Professor  David Lo

David Lo is the OUB Chair Professor of Computer Science, Director of the Information Systems and Technology Cluster, and Director of the Center for Research on Intelligent Software Engineering at the School of Computing and Information Systems at Singapore Management University. He was named an ACM Fellow in 2023, recognized "for contributions to synergizing AI and software engineering for human-in-the-loop automation and software analytics." He is also an IEEE Fellow, a Fellow of Automated Software Engineering (ASE), and a National Research Foundation Investigator (Senior Fellow).

Since the mid-2000s, Lo has championed the area of AI for Software Engineering (AI4SE). His work has demonstrated how AI—spanning data mining, machine learning, information retrieval, natural language processing, and search-based algorithms—can transform software engineering data into actionable insights and automation. Through empirical studies, Lo has also explored the pain points faced by practitioners, identified the limitations of AI4SE solutions, and assessed the acceptance thresholds for AI-powered tools. As of December 2024, his research has received over 20 awards and garnered over 37,000 citations, with an H-index of 100. His contributions include two 10-year Test-of-Time (Most Influential Paper) Awards at the 29th IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER'22) and the 33rd IEEE International Symposium on Software Reliability Engineering (ISSRE'22) for papers on AI-powered automation and AI reliability. Lo’s work has also earned eleven ACM SIGSOFT/IEEE TCSE Distinguished Paper Awards, most recently at the 33rd ACM International Symposium on Software Testing and Analysis (ISSTA'24) for proposing the first programming language for AI agents.

Lo has served on over 40 Organizing Committees of international conferences and on the Editorial Boards of more than 10 journals. His notable leadership roles include General Chair of the 31st IEEE/ACM International Conference on Automated Software Engineering (ASE'16) and the 19th IEEE/ACM International Conference on Mining Software Repositories (MSR'22), and PC Co-Chair for ASE'20, the 32nd ACM International Conference on the Foundations of Software Engineering (FSE'24), and the 37th ACM/IEEE International Conference on Software Engineering (ICSE'25). He has been elected to the ACM SIGSOFT Executive Committee for two terms (2021–24, 2024–27), serving as Award Chair (2021–24) and Treasurer (2024–27). He is also serving in multiple Steering Committees and is the Vice Chair of the ASE Steering Committee. In 2021, he received the IEEE TCSE Distinguished Service Award. He has also received many Distinguished PC Member/Reviewer Awards.

As an educator and mentor, Lo received a university-wide Teaching Excellence Award in 2022 and an inaugural university-wide Outstanding Graduate Supervisor Award in 2024. His trainees have become faculty members and R&D experts in Asia, North America, Europe, and Australia. Lo is also a frequent keynote speaker, having recently presented at the 32nd IEEE International Requirements Engineering Conference (RE'24), 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM'24), 10th IEEE International Conference on Data and Software Engineering (ICoDSE'24), 35th IEEE International Symposium on Software Reliability Engineering (ISSRE'24), and 31st Asia-Pacific Software Engineering Conference (APSEC'24).

• Charting New Frontiers: Exploring Limits, Threats, and Ecosystems of LLMs in Software Engineering
  Large language models (LLMs) are transforming software engineering, but their adoption brings critical challenges. In this lecture, I will explore three key thrusts: the *limits* of LLMs,...
• Efficacy, Efficiency, and Security of Code LLMs: Promises and Perils
  Researchers have explored methods to automate software engineering (ASE) tasks for decades. In recent years, we have been excited about the potential of code Large Language Models (code LLMs) for...
• Efficient and Green Code LLMs: Happier Software Engineers, Happier Planet
  Many have been excited about the potential of code Large Language Models (code LLMs). However, code LLMs are large, slow, and energy-hungry compared to traditional ASE solutions, which...
• Requirements Engineering for Trustworthy Human-AI Synergy in Software Engineering 2.0
  Software Engineering 2.0 envisions trustworthy and synergistic collaborations between humans and AI agents that are diverse, responsible, and autonomous, aiming to build the software of...
• Software Reliability in the Era of Large Language Models: A Dual Perspective
  Much software engineering research has been dedicated to building reliable software systems. The last two decades have witnessed the growth of software engineering data availability that spurred...