Keynote Speech Information - Lei MA
- Associate Professor at The University of Tokyo and the University of Alberta; Canada CIFAR AI Chair
- Research in human-centered trustworthy AI, software engineering, and cyber-physical systems, focusing on quality, reliability, safety, and security assurance
- Multiple awards, including four ACM SIGSOFT Distinguished Paper Awards (ASE 16, ASE 18, ASE 18, FSE 23) and the 2022 IEEE Transactions on Software Engineering annual best paper award
Biography: Lei Ma is currently an Associate Professor with The University of Tokyo; as
well as an associate professor and Canada CIFAR AI Chair with University of Alberta. His
research centers around the interdisciplinary fields of human-centered trustworthy artificial
intelligence (AI), software engineering (SE), and cyber-physical system (CPS) with a special
focus on quality, reliability, safety, and security assurance, as well as the interpretation
and human interactivity of and AI Systems. Many of his works were published in top-tier AI,
software engineering, and security venues (e.g., TSE, TOSEM, ICSE, FSE, ASE, CAV, ICML,
NeurIPS, AAAI, IJCAI, TDSC), among which four papers receive the ACM SIGSOFT Distinguished
Paper Awards (ASE 16, ASE 18, ASE 18, FSE 23), and an annual best paper award of 2022 IEEE
Transactions on Software Engineering (TSE 2022). More information about his recent activities
can be found at https://www.malei.org
Title: Towards Trustworthy Assurance of AI Systems in the LLM Era
Abstract: In recent years, deep learning-enabled systems have made remarkable progress,
powering a surge in advanced intelligent applications. This growth and its real-world impact
have been further amplified by the advent of large foundation models (e.g., LLM, Stable
Diffusion). Yet, the rapid evolution of these AI systems often proceeds without comprehensive
quality assurance and engineering support. This gap is evident in the integration of standards
for quality, reliability, and safety assurance, as well as the need for mature toolchain
support that provides systematic and explainable feedback of the development lifecycle. In
this talk, I will present a high-level overview of our team's ongoing initiatives to lay the
groundwork for Trustworthy Assurance of AI Systems and its industrial applications.