Speaker:

樊文飞 (Shenzhen Institute of Computing Sciences)

Time:

• 10:00-11:30 Beijing Time
• May 14, 2026 (Thursday)

Venue:

四号科研楼A区 518

Abstract:

Large Language Models (LLMs) are transforming many applications, yet their use in industrial and high-stakes settings remains limited. LLMs often suffer from hallucinations, limited interpretability, weak explicit reasoning, and heavy data dependence, making it difficult to ensure reliable, fair, and robust decision-making in real time.

This talk advocates a multi-paradigm AI approach that integrates machine learning with logical reasoning. By embedding data-driven models within logical rules as predicates, this approach improves reasoning consistency, interpretability, and controllability while reducing reliance on large training data. We present practical case studies spanning industrial manufacturing, cyber security, early-stage drug discovery, intelligent recommendation, and banking risk control, demonstrating how multi-paradigm AI supports low-cost, high-accuracy, and interpretable decision-making. The talk aims to incite interest in this emerging direction.

Speaker Bio:

Professor Wenfei Fan is the Chair of Web Data Management at the University of Edinburgh, UK, and the Chief Scientist of Shenzhen Institute of Computing Science, China. He is a Foreign Member of Chinese Academy of Sciences, a Fellow of the Royal Society (FRS), a Fellow of the Royal Academy of Engineering (FREng), a Fellow of the Royal Society of Edinburgh (FRSE), a Member of the Academy of Europe (MAE), and an ACM Fellow (FACM). He is a visiting chair professor at Peking University, and a distinguished visiting professor at Tsinghua University.
He received his PhD from the University of Pennsylvania (USA), and his MSc and BSc from Peking University (China). He is a recipient of Royal Society Wolfson Research Merit Award in 2018, ERC Advanced Grant in 2015, the Roger Needham Award in 2008 (UK), Yangtze River Scholar in 2007 (China), the Outstanding Overseas Young Scholar Award in 2003 (China), the Career Award in 2001 (USA), and several Test-of-Time and Best Paper Awards (Alberto O. Mendelzon Test-of-Time Award of ACM PODS 2015 and 2010, Best Paper Awards for SIGMOD 2017, VLDB 2010, ICDE 2007 and Computer Networks 2002). His current research interests include database theory and systems, in particular big data, DB4AI, AI4DB, data quality, parallel models, and unification of logical reasoning and machine learning.