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\u5fae\u8f6f\u4e9a\u6d32\u7814\u7a76\u9662\u673a\u5668\u5b66\u4e60\u9886\u57df\u4ece\u7406\u8bba\u3001\u7b97\u6cd5\u3001\u5e94\u7528\u7b49\u4e0d\u540c\u5c42\u9762\u63a8\u52a8\u673a\u5668\u5b66\u4e60\u7684\u524d\u6cbf\u3002\u6211\u4eec\u7684\u7814\u7a76\u5174\u8da3\u5305\u542b\uff1a\u6df1\u5ea6\u5b66\u4e60\u3001\u5f3a\u5316\u5b66\u4e60\u3001\u56fe\u5b66\u4e60\u3001\u68af\u5ea6\u63d0\u5347\u6811\u3001\u5728\u7ebf\u5b66\u4e60\u3001\u9884\u8bad\u7ec3\u3001\u52a8\u6001\u5b66\u4e60\u3001\u5b66\u4e60\u7406\u8bba\u7b49\u3002\u540c\u65f6\uff0c \u6211\u4eec\u4e5f\u5728\u79ef\u6781\u63a2\u7d22\u4eba\u5de5\u667a\u80fd\u5728\u81ea\u7136\u79d1\u5b66\u548c\u4ea7\u4e1a\u5e94\u7528\u4e2d\u7684\u4ef7\u503c\uff0c\u4ece\u800c\u4e3a\u79d1\u5b66\u5de5\u4f5c\u8005\u548c\u4f20\u7edf\u5de5\u4e1a\u8d4b\u80fd\uff08\u5177\u4f53\u89c1\u7814\u7a76\u6982\u51b5<\/a>\uff09\u3002\u5728\u8fc7\u53bb\u7684\u5341\u51e0\u5e74\u95f4\uff0c\u6211\u4eec\u5728\u9876\u7ea7\u56fd\u9645\u4f1a\u8bae\u548c\u671f\u520a\u4e0a\u53d1\u8868\u4e86\u5927\u91cf\u88ab\u9ad8\u5ea6\u5f15\u7528\u7684\u9ad8\u8d28\u91cf\u8bba\u6587\uff0c\u5411\u5fae\u8f6f\u7684\u4ea7\u54c1\u90e8\u95e8\u8f6c\u5316\u4e86\u5927\u91cf\u6838\u5fc3\u6280\u672f\uff0c\u5e76\u5e2e\u52a9\u4f17\u591a\u7684\u4f01\u4e1a\u5408\u4f5c\u4f19\u4f34\u5b9e\u73b0\u4e86\u6570\u5b57\u5316\u8f6c\u578b\u3002\u6211\u4eec\u4e5f\u5411\u5f00\u6e90\u793e\u533a\u8d21\u732e\u4e86\u5927\u91cf\u9ad8\u8d28\u91cf\u5f00\u6e90\u5de5\u5177\uff0c\u4f8b\u5982 LightGBM\u3001LigthLDA\u3001\u5fae\u8f6f\u56fe\u5f15\u64ce\uff0c\u591a\u667a\u80fd\u4f53\u8d44\u6e90\u4f18\u5316\u5e73\u53f0\u201c\u7fa4\u7b56 MARO\u201c\uff0c\u4e1a\u5185\u9996\u4e2aAI\u91cf\u5316\u6295\u8d44\u5e73\u53f0\u201c\u5fae\u77ffQlib\u201d\uff0c\u4ee5\u53ca\u6700\u65b0\u7684\u65f6\u7a7a\u9884\u6d4b\u5e73\u53f0”FOST”\u3002\u8fd9\u4e9b\u5de5\u5177\u53d7\u5230\u5f00\u6e90\u793e\u533a\u7684\u5e7f\u6cdb\u5173\u6ce8\uff0c\u5df2\u5728Github\u4e0a\u7d2f\u8ba1\u6536\u83b7\u4e09\u4e07\u4f59\u9897\u661f\u3002<\/p>\n Principal Research Manager<\/p>\n<\/div>\n Senior Researcher<\/p>\n<\/div>\n Senior Researcher<\/p>\n<\/div>\n Principal Researcher<\/p>\n<\/div>\n Our current research focus is on deep\/reinforcement learning, distributed machine learning, and graph learning. Other research projects from our group include learning to rank, computational advertising, and cloud pricing. <\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr_group_start":"2016-08-03","footnotes":""},"research-area":[13556],"msr-group-type":[243694],"msr-locale":[268875],"msr-impact-theme":[],"class_list":["post-269241","msr-group","type-msr-group","status-publish","hentry","msr-research-area-artificial-intelligence","msr-group-type-group","msr-locale-en_us"],"msr_group_start":"2016-08-03","msr_detailed_description":"","msr_further_details":"","msr_hero_images":[],"msr_research_lab":[199560,1012650],"related-researchers":[{"type":"user_nicename","display_name":"Jiang Bian","user_id":38481,"people_section":"Group 1","alias":"jiabia"},{"type":"user_nicename","display_name":"Tianyu He","user_id":42141,"people_section":"Group 1","alias":"tianyuhe"},{"type":"user_nicename","display_name":"Junjie Li","user_id":32446,"people_section":"Group 1","alias":"junli"},{"type":"user_nicename","display_name":"Zhong Li","user_id":42324,"people_section":"Group 1","alias":"lzhong"},{"type":"user_nicename","display_name":"Weiqing Liu","user_id":39300,"people_section":"Group 1","alias":"weiqiliu"},{"type":"user_nicename","display_name":"Lei Song","user_id":41078,"people_section":"Group 1","alias":"lesong"},{"type":"user_nicename","display_name":"Jinyu Wang","user_id":42963,"people_section":"Group 1","alias":"jinywan"},{"type":"user_nicename","display_name":"Chang Xu","user_id":41107,"people_section":"Group 1","alias":"chanx"},{"type":"user_nicename","display_name":"Xianliang Yang","user_id":41170,"people_section":"Group 1","alias":"xianya"},{"type":"user_nicename","display_name":"Li Zhao","user_id":36152,"people_section":"Group 1","alias":"lizo"},{"type":"user_nicename","display_name":"Shun Zheng","user_id":41072,"people_section":"Group 1","alias":"shunzhen"}],"related-publications":[439545,439536,152546,915819,557037,709714,1080309,565296,709741,565332,755131,565359,758137,567195,758143,619116,846247,650772,846265,699730,887085,701680,915780,511625,705331,915804,548295,709309],"related-downloads":[650781,710773],"related-videos":[],"related-projects":[],"related-events":[],"related-opportunities":[1059561,1059567,1059576,1059339,1059393,1059489],"related-posts":[],"tab-content":[{"id":0,"name":"Areas","content":" The Deep and Reinforcement Learning area pushes forward the research of deep learning and reinforcement learning from both algorithmic and practical aspects. We focus on speech and language processing (especially speech synthesis, machine translation, music composition), drug discovery, game testing, and intelligence logistics.<\/p>\r\n\r\n<\/div>\r\n<\/div>\r\n The Machine Learning Solutions & Services (MLSS) area was born with the rapidly increasing demand on AI-driven digital transformation from a wide range of industrial domains. Inspired by the mission of innovating disruptive AI technologies and even becoming the thought leader in critical industrial verticals, MLSS group has been diving into a few critical industrial domains, including sustainability, supply-chain, medical, and financials, and identified a couple of research directions through close collaboration with key domain partners, i.e., leading companies within respective domains.<\/p>\r\n\r\n<\/div>\r\n<\/div>\r\n The Computing and Learning Theory area investigates the utility, complexity, trustworthiness of learning and computing for AI, in the theoretical perspective. We aim to push the frontier of learning and computing by theory. Our current research focus is on deep learning theory, reinforcement learning theory, differential privacy, causality, and game theory.<\/p>\r\n\r\n<\/div>\r\n<\/div>\r\n<\/div>"},{"id":1,"name":"Research","content":"Leaders<\/h2>\n
![\"Portrait](\"\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2019\/07\/WeChat-Image_20180503124122.jpg\")
<\/div>\n<\/div>\nJiang Bian<\/h3>\n
![\"\"](\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2016\/10\/avatar_user__1476158324-96x96.jpg\"){kind=avatar}
<\/div>\n<\/div>\nLi Zhao<\/h3>\n
![\"\"](\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/02\/bio_weiqing_liu.jpg\")
<\/div>\n<\/div>\nWeiqing Liu<\/h3>\n
![\"\"](\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/04\/Picture1.png\")
<\/div>\n<\/div>\nLei Song<\/h3>\n
News<\/h2>\n
\n
![\"Machine](\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/11\/machine-learning-research-1024x790.jpg\")
\r\n\r\n \r\nMachine Learning<\/h2>\r\n
Algorithm<\/h4>\r\nWe work on challenging machine learning problems and design cutting-edge algorithms for both real-world applications and fundamental science problems. In particular, we are interested in deep learning, reinforcement learning, neural architecture search, pre-training, causal learning, etc.\u00a0 [accordion][panel header=\"Projects\"]\r\n
\r\n \t
Theory<\/h4>\r\nMachine learning, with deep neural networks, has achieved breakthroughs in AI tasks and impressive progress in science. However, due to the non-convexity of neural networks, machine learning is suffering from high computation and sample cost. In addition, it has high variance and is hard to generalize to new domains. We take the theoretical view to understand and improve the technologies in machine learning taxonomy, in terms of sample\/computation efficiency, robustness, interpretability, privacy-preservation, etc. [accordion] [panel header=\"Projects\"]\r\n
\r\n \t
Application<\/h4>\r\nIn recent years, machine learning algorithms (especially deep learning and reinforcement learning) have greatly boosted the performance of many real-world applications, and applications have also driven the progress of machine learning research (e.g., ResNet originated from image classification, Transformer from machine translation). We work on multiple key applications, including language and speech, music, game testing, finance, logistics, healthcare (including drug discovery and precision medicine). [accordion] [panel header=\"Projects\"]\r\n
\r\n \t
AI for Science<\/h2>\r\n
Physics<\/h4>\r\nMachine learning is disrupting physics research. We are using machine learning, especially deep learning, to tackle physics problems that are extremely challenging to solve before. The laws of nature are described as partial differential equations (PDEs). With AI techniques, we can leverage big data to solve, simulate, or predict known PDEs more efficiently and discover unknown laws. Specifically, we take machine learning approaches to solve parametric PDEs through physics-based self-supervision, to predict the stationary state of PDEs from simulated data, to detect parameters of PDEs by modeling their symmetry groups, and to speed up molecular dynamics simulation. [accordion] [panel header=\"Projects\"]\r\n
\r\n \t
Biology<\/h4>\r\nBiology comes to the big data era. At Microsoft Research Asia, we seek to unlock the big biological data and reveal the secret of life with computation. We are working on advanced techniques for computational molecular biology to help scientists obtain deep insights into life at the molecular level.\u00a0We are carrying out research in the following areas: molecular dynamics simulation, genomics, immunomics, microbiome, and protein folding. [accordion] [panel header=\"Projects\"]\r\n
\r\n \t
Sustainability<\/h4>\r\nSustainability involves a few systematic and scientific challenges, including energy crisis, global warming, air pollution, and other thorny issues. Most of them essentially lie in over-reliance on fossil fuels and are accompanied by high carbon emissions<\/em>. To accelerate the energy transition and facilitate carbon removal, we need computational physics to develop new technologies. For example, density functional theory is the foundation to calculate the properties of crystals so to aid material discovery, and molecular dynamics plays the basis to accelerate the adsorption and conversion of carbon dioxide. We work on these core techniques to benefit sustainability research. [accordion] [panel header=\"Projects\"]<\/span>\r\n
\r\n \t
AI for Industry<\/h2>\r\n
Healthcare<\/h4>\r\nHealthcare plays an important role in human beings. To understand the human body and better care human health, we pay great efforts on the research of biology understanding, drug discovery, and medical forecasting. The biology sequence data, e.g., DNA, RNA, protein, is fundamental to the life system. Drug discovery with AI technique hugely increases the speed of drug innovation. Medical forecasting with accurate and robust predictions can greatly support clinical decision-making and treatment. We exploit the machine learning power in these fields to benefit healthcare research. [accordion][panel header=\"Projects\"]\r\n
\r\n \t
Finance<\/h4>\r\nThe financial industry has adopted statistical analysis for different tasks for a long time and has accumulated tremendous valuable data. These conditions leave a big potential for AI technologies to empower the financial industry. In particular, we start with the intelligent quant investment as our first exploration area. Now we also expand our research on RegTech like anti-money laundry. We mainly focus on several typical challenges\/research directions in applying AI techniques to Machine learning. We also build an open-source AI-oriented quant investment platform Qlib to accelerate the research exploration and algorithm landing.\u00a0[accordion][panel header=\"Projects\"]\r\n
\r\n \t
Supply Chain<\/h4>\r\nIn supply chain and logistics systems, we need to solve complex decision-making problems to maximize revenue while minimizing operational costs, for instance, to optimize inventory and vehicle routing. At Microsoft Research Asia, we focus on developing efficient Deep Reinforcement Learning algorithms for optimization problems in logistics. We are targeting a scalable and generalizable DRL approach that can be applied to solving problems in practice, hence, to empower the companies and customers in the logistic industry. [accordion][panel header=\"Projects\"]\r\n
\r\n \t
Machine Learning<\/h3>\r\n
\r\n \t