Timeline<\/h5>\n\n\n\n\n- October 15, 2025: Application opens.<\/li>\n\n\n\n
- November 30, 2025: Registration deadline.<\/li>\n\n\n\n
- December 15, 2025: Application deadline.<\/li>\n\n\n\n
- December 16 \u2013 February 26, 2026: Confirmation of Visit Plans and Selection.<\/li>\n\n\n\n
- Early March 2026: Announcement of Results.<\/li>\n\n\n\n
- March 2026 \u2013 January 31, 2027: Visits commence.<\/li>\n<\/ul>\n\n\n\n
Criteria<\/h5>\n\n\n\n\n- Applicants are from collaborative universities with Microsoft Research and can provide recommendations from experts in the relevant field, as well as from their affiliated department\u2019s representative or supervisor.<\/li>\n\n\n\n
- Applicants should be university\/institution faculty members and meet the requirement of having obtained a Ph.D. degree within the past 7 years (inclusive of the 7th year).<\/li>\n\n\n\n
- Applicants\u2019 research areas should fall within the research domains of interest in the StarTrack Scholars Program (refer to the \u201cFields\u201d page for details).<\/li>\n\n\n\n
- The program requires a full-time, three-month on-site visit. In exceptional cases, with committee approval, up to one month of remote access may be granted during the three-month visit.<\/li>\n<\/ol>\n\n\n\n
Application<\/h5>\n\n\n\n\n- Click the Registration Link (opens in new tab)<\/span><\/a> to submit your registration information.<\/li>\n\n\n\n
- Download the StarTrack Scholars Program Application Form.docx<\/a>, along with the Letter of Recommendation.docx<\/a> and Letter of Institutional Endorsement.docx<\/a> templates. Utilize the form and templates to provide formal application details, then forward them via email to Yanxuan Wu at v-yanxuanwu@microsoft.com<\/a>.<\/li>\n<\/ol>\n\n\n\n
If you have any questions, please email Ms. Yanxuan Wu, program manager of the Microsoft Research Asia StarTrack Scholars, at\u202fv-yanxuanwu@microsoft.com<\/a>. <\/em><\/p>\n\n\n\n<\/div>\n\n\n\n\n\n1. AI-Transformed Medical Research<\/h5>\n\n\n\n
This project focuses on developing media foundation models that integrate multiple modalities, including text, audio, image, video, and other signals. By leveraging these comprehensive models, we aim to explore and innovate across several research themes, including medical LLMs, multi-modal medical LLM integration, education agents, human-agent interaction, biomedical data synthesis and clinical translation and deployment.\u202f\u202f <\/p>\n\n\n\n
1) Foundation Model: This theme delves into the development of medical LLM for medical diagnostics, treatment planning, and patient care. Leveraging medical specific data and advanced pre-training and fine-tuning algorithms, we develop medical foundation models to improve the accuracy and efficiency of medical decision-making, ultimately enhancing patient outcomes.\u202f <\/p>\n\n\n\n
2\uff09Multi-Modal Medical LLM Integration. Building a universal model for all medical modalities is unrealistic due to limited data and the need for tailored domain knowledge integration for each modality. Instead, we unify unimodal foundation models into a single framework, fine-tuned for multi-modality tasks. This approach enables scalable and flexible deployment across diverse medical scenarios while preserving modality-specific strengths.\u202f <\/p>\n\n\n\n
3) Agentic System: This theme focuses on building a multi-agentic system powered by LLMs and VLMs to support doctors and medical researchers in their professional development and clinical practice. The system simulates collaborative environments involving expert agents that represent clinicians, researchers, and patients. It facilitates clinical scenario simulations for diagnostic reasoning and treatment planning, research collaboration simulations to support hypothesis generation, literature synthesis, and experimental design, and interactive training modules that enhance communication, decision-making, and interdisciplinary teamwork.\u202f\u202f <\/p>\n\n\n\n
4) AI-Transformed Medical Education. Leverage AI to revolutionize medical education for both students and professionals. The system provides personalized learning pathways using adaptive LLMs Immersive simulations with agent-based role-play (e.g., educators, patients, peers), real-time feedback and assessment to reinforce clinical reasoning and communication skills, and scalable platforms for continuous medical education (CME) and certification.\u202f <\/p>\n\n\n\n
5\uff09Medical Data Synthesis. This theme focusses on building biomedical data synthesis models that learn implicit medical distributions, generate diverse synthetic samples, and support causal reasoning to enhance the generalization of medical AI. By introducing a temporal disease progression framework that integrates longitudinal imaging, patient-specific conditions, and medical interventions, our approach seeks to provide a more comprehensive perspective on disease progression and treatment responses.\u202f <\/p>\n\n\n\n
6) Clinical Translation and Deployment for improved patient care. We focus on bridging the gap between research and real-world application by enabling the translation and deployment of AI systems in clinical settings. This includes collaborating with healthcare institutions, adapting and validating foundation models, and integrating solutions into healthcare workflows.\u202f <\/p>\n\n\n\n
Through these research themes, our project aims to achieve several key goals including cultivating future medical talents, fostering technological breakthroughs and accelerating translation to practical applications by developing SOTA performance models for improved patient care and publishing papers in top-tier conferences and journals.\u202f <\/p>\n\n\n\n
For more detailed information, please refer to the article Microsoft Research Asia StarTrack Scholars 2026: AI-Transformed Medical Research – Microsoft Research (opens in new tab)<\/span><\/a>.<\/p>\n\n\n\n\n\n\n- Medical LLMs\u202f<\/li>\n\n\n\n
- Multi-modal LLMs\u202f<\/li>\n\n\n\n
- Medical AI\u202f<\/li>\n\n\n\n
- Medical Agents\u202f<\/li>\n\n\n\n
- Medical Education\u202f<\/li>\n\n\n\n
- Medical Data Synthesis\u202f<\/li>\n\n\n\n
- Medical Diagnosis\u202f<\/li>\n\n\n\n
- Disease Progression Prediction\u202f<\/li>\n<\/ul>\n\n\n\n\n\n\n\n\n\n
![\"a](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/09\/image.png\")
<\/figure>\n\n\n\nShujie Liu<\/a> (Engaging Lead)
Principal Researcher<\/p>\n<\/div>\n\n\n\n\nWe invite researchers passionate about advancing AI in healthcare to join our pioneering research initiatives. Our programs focus on AI foundation models, agentic medical research, and transformative approaches to medical education. We aim to develop intelligent systems that not only analyze and interpret complex medical data but also assist in decision-making, personalized care, and adaptive learning for clinicians and students. We are particularly interested in scholars exploring multimodal AI for medical imaging and diagnostics, autonomous agents for clinical workflows, and innovative educational platforms powered by AI. By fostering interdisciplinary collaboration across medicine, AI, and human-computer interaction, we strive to create groundbreaking technologies that redefine the future of healthcare and medical training. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n![\"a](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/09\/image-1.png\")
<\/figure>\n\n\n\nXinxing Xu<\/a> (Engaging Lead)
Principal Research Manager <\/p>\n<\/div>\n\n\n\n\nWe invite researchers passionate about advancing AI in healthcare to join our pioneering initiatives spanning multimodal AI, generative AI, agentic AI, and the translation and deployment of AI systems in real-world clinical environments. We aim to bridge the gap between cutting-edge AI research and clinical impact by ensuring that AI innovations are not only scientifically rigorous but also validated, adapted, and seamlessly integrated into healthcare workflows. We are committed to transforming foundational AI advances into practical, trustworthy tools that enhance patient outcomes, clinical decision-making, and healthcare system efficiency.<\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n<\/div>\n\n\n\n\n\n![\"a](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/09\/image-2.png\")
<\/figure>\n\n\n\nZilong Wang<\/a>
Senior Researcher\u202f <\/p>\n<\/div>\n\n\n\n\nWe invite researchers with strong interests in advancing the intersection of artificial intelligence and real-world healthcare practice. Our research focuses on evaluating and benchmarking foundation models and AI agents in realistic medical contexts, emphasizing their capabilities, reliability, and collaboration with clinicians. We are particularly interested in developing and assessing practical foundation models for clinical decision support, diagnostic reasoning, and workflow optimization.\u202f <\/p>\n\n\n\n
Our work aims to bridge the gap between AI research and clinical application by exploring user interaction, human-AI collaboration, and agentic intelligence in healthcare environments. We welcome visiting scholars and collaborators passionate about building, adapting, and rigorously testing foundation models and agent-driven systems that advance trustworthy, effective, and human-centered AI for medicine.\u202f <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n![\"a](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/09\/image-3.png\")
<\/figure>\n\n\n\nXinyang Jiang<\/a>
Senior Researcher\u202f <\/p>\n<\/div>\n\n\n\n\nOur research projects centre on developing and applying foundation models using diverse types of medical data and unifiedly integrate different foundation models and modalities to address significant challenges in healthcare research.\u202f We are particularly interested in visiting scholars and collaborators with research interests and expertise in medical AI and multi-modal intelligence, especially those aiming to develop novel foundation models that advance diagnosis, prognosis, and treatment personalization.<\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n![\"a](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/09\/image-4.png\")
<\/figure>\n\n\n\nJinglu Wang
<\/a>Senior Researcher\u202f <\/p>\n<\/div>\n\n\n\n\nWe invite researchers with strong interests in the intersection of artificial intelligence, medicine, and education, particularly those exploring agentic AI. Our overarching goal is to advance the scientific foundations and practical applications of agentic AI systems for healthcare and medical education. We aim to investigate how autonomous, communicative, and trustworthy AI agents can enhance medical reasoning, clinical training, and knowledge dissemination. This includes developing frameworks that integrate multi-modal understanding, collaborative reasoning, and interactive learning environments. We welcome visiting scholars and collaborators who seek to contribute to this emerging paradigm of agentic intelligence in healthcare and medical education, and to jointly explore its theoretical, technical, and societal implications.\u202f<\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n![\"a](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/09\/image-5.png\")
<\/figure>\n\n\n\nJingjing Fu
<\/a>Senior Researcher\u202f <\/p>\n<\/div>\n\n\n\n\nWe invite researchers with strong interests in advancing the frontier of medical data synthesis and shaping its transformative impact on healthcare AI. Our work focuses on developing medical world models and advanced generative frameworks that learn implicit medical distributions, generate diverse and high-fidelity synthetic data, and support causal reasoning to enhance model generalization and robustness. We welcome visiting scholars and collaborators passionate about multimodal generative modeling, disease trajectory simulation, and causal inference in medicine.\u202f<\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n![\"a](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/09\/image-6.png\")
<\/figure>\n\n\n\nChang Xu<\/a>
Senior Researcher\u202f <\/p>\n<\/div>\n\n\n\n\nWe invite researchers passionate about advancing medical AI through foundation models, time series analysis, generative modeling, and agent systems. Our research focuses on developing medical foundation models that integrate multi-modal data\u2014including time series, text, and imaging\u2014to enhance diagnostic reasoning, clinical decision-making, and data-driven discovery. We also explore intelligent medical agents and synthetic data generation frameworks that enable adaptive learning, collaborative reasoning, and robust generalization. We welcome visiting scholars eager to contribute to building trustworthy, interpretable, and impactful AI systems for next-generation healthcare.\u202f <\/p>\n<\/div>\n<\/div>\n\n\n\n\n\n
<\/div>\n\n\n\n2. Agentic AI: Reimagining Future Human\u2013Agent Communication and Collaboration <\/h5>\n\n\n\n
Agentic AI marks a shift from passive systems to active collaborators\u2014AI agents that engage in sustained reasoning, understand complex multimodal environments, and interact naturally with humans over extended time and contexts. Our vision is to build intelligent systems that participate meaningfully in knowledge discovery, content creation, communication, and decision-making. <\/p>\n\n\n\n
We organize our understanding of Agentic AI into three interrelated categories, each reflecting a different facet of the ecosystem these systems must inhabit: <\/p>\n\n\n\n
1. Foundations & Frameworks <\/p>\n\n\n\n
Agentic AI requires new computational foundations to operate effectively across time, context, and modalities. We seek to develop compact and grounded multimodal representations that allow systems to perceive and reason over complex visual, auditory, and sensor-rich environments. <\/p>\n\n\n\n
To support long-term engagement and contextual reasoning, we explore advances in semantic memory and process memory\u2014compression mechanisms that allow agents to retain knowledge across interactions and reason over long horizons. Retrieval-augmented pipelines further enrich reasoning with dynamic access to knowledge bases and structured memory. <\/p>\n\n\n\n
Agents should also coordinate and plan over extended workflows. We encourage research on multi-agent collaboration, process-aligned action spaces, and models that align with the semantics of user-driven tasks. Contributions in this category might include novel architectures, datasets, training methods, or theoretical insights that strengthen the core reasoning and planning capabilities of agentic systems. <\/p>\n\n\n\n
2. User Experiences and Human-Agent Interfaces <\/p>\n\n\n\n
Human\u2013agent interaction must evolve to meet the demands of fluid, multimodal collaboration. We envision interfaces that are generative and dynamic, constructed to suit user intent and task context. Proposals should consider how agents generate or adapt interfaces in real time, across devices, modalities, and immersive environments. <\/p>\n\n\n\n
Agents should be capable of interactive visualization, using media to communicate internal states, uncertainties, and possible outcomes. Interaction should be audience- and context-adaptive, with personalized behaviors that respect different usage settings and preserve user privacy. <\/p>\n\n\n\n
We also encourage work on process-aware collaboration, where agents sustain memory across sessions and adapt over time. Topics such as communicative effectiveness, trust, interpretability, and longitudinal studies are central. Researchers should also consider how interface design, memory, and communication strategies can support natural, explainable, and long-term agentic interaction. <\/p>\n\n\n\n
3. Applications & Societal Impact <\/p>\n\n\n\n
We see Agentic AI as a transformative force across domains like science, healthcare, education, and enterprise decision-making. These applications require agents to perform deep research\u2014gathering evidence, testing hypotheses, and constructing trustworthy narratives with transparency and provenance. <\/p>\n\n\n\n
In the media domain, agentic AI systems could enable advanced content generation, curation, and personalization by understanding and synthesizing information from multiple modalities such as text, audio, and video. These agents support content creators by automating research, summarization, and fact-checking, or by generating interactive and adaptive media experiences tailored to audience preferences. Additionally, agents could facilitate media analysis at scale\u2014detecting trends, ensuring provenance, and helping organizations manage and distribute content more effectively. <\/p>\n\n\n\n
Finally, as these systems scale, we must ensure they operate responsibly. We encourage proposals on provenance tracking, watermarking, privacy-preserving design, and energy-efficient deployment. We also welcome organizational and societal studies that examine how agentic systems are adopted, trusted, and evaluated in the wild. <\/p>\n\n\n\n
For more detailed information, please refer to the article Microsoft Research Asia StarTrack Scholars 2026: Agentic AI: Reimagining Future Human\u2013Agent Communication and Collaboration<\/a><\/p>\n\n\n\n<\/p>\n\n\n\n\n\n
\n- Agentic deep research model training: data, tools, and algorithms\u202f\u202f <\/li>\n\n\n\n
- Deep research applications in science, healthcare, enterprise, and education\u202f\u202f <\/li>\n\n\n\n
- Media foundations: multimodal representation learning, neural compression, multimodal understanding, world models\u202f\u202f <\/li>\n\n\n\n
- Context and memory: semantic memory, process memory, long-context reasoning, retrieval-augmented systems\u202f\u202f <\/li>\n\n\n\n
- Agentic capabilities: planning, tool use, multi-agent collaboration, process-aligned action spaces\u202f\u202f <\/li>\n\n\n\n
- Generative and adaptive user interfaces: UI generation, audience\/context adaptation, interactive visualization, cross-device\/immersive experiences\u202f\u202f <\/li>\n\n\n\n
- Human-centered AI application and evaluation: process-aware collaboration, communicative effectiveness, trust and interpretability, longitudinal and organizational studies\u202f\u202f <\/li>\n\n\n\n
- Responsible media and systems: provenance, watermarking, privacy-preserving design, efficiency, and deployment at scale\u202f <\/li>\n<\/ul>\n\n\n\n\n\n\n\n\n\n
![\"a](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2023\/09\/image009.jpg\")
<\/figure>\n\n\n\nYan Lu<\/a> (Engaging Lead)
Partner Research Manager<\/p>\n<\/div>\n\n\n\n\nWe welcome applicants who possess a strong background in Multimedia, Immersive AI, Agentic AI, HCI or related fields, including individuals with experience in neural video communication, computer vision, 3D and graphics, audio and speech, and other related domains. We also expect participants to be passionate about exploring the paradigm shifts in building future media and communication experience and are eager to contribute to the development of advanced agentic media ecosystem that can enhance human learning and creativity.\u202f <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n<\/div>\n\n\n\n\n\n![\"a](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/09\/image-10.png\")
<\/figure>\n\n\n\nChong Luo<\/a> (Engaging Lead)
Sr. Principal Research Manager <\/p>\n<\/div>\n\n\n\n\nMy team is particularly excited about advancing the deep research agent aspect of Agentic AI. We aim to develop models capable of conducting autonomous, evidence-based research across scientific and enterprise domains. We hope to collaborate closely with visiting researchers on data, tools, and training algorithms that empower agents to reason, hypothesize, and construct transparent knowledge artifacts. Through this collaboration, we expect to jointly explore real-world applications in science, healthcare, and education, and to establish a foundation for trustworthy agentic research systems. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n![\"a](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/09\/image-11.png\")
<\/figure>\n\n\n\nJiahao Li<\/a>
<\/strong>Principal Researcher<\/p>\n<\/div>\n\n\n\n\nWe welcome candidates with expertise in multimedia and AI, such as neural compression, representation learning, and generative modeling. We value individuals passionate about investigating paradigm shifts in video research and contributing to advanced AI systems, including agentic capabilities like long-horizon planning, tool use, and memory-centric workflows, to propel communication, decision-making, and knowledge creation. Through seamless collaboration, we are excited to push the frontiers of AI and media technologies together. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n![\"a](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/09\/image-12.png\")
<\/figure>\n\n\n\nYun Wang<\/a>
Senior Researcher<\/p>\n<\/div>\n\n\n\n\nWe welcome applicants with strong backgrounds in Human\u2013AI Interaction, Intelligent Systems, and related areas such as visualization, multimodal communication, and cognitive modeling. We are particularly interested in individuals who aspire to rethink how intelligence is expressed, shared, and co-evolved between humans and AI. We value researchers who not only advance technical capability but also interrogate the underlying paradigms of collaboration, reasoning, and communication. Visiting researchers are encouraged to explore new frameworks, representations, and processes that connect human cognition with computational intelligence\u2014toward more transparent, adaptive, and meaning-centered systems that augment human understanding and creativity. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n![\"a](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/09\/image-13.png\")
<\/figure>\n\n\n\nXun Guo<\/a>
Principal Researcher <\/p>\n<\/div>\n\n\n\n\nWe welcome applicants with a strong background in Multimedia, Agentic AI, and Multimodal Learning, and particularly value experience in generative models for video, vision-language, and other modalities. Participants are encouraged to explore agentic AI systems that understand user intent across modalities (e.g., video, audio, and text) and generate interactive outputs accordingly, or contribute to related research areas such as multimodal learning, video generation, and vision-language alignment. These efforts aim to support intuitive, intent-driven user experiences and advance the frontier of future media technologies. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n![\"Kai](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/09\/image-14.png\")
<\/figure>\n\n\n\nKai Qiu<\/a>
Senior Researcher <\/p>\n<\/div>\n\n\n\n\nWe welcome applicants with a strong background in reinforcement learning, LLM, and agent-based AI. We also expect participants to be passionate about exploring paradigm shifts in creating advanced autonomous agents \u2013 such as designing agents with long-horizon planning, tool-use integration, and memory-driven reasoning \u2013 and are eager to contribute to the development of next-generation agentic AI systems capable of autonomously tackling complex and augmenting human capabilities. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n![\"a](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/09\/image-15.png\")
<\/figure>\n\n\n\nQi Dai<\/a>
Principal Researcher<\/p>\n<\/div>\n\n\n\n\nWe welcome applicants who are excited to push the frontier of agentic AI research. Ideal candidates will have a solid foundation in reinforcement learning, agents, LLM and VLM. We are particularly interested in researchers who can imagine and build systems in which agents perceive multimodal inputs, remember and reason over extended time horizons, and dynamically orchestrate external tools to solve open-ended tasks. If you are passionate about transforming these ingredients into robust, adaptive agents that expand human potential, we encourage you to apply. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n![\"a](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/09\/image-16.png\")
<\/figure>\n\n\n\nBei Liu<\/a>
Senior Researcher <\/p>\n<\/div>\n\n\n\n\nWe welcome visiting researchers who are motivated, collaborative, and passionate about advancing agentic AI. Candidates with independent ideas and strong interest in general agentic models, Vision-Language Models, and related areas\u2014such as multimodal reasoning, long-horizon reasoning, or reinforcement learning\u2014are highly encouraged to join us and engage in open, productive exchange. <\/p>\n<\/div>\n<\/div>\n\n\n\n\n\n
<\/div>\n\n\n\n3. Brain and Artificial Intelligence <\/h5>\n\n\n\n
We conduct interdisciplinary research at the intersection of artificial intelligence (AI) and neuroscience, aiming both to leverage AI for advancing our understanding of the brain and to use these insights to improve AI systems and brain health. On one front, brain-inspired AI seeks to incorporate neurobiological principles to create energy-efficient, robust, and human-like intelligence, driving the development of next-generation AI technologies. On the other front, human brain signals (e.g., EEG and fMRI) are inherently noisy, with low signal-to-noise ratios that hinder their practical utility. To overcome this challenge, we strive to develop foundational models of brain activity capable of decoding a wide range of human perceptions and supporting efforts to address neurological disorders.\u202f <\/p>\n\n\n\n
For more detailed information, please refer to the article Microsoft Research Asia StarTrack Scholars 2026: Create a synergistic relationship between AI and the brain – Microsoft Research (opens in new tab)<\/span><\/a>.<\/p>\n\n\n\n\n\n\n- Brain-inspired AI\u202f <\/li>\n\n\n\n
- Brain-computer Interface\u202f <\/li>\n\n\n\n
- AI for Brain Health\u202f <\/li>\n<\/ul>\n\n\n\n\n\n\n\n\n\n
![\"a](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/09\/image-17.png\")
<\/figure>\n\n\n\nDongsheng Li<\/a> (Engaging Lead)
Principal Research Manager\u202f <\/p>\n<\/div>\n\n\n\n\nAs AI is transforming the world, it is equally crucial to deepen our understanding of biological intelligence. The integration of AI and brain science research holds immense potential to drive innovation, enhance human well-being, and expand our knowledge of both artificial and biological intelligence. I am eager to collaborate with researchers in this field to revolutionize existing paradigms through interdisciplinary studies, advancing both AI and brain science in groundbreaking ways.\u202f <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n![\"a](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/09\/image-18.png\")
<\/figure>\n\n\n\nYansen Wang<\/u>
<\/a>Senior Researcher<\/p>\n<\/div>\n\n\n\n\nOur brain, encapsulated within a structure no larger than our two fists, is a marvel of complexity and power. It governs every facet of our thoughts, emotions, and actions, enabling feats from artistic expression to scientific innovation. Yet, much of its inner workings remain shrouded in mystery, with countless questions about consciousness, memory, and cognition still unanswered. Understanding the brain is not only essential for unraveling these mysteries but also for constructing interfaces between the brain and artificial intelligence. I hope to see a better synergy in the future.\u202f \u202f <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n![\"Dongqi](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/09\/image-19.png\")
<\/figure>\n\n\n\nDongqi Han<\/a><\/u>
Senior Researcher\u202f<\/p>\n<\/div>\n\n\n\n\nGeoffrey Hinton once said, \u201cI have always been convinced that the only way to get artificial intelligence to work is to do the computation in a way similar to the human brain<\/em>.\u201d This conviction reveals a deep philosophical and practical bridge between neuroscience and AI: by studying how the brain computes, learns, and adapts, we gain principles that can guide more effective artificial systems; conversely, AI models provide testable hypotheses and experimental platforms for exploring brain function. Synergizing AI and brain science means accelerating mutual progress \u2014 using neural insights to inspire new architectures, and leveraging AI to simulate, analyze, and even predict biological behavior \u2014 all toward unraveling the deeper mysteries of intelligence in nature and in machines.\u202f <\/p>\n<\/div>\n<\/div>\n\n\n\n<\/div>\n\n\n\n\n\n![\"a](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/09\/image-20.png\")
<\/figure>\n\n\n\nMingqing Xiao (opens in new tab)<\/span><\/a>
Researcher<\/p>\n<\/div>\n\n\n\n\nThe human brain remains the only known realization of general intelligence, exhibiting remarkable efficiency and adaptability. Its energy efficiency, learning efficiency, and generalization capability are unparalleled compared to current AI models. Drawing inspiration from the brain at multiple levels\u2014from spatiotemporal neural dynamics, through learning principles, to high-level representations\u2014grounded in solid theoretical foundations, is essential for advancing AI toward more robust and human-like intelligence. At the same time, this synergy works both ways: AI can serve as a surrogate computational model to simulate or as a tool to decode brain processes, deepening our understanding of human brains. Exploring this bidirectional relationship between AI and neuroscience holds immense promise for shaping the next generation of intelligent systems and unraveling the mysteries of biological intelligence.\u202f <\/p>\n<\/div>\n<\/div>\n\n\n\n\n\n
<\/div>\n\n\n\n4. Rethink systems and networking in the era of AI\u202f <\/h5>\n\n\n\n
Rapid advances in AI are transforming modern computing infrastructure – bringing both fundamental challenges and unprecedented opportunities. <\/p>\n\n\n\n
We call for proposals that tackle these challenges and harness the emerging opportunities of the AI era. Submissions may span multiple dimensions of AI and computing systems, including but not limited to: <\/p>\n\n\n\n
1) AI-assisted self-driving networking infrastructure; <\/p>\n\n\n\n
2) Disruptive AI-assisted methods for lowering the barrier to building secure and reliable systems; <\/p>\n\n\n\n
3) Next-generation software and hardware architectures for AI; <\/p>\n\n\n\n
4) Development and debugging tools for intelligent systems. <\/p>\n\n\n\n
We particularly encourage unconventional and multidisciplinary approaches that break traditional boundaries and push the frontiers of AI and systems research. <\/p>\n\n\n\n
For more detailed information, please refer to the article Microsoft Research Asia StarTrack Scholars 2026: A Holistic Approach to Systems and Networking in the AI Era – Microsoft Research (opens in new tab)<\/span><\/a>.<\/p>\n\n\n\n\n\n\n- RL infrastructure\u202f <\/li>\n\n\n\n
- Agentic AI systems\u202f <\/li>\n\n\n\n
- Storage systems for AI\u202f <\/li>\n\n\n\n
- Verification for AI\u202f <\/li>\n\n\n\n
- LLM-Assisted Network Optimization\u202f <\/li>\n\n\n\n
- AI Networking Infrastructure\u202f <\/li>\n\n\n\n
- Network Protocol Troubleshooting\u202f <\/li>\n\n\n\n
- Infrastructure-aware resource allocation across compute, network, and devices\u202f <\/li>\n\n\n\n
- Adaptive configuration of collective communication algorithms\u202f <\/li>\n\n\n\n
- Network path selection and routing optimization for AI workloads\u202f <\/li>\n\n\n\n
- Autonomous device configuration and tuning\u202f <\/li>\n\n\n\n
- Integration of workload sensing with system-level orchestration for self-optimizing AI infrastructure\u202f<\/li>\n<\/ul>\n\n\n\n
<\/p>\n\n\n\n\n\n\n\n
\n\n![\"a](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/09\/image-21.png\")
<\/figure>\n\n\n\nYongqiang Xiong<\/a> (Engaging Lead)
<\/a>Sr. Principal Research Manager<\/p>\n<\/div>\n\n\n\n\nJoin us to revisit\/rethink\/rebuild the system and networking infrastructure by AI and for AI with a holistic and clean-slate perspective. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n![\"Fan](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/09\/image-22.png\")
<\/figure>\n\n\n\nFan Yang<\/a> (Engaging Lead)
Sr. Principal Research Manager<\/p>\n<\/div>\n\n\n\n\nWe welcome young systems researchers to join us exploring the disruptive system opportunities in the era of AI. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n![\"Jing](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/09\/image-23.png\")
<\/figure>\n\n\n\nJing Liu
<\/a>Senior Researcher<\/p>\n<\/div>\n\n\n\n\nWe welcome systems researchers to join us and build strong systems for the AI era together. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n![\"a](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/09\/image-24.png\")
<\/figure>\n\n\n\nBaotong Lu<\/a>
Senior Researcher<\/p>\n<\/div>\n\n\n\n\nWe welcome systems researchers to explore the next generation of data systems in the era of AI. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n![\"a](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/09\/image-25.png\")
<\/figure>\n\n\n\nLi Lyna Zhang<\/a>
Principal Researcher <\/p>\n<\/div>\n\n\n\n\nWe welcome researchers who are passionate about advancing agentic RL infrastructure and building general agents! <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n![\"Yi](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/09\/image-27.png\")
<\/figure>\n\n\n\n
Criteria<\/h5>\n\n\n\n\n- Applicants are from collaborative universities with Microsoft Research and can provide recommendations from experts in the relevant field, as well as from their affiliated department\u2019s representative or supervisor.<\/li>\n\n\n\n
- Applicants should be university\/institution faculty members and meet the requirement of having obtained a Ph.D. degree within the past 7 years (inclusive of the 7th year).<\/li>\n\n\n\n
- Applicants\u2019 research areas should fall within the research domains of interest in the StarTrack Scholars Program (refer to the \u201cFields\u201d page for details).<\/li>\n\n\n\n
- The program requires a full-time, three-month on-site visit. In exceptional cases, with committee approval, up to one month of remote access may be granted during the three-month visit.<\/li>\n<\/ol>\n\n\n\n
Application<\/h5>\n\n\n\n\n- Click the Registration Link (opens in new tab)<\/span><\/a> to submit your registration information.<\/li>\n\n\n\n
- Download the StarTrack Scholars Program Application Form.docx<\/a>, along with the Letter of Recommendation.docx<\/a> and Letter of Institutional Endorsement.docx<\/a> templates. Utilize the form and templates to provide formal application details, then forward them via email to Yanxuan Wu at v-yanxuanwu@microsoft.com<\/a>.<\/li>\n<\/ol>\n\n\n\n
If you have any questions, please email Ms. Yanxuan Wu, program manager of the Microsoft Research Asia StarTrack Scholars, at\u202fv-yanxuanwu@microsoft.com<\/a>. <\/em><\/p>\n\n\n\n<\/div>\n\n\n\n\n\n1. AI-Transformed Medical Research<\/h5>\n\n\n\n
This project focuses on developing media foundation models that integrate multiple modalities, including text, audio, image, video, and other signals. By leveraging these comprehensive models, we aim to explore and innovate across several research themes, including medical LLMs, multi-modal medical LLM integration, education agents, human-agent interaction, biomedical data synthesis and clinical translation and deployment.\u202f\u202f <\/p>\n\n\n\n
1) Foundation Model: This theme delves into the development of medical LLM for medical diagnostics, treatment planning, and patient care. Leveraging medical specific data and advanced pre-training and fine-tuning algorithms, we develop medical foundation models to improve the accuracy and efficiency of medical decision-making, ultimately enhancing patient outcomes.\u202f <\/p>\n\n\n\n
2\uff09Multi-Modal Medical LLM Integration. Building a universal model for all medical modalities is unrealistic due to limited data and the need for tailored domain knowledge integration for each modality. Instead, we unify unimodal foundation models into a single framework, fine-tuned for multi-modality tasks. This approach enables scalable and flexible deployment across diverse medical scenarios while preserving modality-specific strengths.\u202f <\/p>\n\n\n\n
3) Agentic System: This theme focuses on building a multi-agentic system powered by LLMs and VLMs to support doctors and medical researchers in their professional development and clinical practice. The system simulates collaborative environments involving expert agents that represent clinicians, researchers, and patients. It facilitates clinical scenario simulations for diagnostic reasoning and treatment planning, research collaboration simulations to support hypothesis generation, literature synthesis, and experimental design, and interactive training modules that enhance communication, decision-making, and interdisciplinary teamwork.\u202f\u202f <\/p>\n\n\n\n
4) AI-Transformed Medical Education. Leverage AI to revolutionize medical education for both students and professionals. The system provides personalized learning pathways using adaptive LLMs Immersive simulations with agent-based role-play (e.g., educators, patients, peers), real-time feedback and assessment to reinforce clinical reasoning and communication skills, and scalable platforms for continuous medical education (CME) and certification.\u202f <\/p>\n\n\n\n
5\uff09Medical Data Synthesis. This theme focusses on building biomedical data synthesis models that learn implicit medical distributions, generate diverse synthetic samples, and support causal reasoning to enhance the generalization of medical AI. By introducing a temporal disease progression framework that integrates longitudinal imaging, patient-specific conditions, and medical interventions, our approach seeks to provide a more comprehensive perspective on disease progression and treatment responses.\u202f <\/p>\n\n\n\n
6) Clinical Translation and Deployment for improved patient care. We focus on bridging the gap between research and real-world application by enabling the translation and deployment of AI systems in clinical settings. This includes collaborating with healthcare institutions, adapting and validating foundation models, and integrating solutions into healthcare workflows.\u202f <\/p>\n\n\n\n
Through these research themes, our project aims to achieve several key goals including cultivating future medical talents, fostering technological breakthroughs and accelerating translation to practical applications by developing SOTA performance models for improved patient care and publishing papers in top-tier conferences and journals.\u202f <\/p>\n\n\n\n
For more detailed information, please refer to the article Microsoft Research Asia StarTrack Scholars 2026: AI-Transformed Medical Research – Microsoft Research (opens in new tab)<\/span><\/a>.<\/p>\n\n\n\n\n\n\n- Medical LLMs\u202f<\/li>\n\n\n\n
- Multi-modal LLMs\u202f<\/li>\n\n\n\n
- Medical AI\u202f<\/li>\n\n\n\n
- Medical Agents\u202f<\/li>\n\n\n\n
- Medical Education\u202f<\/li>\n\n\n\n
- Medical Data Synthesis\u202f<\/li>\n\n\n\n
- Medical Diagnosis\u202f<\/li>\n\n\n\n
- Disease Progression Prediction\u202f<\/li>\n<\/ul>\n\n\n\n\n\n\n\n\n\n
<\/figure>\n\n\n\nShujie Liu<\/a> (Engaging Lead)
Principal Researcher<\/p>\n<\/div>\n\n\n\n\nWe invite researchers passionate about advancing AI in healthcare to join our pioneering research initiatives. Our programs focus on AI foundation models, agentic medical research, and transformative approaches to medical education. We aim to develop intelligent systems that not only analyze and interpret complex medical data but also assist in decision-making, personalized care, and adaptive learning for clinicians and students. We are particularly interested in scholars exploring multimodal AI for medical imaging and diagnostics, autonomous agents for clinical workflows, and innovative educational platforms powered by AI. By fostering interdisciplinary collaboration across medicine, AI, and human-computer interaction, we strive to create groundbreaking technologies that redefine the future of healthcare and medical training. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nXinxing Xu<\/a> (Engaging Lead)
Principal Research Manager <\/p>\n<\/div>\n\n\n\n\nWe invite researchers passionate about advancing AI in healthcare to join our pioneering initiatives spanning multimodal AI, generative AI, agentic AI, and the translation and deployment of AI systems in real-world clinical environments. We aim to bridge the gap between cutting-edge AI research and clinical impact by ensuring that AI innovations are not only scientifically rigorous but also validated, adapted, and seamlessly integrated into healthcare workflows. We are committed to transforming foundational AI advances into practical, trustworthy tools that enhance patient outcomes, clinical decision-making, and healthcare system efficiency.<\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nZilong Wang<\/a>
Senior Researcher\u202f <\/p>\n<\/div>\n\n\n\n\nWe invite researchers with strong interests in advancing the intersection of artificial intelligence and real-world healthcare practice. Our research focuses on evaluating and benchmarking foundation models and AI agents in realistic medical contexts, emphasizing their capabilities, reliability, and collaboration with clinicians. We are particularly interested in developing and assessing practical foundation models for clinical decision support, diagnostic reasoning, and workflow optimization.\u202f <\/p>\n\n\n\n
Our work aims to bridge the gap between AI research and clinical application by exploring user interaction, human-AI collaboration, and agentic intelligence in healthcare environments. We welcome visiting scholars and collaborators passionate about building, adapting, and rigorously testing foundation models and agent-driven systems that advance trustworthy, effective, and human-centered AI for medicine.\u202f <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nXinyang Jiang<\/a>
Senior Researcher\u202f <\/p>\n<\/div>\n\n\n\n\nOur research projects centre on developing and applying foundation models using diverse types of medical data and unifiedly integrate different foundation models and modalities to address significant challenges in healthcare research.\u202f We are particularly interested in visiting scholars and collaborators with research interests and expertise in medical AI and multi-modal intelligence, especially those aiming to develop novel foundation models that advance diagnosis, prognosis, and treatment personalization.<\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nJinglu Wang
<\/a>Senior Researcher\u202f <\/p>\n<\/div>\n\n\n\n\nWe invite researchers with strong interests in the intersection of artificial intelligence, medicine, and education, particularly those exploring agentic AI. Our overarching goal is to advance the scientific foundations and practical applications of agentic AI systems for healthcare and medical education. We aim to investigate how autonomous, communicative, and trustworthy AI agents can enhance medical reasoning, clinical training, and knowledge dissemination. This includes developing frameworks that integrate multi-modal understanding, collaborative reasoning, and interactive learning environments. We welcome visiting scholars and collaborators who seek to contribute to this emerging paradigm of agentic intelligence in healthcare and medical education, and to jointly explore its theoretical, technical, and societal implications.\u202f<\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nJingjing Fu
<\/a>Senior Researcher\u202f <\/p>\n<\/div>\n\n\n\n\nWe invite researchers with strong interests in advancing the frontier of medical data synthesis and shaping its transformative impact on healthcare AI. Our work focuses on developing medical world models and advanced generative frameworks that learn implicit medical distributions, generate diverse and high-fidelity synthetic data, and support causal reasoning to enhance model generalization and robustness. We welcome visiting scholars and collaborators passionate about multimodal generative modeling, disease trajectory simulation, and causal inference in medicine.\u202f<\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nChang Xu<\/a>
Senior Researcher\u202f <\/p>\n<\/div>\n\n\n\n\nWe invite researchers passionate about advancing medical AI through foundation models, time series analysis, generative modeling, and agent systems. Our research focuses on developing medical foundation models that integrate multi-modal data\u2014including time series, text, and imaging\u2014to enhance diagnostic reasoning, clinical decision-making, and data-driven discovery. We also explore intelligent medical agents and synthetic data generation frameworks that enable adaptive learning, collaborative reasoning, and robust generalization. We welcome visiting scholars eager to contribute to building trustworthy, interpretable, and impactful AI systems for next-generation healthcare.\u202f <\/p>\n<\/div>\n<\/div>\n\n\n\n\n\n
<\/div>\n\n\n\n2. Agentic AI: Reimagining Future Human\u2013Agent Communication and Collaboration <\/h5>\n\n\n\n
Agentic AI marks a shift from passive systems to active collaborators\u2014AI agents that engage in sustained reasoning, understand complex multimodal environments, and interact naturally with humans over extended time and contexts. Our vision is to build intelligent systems that participate meaningfully in knowledge discovery, content creation, communication, and decision-making. <\/p>\n\n\n\n
We organize our understanding of Agentic AI into three interrelated categories, each reflecting a different facet of the ecosystem these systems must inhabit: <\/p>\n\n\n\n
1. Foundations & Frameworks <\/p>\n\n\n\n
Agentic AI requires new computational foundations to operate effectively across time, context, and modalities. We seek to develop compact and grounded multimodal representations that allow systems to perceive and reason over complex visual, auditory, and sensor-rich environments. <\/p>\n\n\n\n
To support long-term engagement and contextual reasoning, we explore advances in semantic memory and process memory\u2014compression mechanisms that allow agents to retain knowledge across interactions and reason over long horizons. Retrieval-augmented pipelines further enrich reasoning with dynamic access to knowledge bases and structured memory. <\/p>\n\n\n\n
Agents should also coordinate and plan over extended workflows. We encourage research on multi-agent collaboration, process-aligned action spaces, and models that align with the semantics of user-driven tasks. Contributions in this category might include novel architectures, datasets, training methods, or theoretical insights that strengthen the core reasoning and planning capabilities of agentic systems. <\/p>\n\n\n\n
2. User Experiences and Human-Agent Interfaces <\/p>\n\n\n\n
Human\u2013agent interaction must evolve to meet the demands of fluid, multimodal collaboration. We envision interfaces that are generative and dynamic, constructed to suit user intent and task context. Proposals should consider how agents generate or adapt interfaces in real time, across devices, modalities, and immersive environments. <\/p>\n\n\n\n
Agents should be capable of interactive visualization, using media to communicate internal states, uncertainties, and possible outcomes. Interaction should be audience- and context-adaptive, with personalized behaviors that respect different usage settings and preserve user privacy. <\/p>\n\n\n\n
We also encourage work on process-aware collaboration, where agents sustain memory across sessions and adapt over time. Topics such as communicative effectiveness, trust, interpretability, and longitudinal studies are central. Researchers should also consider how interface design, memory, and communication strategies can support natural, explainable, and long-term agentic interaction. <\/p>\n\n\n\n
3. Applications & Societal Impact <\/p>\n\n\n\n
We see Agentic AI as a transformative force across domains like science, healthcare, education, and enterprise decision-making. These applications require agents to perform deep research\u2014gathering evidence, testing hypotheses, and constructing trustworthy narratives with transparency and provenance. <\/p>\n\n\n\n
In the media domain, agentic AI systems could enable advanced content generation, curation, and personalization by understanding and synthesizing information from multiple modalities such as text, audio, and video. These agents support content creators by automating research, summarization, and fact-checking, or by generating interactive and adaptive media experiences tailored to audience preferences. Additionally, agents could facilitate media analysis at scale\u2014detecting trends, ensuring provenance, and helping organizations manage and distribute content more effectively. <\/p>\n\n\n\n
Finally, as these systems scale, we must ensure they operate responsibly. We encourage proposals on provenance tracking, watermarking, privacy-preserving design, and energy-efficient deployment. We also welcome organizational and societal studies that examine how agentic systems are adopted, trusted, and evaluated in the wild. <\/p>\n\n\n\n
For more detailed information, please refer to the article Microsoft Research Asia StarTrack Scholars 2026: Agentic AI: Reimagining Future Human\u2013Agent Communication and Collaboration<\/a><\/p>\n\n\n\n<\/p>\n\n\n\n\n\n
\n- Agentic deep research model training: data, tools, and algorithms\u202f\u202f <\/li>\n\n\n\n
- Deep research applications in science, healthcare, enterprise, and education\u202f\u202f <\/li>\n\n\n\n
- Media foundations: multimodal representation learning, neural compression, multimodal understanding, world models\u202f\u202f <\/li>\n\n\n\n
- Context and memory: semantic memory, process memory, long-context reasoning, retrieval-augmented systems\u202f\u202f <\/li>\n\n\n\n
- Agentic capabilities: planning, tool use, multi-agent collaboration, process-aligned action spaces\u202f\u202f <\/li>\n\n\n\n
- Generative and adaptive user interfaces: UI generation, audience\/context adaptation, interactive visualization, cross-device\/immersive experiences\u202f\u202f <\/li>\n\n\n\n
- Human-centered AI application and evaluation: process-aware collaboration, communicative effectiveness, trust and interpretability, longitudinal and organizational studies\u202f\u202f <\/li>\n\n\n\n
- Responsible media and systems: provenance, watermarking, privacy-preserving design, efficiency, and deployment at scale\u202f <\/li>\n<\/ul>\n\n\n\n\n\n\n\n\n\n
<\/figure>\n\n\n\nYan Lu<\/a> (Engaging Lead)
Partner Research Manager<\/p>\n<\/div>\n\n\n\n\nWe welcome applicants who possess a strong background in Multimedia, Immersive AI, Agentic AI, HCI or related fields, including individuals with experience in neural video communication, computer vision, 3D and graphics, audio and speech, and other related domains. We also expect participants to be passionate about exploring the paradigm shifts in building future media and communication experience and are eager to contribute to the development of advanced agentic media ecosystem that can enhance human learning and creativity.\u202f <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nChong Luo<\/a> (Engaging Lead)
Sr. Principal Research Manager <\/p>\n<\/div>\n\n\n\n\nMy team is particularly excited about advancing the deep research agent aspect of Agentic AI. We aim to develop models capable of conducting autonomous, evidence-based research across scientific and enterprise domains. We hope to collaborate closely with visiting researchers on data, tools, and training algorithms that empower agents to reason, hypothesize, and construct transparent knowledge artifacts. Through this collaboration, we expect to jointly explore real-world applications in science, healthcare, and education, and to establish a foundation for trustworthy agentic research systems. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nJiahao Li<\/a>
<\/strong>Principal Researcher<\/p>\n<\/div>\n\n\n\n\nWe welcome candidates with expertise in multimedia and AI, such as neural compression, representation learning, and generative modeling. We value individuals passionate about investigating paradigm shifts in video research and contributing to advanced AI systems, including agentic capabilities like long-horizon planning, tool use, and memory-centric workflows, to propel communication, decision-making, and knowledge creation. Through seamless collaboration, we are excited to push the frontiers of AI and media technologies together. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nYun Wang<\/a>
Senior Researcher<\/p>\n<\/div>\n\n\n\n\nWe welcome applicants with strong backgrounds in Human\u2013AI Interaction, Intelligent Systems, and related areas such as visualization, multimodal communication, and cognitive modeling. We are particularly interested in individuals who aspire to rethink how intelligence is expressed, shared, and co-evolved between humans and AI. We value researchers who not only advance technical capability but also interrogate the underlying paradigms of collaboration, reasoning, and communication. Visiting researchers are encouraged to explore new frameworks, representations, and processes that connect human cognition with computational intelligence\u2014toward more transparent, adaptive, and meaning-centered systems that augment human understanding and creativity. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nXun Guo<\/a>
Principal Researcher <\/p>\n<\/div>\n\n\n\n\nWe welcome applicants with a strong background in Multimedia, Agentic AI, and Multimodal Learning, and particularly value experience in generative models for video, vision-language, and other modalities. Participants are encouraged to explore agentic AI systems that understand user intent across modalities (e.g., video, audio, and text) and generate interactive outputs accordingly, or contribute to related research areas such as multimodal learning, video generation, and vision-language alignment. These efforts aim to support intuitive, intent-driven user experiences and advance the frontier of future media technologies. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nKai Qiu<\/a>
Senior Researcher <\/p>\n<\/div>\n\n\n\n\nWe welcome applicants with a strong background in reinforcement learning, LLM, and agent-based AI. We also expect participants to be passionate about exploring paradigm shifts in creating advanced autonomous agents \u2013 such as designing agents with long-horizon planning, tool-use integration, and memory-driven reasoning \u2013 and are eager to contribute to the development of next-generation agentic AI systems capable of autonomously tackling complex and augmenting human capabilities. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nQi Dai<\/a>
Principal Researcher<\/p>\n<\/div>\n\n\n\n\nWe welcome applicants who are excited to push the frontier of agentic AI research. Ideal candidates will have a solid foundation in reinforcement learning, agents, LLM and VLM. We are particularly interested in researchers who can imagine and build systems in which agents perceive multimodal inputs, remember and reason over extended time horizons, and dynamically orchestrate external tools to solve open-ended tasks. If you are passionate about transforming these ingredients into robust, adaptive agents that expand human potential, we encourage you to apply. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nBei Liu<\/a>
Senior Researcher <\/p>\n<\/div>\n\n\n\n\nWe welcome visiting researchers who are motivated, collaborative, and passionate about advancing agentic AI. Candidates with independent ideas and strong interest in general agentic models, Vision-Language Models, and related areas\u2014such as multimodal reasoning, long-horizon reasoning, or reinforcement learning\u2014are highly encouraged to join us and engage in open, productive exchange. <\/p>\n<\/div>\n<\/div>\n\n\n\n\n\n
<\/div>\n\n\n\n3. Brain and Artificial Intelligence <\/h5>\n\n\n\n
We conduct interdisciplinary research at the intersection of artificial intelligence (AI) and neuroscience, aiming both to leverage AI for advancing our understanding of the brain and to use these insights to improve AI systems and brain health. On one front, brain-inspired AI seeks to incorporate neurobiological principles to create energy-efficient, robust, and human-like intelligence, driving the development of next-generation AI technologies. On the other front, human brain signals (e.g., EEG and fMRI) are inherently noisy, with low signal-to-noise ratios that hinder their practical utility. To overcome this challenge, we strive to develop foundational models of brain activity capable of decoding a wide range of human perceptions and supporting efforts to address neurological disorders.\u202f <\/p>\n\n\n\n
For more detailed information, please refer to the article Microsoft Research Asia StarTrack Scholars 2026: Create a synergistic relationship between AI and the brain – Microsoft Research (opens in new tab)<\/span><\/a>.<\/p>\n\n\n\n\n\n\n- Brain-inspired AI\u202f <\/li>\n\n\n\n
- Brain-computer Interface\u202f <\/li>\n\n\n\n
- AI for Brain Health\u202f <\/li>\n<\/ul>\n\n\n\n\n\n\n\n\n\n
<\/figure>\n\n\n\nDongsheng Li<\/a> (Engaging Lead)
Principal Research Manager\u202f <\/p>\n<\/div>\n\n\n\n\nAs AI is transforming the world, it is equally crucial to deepen our understanding of biological intelligence. The integration of AI and brain science research holds immense potential to drive innovation, enhance human well-being, and expand our knowledge of both artificial and biological intelligence. I am eager to collaborate with researchers in this field to revolutionize existing paradigms through interdisciplinary studies, advancing both AI and brain science in groundbreaking ways.\u202f <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nYansen Wang<\/u>
<\/a>Senior Researcher<\/p>\n<\/div>\n\n\n\n\nOur brain, encapsulated within a structure no larger than our two fists, is a marvel of complexity and power. It governs every facet of our thoughts, emotions, and actions, enabling feats from artistic expression to scientific innovation. Yet, much of its inner workings remain shrouded in mystery, with countless questions about consciousness, memory, and cognition still unanswered. Understanding the brain is not only essential for unraveling these mysteries but also for constructing interfaces between the brain and artificial intelligence. I hope to see a better synergy in the future.\u202f \u202f <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nDongqi Han<\/a><\/u>
Senior Researcher\u202f<\/p>\n<\/div>\n\n\n\n\nGeoffrey Hinton once said, \u201cI have always been convinced that the only way to get artificial intelligence to work is to do the computation in a way similar to the human brain<\/em>.\u201d This conviction reveals a deep philosophical and practical bridge between neuroscience and AI: by studying how the brain computes, learns, and adapts, we gain principles that can guide more effective artificial systems; conversely, AI models provide testable hypotheses and experimental platforms for exploring brain function. Synergizing AI and brain science means accelerating mutual progress \u2014 using neural insights to inspire new architectures, and leveraging AI to simulate, analyze, and even predict biological behavior \u2014 all toward unraveling the deeper mysteries of intelligence in nature and in machines.\u202f <\/p>\n<\/div>\n<\/div>\n\n\n\n<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nMingqing Xiao (opens in new tab)<\/span><\/a>
Researcher<\/p>\n<\/div>\n\n\n\n\nThe human brain remains the only known realization of general intelligence, exhibiting remarkable efficiency and adaptability. Its energy efficiency, learning efficiency, and generalization capability are unparalleled compared to current AI models. Drawing inspiration from the brain at multiple levels\u2014from spatiotemporal neural dynamics, through learning principles, to high-level representations\u2014grounded in solid theoretical foundations, is essential for advancing AI toward more robust and human-like intelligence. At the same time, this synergy works both ways: AI can serve as a surrogate computational model to simulate or as a tool to decode brain processes, deepening our understanding of human brains. Exploring this bidirectional relationship between AI and neuroscience holds immense promise for shaping the next generation of intelligent systems and unraveling the mysteries of biological intelligence.\u202f <\/p>\n<\/div>\n<\/div>\n\n\n\n\n\n
<\/div>\n\n\n\n4. Rethink systems and networking in the era of AI\u202f <\/h5>\n\n\n\n
Rapid advances in AI are transforming modern computing infrastructure – bringing both fundamental challenges and unprecedented opportunities. <\/p>\n\n\n\n
We call for proposals that tackle these challenges and harness the emerging opportunities of the AI era. Submissions may span multiple dimensions of AI and computing systems, including but not limited to: <\/p>\n\n\n\n
1) AI-assisted self-driving networking infrastructure; <\/p>\n\n\n\n
2) Disruptive AI-assisted methods for lowering the barrier to building secure and reliable systems; <\/p>\n\n\n\n
3) Next-generation software and hardware architectures for AI; <\/p>\n\n\n\n
4) Development and debugging tools for intelligent systems. <\/p>\n\n\n\n
We particularly encourage unconventional and multidisciplinary approaches that break traditional boundaries and push the frontiers of AI and systems research. <\/p>\n\n\n\n
For more detailed information, please refer to the article Microsoft Research Asia StarTrack Scholars 2026: A Holistic Approach to Systems and Networking in the AI Era – Microsoft Research (opens in new tab)<\/span><\/a>.<\/p>\n\n\n\n\n\n\n- RL infrastructure\u202f <\/li>\n\n\n\n
- Agentic AI systems\u202f <\/li>\n\n\n\n
- Storage systems for AI\u202f <\/li>\n\n\n\n
- Verification for AI\u202f <\/li>\n\n\n\n
- LLM-Assisted Network Optimization\u202f <\/li>\n\n\n\n
- AI Networking Infrastructure\u202f <\/li>\n\n\n\n
- Network Protocol Troubleshooting\u202f <\/li>\n\n\n\n
- Infrastructure-aware resource allocation across compute, network, and devices\u202f <\/li>\n\n\n\n
- Adaptive configuration of collective communication algorithms\u202f <\/li>\n\n\n\n
- Network path selection and routing optimization for AI workloads\u202f <\/li>\n\n\n\n
- Autonomous device configuration and tuning\u202f <\/li>\n\n\n\n
- Integration of workload sensing with system-level orchestration for self-optimizing AI infrastructure\u202f<\/li>\n<\/ul>\n\n\n\n
<\/p>\n\n\n\n\n\n\n\n
\n\n<\/figure>\n\n\n\nYongqiang Xiong<\/a> (Engaging Lead)
<\/a>Sr. Principal Research Manager<\/p>\n<\/div>\n\n\n\n\nJoin us to revisit\/rethink\/rebuild the system and networking infrastructure by AI and for AI with a holistic and clean-slate perspective. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nFan Yang<\/a> (Engaging Lead)
Sr. Principal Research Manager<\/p>\n<\/div>\n\n\n\n\nWe welcome young systems researchers to join us exploring the disruptive system opportunities in the era of AI. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nJing Liu
<\/a>Senior Researcher<\/p>\n<\/div>\n\n\n\n\nWe welcome systems researchers to join us and build strong systems for the AI era together. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nBaotong Lu<\/a>
Senior Researcher<\/p>\n<\/div>\n\n\n\n\nWe welcome systems researchers to explore the next generation of data systems in the era of AI. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nLi Lyna Zhang<\/a>
Principal Researcher <\/p>\n<\/div>\n\n\n\n\nWe welcome researchers who are passionate about advancing agentic RL infrastructure and building general agents! <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\n
Application<\/h5>\n\n\n\n\n- Click the Registration Link (opens in new tab)<\/span><\/a> to submit your registration information.<\/li>\n\n\n\n
- Download the StarTrack Scholars Program Application Form.docx<\/a>, along with the Letter of Recommendation.docx<\/a> and Letter of Institutional Endorsement.docx<\/a> templates. Utilize the form and templates to provide formal application details, then forward them via email to Yanxuan Wu at v-yanxuanwu@microsoft.com<\/a>.<\/li>\n<\/ol>\n\n\n\n
If you have any questions, please email Ms. Yanxuan Wu, program manager of the Microsoft Research Asia StarTrack Scholars, at\u202fv-yanxuanwu@microsoft.com<\/a>. <\/em><\/p>\n\n\n\n<\/div>\n\n\n\n\n\n1. AI-Transformed Medical Research<\/h5>\n\n\n\n
This project focuses on developing media foundation models that integrate multiple modalities, including text, audio, image, video, and other signals. By leveraging these comprehensive models, we aim to explore and innovate across several research themes, including medical LLMs, multi-modal medical LLM integration, education agents, human-agent interaction, biomedical data synthesis and clinical translation and deployment.\u202f\u202f <\/p>\n\n\n\n
1) Foundation Model: This theme delves into the development of medical LLM for medical diagnostics, treatment planning, and patient care. Leveraging medical specific data and advanced pre-training and fine-tuning algorithms, we develop medical foundation models to improve the accuracy and efficiency of medical decision-making, ultimately enhancing patient outcomes.\u202f <\/p>\n\n\n\n
2\uff09Multi-Modal Medical LLM Integration. Building a universal model for all medical modalities is unrealistic due to limited data and the need for tailored domain knowledge integration for each modality. Instead, we unify unimodal foundation models into a single framework, fine-tuned for multi-modality tasks. This approach enables scalable and flexible deployment across diverse medical scenarios while preserving modality-specific strengths.\u202f <\/p>\n\n\n\n
3) Agentic System: This theme focuses on building a multi-agentic system powered by LLMs and VLMs to support doctors and medical researchers in their professional development and clinical practice. The system simulates collaborative environments involving expert agents that represent clinicians, researchers, and patients. It facilitates clinical scenario simulations for diagnostic reasoning and treatment planning, research collaboration simulations to support hypothesis generation, literature synthesis, and experimental design, and interactive training modules that enhance communication, decision-making, and interdisciplinary teamwork.\u202f\u202f <\/p>\n\n\n\n
4) AI-Transformed Medical Education. Leverage AI to revolutionize medical education for both students and professionals. The system provides personalized learning pathways using adaptive LLMs Immersive simulations with agent-based role-play (e.g., educators, patients, peers), real-time feedback and assessment to reinforce clinical reasoning and communication skills, and scalable platforms for continuous medical education (CME) and certification.\u202f <\/p>\n\n\n\n
5\uff09Medical Data Synthesis. This theme focusses on building biomedical data synthesis models that learn implicit medical distributions, generate diverse synthetic samples, and support causal reasoning to enhance the generalization of medical AI. By introducing a temporal disease progression framework that integrates longitudinal imaging, patient-specific conditions, and medical interventions, our approach seeks to provide a more comprehensive perspective on disease progression and treatment responses.\u202f <\/p>\n\n\n\n
6) Clinical Translation and Deployment for improved patient care. We focus on bridging the gap between research and real-world application by enabling the translation and deployment of AI systems in clinical settings. This includes collaborating with healthcare institutions, adapting and validating foundation models, and integrating solutions into healthcare workflows.\u202f <\/p>\n\n\n\n
Through these research themes, our project aims to achieve several key goals including cultivating future medical talents, fostering technological breakthroughs and accelerating translation to practical applications by developing SOTA performance models for improved patient care and publishing papers in top-tier conferences and journals.\u202f <\/p>\n\n\n\n
For more detailed information, please refer to the article Microsoft Research Asia StarTrack Scholars 2026: AI-Transformed Medical Research – Microsoft Research (opens in new tab)<\/span><\/a>.<\/p>\n\n\n\n\n\n\n- Medical LLMs\u202f<\/li>\n\n\n\n
- Multi-modal LLMs\u202f<\/li>\n\n\n\n
- Medical AI\u202f<\/li>\n\n\n\n
- Medical Agents\u202f<\/li>\n\n\n\n
- Medical Education\u202f<\/li>\n\n\n\n
- Medical Data Synthesis\u202f<\/li>\n\n\n\n
- Medical Diagnosis\u202f<\/li>\n\n\n\n
- Disease Progression Prediction\u202f<\/li>\n<\/ul>\n\n\n\n\n\n\n\n\n\n
<\/figure>\n\n\n\nShujie Liu<\/a> (Engaging Lead)
Principal Researcher<\/p>\n<\/div>\n\n\n\n\nWe invite researchers passionate about advancing AI in healthcare to join our pioneering research initiatives. Our programs focus on AI foundation models, agentic medical research, and transformative approaches to medical education. We aim to develop intelligent systems that not only analyze and interpret complex medical data but also assist in decision-making, personalized care, and adaptive learning for clinicians and students. We are particularly interested in scholars exploring multimodal AI for medical imaging and diagnostics, autonomous agents for clinical workflows, and innovative educational platforms powered by AI. By fostering interdisciplinary collaboration across medicine, AI, and human-computer interaction, we strive to create groundbreaking technologies that redefine the future of healthcare and medical training. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nXinxing Xu<\/a> (Engaging Lead)
Principal Research Manager <\/p>\n<\/div>\n\n\n\n\nWe invite researchers passionate about advancing AI in healthcare to join our pioneering initiatives spanning multimodal AI, generative AI, agentic AI, and the translation and deployment of AI systems in real-world clinical environments. We aim to bridge the gap between cutting-edge AI research and clinical impact by ensuring that AI innovations are not only scientifically rigorous but also validated, adapted, and seamlessly integrated into healthcare workflows. We are committed to transforming foundational AI advances into practical, trustworthy tools that enhance patient outcomes, clinical decision-making, and healthcare system efficiency.<\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nZilong Wang<\/a>
Senior Researcher\u202f <\/p>\n<\/div>\n\n\n\n\nWe invite researchers with strong interests in advancing the intersection of artificial intelligence and real-world healthcare practice. Our research focuses on evaluating and benchmarking foundation models and AI agents in realistic medical contexts, emphasizing their capabilities, reliability, and collaboration with clinicians. We are particularly interested in developing and assessing practical foundation models for clinical decision support, diagnostic reasoning, and workflow optimization.\u202f <\/p>\n\n\n\n
Our work aims to bridge the gap between AI research and clinical application by exploring user interaction, human-AI collaboration, and agentic intelligence in healthcare environments. We welcome visiting scholars and collaborators passionate about building, adapting, and rigorously testing foundation models and agent-driven systems that advance trustworthy, effective, and human-centered AI for medicine.\u202f <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nXinyang Jiang<\/a>
Senior Researcher\u202f <\/p>\n<\/div>\n\n\n\n\nOur research projects centre on developing and applying foundation models using diverse types of medical data and unifiedly integrate different foundation models and modalities to address significant challenges in healthcare research.\u202f We are particularly interested in visiting scholars and collaborators with research interests and expertise in medical AI and multi-modal intelligence, especially those aiming to develop novel foundation models that advance diagnosis, prognosis, and treatment personalization.<\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nJinglu Wang
<\/a>Senior Researcher\u202f <\/p>\n<\/div>\n\n\n\n\nWe invite researchers with strong interests in the intersection of artificial intelligence, medicine, and education, particularly those exploring agentic AI. Our overarching goal is to advance the scientific foundations and practical applications of agentic AI systems for healthcare and medical education. We aim to investigate how autonomous, communicative, and trustworthy AI agents can enhance medical reasoning, clinical training, and knowledge dissemination. This includes developing frameworks that integrate multi-modal understanding, collaborative reasoning, and interactive learning environments. We welcome visiting scholars and collaborators who seek to contribute to this emerging paradigm of agentic intelligence in healthcare and medical education, and to jointly explore its theoretical, technical, and societal implications.\u202f<\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nJingjing Fu
<\/a>Senior Researcher\u202f <\/p>\n<\/div>\n\n\n\n\nWe invite researchers with strong interests in advancing the frontier of medical data synthesis and shaping its transformative impact on healthcare AI. Our work focuses on developing medical world models and advanced generative frameworks that learn implicit medical distributions, generate diverse and high-fidelity synthetic data, and support causal reasoning to enhance model generalization and robustness. We welcome visiting scholars and collaborators passionate about multimodal generative modeling, disease trajectory simulation, and causal inference in medicine.\u202f<\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nChang Xu<\/a>
Senior Researcher\u202f <\/p>\n<\/div>\n\n\n\n\nWe invite researchers passionate about advancing medical AI through foundation models, time series analysis, generative modeling, and agent systems. Our research focuses on developing medical foundation models that integrate multi-modal data\u2014including time series, text, and imaging\u2014to enhance diagnostic reasoning, clinical decision-making, and data-driven discovery. We also explore intelligent medical agents and synthetic data generation frameworks that enable adaptive learning, collaborative reasoning, and robust generalization. We welcome visiting scholars eager to contribute to building trustworthy, interpretable, and impactful AI systems for next-generation healthcare.\u202f <\/p>\n<\/div>\n<\/div>\n\n\n\n\n\n
<\/div>\n\n\n\n2. Agentic AI: Reimagining Future Human\u2013Agent Communication and Collaboration <\/h5>\n\n\n\n
Agentic AI marks a shift from passive systems to active collaborators\u2014AI agents that engage in sustained reasoning, understand complex multimodal environments, and interact naturally with humans over extended time and contexts. Our vision is to build intelligent systems that participate meaningfully in knowledge discovery, content creation, communication, and decision-making. <\/p>\n\n\n\n
We organize our understanding of Agentic AI into three interrelated categories, each reflecting a different facet of the ecosystem these systems must inhabit: <\/p>\n\n\n\n
1. Foundations & Frameworks <\/p>\n\n\n\n
Agentic AI requires new computational foundations to operate effectively across time, context, and modalities. We seek to develop compact and grounded multimodal representations that allow systems to perceive and reason over complex visual, auditory, and sensor-rich environments. <\/p>\n\n\n\n
To support long-term engagement and contextual reasoning, we explore advances in semantic memory and process memory\u2014compression mechanisms that allow agents to retain knowledge across interactions and reason over long horizons. Retrieval-augmented pipelines further enrich reasoning with dynamic access to knowledge bases and structured memory. <\/p>\n\n\n\n
Agents should also coordinate and plan over extended workflows. We encourage research on multi-agent collaboration, process-aligned action spaces, and models that align with the semantics of user-driven tasks. Contributions in this category might include novel architectures, datasets, training methods, or theoretical insights that strengthen the core reasoning and planning capabilities of agentic systems. <\/p>\n\n\n\n
2. User Experiences and Human-Agent Interfaces <\/p>\n\n\n\n
Human\u2013agent interaction must evolve to meet the demands of fluid, multimodal collaboration. We envision interfaces that are generative and dynamic, constructed to suit user intent and task context. Proposals should consider how agents generate or adapt interfaces in real time, across devices, modalities, and immersive environments. <\/p>\n\n\n\n
Agents should be capable of interactive visualization, using media to communicate internal states, uncertainties, and possible outcomes. Interaction should be audience- and context-adaptive, with personalized behaviors that respect different usage settings and preserve user privacy. <\/p>\n\n\n\n
We also encourage work on process-aware collaboration, where agents sustain memory across sessions and adapt over time. Topics such as communicative effectiveness, trust, interpretability, and longitudinal studies are central. Researchers should also consider how interface design, memory, and communication strategies can support natural, explainable, and long-term agentic interaction. <\/p>\n\n\n\n
3. Applications & Societal Impact <\/p>\n\n\n\n
We see Agentic AI as a transformative force across domains like science, healthcare, education, and enterprise decision-making. These applications require agents to perform deep research\u2014gathering evidence, testing hypotheses, and constructing trustworthy narratives with transparency and provenance. <\/p>\n\n\n\n
In the media domain, agentic AI systems could enable advanced content generation, curation, and personalization by understanding and synthesizing information from multiple modalities such as text, audio, and video. These agents support content creators by automating research, summarization, and fact-checking, or by generating interactive and adaptive media experiences tailored to audience preferences. Additionally, agents could facilitate media analysis at scale\u2014detecting trends, ensuring provenance, and helping organizations manage and distribute content more effectively. <\/p>\n\n\n\n
Finally, as these systems scale, we must ensure they operate responsibly. We encourage proposals on provenance tracking, watermarking, privacy-preserving design, and energy-efficient deployment. We also welcome organizational and societal studies that examine how agentic systems are adopted, trusted, and evaluated in the wild. <\/p>\n\n\n\n
For more detailed information, please refer to the article Microsoft Research Asia StarTrack Scholars 2026: Agentic AI: Reimagining Future Human\u2013Agent Communication and Collaboration<\/a><\/p>\n\n\n\n<\/p>\n\n\n\n\n\n
\n- Agentic deep research model training: data, tools, and algorithms\u202f\u202f <\/li>\n\n\n\n
- Deep research applications in science, healthcare, enterprise, and education\u202f\u202f <\/li>\n\n\n\n
- Media foundations: multimodal representation learning, neural compression, multimodal understanding, world models\u202f\u202f <\/li>\n\n\n\n
- Context and memory: semantic memory, process memory, long-context reasoning, retrieval-augmented systems\u202f\u202f <\/li>\n\n\n\n
- Agentic capabilities: planning, tool use, multi-agent collaboration, process-aligned action spaces\u202f\u202f <\/li>\n\n\n\n
- Generative and adaptive user interfaces: UI generation, audience\/context adaptation, interactive visualization, cross-device\/immersive experiences\u202f\u202f <\/li>\n\n\n\n
- Human-centered AI application and evaluation: process-aware collaboration, communicative effectiveness, trust and interpretability, longitudinal and organizational studies\u202f\u202f <\/li>\n\n\n\n
- Responsible media and systems: provenance, watermarking, privacy-preserving design, efficiency, and deployment at scale\u202f <\/li>\n<\/ul>\n\n\n\n\n\n\n\n\n\n
<\/figure>\n\n\n\nYan Lu<\/a> (Engaging Lead)
Partner Research Manager<\/p>\n<\/div>\n\n\n\n\nWe welcome applicants who possess a strong background in Multimedia, Immersive AI, Agentic AI, HCI or related fields, including individuals with experience in neural video communication, computer vision, 3D and graphics, audio and speech, and other related domains. We also expect participants to be passionate about exploring the paradigm shifts in building future media and communication experience and are eager to contribute to the development of advanced agentic media ecosystem that can enhance human learning and creativity.\u202f <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nChong Luo<\/a> (Engaging Lead)
Sr. Principal Research Manager <\/p>\n<\/div>\n\n\n\n\nMy team is particularly excited about advancing the deep research agent aspect of Agentic AI. We aim to develop models capable of conducting autonomous, evidence-based research across scientific and enterprise domains. We hope to collaborate closely with visiting researchers on data, tools, and training algorithms that empower agents to reason, hypothesize, and construct transparent knowledge artifacts. Through this collaboration, we expect to jointly explore real-world applications in science, healthcare, and education, and to establish a foundation for trustworthy agentic research systems. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nJiahao Li<\/a>
<\/strong>Principal Researcher<\/p>\n<\/div>\n\n\n\n\nWe welcome candidates with expertise in multimedia and AI, such as neural compression, representation learning, and generative modeling. We value individuals passionate about investigating paradigm shifts in video research and contributing to advanced AI systems, including agentic capabilities like long-horizon planning, tool use, and memory-centric workflows, to propel communication, decision-making, and knowledge creation. Through seamless collaboration, we are excited to push the frontiers of AI and media technologies together. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nYun Wang<\/a>
Senior Researcher<\/p>\n<\/div>\n\n\n\n\nWe welcome applicants with strong backgrounds in Human\u2013AI Interaction, Intelligent Systems, and related areas such as visualization, multimodal communication, and cognitive modeling. We are particularly interested in individuals who aspire to rethink how intelligence is expressed, shared, and co-evolved between humans and AI. We value researchers who not only advance technical capability but also interrogate the underlying paradigms of collaboration, reasoning, and communication. Visiting researchers are encouraged to explore new frameworks, representations, and processes that connect human cognition with computational intelligence\u2014toward more transparent, adaptive, and meaning-centered systems that augment human understanding and creativity. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nXun Guo<\/a>
Principal Researcher <\/p>\n<\/div>\n\n\n\n\nWe welcome applicants with a strong background in Multimedia, Agentic AI, and Multimodal Learning, and particularly value experience in generative models for video, vision-language, and other modalities. Participants are encouraged to explore agentic AI systems that understand user intent across modalities (e.g., video, audio, and text) and generate interactive outputs accordingly, or contribute to related research areas such as multimodal learning, video generation, and vision-language alignment. These efforts aim to support intuitive, intent-driven user experiences and advance the frontier of future media technologies. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nKai Qiu<\/a>
Senior Researcher <\/p>\n<\/div>\n\n\n\n\nWe welcome applicants with a strong background in reinforcement learning, LLM, and agent-based AI. We also expect participants to be passionate about exploring paradigm shifts in creating advanced autonomous agents \u2013 such as designing agents with long-horizon planning, tool-use integration, and memory-driven reasoning \u2013 and are eager to contribute to the development of next-generation agentic AI systems capable of autonomously tackling complex and augmenting human capabilities. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nQi Dai<\/a>
Principal Researcher<\/p>\n<\/div>\n\n\n\n\nWe welcome applicants who are excited to push the frontier of agentic AI research. Ideal candidates will have a solid foundation in reinforcement learning, agents, LLM and VLM. We are particularly interested in researchers who can imagine and build systems in which agents perceive multimodal inputs, remember and reason over extended time horizons, and dynamically orchestrate external tools to solve open-ended tasks. If you are passionate about transforming these ingredients into robust, adaptive agents that expand human potential, we encourage you to apply. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nBei Liu<\/a>
Senior Researcher <\/p>\n<\/div>\n\n\n\n\nWe welcome visiting researchers who are motivated, collaborative, and passionate about advancing agentic AI. Candidates with independent ideas and strong interest in general agentic models, Vision-Language Models, and related areas\u2014such as multimodal reasoning, long-horizon reasoning, or reinforcement learning\u2014are highly encouraged to join us and engage in open, productive exchange. <\/p>\n<\/div>\n<\/div>\n\n\n\n\n\n
<\/div>\n\n\n\n3. Brain and Artificial Intelligence <\/h5>\n\n\n\n
We conduct interdisciplinary research at the intersection of artificial intelligence (AI) and neuroscience, aiming both to leverage AI for advancing our understanding of the brain and to use these insights to improve AI systems and brain health. On one front, brain-inspired AI seeks to incorporate neurobiological principles to create energy-efficient, robust, and human-like intelligence, driving the development of next-generation AI technologies. On the other front, human brain signals (e.g., EEG and fMRI) are inherently noisy, with low signal-to-noise ratios that hinder their practical utility. To overcome this challenge, we strive to develop foundational models of brain activity capable of decoding a wide range of human perceptions and supporting efforts to address neurological disorders.\u202f <\/p>\n\n\n\n
For more detailed information, please refer to the article Microsoft Research Asia StarTrack Scholars 2026: Create a synergistic relationship between AI and the brain – Microsoft Research (opens in new tab)<\/span><\/a>.<\/p>\n\n\n\n\n\n\n- Brain-inspired AI\u202f <\/li>\n\n\n\n
- Brain-computer Interface\u202f <\/li>\n\n\n\n
- AI for Brain Health\u202f <\/li>\n<\/ul>\n\n\n\n\n\n\n\n\n\n
<\/figure>\n\n\n\nDongsheng Li<\/a> (Engaging Lead)
Principal Research Manager\u202f <\/p>\n<\/div>\n\n\n\n\nAs AI is transforming the world, it is equally crucial to deepen our understanding of biological intelligence. The integration of AI and brain science research holds immense potential to drive innovation, enhance human well-being, and expand our knowledge of both artificial and biological intelligence. I am eager to collaborate with researchers in this field to revolutionize existing paradigms through interdisciplinary studies, advancing both AI and brain science in groundbreaking ways.\u202f <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nYansen Wang<\/u>
<\/a>Senior Researcher<\/p>\n<\/div>\n\n\n\n\nOur brain, encapsulated within a structure no larger than our two fists, is a marvel of complexity and power. It governs every facet of our thoughts, emotions, and actions, enabling feats from artistic expression to scientific innovation. Yet, much of its inner workings remain shrouded in mystery, with countless questions about consciousness, memory, and cognition still unanswered. Understanding the brain is not only essential for unraveling these mysteries but also for constructing interfaces between the brain and artificial intelligence. I hope to see a better synergy in the future.\u202f \u202f <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nDongqi Han<\/a><\/u>
Senior Researcher\u202f<\/p>\n<\/div>\n\n\n\n\nGeoffrey Hinton once said, \u201cI have always been convinced that the only way to get artificial intelligence to work is to do the computation in a way similar to the human brain<\/em>.\u201d This conviction reveals a deep philosophical and practical bridge between neuroscience and AI: by studying how the brain computes, learns, and adapts, we gain principles that can guide more effective artificial systems; conversely, AI models provide testable hypotheses and experimental platforms for exploring brain function. Synergizing AI and brain science means accelerating mutual progress \u2014 using neural insights to inspire new architectures, and leveraging AI to simulate, analyze, and even predict biological behavior \u2014 all toward unraveling the deeper mysteries of intelligence in nature and in machines.\u202f <\/p>\n<\/div>\n<\/div>\n\n\n\n<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nMingqing Xiao (opens in new tab)<\/span><\/a>
Researcher<\/p>\n<\/div>\n\n\n\n\nThe human brain remains the only known realization of general intelligence, exhibiting remarkable efficiency and adaptability. Its energy efficiency, learning efficiency, and generalization capability are unparalleled compared to current AI models. Drawing inspiration from the brain at multiple levels\u2014from spatiotemporal neural dynamics, through learning principles, to high-level representations\u2014grounded in solid theoretical foundations, is essential for advancing AI toward more robust and human-like intelligence. At the same time, this synergy works both ways: AI can serve as a surrogate computational model to simulate or as a tool to decode brain processes, deepening our understanding of human brains. Exploring this bidirectional relationship between AI and neuroscience holds immense promise for shaping the next generation of intelligent systems and unraveling the mysteries of biological intelligence.\u202f <\/p>\n<\/div>\n<\/div>\n\n\n\n\n\n
<\/div>\n\n\n\n4. Rethink systems and networking in the era of AI\u202f <\/h5>\n\n\n\n
Rapid advances in AI are transforming modern computing infrastructure – bringing both fundamental challenges and unprecedented opportunities. <\/p>\n\n\n\n
We call for proposals that tackle these challenges and harness the emerging opportunities of the AI era. Submissions may span multiple dimensions of AI and computing systems, including but not limited to: <\/p>\n\n\n\n
1) AI-assisted self-driving networking infrastructure; <\/p>\n\n\n\n
2) Disruptive AI-assisted methods for lowering the barrier to building secure and reliable systems; <\/p>\n\n\n\n
3) Next-generation software and hardware architectures for AI; <\/p>\n\n\n\n
4) Development and debugging tools for intelligent systems. <\/p>\n\n\n\n
We particularly encourage unconventional and multidisciplinary approaches that break traditional boundaries and push the frontiers of AI and systems research. <\/p>\n\n\n\n
For more detailed information, please refer to the article Microsoft Research Asia StarTrack Scholars 2026: A Holistic Approach to Systems and Networking in the AI Era – Microsoft Research (opens in new tab)<\/span><\/a>.<\/p>\n\n\n\n\n\n\n- RL infrastructure\u202f <\/li>\n\n\n\n
- Agentic AI systems\u202f <\/li>\n\n\n\n
- Storage systems for AI\u202f <\/li>\n\n\n\n
- Verification for AI\u202f <\/li>\n\n\n\n
- LLM-Assisted Network Optimization\u202f <\/li>\n\n\n\n
- AI Networking Infrastructure\u202f <\/li>\n\n\n\n
- Network Protocol Troubleshooting\u202f <\/li>\n\n\n\n
- Infrastructure-aware resource allocation across compute, network, and devices\u202f <\/li>\n\n\n\n
- Adaptive configuration of collective communication algorithms\u202f <\/li>\n\n\n\n
- Network path selection and routing optimization for AI workloads\u202f <\/li>\n\n\n\n
- Autonomous device configuration and tuning\u202f <\/li>\n\n\n\n
- Integration of workload sensing with system-level orchestration for self-optimizing AI infrastructure\u202f<\/li>\n<\/ul>\n\n\n\n
<\/p>\n\n\n\n\n\n\n\n
\n\n<\/figure>\n\n\n\nYongqiang Xiong<\/a> (Engaging Lead)
<\/a>Sr. Principal Research Manager<\/p>\n<\/div>\n\n\n\n\nJoin us to revisit\/rethink\/rebuild the system and networking infrastructure by AI and for AI with a holistic and clean-slate perspective. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nFan Yang<\/a> (Engaging Lead)
Sr. Principal Research Manager<\/p>\n<\/div>\n\n\n\n\nWe welcome young systems researchers to join us exploring the disruptive system opportunities in the era of AI. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nJing Liu
<\/a>Senior Researcher<\/p>\n<\/div>\n\n\n\n\nWe welcome systems researchers to join us and build strong systems for the AI era together. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nBaotong Lu<\/a>
Senior Researcher<\/p>\n<\/div>\n\n\n\n\nWe welcome systems researchers to explore the next generation of data systems in the era of AI. <\/p>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\nLi Lyna Zhang<\/a>
Principal Researcher <\/p>\n<\/div>\n\n\n\n\nWe welcome researchers who are passionate about advancing agentic RL infrastructure and building general agents! <\/p>\n<\/div>\n<\/div>\n\n\n\n
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If you have any questions, please email Ms. Yanxuan Wu, program manager of the Microsoft Research Asia StarTrack Scholars, at\u202fv-yanxuanwu@microsoft.com<\/a>. <\/em><\/p>\n\n\n\n This project focuses on developing media foundation models that integrate multiple modalities, including text, audio, image, video, and other signals. By leveraging these comprehensive models, we aim to explore and innovate across several research themes, including medical LLMs, multi-modal medical LLM integration, education agents, human-agent interaction, biomedical data synthesis and clinical translation and deployment.\u202f\u202f <\/p>\n\n\n\n 1) Foundation Model: This theme delves into the development of medical LLM for medical diagnostics, treatment planning, and patient care. Leveraging medical specific data and advanced pre-training and fine-tuning algorithms, we develop medical foundation models to improve the accuracy and efficiency of medical decision-making, ultimately enhancing patient outcomes.\u202f <\/p>\n\n\n\n 2\uff09Multi-Modal Medical LLM Integration. Building a universal model for all medical modalities is unrealistic due to limited data and the need for tailored domain knowledge integration for each modality. Instead, we unify unimodal foundation models into a single framework, fine-tuned for multi-modality tasks. This approach enables scalable and flexible deployment across diverse medical scenarios while preserving modality-specific strengths.\u202f <\/p>\n\n\n\n 3) Agentic System: This theme focuses on building a multi-agentic system powered by LLMs and VLMs to support doctors and medical researchers in their professional development and clinical practice. The system simulates collaborative environments involving expert agents that represent clinicians, researchers, and patients. It facilitates clinical scenario simulations for diagnostic reasoning and treatment planning, research collaboration simulations to support hypothesis generation, literature synthesis, and experimental design, and interactive training modules that enhance communication, decision-making, and interdisciplinary teamwork.\u202f\u202f <\/p>\n\n\n\n 4) AI-Transformed Medical Education. Leverage AI to revolutionize medical education for both students and professionals. The system provides personalized learning pathways using adaptive LLMs Immersive simulations with agent-based role-play (e.g., educators, patients, peers), real-time feedback and assessment to reinforce clinical reasoning and communication skills, and scalable platforms for continuous medical education (CME) and certification.\u202f <\/p>\n\n\n\n 5\uff09Medical Data Synthesis. This theme focusses on building biomedical data synthesis models that learn implicit medical distributions, generate diverse synthetic samples, and support causal reasoning to enhance the generalization of medical AI. By introducing a temporal disease progression framework that integrates longitudinal imaging, patient-specific conditions, and medical interventions, our approach seeks to provide a more comprehensive perspective on disease progression and treatment responses.\u202f <\/p>\n\n\n\n 6) Clinical Translation and Deployment for improved patient care. We focus on bridging the gap between research and real-world application by enabling the translation and deployment of AI systems in clinical settings. This includes collaborating with healthcare institutions, adapting and validating foundation models, and integrating solutions into healthcare workflows.\u202f <\/p>\n\n\n\n Through these research themes, our project aims to achieve several key goals including cultivating future medical talents, fostering technological breakthroughs and accelerating translation to practical applications by developing SOTA performance models for improved patient care and publishing papers in top-tier conferences and journals.\u202f <\/p>\n\n\n\n For more detailed information, please refer to the article Microsoft Research Asia StarTrack Scholars 2026: AI-Transformed Medical Research – Microsoft Research (opens in new tab)<\/span><\/a>.<\/p>\n\n\n\n\n\n Shujie Liu<\/a> (Engaging Lead) We invite researchers passionate about advancing AI in healthcare to join our pioneering research initiatives. Our programs focus on AI foundation models, agentic medical research, and transformative approaches to medical education. We aim to develop intelligent systems that not only analyze and interpret complex medical data but also assist in decision-making, personalized care, and adaptive learning for clinicians and students. We are particularly interested in scholars exploring multimodal AI for medical imaging and diagnostics, autonomous agents for clinical workflows, and innovative educational platforms powered by AI. By fostering interdisciplinary collaboration across medicine, AI, and human-computer interaction, we strive to create groundbreaking technologies that redefine the future of healthcare and medical training. <\/p>\n<\/div>\n<\/div>\n\n\n\n Xinxing Xu<\/a> (Engaging Lead) We invite researchers passionate about advancing AI in healthcare to join our pioneering initiatives spanning multimodal AI, generative AI, agentic AI, and the translation and deployment of AI systems in real-world clinical environments. We aim to bridge the gap between cutting-edge AI research and clinical impact by ensuring that AI innovations are not only scientifically rigorous but also validated, adapted, and seamlessly integrated into healthcare workflows. We are committed to transforming foundational AI advances into practical, trustworthy tools that enhance patient outcomes, clinical decision-making, and healthcare system efficiency.<\/p>\n<\/div>\n<\/div>\n\n\n\n Zilong Wang<\/a> We invite researchers with strong interests in advancing the intersection of artificial intelligence and real-world healthcare practice. Our research focuses on evaluating and benchmarking foundation models and AI agents in realistic medical contexts, emphasizing their capabilities, reliability, and collaboration with clinicians. We are particularly interested in developing and assessing practical foundation models for clinical decision support, diagnostic reasoning, and workflow optimization.\u202f <\/p>\n\n\n\n Our work aims to bridge the gap between AI research and clinical application by exploring user interaction, human-AI collaboration, and agentic intelligence in healthcare environments. We welcome visiting scholars and collaborators passionate about building, adapting, and rigorously testing foundation models and agent-driven systems that advance trustworthy, effective, and human-centered AI for medicine.\u202f <\/p>\n<\/div>\n<\/div>\n\n\n\n Xinyang Jiang<\/a> Our research projects centre on developing and applying foundation models using diverse types of medical data and unifiedly integrate different foundation models and modalities to address significant challenges in healthcare research.\u202f We are particularly interested in visiting scholars and collaborators with research interests and expertise in medical AI and multi-modal intelligence, especially those aiming to develop novel foundation models that advance diagnosis, prognosis, and treatment personalization.<\/p>\n<\/div>\n<\/div>\n\n\n\n Jinglu Wang We invite researchers with strong interests in the intersection of artificial intelligence, medicine, and education, particularly those exploring agentic AI. Our overarching goal is to advance the scientific foundations and practical applications of agentic AI systems for healthcare and medical education. We aim to investigate how autonomous, communicative, and trustworthy AI agents can enhance medical reasoning, clinical training, and knowledge dissemination. This includes developing frameworks that integrate multi-modal understanding, collaborative reasoning, and interactive learning environments. We welcome visiting scholars and collaborators who seek to contribute to this emerging paradigm of agentic intelligence in healthcare and medical education, and to jointly explore its theoretical, technical, and societal implications.\u202f<\/p>\n<\/div>\n<\/div>\n\n\n\n Jingjing Fu We invite researchers with strong interests in advancing the frontier of medical data synthesis and shaping its transformative impact on healthcare AI. Our work focuses on developing medical world models and advanced generative frameworks that learn implicit medical distributions, generate diverse and high-fidelity synthetic data, and support causal reasoning to enhance model generalization and robustness. We welcome visiting scholars and collaborators passionate about multimodal generative modeling, disease trajectory simulation, and causal inference in medicine.\u202f<\/p>\n<\/div>\n<\/div>\n\n\n\n Chang Xu<\/a> We invite researchers passionate about advancing medical AI through foundation models, time series analysis, generative modeling, and agent systems. Our research focuses on developing medical foundation models that integrate multi-modal data\u2014including time series, text, and imaging\u2014to enhance diagnostic reasoning, clinical decision-making, and data-driven discovery. We also explore intelligent medical agents and synthetic data generation frameworks that enable adaptive learning, collaborative reasoning, and robust generalization. We welcome visiting scholars eager to contribute to building trustworthy, interpretable, and impactful AI systems for next-generation healthcare.\u202f <\/p>\n<\/div>\n<\/div>\n\n\n\n\n\n Agentic AI marks a shift from passive systems to active collaborators\u2014AI agents that engage in sustained reasoning, understand complex multimodal environments, and interact naturally with humans over extended time and contexts. Our vision is to build intelligent systems that participate meaningfully in knowledge discovery, content creation, communication, and decision-making. <\/p>\n\n\n\n We organize our understanding of Agentic AI into three interrelated categories, each reflecting a different facet of the ecosystem these systems must inhabit: <\/p>\n\n\n\n 1. Foundations & Frameworks <\/p>\n\n\n\n Agentic AI requires new computational foundations to operate effectively across time, context, and modalities. We seek to develop compact and grounded multimodal representations that allow systems to perceive and reason over complex visual, auditory, and sensor-rich environments. <\/p>\n\n\n\n To support long-term engagement and contextual reasoning, we explore advances in semantic memory and process memory\u2014compression mechanisms that allow agents to retain knowledge across interactions and reason over long horizons. Retrieval-augmented pipelines further enrich reasoning with dynamic access to knowledge bases and structured memory. <\/p>\n\n\n\n Agents should also coordinate and plan over extended workflows. We encourage research on multi-agent collaboration, process-aligned action spaces, and models that align with the semantics of user-driven tasks. Contributions in this category might include novel architectures, datasets, training methods, or theoretical insights that strengthen the core reasoning and planning capabilities of agentic systems. <\/p>\n\n\n\n 2. User Experiences and Human-Agent Interfaces <\/p>\n\n\n\n Human\u2013agent interaction must evolve to meet the demands of fluid, multimodal collaboration. We envision interfaces that are generative and dynamic, constructed to suit user intent and task context. Proposals should consider how agents generate or adapt interfaces in real time, across devices, modalities, and immersive environments. <\/p>\n\n\n\n Agents should be capable of interactive visualization, using media to communicate internal states, uncertainties, and possible outcomes. Interaction should be audience- and context-adaptive, with personalized behaviors that respect different usage settings and preserve user privacy. <\/p>\n\n\n\n We also encourage work on process-aware collaboration, where agents sustain memory across sessions and adapt over time. Topics such as communicative effectiveness, trust, interpretability, and longitudinal studies are central. Researchers should also consider how interface design, memory, and communication strategies can support natural, explainable, and long-term agentic interaction. <\/p>\n\n\n\n 3. Applications & Societal Impact <\/p>\n\n\n\n We see Agentic AI as a transformative force across domains like science, healthcare, education, and enterprise decision-making. These applications require agents to perform deep research\u2014gathering evidence, testing hypotheses, and constructing trustworthy narratives with transparency and provenance. <\/p>\n\n\n\n In the media domain, agentic AI systems could enable advanced content generation, curation, and personalization by understanding and synthesizing information from multiple modalities such as text, audio, and video. These agents support content creators by automating research, summarization, and fact-checking, or by generating interactive and adaptive media experiences tailored to audience preferences. Additionally, agents could facilitate media analysis at scale\u2014detecting trends, ensuring provenance, and helping organizations manage and distribute content more effectively. <\/p>\n\n\n\n Finally, as these systems scale, we must ensure they operate responsibly. We encourage proposals on provenance tracking, watermarking, privacy-preserving design, and energy-efficient deployment. We also welcome organizational and societal studies that examine how agentic systems are adopted, trusted, and evaluated in the wild. <\/p>\n\n\n\n For more detailed information, please refer to the article Microsoft Research Asia StarTrack Scholars 2026: Agentic AI: Reimagining Future Human\u2013Agent Communication and Collaboration<\/a><\/p>\n\n\n\n <\/p>\n\n\n\n\n\n Yan Lu<\/a> (Engaging Lead) We welcome applicants who possess a strong background in Multimedia, Immersive AI, Agentic AI, HCI or related fields, including individuals with experience in neural video communication, computer vision, 3D and graphics, audio and speech, and other related domains. We also expect participants to be passionate about exploring the paradigm shifts in building future media and communication experience and are eager to contribute to the development of advanced agentic media ecosystem that can enhance human learning and creativity.\u202f <\/p>\n<\/div>\n<\/div>\n\n\n\n Chong Luo<\/a> (Engaging Lead) My team is particularly excited about advancing the deep research agent aspect of Agentic AI. We aim to develop models capable of conducting autonomous, evidence-based research across scientific and enterprise domains. We hope to collaborate closely with visiting researchers on data, tools, and training algorithms that empower agents to reason, hypothesize, and construct transparent knowledge artifacts. Through this collaboration, we expect to jointly explore real-world applications in science, healthcare, and education, and to establish a foundation for trustworthy agentic research systems. <\/p>\n<\/div>\n<\/div>\n\n\n\n Jiahao Li<\/a> We welcome candidates with expertise in multimedia and AI, such as neural compression, representation learning, and generative modeling. We value individuals passionate about investigating paradigm shifts in video research and contributing to advanced AI systems, including agentic capabilities like long-horizon planning, tool use, and memory-centric workflows, to propel communication, decision-making, and knowledge creation. Through seamless collaboration, we are excited to push the frontiers of AI and media technologies together. <\/p>\n<\/div>\n<\/div>\n\n\n\n Yun Wang<\/a> We welcome applicants with strong backgrounds in Human\u2013AI Interaction, Intelligent Systems, and related areas such as visualization, multimodal communication, and cognitive modeling. We are particularly interested in individuals who aspire to rethink how intelligence is expressed, shared, and co-evolved between humans and AI. We value researchers who not only advance technical capability but also interrogate the underlying paradigms of collaboration, reasoning, and communication. Visiting researchers are encouraged to explore new frameworks, representations, and processes that connect human cognition with computational intelligence\u2014toward more transparent, adaptive, and meaning-centered systems that augment human understanding and creativity. <\/p>\n<\/div>\n<\/div>\n\n\n\n Xun Guo<\/a> We welcome applicants with a strong background in Multimedia, Agentic AI, and Multimodal Learning, and particularly value experience in generative models for video, vision-language, and other modalities. Participants are encouraged to explore agentic AI systems that understand user intent across modalities (e.g., video, audio, and text) and generate interactive outputs accordingly, or contribute to related research areas such as multimodal learning, video generation, and vision-language alignment. These efforts aim to support intuitive, intent-driven user experiences and advance the frontier of future media technologies. <\/p>\n<\/div>\n<\/div>\n\n\n\n Kai Qiu<\/a> We welcome applicants with a strong background in reinforcement learning, LLM, and agent-based AI. We also expect participants to be passionate about exploring paradigm shifts in creating advanced autonomous agents \u2013 such as designing agents with long-horizon planning, tool-use integration, and memory-driven reasoning \u2013 and are eager to contribute to the development of next-generation agentic AI systems capable of autonomously tackling complex and augmenting human capabilities. <\/p>\n<\/div>\n<\/div>\n\n\n\n Qi Dai<\/a> We welcome applicants who are excited to push the frontier of agentic AI research. Ideal candidates will have a solid foundation in reinforcement learning, agents, LLM and VLM. We are particularly interested in researchers who can imagine and build systems in which agents perceive multimodal inputs, remember and reason over extended time horizons, and dynamically orchestrate external tools to solve open-ended tasks. If you are passionate about transforming these ingredients into robust, adaptive agents that expand human potential, we encourage you to apply. <\/p>\n<\/div>\n<\/div>\n\n\n\n Bei Liu<\/a> We welcome visiting researchers who are motivated, collaborative, and passionate about advancing agentic AI. Candidates with independent ideas and strong interest in general agentic models, Vision-Language Models, and related areas\u2014such as multimodal reasoning, long-horizon reasoning, or reinforcement learning\u2014are highly encouraged to join us and engage in open, productive exchange. <\/p>\n<\/div>\n<\/div>\n\n\n\n\n\n We conduct interdisciplinary research at the intersection of artificial intelligence (AI) and neuroscience, aiming both to leverage AI for advancing our understanding of the brain and to use these insights to improve AI systems and brain health. On one front, brain-inspired AI seeks to incorporate neurobiological principles to create energy-efficient, robust, and human-like intelligence, driving the development of next-generation AI technologies. On the other front, human brain signals (e.g., EEG and fMRI) are inherently noisy, with low signal-to-noise ratios that hinder their practical utility. To overcome this challenge, we strive to develop foundational models of brain activity capable of decoding a wide range of human perceptions and supporting efforts to address neurological disorders.\u202f <\/p>\n\n\n\n For more detailed information, please refer to the article Microsoft Research Asia StarTrack Scholars 2026: Create a synergistic relationship between AI and the brain – Microsoft Research (opens in new tab)<\/span><\/a>.<\/p>\n\n\n\n\n\n Dongsheng Li<\/a> (Engaging Lead) As AI is transforming the world, it is equally crucial to deepen our understanding of biological intelligence. The integration of AI and brain science research holds immense potential to drive innovation, enhance human well-being, and expand our knowledge of both artificial and biological intelligence. I am eager to collaborate with researchers in this field to revolutionize existing paradigms through interdisciplinary studies, advancing both AI and brain science in groundbreaking ways.\u202f <\/p>\n<\/div>\n<\/div>\n\n\n\n Yansen Wang<\/u> Our brain, encapsulated within a structure no larger than our two fists, is a marvel of complexity and power. It governs every facet of our thoughts, emotions, and actions, enabling feats from artistic expression to scientific innovation. Yet, much of its inner workings remain shrouded in mystery, with countless questions about consciousness, memory, and cognition still unanswered. Understanding the brain is not only essential for unraveling these mysteries but also for constructing interfaces between the brain and artificial intelligence. I hope to see a better synergy in the future.\u202f \u202f <\/p>\n<\/div>\n<\/div>\n\n\n\n Dongqi Han<\/a><\/u> Geoffrey Hinton once said, \u201cI have always been convinced that the only way to get artificial intelligence to work is to do the computation in a way similar to the human brain<\/em>.\u201d This conviction reveals a deep philosophical and practical bridge between neuroscience and AI: by studying how the brain computes, learns, and adapts, we gain principles that can guide more effective artificial systems; conversely, AI models provide testable hypotheses and experimental platforms for exploring brain function. Synergizing AI and brain science means accelerating mutual progress \u2014 using neural insights to inspire new architectures, and leveraging AI to simulate, analyze, and even predict biological behavior \u2014 all toward unraveling the deeper mysteries of intelligence in nature and in machines.\u202f <\/p>\n<\/div>\n<\/div>\n\n\n\n Mingqing Xiao (opens in new tab)<\/span><\/a> The human brain remains the only known realization of general intelligence, exhibiting remarkable efficiency and adaptability. Its energy efficiency, learning efficiency, and generalization capability are unparalleled compared to current AI models. Drawing inspiration from the brain at multiple levels\u2014from spatiotemporal neural dynamics, through learning principles, to high-level representations\u2014grounded in solid theoretical foundations, is essential for advancing AI toward more robust and human-like intelligence. At the same time, this synergy works both ways: AI can serve as a surrogate computational model to simulate or as a tool to decode brain processes, deepening our understanding of human brains. Exploring this bidirectional relationship between AI and neuroscience holds immense promise for shaping the next generation of intelligent systems and unraveling the mysteries of biological intelligence.\u202f <\/p>\n<\/div>\n<\/div>\n\n\n\n\n\n Rapid advances in AI are transforming modern computing infrastructure – bringing both fundamental challenges and unprecedented opportunities. <\/p>\n\n\n\n We call for proposals that tackle these challenges and harness the emerging opportunities of the AI era. Submissions may span multiple dimensions of AI and computing systems, including but not limited to: <\/p>\n\n\n\n 1) AI-assisted self-driving networking infrastructure; <\/p>\n\n\n\n 2) Disruptive AI-assisted methods for lowering the barrier to building secure and reliable systems; <\/p>\n\n\n\n 3) Next-generation software and hardware architectures for AI; <\/p>\n\n\n\n 4) Development and debugging tools for intelligent systems. <\/p>\n\n\n\n We particularly encourage unconventional and multidisciplinary approaches that break traditional boundaries and push the frontiers of AI and systems research. <\/p>\n\n\n\n For more detailed information, please refer to the article Microsoft Research Asia StarTrack Scholars 2026: A Holistic Approach to Systems and Networking in the AI Era – Microsoft Research (opens in new tab)<\/span><\/a>.<\/p>\n\n\n\n\n\n <\/p>\n\n\n\n\n\n\n\n Yongqiang Xiong<\/a> (Engaging Lead) Join us to revisit\/rethink\/rebuild the system and networking infrastructure by AI and for AI with a holistic and clean-slate perspective. <\/p>\n<\/div>\n<\/div>\n\n\n\n Fan Yang<\/a> (Engaging Lead) We welcome young systems researchers to join us exploring the disruptive system opportunities in the era of AI. <\/p>\n<\/div>\n<\/div>\n\n\n\n Jing Liu We welcome systems researchers to join us and build strong systems for the AI era together. <\/p>\n<\/div>\n<\/div>\n\n\n\n Baotong Lu<\/a> We welcome systems researchers to explore the next generation of data systems in the era of AI. <\/p>\n<\/div>\n<\/div>\n\n\n\n Li Lyna Zhang<\/a> We welcome researchers who are passionate about advancing agentic RL infrastructure and building general agents! <\/p>\n<\/div>\n<\/div>\n\n\n\n1. AI-Transformed Medical Research<\/h5>\n\n\n\n
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