Sumon Biswas

Assistant Professor

Department of Computer and Data Sciences

Case School of Engineering

Case Western Reserve University

Email: sumon@case.edu

About

I’m a tenure-track Assistant Professor at Case Western Reserve University. I joined the Department of Computer and Data Sciences in Fall 2024. Before that I was a Postdoctoral Researcher at Carnegie Mellon University, where I worked with Dr. Eunsuk Kang in the Institute for Software Research (ISR). I received my Ph.D. in Computer Science from Iowa State University (ISU) under the supervision of Dr. Hridesh Rajan. For my dissertation, I worked on verifying and reasoning algorithmic fairness in ML pipeline.

My research interests are broadly at the intersection of Software Engineering (SE) and AI, focusing on engineering responsible AI properties. I’m currently working on fomal verificaiton and design of fairness and safety of AI systems that endure for long-term.

[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.

Research

I’m broadly interested in the intersection of SE and AI with particular focus in the Responsible AI Engineering. Unlike traditional software systems, AI-enabled software is under-specified, uncertain and data-driven, which are introducing several ethical and safety risks for society. My research established fairness as a non-functional SE property for societal systems. By adopting both formal and empirical approaches, I aim to achieve safety and fairness through analysis of software abstractions and their real-world implementations.

Fairness verification and reasoning: fairness verification of DNN (ICSE'23), causal reasoning in ML (FSE'21), compositional fairness (ICSE'23)
Designing fair and safe AI: root cause analysis of bias (FSE'20), fair AutoML for improved trade-off (FSE'23), position paper on long-term safety (FSE-SE4SafeML'23)
AI engineering and static analysis: AI software architecture (ICSE'22), ML technical debts (FSE'22), Repaying debts using LLM (ICSE'24), mining open-source ML (MSR'19)

Currently, I’m working on ensuring fairness and safety of learning-enabled software systems (sociotechnical and cyber-physical). In particular, I’m designing novel analysis framework and safe-by-construction techniques that guarantee certain properties. I’m also aiming to build adaptive techniques towards sustainable AI/ML software.

Publications

Yining She, Sumon Biswas, Christian Kästner, Eunsuk Kang

FairSense: Long-Term Fairness Analysis of ML-Enabled Systems

In 47th International Conference on Software Engineering (ICSE), Ottawa, Canada, 2025.

David OBrien, Sumon Biswas, Sayem Imtiaz, Rabe Abdalkareem, Emad Shihab, Hridesh Rajan

Are Prompt Engineering and TODO Comments Friends or Foes? An Evaluation on GitHub Copilot

In 46th International Conference on Software Engineering (ICSE), Lisbon, Portugal, 2024.

Sumon Biswas, Hridesh Rajan

Fairify: Fairness Verification of Neural Networks

In 45th International Conference on Software Engineering (ICSE), Melbourne, Australia, 2023.

Usman Gohar, Sumon Biswas, Hridesh Rajan

Towards Understanding Fairness and its Composition in Ensemble Machine Learning

In 45th International Conference on Software Engineering (ICSE), Melbourne, Australia, 2023.

Giang Nguyen, Sumon Biswas, Hridesh Rajan

Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoML

In 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), San Francisco, California, 2023.

Sumon Biswas, Mohammad Wardat, 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 44th International Conference on Software Engineering (ICSE), Pittsburgh, Pennsylvania, 2022.

David OBrien, Sumon Biswas, Sayem Imtiaz, Rabe Abdalkareem, Emad Shihab, Hridesh Rajan

23 Shades of Self-Admitted Technical Debt: An Empirical Study on Machine Learning Software

In 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), Singapore, 2022.

Sumon Biswas, Hridesh Rajan

Fair Preprocessing: Towards Understanding Compositional Fairness of Data Transformers in Machine Learning Pipeline

In 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), Athens, Greece, 2021.

Sumon Biswas, Hridesh Rajan

Do the Machine Learning Models on a Crowd Sourced Platform Exhibit Bias? An Empirical Study on Model Fairness

In 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), Sacramento, California, 2020.

See all publications

Services

Journal Reviewer

TOSEM: Serving in the Board of Distinguished Reviewers for ACM Transactions on Software Engineering and Methodology (TOSEM).
TSE: Serving as the reviewer for IEEE Transactions on Software Engineering (TSE).
EMSE: Serving as a reviewer for Springer Journal - Empirical Software Engineering (EMSE).

Conference Program Committees (PC)

ICSE'25: Serving as the Program Committee member of Technical Track of ICSE 2025.
ICSE'24: Served as the Program Committee member of Technical Track of ICSE 2024.
ASE'24: Serving as the Program Committee member of Technical Track of ASE 2024.
ASE'23: Serving as the Program Committee member of Technical Track of ASE 2023.
ASE'23: Serving as the Program Committee member of Industry Showcase (Papers) Track of ASE 2023.
ICSE'23: Served as the Program Committee member of Student Research Competition track of ICSE 2023.
ASE'22: Served as the Program Committee member of Industry Showcase track of ASE 2022.
ASE'22: Served as the Program Committee member of Late Breaking Results track of ASE 2022.
ESEC/FSE'22: Served as the Program Committee member of Doctoral Symposium track of ESEC/FSE 2022.
OOPSLA'21 AEC: Served in the program committee of Artifact Evaluation Track of ACM SIGPLAN conference on Object-Oriented Programming, Systems, Languages & Applications (OOPSLA'21).
MSR'21: Served as the shadow program committee member of the Technical Track of International Conference on Mining Software Repositories (MSR 2021).

Organizing Committees

Mentor @ ICSE SMeW : Served as a Mentor in ICSE 2023 SMeW - Student Mentoring Workshop.
Onsite Judge @ ICSE'23 SRC: Served as the Onsite Judge of ICSE SRC held at Melbourne, Australia.
Accessibility Chair @ SPLASH'20-21: Served the organizing committee of ACM SIGPLAN conference on Systems, Programming, Languages, and Applications (SPLASH 2020 and SPLASH 2021) with OOPSLA, ECOOP and REBASE in Chicago, IL.
Web Chair @ Midwest Big Data Summer School: Served in the organizing committee of the Midwest Big Data Summer School 2021 in Ames, IA, United States.
WiDS Ambassador: Served as the WiDS Ambassador (Women in Data Science) to organize and promote ISU event as part of the annual WiDS Worldwide Conference organized by Stanford University.
Panelist @ ESEC/FSE'20: Served the panel discussion of the session on Fairness at ACM ESEC/FSE 2020 in Sacramento, CA.

Research Projects

Design and Architecture of Data Science Pipelines

We study, design, and analyze the DS pipeline architecture consisting stages such as preprocessing, modeling, training, evaluation, etc.

Safety Assurance of Predictive Systems

We built abstractions of ML systems and inferred preconditions to provide assurance in safety-critical predictions.

Verifying Neural Networks for Individual Fairness

A modular approach to formally verify neural networks. We specified individual property for SMT solver and verified fairness for specific subpopulations.

Causal Fairness in Machine Learning Pipeline

We used causal reasoning to measure fairness of components and remove them from machine learning pipeline.

Fairness Engineering in ML Models

We have studied the software engineering concerns of fairness in real-world machine learning models.

ML Repo Dataset from GitHub

This dataset is created by mining 5M Python program snapshots. The code is transformed to AST for static analysis.

Large-Scale Mining of Data-Science Software from GitHub

Mining and analyzing data-science repositories can provide insights from historical data.

Teaching

COMS 309 - Software Development Practices

This is one of the largest computer science classes taught at Iowa State (~250 class size). I taught it for three semesters. One of the great experience in the course is to supervise many software project teams.

Topics: Develop complex software in a team: from idea to release 🔘 Software development criteria: client-server architecture, relational database, multi-user setting, concurrent features, e.g., online chat 🔘 Utilizing SE tools e.g., IDE, source-control, Git.

COMS 327: Advanced Programming Techniques

This is also a very large class which I TA’ed for three semesters. Being heavy on the programming practices, I taught problem-solving, live coding, debugging, and testing.

Topics: Differences between managed (Java) and unmanaged languages (C/C++) 🔘 Design and build large programs from specification 🔘 Memory management in C and C++ 🔘 Templates and standard library 🔘 Concurrent and network programming.

Recent News


Nov 2024:	Our paper on long-term fairness analysis of ML systems got accepted to ICSE 2025 to be held at Ottawa, Canada.
Aug 2024:	Excited to share that I joined Case Western Reserve University as a tenure-track faculty.
Dec 2023:	Presented our paper on Safe ML Systems at FSE-SE4SafeML at San Francisco, CA.
Nov 2023:	Paper accepted in ICSE'24 research track on the risks and opportunities brought by LLM for technical debts.
Aug 2023:	Our position paper got accepted in the FSE'23 Workshop on Dependability and Trustworthiness of Safety-Critical Systems with Machine Learned Components
May 2023:	Presented two research papers in ICSE'23 at Melbourne
May 2023:	Served as onsite judge in ACM Student Research Competition at ICSE 2023
May 2023:	Joined the ICSE'23 SMeW - Student Mentoring Workshop as a Mentor
May 2023:	Paper accepted in ESEC/FSE'23 research track.
Feb 2023:	ICSE paper artifact accepted and got three badges.
Dec 2022:	Two papers accepted at ICSE'22 research track.
Sep 2022:	Joined the Program Committee of ICSE 2024 technical track.
Aug 2022:	I’ve been invited to serve the Board of Distinguished Reviewers for the Journal TOSEM (ACM Transactions on Software Engineering and Methodology).
Jun 2022:	Paper accepted in ESEC/FSE'22 research track.
May 2022:	Presented ICSE'22 paper on Data Science Pipeline in-person at Pittsburgh, PA.
May 2022:	Joined ISR at Carnegie Mellon University as a Postdoctoral Researcher
May 2022:	Received Research Excellence Award from Iowa State University.
Apr 2022:	Organized the Women in Data Science (WiDS) event at ISU and served as an ambassador of WiDS Global, Stanford University.
Apr 2022:	Defended my Ph.D. thesis “Understanding and Reasoning Fairness in Machine Learning Pipelines”.
Apr 2022:	I’ve been awarded ACM SIGSOFT CAPS grant to attend ICSE'22 in-person at Pittsburgh.
Mar 2022:	Artifact accepted for my ICSE'22 paper on Data Science Pipelines.
Feb 2022:	Gave an invited talk at the CREATE SE4AI group organized by Concordia University, Polytechnique Montreal, Queen’s University, and University of Alberta.
Dec 2021:	Paper accepted at ICSE'22 research track.
Aug 2021:	Present paper on Fair Preprocessing in ESEC/FSE'21.
Jul 2021:	Awarded ACM SIGSOFT CAPS grant to attend ESEC/FSE'21.
Jun 2021:	Artifact accepted for ESEC/FSE'21 paper on Fair Preprocessing.
Jun 2021:	Joined the program committee of the artifact track of OOPSLA'21.
Jun 2021:	Received publication award from ISU computer science department for publishing in the top-tier venues.
Jul 2020:	Attending ICSE 2021 conference.
Apr 2021:	Paper accepted in ESEC/FSE'21 research track.
May 2021:	Passed the Ph.D. candidacy exam.
Jan 2021:	Selected for Shadow PC for the MSR'21 technical track.
Nov 2020:	Attending OOPSLA'21 conference.
Nov 2020:	Present paper on ML Fairness in ESEC/FSE'20.
Jul 2020:	Attending ICSE 2020 conference.
May 2020:	Organizing Mid-West Big Data Summer School.