Shiqiang Wang

Shiqiang Wang is a Professor of Artificial Intelligence in the Department of Computer Science, University of Exeter, United Kingdom. He was a researcher at IBM T. J. Watson Research Center, NY, United States until Oct. 2025. He received his Ph.D. from Imperial College London, United Kingdom, in 2015. His research focuses on the intersection of artificial intelligence (AI), distributed computing, and optimization, with a broad range of applications including large language models (LLMs), agentic AI, efficient model training and inference, and AI in distributed systems. He has made foundational contributions to edge computing and federated learning that generated both academic and industrial impact. Dr. Wang served as an associate editor of the IEEE Transactions on Mobile Computing, IEEE Transactions on Parallel and Distributed Systems, and IEEE Transactions on Computational Social Systems. He also served as an area chair of major AI and machine learning conferences, including AAAI, ICLR, ICML, and NeurIPS, and an organizer of multiple workshops at ACM conferences. He received the IEEE Communications Society (ComSoc) Leonard G. Abraham Prize in 2021, IEEE ComSoc Best Young Professional Award in Industry in 2021, Best Paper Runner-Up of ACM MobiHoc 2025, IBM Outstanding Technical Achievement Awards (OTAA) in 2019, 2021, 2022, and 2023, multiple Invention Achievement Awards from IBM since 2016, and Best Student Paper Award of the Network and Information Sciences International Technology Alliance (NIS-ITA) in 2015. He is an IEEE Fellow and a member of ACM and ELLIS.

• Balancing Quality and Efficiency in Future AI Systems
  Quality and efficiency are both essential in AI systems as data sources become more diverse and model sizes grow. In this talk, I will present techniques to address challenges in data and model...
• Federated Learning with Imbalanced Client Participation
  Data is essential for machine learning, but in real-world scenarios, it is often distributed unevenly across sources with varying sizes, quality, and availability. This imbalance poses challenges...
• Navigating Through the LLM Zoo: How to Find the Best Model?
  Open-weight large language model (LLM) zoos provide access to numerous high-quality models, but selecting the appropriate model for specific tasks remains challenging and requires technical...
• Towards Agentic AI at the Edge: Challenges and Opportunities
  Agentic AI systems that can reason, plan, and act with tools are becoming a promising paradigm for real world applications, especially at the edge where low latency, privacy, and resilience are...
• Towards Distributed MLOps: Theory and Practice
  As machine learning (ML) technologies get widely applied to many domains, it has become essential to rapidly develop and deploy ML models. Towards this goal, MLOps has recently emerged as a...