conf.researchr.org / Sumon Biswas
Registered user since Fri 15 Mar 2019
Name:Sumon Biswas
Bio:
I’m a tenure-track Assistant Professor at Case Western Reserve University, where I direct the Responsible Software and AI Design (reSAID) Lab.
My research focuses on the intersection of Software Engineering (SE) and Artificial Intelligence, with a focus on engineering responsible AI systems. I develop formal and empirical approaches to improve the safety, fairness and robustness of AI-enabled software. My current research particularly focuses on LLMs and agentic AI systems, including the safety of coding agents, LLM reasoning, planning and verification of AI systems.
Country:United States
Affiliation:Case Western Reserve University
Personal website: https://sumonbis.github.io
Research interests:Software Engineering, Artificial Intelligence
Contributions
2026
International Conference on Software Engineering for Adaptive and Self-Managing Systems
2025
2024
ICSE
- Committee Member in Research Track within the Research Track-track
- Author of Artifact for "Are Prompt Engineering and TODO Comments Friends or Foes? An Evaluation on GitHub Copilot" within the Artifact Evaluation-track
- Author of Are Prompt Engineering and TODO Comments Friends or Foes? An Evaluation on GitHub Copilot within the Research Track-track
2023
ASE
ICSE
- Author of Fairify: Fairness Verification of Neural Networks within the Technical Track-track
- Author of Towards Understanding Fairness and its Composition in Ensemble Machine Learning within the Technical Track-track
- Committee Member in Onsite Judges within the SRC - ACM Student Research Competition-track
- Author of Replication Package of the ICSE 2023 Paper Entitled "Fairify: Fairness Verification of Neural Networks" within the Artifact Evaluation-track
- Committee Member in ACM Student Research Competition within the SRC - ACM Student Research Competition-track
- Mentor in Mentors within the SMeW - Student Mentoring Workshop-track
- Author of Artifact for the ICSE 2023 Paper Entitled "Towards Understanding Fairness and its Composition in Ensemble Machine Learning" within the Artifact Evaluation-track