CPIL members

Prof. Xue (Steve) Liu leads the Cyber-Physical Intelligence Lab. He is an IEEE Fellow, Fellow of the Canadian Academy of Engineering, Professor and William Dawson Scholar at McGill University, and affiliated with Mila.

I am a Ph.D. candidate in Computer Science at McGill University, affiliated with Mila – Quebec AI Institute. My research focuses on large language models, reinforcement learning, retrieval-augmented generation, and intelligent systems for telecommunications and real-world infrastructure. I am particularly interested in trustworthy reasoning, knowledge-enhanced AI, and learning-based optimization for complex systems. My recent work spans LLM post-training, graph-grounded and provenance-aware AI, telecom knowledge intelligence, digital twins, and multi-agent reinforcement learning for autonomous control. My research has led to publications in venues including ICLR, IEEE Wireless Communications, IEEE Communications Surveys & Tutorials, ICC Workshops, and ICML workshops.

I am an AI researcher and machine learning engineer interested in building practical and reliable AI systems for real-world applications. My research interests are centered on AI for finance and neuroscience, with broader interests in applied machine learning, computer vision, optimization, healthcare AI, and modeling complex real-world systems. I am particularly interested in research problems that arise in competitive and data-rich environments, including financial markets, brain signals, medical imaging, and machine learning competitions. I also enjoy using competitions as a research setting: a way to test ideas under strong empirical feedback, compare methods rigorously, and develop approaches that generalize beyond a single benchmark. My recent work spans clinical AI, computer vision, optimization, and applied machine learning in industry settings. I have contributed to clinical AI research, participated in research-oriented competitions including Kaggle and NeurIPS competitions, and reached Kaggle Competitions Master status with a top-14 global ranking. Beyond research, I am involved in AI education and community building. I founded and help grow DSML.KZ, a data science and AI community in Kazakhstan and Central Asia, and have been involved in organizing AI olympiad initiatives for school students, including Kazakhstan’s national AI Olympiad and the International Olympiad in Artificial Intelligence.

I’m currently a Ph.D. candidate at McGill University and Mila. I am currently enthusiastic about extending the boundary of test-time scaling beyond language foundation models. Correspondingly, I am also building modality-native foundation models in collaboration with other amazing researchers. Specifically, this involves formats such as time series, tabular, and behavioural sequences.

Hi👋 My name is Ye Yuan袁野(https://stevenyuan666.github.io). I’m currently a fourth-year PhD candidate at McGill University and Mila-Quebec AI institute, supervised by Professor Xue Liu. My research centers on developing vision language models / large language models and score-based generative models to advance intelligent systems, with applications in (i) addressing challenges in offline black-box optimization, (ii) enabling knowledge-centric NLP tasks such as automatic knowledge base construction and retrieval augmented generation, and (iii) improving the efficiency and efficacy of agentic systems for real-world applications. My work is supported by multiple fellowships and has been published at top venues including NeurIPS, ICLR, ICML, ACL, EMNLP, and TMLR. Representative contributions include authoring a comprehensive survey on offline black-box optimization with Turing Award Winner Prof. Yoshua Bengio, which is accepted by TMLR with the Survey Certification, presenting an EMNLP 2024 oral paper (top 3%), and contributing to an ICML 2026 spotlight paper (top 2%). My journey is also complemented by industrial research experiences or close collaborations at Microsoft Research, RBC Borealis, Samsung Research America, ByteDance, Amazon, and Meta.

Changjiang Han is an MSc student in Machine Learning at MBZUAI, supervised by Prof. Xue (Steve) Liu in the Cyber-Physical Intelligence Lab. His research centers on Agentic AI, with particular interests in agent memory architectures and generative recommendation systems. He is currently a Research Intern at JD.com, working on Agentic AI and generative recommendation. Prior to MBZUAI, he received his Bachelor of Engineering from Huazhong University of Science and Technology (HUST), where he was recognized with the Outstanding Thesis Award and Outstanding Graduate Award, and conducted research on brain-computer interfaces and neuromorphic computing at Zhejiang University's State Key Laboratory of Brain-Machine Intelligence.

Bowei He is a Postdoctoral Fellow in the Department of Machine Learning at MBZUAI. His recent research centers on AI agents, with interests spanning agent reinforcement learning, agent memory, agent lifelong learning and evolution, agent world modeling, and scaling laws for agent intelligence. He has extensive internship and collaboration experience with leading industry research groups, including Tencent Hunyuan LLM and Meta. His work has been published in top-tier conferences such as NeurIPS, ICLR, ICML, AAAI, KDD, and CVPR, with several papers selected for highlight and oral presentations. He has also served the community as a program committee member, reviewer, and area chair for major international AI conferences. He has been recognized as an ACM MobiSys Rising Star and selected as a Heidelberg Laureate Forum Young Researcher.

Dr. Yankai Chen is currently a Postdoctoral Associate supervised by Prof. Xue (Steve) Liu at MBZUAI. He previously held postdoctoral associate positions at Cornell University and The Chinese University of Hong Kong (CUHK). He received his Ph.D. in Computer Science and Engineering from CUHK in 2023 and his B.S. from Nanjing University in 2016. His research lies in Machine Learning and Human-centered AI. Details can be found at https://yankai-chen.github.io/.

Zonghang Li obtained his Bachelor and PhD degrees from UESTC. He was a visiting scholar at the University of Oxford, the Peng Cheng Laboratory, and the Nanyang Technological University. He is now a postdoc researcher at MBZUAI. His research interests are mainly in Distributed AI Systems, including distributed ML systems, LLM systems, and on-device/edge AI systems. He is also interested in Network AI and Embodied AI. He has published in top journals including IEEE/ACM TON, IEEE TSC, IEEE TMC, IEEE JSAC, IEEE COMST, and top conferences such as ICLR, MLSys, KDD, with a total citation of 1900+. He obtained 6 CN Patents, 1 National Group Standard, and authored 1 book. His work won the Future Network Leading Innovative Scientific and Technological Award of China Institute of Communications (CIC), the Best Paper Award from Guangdong Computer Society, the Best Paper Award from IEEE Transactions on Cloud Computing, and the Best Paper Award from IEEE IWCMC 2025.

Haolun Wu received his Ph.D. in Computer Science from McGill University and Mila – Quebec AI Institute in 2026, and was a visiting scholar at Stanford University in 2025. His research focuses on human-centered and data-centric AI, especially learning from human feedback to make AI systems more trustworthy, responsible, and aligned with human needs. His work spans personalization, information retrieval, recommendation, AI alignment, and interdisciplinary applications in education and psychology. During Ph.D., he published over 30 papers in leading venues including NeurIPS, ICML, ICLR, WWW, SIGIR, KDD, EMNLP, CHI, and CSCW, and has interned and collaborated with Google DeepMind, Google Research, Microsoft Research, Samsung AI Research, etc. He received Microsoft Research & Mila Research Grant (twice), MITACS Accelerate Fellowship, Borealis AI Fellowship (10 across Canada), Samsung Global Research Outreach Grant, Tinker Research Grants, Cohere Catalyst Grants, and FRQNT PhD Scholarship (ranked 1st place). He also received the Governor General’s Gold Medal, the most prestigious academic honors for graduate students in Canada, as well as McGill University’s D. W. Ambridge Prize, both recognizing him among the most outstanding graduating graduate students at McGill.