Sumon Biswas

Sumon Biswas is a tenure-track Assistant Professor in the Department of Computer and Data Sciences at Case School of Engineering, Case Western Reserve University. Previously, he was a Postdoctoral Researcher at the Institute for Software Research (ISR) at Carnegie Mellon University, working with Dr. Eunsuk Kang. He received his Ph.D. in Computer Science from Iowa State University under the supervision of Dr. Hridesh Rajan.

His research focuses on the intersection of Software Engineering and Artificial Intelligence with particular emphasis on responsible AI engineering. His work spans several key areas:

• Formal verification and reasoning of fairness in AI systems
• Designing fair and safe AI systems
• AI engineering and analysis of machine learning software
• Long-term risks in machine learning systems
• Analysis of technical debt in AI/ML systems

Dr. Biswas has made significant contributions to understanding and addressing fairness in machine learning pipelines, verification of neural networks, causal reasoning in ML pipelines, and safety assurance of predictive systems. His recent work increasingly focuses on foundation models and large language models (LLMs), with an emphasis on safety and responsible deployment of AI agents. His lab operates the state-of-the-art AISC2 cluster with five HGX H200 servers featuring 40 NVIDIA H200 GPUs.

His publications show a consistent trend toward addressing both theoretical and practical challenges in responsible AI, with increasing focus on long-term system behavior, LLMs, and practical deployment challenges. The research spans formal methods, empirical studies, and practical tool development.

Dr. Biswas has received several awards including the Research Excellence Award from Iowa State University and has been invited to serve on the Board of Distinguished Reviewers for ACM Transactions on Software Engineering and Methodology (TOSEM).

He serves on the program committees of major software engineering conferences including ICSE, ASE, and ESEC/FSE, and has reviewed for prestigious journals such as IEEE Transactions on Software Engineering. As an educator, he teaches courses on Responsible AI Engineering and Software Engineering, focusing on building high-quality software systems that meet responsible AI principles including fairness, robustness, explainability, and safety.