Baining Guo, Microsoft Research Asia
Baining Guo is a Distinguished Scientist with Microsoft Research. Prior to joining Microsoft in 1999, Baining was a senior staff researcher with Intel Research in Santa Clara, California. Baining received his PhD and MS degrees from Cornell University, and his BS from Peking University. He is a fellow of ACM, IEEE, and Canadian Academy of Engineering.
Baining works in computer graphics, geometric modeling, virtual reality, and computer vision. His research focuses on three areas: DNN models for 3D graphics and imaging, statistical modeling of textures and appearances, and geometric modeling. His work is motivated by applications in the fields of virtual reality, video communication, digital content creation, and video gaming. He was a keynote speaker in many graphics and visual computing conferences, including ACM/SIAM Solid and Physical Modeling (SPM), IEEE Shape Modeling International (SMI), IEEE Virtual Reality (IEEE-VR), IEEE Multimedia and Expo (ICME), IEEE Visual Communication and Image Processing (VCIP), Pacific Graphics (PG), Computer Animation and Social Agents (CASA), and IEEE ICASSP.
He served on program committees of most major graphics conferences, including ACM SIGGRAPH, ACM SIGGRAPH Asia, and IEEE Visualization. He was a member of ACM Siggraph Papers Advisory Group (2013-2015). He was the technical papers program chair of ACM SIGGRAPH Asia in 2014. He also served on the editorial boards of IEEE Transactions on Visualization and Computer Graphics, IEEE Computer Graphics and Applications, and Elsevier Journal of Computer and Graphics.
Beibei Shi, Microsoft Research Asia
Beibei Shi is senior research program manager at Microsoft Research Asia, taking the responsibility of MSR Asia Open Collaborative Research Program and StarTrack Program, as well as university relations between MSR Asia and universities in Central China, South China, China Hongkong and Taiwan. Besides, she takes the responsibility of the strategic cooperation between Microsoft Research Asia and the Ministry of Education of the People’s Republic of China. She focuses on the research theme of Resilience and Trust, has successfully led several open collaborative research sub-themes establishment with related MSR Asia research team, such as AIER Platform, OpenNetLab, Computing for Carbon Negative and Responsible AI.
Before joined MSR Asia, she joined IBM Research China Institute as a researcher in the cross field of environment and computer, after earned master’s degree in environmental science school of China Agricultural University in 2019. Then, she joined the University Partnership Department of IBM China as a program manager. During that period, she participated to design and led to execute industry-academic cooperative research program “green horizon plan,” making very solid contribution to technology innovation of air pollution control.
Cheng Tan, Northeastern University (opens in new tab)
Dr. Cheng Tan is an Assistant Professor in the Khoury College of Computer Sciences at Northeastern University. He received his Ph.D. in Computer Science from New York University in 2020, his Master’s degree in Computer Science from Fudan University in 2014, and his Bachelor’s degree in Software Engineering from Nanjing University in 2011.
Dr. Tan’s research focuses on systems and ML systems, particularly ensuring the correctness and reliability of large-scale distributed training and inference in the era of large language models (LLMs). His work is supported by an NSF CAREER Award and an NIH R01 grant. He has broad interests in wireless sensing, smart IoT, cyber-physical systems, mobile computing, and wireless networking.
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Title: Introduction to Verified NN4Sys: the What, Why, and a Glimpse of How
Abstract: Neural networks are powerful tools. Applying them in computer systems—operating systems, databases, and networked systems—attracts much attention. However, neural networks are complicated black boxes that may produce unexpected results. Our vision is to build neural networks for computer systems (NN4Sys) that satisfy pre-defined correctness properties. We call these verified NN4Sys. In this talk, I will introduce our recent attempts to pursue this vision, including building NN4Sys benchmarks, training verified NN4Sys, and applying NN4Sys in multiple systems.
Danfeng Shan, Xi’an Jiaotong University (opens in new tab)
Dr. Shan is an Associate Professor at Xi’an Jiaotong University in the School of Computer Science and Technology. He received his Bachelor’s degree in Computer Science and Technology from Xi’an Jiaotong University in 2013 and his Ph.D. in Computer Science and Technology from Tsinghua University in 2018. His research interests include computer networking, with a focus on efficient cache management strategies for RoCE (RDMA over Converged Ethernet) networks.
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Title: Traffic Management in Shallow-Buffer Switches
Abstract: To support the ever-growing datacenter traffic, the switching capacity is rapidly increasing over the last decade, roughly doubled every two years. To support fast packet access, the packet buffer is embedded into the switch chip. However, due to the slowdown of Moore’s law, the switch buffer size is limited by the chip area and does not scale with the switching capacity. As a result, the buffer is becoming increasingly insufficient and this trend inclines to continue. In this talk, I will introduce our attempts on traffic management schemes in face of this situation, including high-speed traffic shaping algorithm at end hosts and efficient buffer management schemes at switch.
Fan Yang, Microsoft Research Asia
Dr. Fan Yang is a systems researcher and research manager of the Systems Research Group at Microsoft Research Asia (MSR-Asia). He received his B.S. and Ph.D. degrees in computer science from Nanjing University. He joined MSR-Asia after completing his doctoral studies. Dr. Yang’s research focuses on computer systems, particularly those supporting machine learning-based intelligent tasks. He is the architect of machine learning systems OpenPAI, NNFusion, and NNI, used in Microsoft products like Bing and Azure and widely adopted as open-source projects. Previously, Dr. Yang co-developed graph systems like GraM, setting new records for trillion-scale graph analytics, and contributed to SCOPE, Microsoft’s big data engine. His work on the SCOPE-based analytic pipeline for Bing Ads improved processing capacity by over 50%, generating significant additional revenue.
Furu Wei, Microsoft Research Asia
Dr. Furu Wei is a Partner Research Manager (全球研究合伙人) at Microsoft Research Asia, where he leads and oversees research on Foundation Models (across tasks, languages and modalities), NLP, MT, Speech and Multimodal AI. More recently, he has also been driving the mission-focused research on General AI, focusing on fundamental research of the Foundation of A(G)I. Furu received his B.S. and Ph.D. in computer science from Wuhan University in 2004 and 2009, respectively. He was a Staff Researcher at IBM Research – China (IBM CRL) from Jul. 2009 to Nov. 2010, and a Research Assistant at Department of Computing, The Hong Kong Polytechnic University from Jan. 2007 to Jun. 2009.
Furu published over 200 research papers in prestigious conferences and journals in natural language processing and artificial intelligence, including ACL, EMNLP, NAACL, COLING, Computational Linguistics, ICML, NeurIPS, ICLR, SIGIR, KDD, AAAI, IJCAI, etc. According to Google Scholar, his H-index is 80 with more than 30,000 citations (as of September 2023). As a co-author, Furu received the Best Paper Runners Up award at AAAI 2021, and the Best Student Paper award at KDD 2018. Furu served as a Senior Area Chair in ACL 2021, an Area Chair in EMNLP 2015, NAACL-HIT 2016, EMNLP 2019, and NeurIPS 2021. He has more than 20 patents filed or granted. The research from Furu and his team has been widely integrated in Microsoft products, including Office (Word, PowerPoint, Outlook and Microsoft Designer), Bing, Microsoft Ads, Azure (Cognitive Services), Dynamics, Windows, LinkedIn, etc.
Hao Wu, Nanjing University (opens in new tab)
Hao Wu is currently an Assistant Researcher at Nanjing University, where he obtained his Bachelor’s and Ph.D. degrees in Computer Science. His research focuses on intelligent edge computing, particularly utilizing large models as the foundation for diverse intelligent services across various applications. In his previous research, Wu made significant contributions to security-reversible video surveillance privacy protection systems, user data privacy protection technologies for intelligent inference services, and fully automatic object detection systems based on given descriptions for open scenes. He authored and led these projects, resulting in publications at prestigious conferences such as MobiCom, WWW, and SIGIR, along with patent approvals.
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Title: Trustworthy Edge AI System
Abstract: Edge intelligence has gained popularity in recent years due to its low latency and high accessibility. However, there are numerous security challenges in edge intelligent computing scenarios, such as high sensitivity of data, weak device protection capabilities, and difficulties in robustness testing of edge AI models. These security issues hinder the deployment of edge intelligence. This talk will clarify the attack surface of edge intelligence systems and introduce the progress we have made in building trustworthy edge systems from the perspectives of data layer, system layer, and application layer. Lastly, the talk will present our recent research progress in edge foundation models, specifically addressing the important issue of continuous perception.
Jian Li, Institute of Information Engineering, Chinese Academy of Sciences (opens in new tab)
Jian Li serves as an Associate Researcher at the Sixth Laboratory of the Institute of Information Engineering, Chinese Academy of Sciences, specializing in artificial intelligence. He obtained his Bachelor’s degree in Software Engineering from Northeastern University in 2015 and his Ph.D. in Network Space Security from the University of Chinese Academy of Sciences in 2020.
In his previous research, Li has made significant contributions to large-scale kernel method model selection and the generalization theory of large-scale machine learning methods. He has published numerous papers in top-tier conferences and journals in the fields of artificial intelligence and machine learning, earning awards such as the Best Student Paper Award at PRICAI 2021.
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Title: Generalization Theory of Large-Scale Machine Learning Methods
Abstract: With the explosive growth of data in the mobile internet era, large-scale machine learning techniques have been widely adopted. However, the generalization theory for these methods lags behind, hindering the understanding of their underlying principles. This talk introduces the generalization theories of large-scale machine learning methods, including distributed learning, subsampling, and dimensionality reduction. These works aim to bridge the gap between theory and practical algorithms, improving existing methods to balance computational efficiency and generalization performance.
Jiang Bian, Microsoft Research Asia
Dr. Jiang Bian is a Principal Research Manager at Microsoft Research. He is leading the machine learning solutions and services group, with the main focus on designing cutting-edge machine learning algorithms into real-world application scenarios, including finance, healthcare, supply-chain and sustainability. Prior to this, he was a Scientist at Yahoo! Labs in the United States, responsible for the content optimization and personalization and Web search modules in Yahoo! Homepage. After that, he jointed a leading content distribution platform in China, i.e., Yidian Inc., and became one of the core members of this startup company, with the major responsibility of developing advanced recommendation models. Dr. Bian has authored tens of research papers in many well-recognized international conferences and has submitted a couple of US patents. He has also served as PC Member for several international conferences and Peer Reviewer for a few well-known journals. Dr. Bian graduated from Peking University in China with a bachelor’s degree and then received the Ph.D. degree in computer science at Georgia Institute of Technology in the United States.
Jianxun Lian, Microsoft Research Asia
Jianxun Lian is a senior researcher at Microsoft Research Asia. His research interests include Humanoid AI, LLM-based agents, and recommendation systems. Lian has published numerous academic papers at top international conferences such as KDD, IJCAI, and WWW. He is an active member of the research community, serving on the program committees for several prestigious conferences including KDD, SIGIR, WWW, AAAI, and IJCAI.
Lian received his Ph.D. and B.S. degrees from the School of Computer Science and Technology at the University of Science and Technology of China. He joined MSRA in July 2018 and has since been contributing to advancements in his areas of expertise.
Jiaolong Yang, Microsoft Research Asia
Jiaolong YANG is a Principal Researcher and Research Manager at the Microsoft Research Asia (MSRA). He specializes in 3D Computer Vision, with research interests that include 3D reconstruction and generative modeling, digital human and AI avatars, and real-time immersive 3D communication. Lately, he is also interested in Embodied AI, delving into the development of foundational action models that are poised to revolutionize robotics with advanced 3D perception. He regularly serves as a program committee member and reviewer for major computer vision conferences and journals, including CVPR, ICCV, ECCV, TPAMI, and IJCV. He also holds the position of Area Chair for CVPR, ICCV, ECCV, WACV, etc.
Before joining MSRA in September 2016, Jiaolong earned dual PhD degrees from The Australian National University (ANU) and Beijing Institute of Technology (BIT) in 2016. He was a research intern at MSRA from November 2015 to March 2016, and a visiting graduate researcher at Harvard University from July 2016 to August 2016. He received the Excellent PhD Thesis Award from China Society of Image and Graphics (CSIG) in 2017, an honor bestowed upon only four recipients in China. He also recieved the Best Paper Award at IEEE VR 2022.
Jie Xiong, Microsoft Research Asia
Dr. Jie Xiong received a Ph.D. in Computer Science from University College London in 2015, supported by a Google European Doctoral Fellowship. His research is supported by an NSF Career Award and an NIH R01 grant. Dr. Xiong’s research interests span wireless sensing, smart IoT, cyber-physical systems, mobile computing, and wireless networking.
Tengchao Lv, Microsoft Research Asia
Tengchao Lv is a Researcher in Natural Language Computing group at Microsoft Research Asia, Beijing, China.
Ting Cao, Microsoft Research Asia
Ting Cao is the Principal Research Manager of the Heterogeneous Extreme Computing (HEX) group within the Systems and Networking Research Area at Microsoft Research. Her research interests span deep learning system and algorithm design, hardware/software co-design, high-level languages, energy-efficient hardware, management of heterogeneous hardware, and big data systems. Currently, she is concentrating on empowering large language models on client devices and simplifying traditional systems, such as compilers, using large generative models.
Ting Cao earned her PhD from the Research School of Computer Science at the Australian National University, where she had the privilege of being supervised by Professors Steve Blackburn and Kathryn McKinley. Her thesis, titled “Power, Performance, and Upheaval: An Opportunity for Managed Languages,” laid the groundwork for her subsequent research endeavors. Before joining Microsoft Research, she held positions at the State Key Lab of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, and the Compiler and Computing Language Lab in 2012 Labs at Huawei Technologies.
Her research contributions have been featured in esteemed computer science conferences such as MobiCom, PLDI, ISCA, ASPLOS, and MobiSys. Additionally, she has actively participated in the academic community by serving as a Program Committee member or External Review Committee member in conferences including PLDI, MobiSys, OOPSLA, VEE, ISMM, ICPAS, MPLR, and ChinaSys. Ting Cao has received numerous awards for her contributions, including the 2012 ACM Research Highlights, 2012 IEEE Micro Top Picks, 2021 ACM SIGMOBILE Research Highlights, Best Paper Awards in PPoPP’24, MobiSys’21, NAS’14, ICCD’10, and Huawei’s Future Star Award.
Xu Chen, High Leng AI Institute, Renmin University of China (opens in new tab)
Chen Xu is an Associate Professor at the High Leng AI Institute, Renmin University of China. He received his Ph.D. in Software Engineering from Tsinghua University in 2019. With expertise in artificial intelligence, Chen’s current research focuses on simulating social media poisoning attacks using large language models. He aims to develop a simulation framework to study the impact of these attacks and propose mitigation strategies. Chen has published extensively in areas such as reinforcement learning and recommendation systems, with research experience gained from visits to the UK and the US.
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Title: User behavior simulation with large language model based agent
Abstract: In recent years, Human-centered AI has received widespread attention from academia and industry. Applications in this field, such as recommendation systems and social networks, have brought great convenience to people’s lives and production. However, one of the key issues restricting the development of this field has always been how to obtain high-quality user behavior data. In this report, the presenter will share ideas for mitigating this problem from the perspective of LLM-based Agent, and introduce the user behavior simulation agent RecAgent based on a large language model developed by his team. This work simulates a variety of user behaviors in recommendation systems and social networks. Each user is an Agent. Different Agents can freely talk, post, search, self-evolve, etc. in the simulated environment. The speaker will introduce in detail the original design intention, structural characteristics, usage methods and experimental evaluation of RecAgent. Finally, the presenter will introduce the potential impact of RecAgent on the future Human-centered AI field.
Yang Wang, Microsoft Research Asia
Yang Wang is a Researcher in the System Research Group at Microsoft Research Asia (MSRA). His research interests include system optimization based on hardware characteristics, neural network inference optimization, and topics related to computer architecture. Wang’s work focuses on improving the efficiency and performance of systems by leveraging unique hardware features. He is actively involved in research areas such as systems and networking, contributing to the Heterogeneous Extreme Computing and Systems and Networking Research Group at MSRA. Wang has published extensively in his field, demonstrating his expertise in optimizing complex systems and enhancing computational processes.
Ye Pan is an Associate Professor at the School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University. She earned her Ph.D. in Computer Science from University College London in 2017. With a focus on computer graphics and digital humans, Ye’s research explores personalized 3D character modeling and animation. Her current project aims to develop techniques for mapping realistic facial expressions to cartoon characters, emphasizing individuality and exaggerated artistic expression. Ye has extensive experience in both academia and industry, having worked as a researcher at the Disney Research Lab in Los Angeles from 2017 to 2020. During her tenure, she made significant contributions to computer graphics and virtual reality, earning multiple patents and awards. She has also been actively involved in international collaborations and has published numerous papers in top-tier conferences and journals, contributing to the advancement of her field.
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Title: Stylized 3D Avatar Generation and Real Time Animation
Abstract: The production process of 3D digital avatars is typically time-consuming and costly. However, with the increasing maturity of AIGC technology, using this technology to accelerate the production process of 3D digital avatars is becoming more and more feasible. This project aims to develop a stylized 3D chat avatar system and explore its application in the production of game release materials. By combining advanced AIGC technology, and computer graphics techniques, users will be able to easily create personalized and uniquely styled 3D talking avatars. We’ll delve into various techniques for creating stylized 3D avatars and achieving real-time animation.
Yongqiang Xiong, Microsoft Research Asia
Dr. Xiong is now with Networking Researching Group at Microsoft Research Asia as a principal researcher and research manager. Dr. Xiong received his B.S., M.S., and Ph.D degrees from Tsinghua University, Beijing, China in 1996, 1998 and 2001, respectively, all in computer science. His research interests include system and networking, as well as network security. He has published over 80 papers and served as TPC member or reviewers for the international key conferences and leading journals in the areas of system and networking. Dr. Xiong is a member of IEEE.
Dr. Xiong has been working on the system and networking area for a while, originally, he worked on Internet routing protocols, after that, he turned to mobile ad hoc networks and peer-to-peer networks, which were both similarly end system based networks. He is now focusing on data center networking systems, especially on the architecture design, optimal scheduling problem, switch constructions to improve resilience, performance, and diagnosis, as well as its security problem such as handing the DDoS attacks. He is also interested in building hardware networking systems, doing measurement and security related research.
Yujiang Wang, Oxford Suzhou Centre for Advanced Research
Yujiang Wang is a Research Fellow and Deputy Director of the Machine Learning Laboratory at the Oxford Suzhou Centre for Advanced Research. He earned his Ph.D. in Computer Vision from Imperial College London in 2021 and has focused his research on Natural Language Computing. His current project, ICU-LLM, aims to develop multi-modal large language models for understanding medical records in intensive care units. Dr. Wang has published in prestigious venues like Nature Communications and TPAMI, and received the Best Student Paper Award in KDD 2016. He is a member of the IEEE and a Distinguished Member of ACM.
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Title: Medical records condensation: a roadmap towards healthcare data democratisation
Abstract: The prevalence of artificial intelligence has envisioned an era of healthcare democratisation that promises every stakeholder a new and better way of life. However, the advancement of clinical AI research is significantly hurdled by the dearth of data democratisation in healthcare. This talk will present dataset condensation as a potential and promising solution for data democratisation through experimental results on varying healthcare datasets.