Deciding between fundamental and applied research is a dilemma that confronts many in the scientific community. Dongqi Han, on the cusp of graduation, ambitiously aspired to bridge this divide by pursuing both avenues of research in his future endeavors.
After a comprehensive evaluation, Dongqi Han selected Microsoft Research Asia (MSR Asia) for his initial foray into fulfilling his aspirations. Prior to completing his doctorate, he undertook an internship at MSR Asia – Shanghai. During his six-month internship, Han gained firsthand experience of the lab’s commitment to pioneering basic research and its active engagement in fostering industrial collaborations, thereby facilitating the practical application of innovative findings. This experience sowed the seeds for his eventual formal engagement with the lab.
“MSR Asia has established a dynamic platform that seamlessly integrates fundamental research with practical industrial applications. Within this environment, I have the opportunity to work alongside eminent researchers, delving into the underlying principles and methodologies of intelligence. Moreover, I am able to harness the power of AI in domains such as healthcare. This synergy of theory and practice was a pivotal factor in my decision to join the lab after graduation. Undoubtedly, it represents an ideal launchpad for my career in scientific research,” Dongqi Han articulated.
Unveiling the core of intelligence: At the crossroads of AI and neuroscience
In recent years, the synergy between computer technologies like AI and various other fields has grown remarkably. MSR Asia is at the forefront, spearheading pivotal research and progressively amplifying its investments. Shanghai, a metropolis celebrated for its diversity and home to esteemed academic and leading medical institutions, offers fertile ground for the interdisciplinary fusion of AI and other fields. Consequently, the confluence of AI with neuroscience and other healthcare domains has emerged as a key research focus for MSR Asia – Shanghai.
Dr. Dongqi Han, a graduate of the Okinawa Institute of Science and Technology Graduate University (OIST) in Japan, majored in cognitive neuro-robotics, which encompasses the study of robotics integrated with neuroscience. As a neuroscientist, Dr. Han’s expertise significantly enhances the professional capabilities of MSR Asia’s interdisciplinary team, focusing on AI and brain science research.
Dongqi Han’s research primarily explores two areas: the convergence of AI with neuroscience, and AI’s applications in healthcare. He believes that this synergy is not only theoretically profound but also immensely practical. Dr. Han asserts, “To create more intuitive and effective interfaces, whether they be brain-computer or human-computer, a more intricate comprehension of the human cognitive and perceptual processes is essential.” In healthcare, neurological disorders impact approximately one billion individuals globally. Studies at the nexus of AI and brain science have the potential to enrich the knowledge base for both clinicians and patients, leading to improved diagnosis, prevention, and management of these conditions.
“AI and brain science are deeply intertwined, both delving into the core and mechanisms of intelligence. They encounter similar issues and can mutually benefit from shared insights.”
Indeed, AI technologies often draw from the brain’s neural networks, with structures like multilayer perceptron (MLP) and long short-term memory (LSTM) networks mirroring our own cognitive architecture. By examining human and animal cognition—learning, memory, decision-making—we can augment AI’s capabilities. A critical hurdle for AI is “catastrophic forgetting”, where new learning can erase old knowledge, a flaw not seen in the human brain. Dr. Han and his team colleagues are dedicated to resolving such AI challenges by gleaning lessons from our neurological processes.
Conversely, the robust data processing and modeling prowess of AI holds the potential to advance neuroscience research and its applications significantly. The human brain is composed of approximately 10^11 neurons interconnected by roughly 10^15 synapses. Harnessing AI to model the brain’s operational principles and computational strategies is essential to manage this immense data complexity and to substantiate the veracity of theoretical frameworks in neuroscience.
Presently, Dr. Han and his team colleagues, along with collaborators, have garnered preliminary findings from their research endeavors. Their study primarily examines two distinct human behavioral types: habitual and goal-directed behaviors [1]. For instance, habitual behavior, akin to the automatic act of selecting a familiar route home post-work, requires no conscious deliberation. In contrast, goal-directed behavior involves intentional consideration of both purpose and outcome, exemplified by plotting a course to the Civil Affairs Bureau to obtain a marriage certificate. While these behavioral models elucidate numerous aspects of biological conduct, the mechanisms by which the brain decides between these patterns and their mutual interactions remain an enigma.
Dongqi Han said, “Our team employs deep learning and machine learning methodologies to model and investigate the characteristics and underlying neural mechanisms of two distinct behavioral types. This endeavor not only contributes to the advancement of cognitive science and psychology but also serves as a source of inspiration for the development of innovative AI algorithms.”
Dr. Han’s recent research, conducted alongside his colleagues, revolves around emulating the brain’s neural circuitry. This has culminated in the development of a novel neural network model named CircuitNet [2]. Characterized by densely interconnected neurons within neural clusters and sparse connections across different brain regions, this model mirrors the human brain’s unique wiring. The team at MSR Asia is delving into the intricacies and benefits of such a neural architecture. Dr. Han, who has been involved in this project since his internship, has seen CircuitNet come to fruition through collaborative efforts, culminating in its selection for presentation at ICML 2023.
CircuitNet represents an advancement in neural network architectures, offering enhanced performance with a reduced parameter count, thus leading to greater energy efficiency. Remarkably, the human brain operates on less than 20 watts of power on average—a stark contrast to the substantial energy demands of large-scale AI models such as GPT-4, which may require hundreds to thousands of watts. Moving forward, Dongqi Han and his team colleagues are dedicated to unraveling the human brain’s mechanisms for energy conservation, drawing inspiration from CircuitNet’s design.
Dr. Han’s research extends to deep reinforcement learning and embodied AI, with the goal of refining AI to improve learning, decision-making, and real-world interaction capabilities of intelligent robots. He observes that while current large AI models predominantly generate content like text and images, embodied AI outputs dynamic actions, introducing a myriad of real-world uncertainties. For instance, a robot engaged in painting might encounter various challenges such as errors, equipment failure, or interference, all influencing the final outcome. Navigating these complexities requires sophisticated action selection processes. Dr. Han believes that by drawing parallels to human cognitive decision-making, we can expedite the advancement of embodied intelligence.
Fostering innovation: The power of interdisciplinary learning
Dongqi Han’s insatiable curiosity about the world fuels his wide-ranging and intense passion for scientific inquiry. His academic journey began with an undergraduate major in theoretical physics—a field he regards as exceptionally demanding. It necessitates a learner to possess stringent logical reasoning, robust mathematical prowess, and the capacity for experimental design and data analysis. These skills are instrumental in enhancing an individual’s intellectual caliber. Furthermore, physical science serves as the bedrock for numerous contemporary technologies, offering expansive applications.
During his undergraduate study in physics, Dongqi Han was deeply engrossed in the detection of physical parameters within tokamaks—devices pivotal for controlled nuclear fusion [4]. He considers nuclear fusion as a boundless, efficient, and clean energy source, believing its mastery to herald unparalleled benefits for humanity. Initially, Dr. Han viewed achieving controlled nuclear fusion as his scientific beacon. Yet, as his studies progressed, he discerned that the real hurdles to implementation lay not in the realm of theoretical physics, but within the ambit of engineering challenges. This revelation steered him to realize that his knowledge in theoretical physics might not directly contribute to the fruition of his aspirations. It was during this phase, amidst his tokamak investigations, that Dr. Han’s encounter with machine learning technology sparked a transformative shift in his academic pursuit, leading him to specialize in cognitive neuro-robotics for his doctoral research.
Embracing interdisciplinary learning, Dongqi Han embarked on a journey of discovery, building his foundation from the ground up. His initial year in the doctoral program was marked by an intensive curriculum that spanned basic neuroscience, machine learning, robotics control automation, and the integrative domains of cognitive science and psychology. Despite the rigorous challenges of navigating multiple disciplines, this cross-pollination of knowledge ignited innovative ideas. These insights have proven to be invaluable, enriching his research in AI and brain science.
In exploring the domains of reinforcement learning and embodied intelligence, Dongqi Han aims to integrate the methodical thinking approaches of physics. This will involve transferring the discipline’s rigorous thought processes (for example, commonly used measurement statistical methods) and logical reasoning, honed through physical experimentation, into the realm of AI study.
“The benefits of interdisciplinary learning are not only reflected in the cross-domain application of knowledge but also in the borrowing and inspiration of logical thinking. For example, when solving problems in neural network machine learning, traditional machine learning thinking often first considers data volume and model scale. Training in neuroscience can allow us to start from the perspective of the human or animal brain, thinking about problems in a more expansive and flexible way,” said Dongqi Han.
Collaborative synergy: Harnessing advanced technology for real-world solutions
Dongqi Han and his team colleagues are not only dedicated to interdisciplinary research but also actively engage in cross-disciplinary collaborations. They partner with universities and medical institutions around the globe, harnessing advanced technologies to tackle real-world challenges and forge superior solutions.
Dongqi Han and his team colleagues, in partnership with Fudan University, have pioneered an AI model for machine vision that replicates human visual perception [3], with a focus on boosting the energy efficiency of computer vision systems. Dr. Han notes, “Through collaborative research, we have discovered notable distinctions between human and computer vision. Human vision exhibits a significantly higher resolution at the fovea—the central point—compared to the peripheral vision. Additionally, the human brain processes information by transmitting spike-based signals, a characteristic that is mirrored in the architecture of spiking neural networks.” To harness the superior aspects of human vision, the team employed a spiking neural network to emulate a visual system with variable resolution and neuronal spike-based communication. This innovative model is poised to revolutionize energy efficiency, potentially achieving up to a hundredfold improvement in performing visual tasks with significantly reduced energy demands.
In a groundbreaking robotics initiative, researchers from MSR Asia and Korea Advanced Institute of Science and Technology (KAIST) have been pioneering the use of wearable, non-invasive devices to decipher human brainwave signals. This technology is poised to significantly enhance a robot’s ability to interpret human intentions with greater precision. “Consider an elderly individual reaching towards the kitchen—does this gesture indicate hunger or the need to retrieve an item? Or when they gesture towards a table adorned with both a water bottle and a tissue box, which is their intended choice?” Dongqi Han said, “KAIST’s robust expertise in neuroscience, combined with our advanced AI algorithms, creates a powerful alliance. Together, we’re pushing the boundaries of what’s possible in robotics, enabling more nuanced task execution through a fusion of brain science and AI. This interdisciplinary collaboration is sparking innovative research avenues for both teams.
From delving into games to persevering in scientific research
Dongqi Han’s diverse interests extend beyond his professional pursuits. He likes hiking and badminton, as well as various types of video games. “My passion for video games began in elementary school, evolving into a dedication to a particularly challenging game that demanded patience and perseverance. The process of advancing through persistent practice instilled in me a profound sense of accomplishment and shaped my approach to life, fostering patience and resilience against adversity” , Dr. Han shares.
This mindset has also permeated his research philosophy, which is characterized by deep, sustained inquiry. The supportive and diverse research environment at MSR Asia encourages vibrant collaboration and communication with colleagues from diverse disciplines and walks of life, bolstering his commitment to long-term research and connecting him with fellow researchers who share his vision.
Related links:
[1] Synergizing Habits and Goals with Variational Bayes, Nature Communications, 2024
(preprint at https://osf.io/preprints/psyarxiv/v63yj)
[2] CircuitNet：A Generic Neural Network to Realize Universal Circuit Motif Modeling
https://proceedings.mlr.press/v202/wang23k/wang23k.pdf
[3] Energy-Efficient Visual Search by Eye Movement and Low-Latency Spiking Neural Network
https://arxiv.org/abs/2310.06578
[4] In situ relative self-dependent calibration of electron cyclotron emission imaging via shape matching
https://doi.org/10.1063/1.5038866