AI Agent Reasoning and Decision-Making Workshop 2025 (AIR 2025)

Speakers

Yuandong Tian. Yuandong Tian is a Research Scientist Director in Meta GenAI, leading a group for Llama reasoning. His research direction covers multiple aspects of decision making, including reinforcement learning, planning and efficiency, as well as theoretical understanding of LLMs. He is the project lead for OpenGo project, an efficient replicate of AlphaZero that beats professional players with a single GPU during inference, serves as the main mentor of StreamingLLM and GaLore that improve the training and inference of LLM, and is the first-author recipient of 2021 ICML Outstanding Paper Honorable Mentions and 2013 ICCV Marr Prize Honorable Mentions, and also received the 2022 CGO Distinguished Paper Award. Prior to that, he worked in Google Self-driving Car team in 2013-2014 and received a Ph.D in Robotics Institute, Carnegie Mellon University in 2013. He has been appointed as area chairs for NeurIPS, ICML, AAAI, CVPR and AIStats.

  

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Dawn Song. Dawn Song is a Professor in the Department of Electrical Engineering and Computer Science at UC Berkeley. Her research interest lies in AI and deep learning, blockchain/web3, security and privacy. She is the recipient of various awards including the MacArthur Fellowship, the Guggenheim Fellowship, the NSF CAREER Award, the Alfred P. Sloan Research Fellowship, the MIT Technology Review TR-35 Award, and several Test-of-Time and Best Paper Awards from top conferences in Computer Security and Deep Learning. She is an ACM Fellow and an IEEE Fellow. She is ranked the most cited scholar in computer security (AMiner Award). She obtained her Ph.D. degree from UC Berkeley. Prior to joining UC Berkeley as a faculty, she was a faculty at Carnegie Mellon University from 2002 to 2007. She is also a serial entrepreneur and has been named on the Female Founder 100 List by Inc. and Wired25 List of Innovators.

  

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Hannaneh Hajishirzi. Hanna Hajishirzi is the Torode Family Associate Professor in the Allen School of Computer Science and Engineering at the University of Washington and a Senior Director of NLP at AI2. Her current research delves into various domains within Natural Language Processing (NLP) and Artificial Intelligence (AI), with a particular emphasis on accelerating the science of language modeling, broadening their scope, and enhancing their applicability and usefulness for human lives. She has published over 140 scientific articles in prestigious journals and conferences across ML, AI, NLP, and Computer Vision. She is the recipient of numerous awards, including the Sloan Fellowship, NSF CAREER Award, Intel Rising Star Award, Allen Distinguished Investigator Award, Academic Achievement UIUC Alumni Award, and Innovator of the Year Award by GeekWire. The work from her lab has been nominated for or has received best paper awards at various conferences and has been featured in numerous magazines and newspapers.

  

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Bo An. Bo An is a President's Chair Professor and Head of Division of Artificial Intelligence at the College of Computing and Data Science of the Nanyang Technological University (NTU). He is also Director for Centre of AI-for-X of NTU. He was a Nanyang Assistant Professor during 2014-2018. Prior to join NTU in 2013, he spent one year as an Associate Professor at the Institute of Computing Technology of the Chinese Academy of Sciences. During October 2010 to June 2012, he was a Postdoctoral Researcher at the University of Southern California, working with Professor Milind Tambe. He received the Ph.D degree in Computer Science from the University of Massachusetts, Amherst, where he was advised by Professor Victor Lesser. His research interests include artificial intelligence, multi-agent systems, computational game theory, reinforcement learning, automated negotiation, and optimization. He has published over 150 referred papers at top conferences (AAMAS, IJCAI, AAAI, ICML, NeurIPS, ICLR, KDD, ICAPS, EC, UAI, AISTATS, and WWW) and journals (JAAMAS, AIJ and ACM/IEEE Transactions). His work on applying game theory to security has been applied to develop game-theoretic randomization software that is currently deployed by the United States Federal Air Marshals Service, the United States Coast Guard, and wildlife conservation organizations. He has served as program committee members for many top conferences and was co-chair for some key international conferences/symposia. He was named to IEEE Intelligent Systems' "AI's 10 to Watch" list for 2018. He is a member of the editorial board of Journal of Artificial Intelligence Research (JAIR) and the Associate Editor of Artificial Intelligence Journal (AIJ), Journal of Autonomous Agents and Multi-agent Systems (JAAMAS), IEEE Intelligent Systems, ACM Transactions on Intelligent Systems and Technology, and ACM Transactions on Autonomous and Adaptive Systems. He was elected to the board of directors of IFAAMAS, senior member of AAAI, and ACM Distinguished Member. He was PC Co-Chair of AAMAS'20 and General Co-Chair of AAMAS'23. He will be PC Chair of IJCAI'27.

  

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Graham Neubig. Graham Neubig is an Associate Professor at the Carnegie Mellon University Language Technology Institute in the School of Computer Science, and work with a bunch of great students in the lab NeuLab. His is also a chief scientist at All Hands AI, where AI agents are built for software development. His research focuses on machine learning and natural language processing. In particular, his is interested in basic research and applications of large language models, with a particular focus on question answering, code generation, multilingual processing, and evaluation/interpretability.

  

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Chi Jin. Chi Jin is an assistant professor at the Electrical and Computer Engineering department of Princeton University. He obtained his PhD degree in Computer Science at University of California, Berkeley, advised by Michael I. Jordan. His research mainly focuses on theoretical machine learning, with special emphasis on nonconvex optimization, reinforcement learning, the reasoning abilities of LLM and developing LLM agents for tasks such as mathematics, coding, and complex games. His representative work includes proving noisy gradient descent escape saddle points efficiently and proving the efficiency of Q-learning and least-squares value iteration when combined with optimism in reinforcement learning.

  

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Junyang Lin. Junyang Lin, a senior staff engineer at Alibaba, currently serves as the tech lead for Qwen. His research areas include natural language processing and multi-modal representation learning, with a particular focus on large-scale foundation models. He has published papers in top-tier conferences such as NeurIPS, ICML, and ACL, and his Google Scholar citation count exceeds 9,700. Since 2023, he has primarily been responsible for the development, open-sourcing, and application of the Qwen series of large models. The models he has developed include the large language model Qwen2.5, the vision-language large model Qwen2-VL, the speech-language large model Qwen2-Audio, the code large model Qwen2.5-Coder, and the math large model Qwen2.5-Math. He is dedicated to promoting the open-source availability of large models. Currently, the Qwen series of models has been downloaded over 100 million times globally, with 87,000 derivative models created based on Qwen and more than 8 million developers worldwide.

  

Talk Title: Qwen: Towards Generalist Models.

  

Talk Abstract: Since Alibaba launched the Qwen series of large models in 2023, the Qwen series of large language models and multimodal large models have been continuously updated and improved. This presentation will introduce the latest developments in the Qwen series of models, including the current performance and technical implementation behind the large language models Qwen2.5, the mathematical large models Qwen2.5-Math, the coding large models Qwen2.5-Coder, the vision-language large models Qwen2-VL, and the speech-language large models Qwen2-Audio, etc. Additionally, this presentation will also cover the future development directions of the Qwen series.








Huan Sun. Huan Sun is an associate professor (with tenure) in the CSE Department and endowed College of Engineering Innovation Scholar at The Ohio State University. She was a visiting scientist at the University of Washington (2016) and received a Ph.D. in Computer Science from University of California, Santa Barbara (2015) and a B.S. in EEIS from the University of Science and Technology of China (2010). Her research interests lie in natural language processing and AI, with emphasis on large language models and agents. Her research received Honorable Mention for Best Paper Awards at ACL (two papers), ACM SIGMOD Research Highlight Award and the Best Paper Award from the IEEE International Conference on Bioinformatics and Biomedicine (BIBM). She is a recipient of NSF CAREER Award, Google Research Scholar and Google Faculty Award, OSU Lumley Research Award, OSU President’s Research Excellence Award, and SIGKDD Ph.D. Dissertation Runner-Up Award, among others. .

  

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