FAISys 2025

Jidong Zhai is a Professor in the Department of Computer Science and Technology of Tsinghua University. He was a Visiting Professor of Stanford University from 2015 to 2016 and a Visiting Scholar of MSRA (Microsoft Research Asia) in 2013. His current research interests include parallel computing, compiler, and performance evaluation. He has published more than 100 papers in prestigious conferences (such as SC, ICS, PPOPP, ASPLOS, MICRO, OSDI, ATC, and PACT) and top-tier journals (such as IEEE TC and IEEE TPDS). His research received 2021 Best Paper Award for IEEE TPDS, Best Paper Award at CLUSTER'21, Best Student Paper Award at ICS’21, Best Paper Honorable Mention Award at ICDCS’20, and Best Paper Finalist at SC’14. He has served as PC members for a number of international conferences including SC, ICS, PPoPP, IPDPS, ICPP, Cluster, and PACT. He was a program co-chair of NPC 2018. He is currently on the editorial boards of IEEE Transactions on Computers (TC), IEEE Transactions on Cloud Computing (TCC), Journal of Parallel and Distributed Computing (JPDC), and Journal of Parallel Computing (PARCO).

Prof. Minchen Yu, CUHK (SZ)

Minchen Yu is an Assistant Professor at the School of Data Science, The Chinese University of Hong Kong, Shenzhen. He received his Ph.D. from Hong Kong University of Science and Technology and B.Eng. from Nanjing University. His research interests cover cloud computing and distributed systems, with the recent focus on serverless computing and machine learning systems. His work has been published at various prestigious venues, including NSDI, ATC, EuroSys, INFOCOM, and SoCC. He received the Best Paper Runner-Up Award at IEEE ICDCS 2021.

Chenyu Guo, Ant Group

Bojie Li, Pine AI

Bojie Li is the Co‑Founder and Chief Scientist at Pine AI. He received his Ph.D. in Computer Science in 2019 from the University of Science and Technology of China (USTC) in collaboration with Microsoft Research Asia (MSRA). His research has appeared at top venues including SIGCOMM, SOSP, NSDI, ATC, and PLDI. He is a recipient of the ACM China Doctoral Dissertation Award and the Microsoft Research Asia Ph.D. Fellowship.

Prof. Jidong Zhai, Tsinghua University

Prof. Minchen Yu, CUHK (SZ)

Zhenyu Guo, Ant Group

Zhenyu Guo is the director of Data and AI Infrastructure at Ant Security. His recent work focuses on building novel systems and algorithms for training, simulation, and serving in risk-related scenarios. Previously, he was the leader of the Distributed Computing Group at Ant Group and a researcher at Microsoft Research Asia. His research interest spans compilers, distributed systems, big data, and large models, with an emphasis on co-designing these components to build accessible, controllable, adaptable, and trustworthy systems. He has authored 20+ papers in top-tier conferences, such as OSDI, NSDI, SIGMOD, and HotOS.

Bojie Li, Pine AI

Keynote & Challenge Info

Keynote speaker: Prof. Ji-Rong Wen, Renmin University of China

Ji-Rong Wen is a Professor and Founding Dean of the Gaoling School of Artificial Intelligence at Renmin University of China. He previously served as a Senior Researcher and Manager of the Web Search and Mining Group at Microsoft Research Asia. Over his career, he has published 500+ papers at leading conferences and journals, with 50,000+ citations and an H-index of 100+. In recent years, he has focused on large-scale foundation models, leading teams that developed “Wenlan,” the first Chinese multimodal large model; the “Yulan” series of large language models; and LLaDA, the first open-source large language diffusion model.

Keynote: "LLaDA: A New Paradigm for Large Language Diffusion Models"

Abstract: This talk focuses on a single question: Is autoregression the only path to SOTA, and beyond, generative AI? I will first survey the evolution of generative models through the lens of unified probabilistic modeling and argue that core LLM properties - scalability, instruction following, in-context learning, multi-turn conversations, and lossless compression - stem primarily from the generative objective rather than from autoregression per se. Building on this perspective, I will introduce our LLaDA series of diffusion-based large language models, covering core theory, scaling laws, large-scale training, SFT, alignment, MoE, and multimodal modeling. Using a non-autoregressive approach, LLaDA exhibits surprising scalability and strong multi-turn conversational abilities. Together, our results show that diffusion is a highly promising path for large language modeling and challenge the prevailing assumption that core LLM capabilities inherently depend on autoregression.