Sumon Biswas

Assistant Professor, Computer and Data Sciences Department

Software engineering (SE), Artificial Intelligence (AI), Responsible AI Engineering, Fairness and Safety. 

Office: 608 Olin Building Phone Number: (216) 368-1494 Email: sumon@case.edu

Research Interests

My research at the intersection of Software Engineering (SE) and AI aims at modeling, verification, and design of responsible AI systems. I apply both formal and empirical SE to ensure algorithmic fairness and safety of ML pipelines, through rigorous analysis of the software abstractions and their real-world implementations.

Teaching Interests

CSDS 600: Responsible AI Engineering
CSDS 393/493: Software Engineering

Academic Qualifications

Postdoc, Carnegie Mellon University
Ph.D. in Computer Science, Iowa State University
M.S. in Computer Science, Iowa State University

Leadership and Services

  • Distinguished Reviewer for ACM Transactions on Software Engineering and Methodology (TOSEM).
  • Reviewers of the journals: IEEE Transactions on Software Engineering (TSE), IEEE Software, and Empirical Software Engineering (EMSE).
  • Program Committee Member of IEEE/ACM International Conference on Software Engineering (ICSE), 2024-2025.
  • Program Committee Member of IEEE/ACM International Conference on Automated Software Engineering (ASE), 2023-2024.

Selected Publications

Yining She, Sumon Biswas, Christian Kästner and Eunsuk Kang. FairSense: Long-Term Fairness Analysis of ML-Enabled Systems, In Proceedings of the 47th IEEE/ACM International Conference on Software Engineering (ICSE), 2025.

David OBrieng , Sumon Biswas, Sayem Mohammad Imtiaz, Rabe Abdalkareem, Emad Shihab and Hridesh Rajan. Are Prompt Engineering and TODO Comments Friends or Foes? An Evaluation on GitHub Copilot, In Proceedings of the 46th IEEE/ACM International Conference on Software Engineering (ICSE), 2024.

Sumon Biswas and Hridesh Rajan. Fairify: Fairness Verification of Neural Networks. In Proceedings of the 45th IEEE/ACM International Conference on Software Engineering (ICSE), 2023. 

Usman Gohar, Sumon Biswas and Hridesh Rajan. Towards Understanding Fairness and its Composition in Ensemble Machine Learning. In Proceedings of the 45th IEEE/ACM International Conference on Software Engineering (ICSE), 2023.

Giang Nguyen , Sumon Biswas and Hridesh Rajan. Fix Fairness, Don’t Ruin Accuracy: Performance Aware Fairness Repair using AutoML. In Proceedings of the 31st ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), 2023.

Sumon Biswas, Mohammad Wardat and Hridesh Rajan. The Art and Practice of Data Science Pipelines: A Comprehensive Study of Data Science Pipelines In Theory, In-The-Small, and In-The-Large. In Proceedings of The 44th International Conference on Software Engineering (ICSE), 2022.

David OBrien, Sumon Biswas, Sayem Mohammad Imtiaz, Rabe Abdalkareem, Emad Shihab and Hridesh Rajan. 23 Shades of Technical Debt: An Empirical Study on Machine Learning Software. In Proceedings of the 30th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), 2022.

Sumon Biswas and Hridesh Rajan. Fair preprocessing: Towards understanding compositional fairness of data transformers in machine learning pipeline. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), 2021.

Sumon Biswas and Hridesh Rajan. Do the machine learning models on a crowd sourced platform exhibit bias? an empirical study on model fairness. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), 2020.

Sumon Biswas, Md Johirul Islam, Yijia Huang, Hridesh Rajan. Boa Meets Python: A Boa Dataset of Data Science Software in Python Language. In 16th International Conference on Mining Software Repositories (MSR), 2019.
 

 

Personal website: https://sumonbis.github.io

[For prospective students] I’m seeking multiple self-motivated students (M.S. and Ph.D.) to join my research group. If you are interested, please email me your CV and unofficial transcripts.