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 * 詹仙园 | ZHAN Xianyuan
 * Publications
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 * 中文简介


詹仙园|XIANYUAN ZHAN

Research Assistant Professor at Institute for AI Industry Research (AIR),
Tsinghua University

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BIOGRAPHY

Dr. Xianyuan Zhan is a research assistant professor at the Institute for AI
Industry Research (AIR), Tsinghua University. He received a dual Master’s degree
in Computer Science and Transportation Engineering, and a PhD degree in
Transportation Engineering from Purdue University. Before joining AIR, Dr. Zhan
was a data scientist at JD Technology and also a researcher at Microsoft
Research Asia (MSRA). Dr. Zhan previously led the research and development of
AI-driven industrial system optimization products at JD Technology. He has
published more than 70 papers in key journals and conferences in the field of
Transportation Engineering and Computer Science. He is also a reviewer for many
top transportation and computer science journals and conferences. He is
currently a committee member of China Computer Federation-Artificial
Intelligence & Pattern Recognition (CCF-AI) Committee.

 * Group Website: https://air-dream.netlify.app/
 * Group Code Repository: https://github.com/AIR-DI


RESEARCH INTERESTS

 * Offline deep reinforcement learning
 * Offline imitation learning
 * Foundation models for decision-making
 * Complex system optimization
 * Autonomous driving


WE ARE HIRING!!!

Our team is looking for student interns/postdocs at AIR! If you are interested
in the research directions of offline RL/IL, AI alignment/AI safety, embodied
AI, decision-making in autonomous driving, please feel free to send me an e-mail
at zhanxianyuan@air.tsinghua.edu.cn!


RECENT NEWS AND ACTIVITIES

 * Sep. 2024: Our two recent papers “Instruction-Guided Visual Masking” and
   “Diffusion-DICE: In-Sample Diffusion Guidance for Offline Reinforcement
   Learning” have been accepted in NeurIPS 2024!
 * Jul. 2024: Our two recent papers “DecisionNCE: Embodied Multimodal
   Representations via Implicit Preference Learning” and “Instruction-Guided
   Visual Masking” have won the Outstanding Paper Awards at ICML 2024 Workshop
   on Multi-modal Foundation Model meets Embodied AI (MFM-EAI).
 * May. 2024: Our four recent papers “DecisionNCE: Embodied Multimodal
   Representations via Implicit Preference Learning”, “OMPO: A Unified Framework
   for Reinforcement Learning under Policy and Dynamics Shifts”,
   “Offline-Boosted Actor-Critic: Adaptively Blending Optimal Historical
   Behaviors in Deep Off-Policy RL”, “Seizing Serendipity: Exploiting the Value
   of Past Success in Off-Policy Actor-Critic” have been accepted in ICML 2024!
 * Apr. 2024: Our recent survey paper “A Comprehensive Survey of Cross-Domain
   Policy Transfer for Embodied Agents” has been accepted in IJCAI 2024.
 * Jan. 2024: Our four recent papers “Revealing the Mystery of Distribution
   Correction Estimation via Orthogonal-gradient Update”, “Safe Offline
   Reinforcement Learning with Feasibility-Guided Diffusion Model”,
   “Query-Policy Misalignment in Preference-Based Reinforcement Learning”, and
   “OpenChat: Advancing Open-source Language Models with Mixed-Quality Data”
   have been accepted in ICLR 2024!
 * Oct. 2023: We have released “Data-Driven Control Library (D2C)”, which
   provides an easy-to-use and comprehensive library for real-world data-driven
   control & decision-making problems! Project page available at
   https://github.com/AIR-DI/D2C.
 * Sep. 2023: We have released “OpenChat: Advancing Open-source Language Models
   with Mixed-Quality Data”, which uses ideas from offline RL to fine-tune
   open-source large language models! Project page is available at
   https://github.com/imoneoi/openchat.
 * Sep. 2023: Our two recent papers “Look Beneath the Surface: Exploiting
   Fundamental Symmetry for Sample-Efficient Offline RL” and “Offline
   Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value
   Regularization” have been accepted in NeurIPS 2023!


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