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, in the Department of Computer and Data Sciences. Before joining CWRU, 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 systems. I’m currently working on formal verification and design of fairness and safety of AI systems.
[For prospective students] I’m seeking multiple self-motivated students (Ph.D. and M.S.) to join my research group. If you are interested, please email me your CV and unofficial transcripts.
Research
I adopt both formal and empirical approaches to achieve safety and fairness through analysis of software abstractions and their real-world implementations.
- Formal verification and reasoning: fairness verification of DNN (ICSE'23), causal reasoning in ML (FSE'21), compositional fairness (ICSE'23)
- Designing fair and safe AI: Long-term fairness (ICSE'25), safety in presence of feedback loop (FSE'23), root cause analysis of bias (FSE'20), fair AutoML (FSE'23)
- AI engineering and 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 focusing on foundation models and LLMs, with an emphasis on safety and responsible deployment of AI agents and systems. Our lab runs the state-of-the-art AISC2 cluster, comprising five HGX H200 servers featuring 40 NVIDIA H200 GPUs (141GB memory each). Fueled by ultra-fast InfiniBand fabric, the cluster delivers industry grade LLM development, multi-tenant runs, and seamless scale for LLM training, fine-tuning, and inference. If you’re excited to push the frontiers of LLMs, let’s talk.
Publications
Preprints
Conference & Journal Papers
Services
Federal Agency
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NSF AI Institute Site Visitor (2026): Served on the NSF Site Visit Team Panel at the University at Buffalo for the NSF AI Institute on AI and Speech Language Therapy.
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NSF Panelist (2025): Served as a panelist for the National Science Foundation (NSF) proposal review panel for the Division of Computing and Communications Foundations (CCF).
Journal Reviewer
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TOSEM: Serving in the Board of Distinguished Reviewers for ACM Transactions on Software Engineering and Methodology (TOSEM).
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TSE: Serving as the reviewer for IEEE Transactions on Software Engineering (TSE).
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IEEE Software: Reviewer for IEEE Software.
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EMSE: Serving as a reviewer for Springer Journal - Empirical Software Engineering (EMSE).
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Other review services:
Conference Program Committees (PC)
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FSE'26: Serving as the Program Committee member of Technical Track of FSE 2026.
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ICSE'25: Serving as the Program Committee member of Technical Track of ICSE 2025.
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ICSE'24: Served as the Program Committee member of Technical Track of ICSE 2024.
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ASE'24: Serving as the Program Committee member of Technical Track of ASE 2024.
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ASE'23: Serving as the Program Committee member of Technical Track of ASE 2023.
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ASE'23: Serving as the Program Committee member of Industry Showcase (Papers) Track of ASE 2023.
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ICSE'23: Served as the Program Committee member of Student Research Competition track of ICSE 2023.
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ASE'22: Served as the Program Committee member of Industry Showcase track of ASE 2022.
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ASE'22: Served as the Program Committee member of Late Breaking Results track of ASE 2022.
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ESEC/FSE'22: Served as the Program Committee member of Doctoral Symposium track of ESEC/FSE 2022.
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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).
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MSR'21: Served as the shadow program committee member of the Technical Track of International Conference on Mining Software Repositories (MSR 2021).
Organizing Committees
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General Chair @ LLMTrust'26 (2026): General Chair of the International Workshop on Trustworthy Large Language Models for Software Engineering (LLMTrust).
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Mentor @ ICSE SMeW : Served as a Mentor in ICSE 2023 SMeW - Student Mentoring Workshop.
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Onsite Judge @ ICSE'23 SRC: Served as the Onsite Judge of ICSE SRC held at Melbourne, Australia.
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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.
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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.
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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.
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Panelist @ ESEC/FSE'20: Served the panel discussion of the session on Fairness at ACM ESEC/FSE 2020 in Sacramento, CA.
Research Projects
Long-Term Risks in ML Systems
This project investigates how feedback loops in ML systems can cause long-term, harmful impacts, and develops tools to detect, analyze, and prevent them before deployment.
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
CSDS 447 - Responsible AI Engineering
Introduces methods for designing and maintaining AI-enabled software systems that meet key responsible AI principles—fairness, robustness, explainability, and safety. Students learn how to identify risks such as bias, unsafe autonomy, and hallucinations, and apply software engineering techniques for requirement analysis, testing, verification, and mitigation.
- Topics: Introduction to responsible AI and AI engineering · Fairness (metrics, bias mitigation, verification) · Robustness (design and testing for resilient AI) · Safety (requirements for safety-critical AI, production safety) · Explainability (black-box vs. white-box analysis, trustworthiness) · Synergy/trade-offs between properties and accuracy
CSDS 393/493 - Software Engineering
Covers the principles, processes, and practices of building high-quality software systems. Students work on a semester-long team project while learning about requirements, design, implementation, testing, and maintenance.
- Topics: Software development processes (Waterfall, Agile) · Requirements engineering and design principles · Implementation best practices and version control · Testing strategies and quality assurance · Maintenance, evolution, and professional ethics
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. |
Talks
| Kent State University | Gave an invited talk “Engineering Responsible AI: From Fairness to Long-term Impact” at the Robotics and Autonomous Systems (AUTOBOT) Program at the College of Aeronautics and Engineering, Kent State University, 2025. Kent, OH |
| Amazon | Presented my research on “Fairness verification and debugging” at the Automated Reasoning Group in AWS, 2024. New York |
| Oracle Labs | Presented my research on “Mining and analysis of AI-Enabled Software Systems” in the Oracle Labs, 2024. Boston |
| SE4SafeML Workshop | Presented the position paper “Towards Safe ML-based Systems in Presence of Feedback Loops” in the workshop of ESEC/FSE 2023. San Francisco |
| DARPA PI Meeting | Presented “Software Architecture for Unmanned Aerial Vehicles (UAV): Automatic AADL Model Extraction and Reasoning” in the DARPA V-SPELLS PI Meeting, 2023. Virtual |
| ICSE'23 | Presented the technical track paper “Fairify: Fairness Verification of Neural Networks” at the 45th ACM/IEEE ICSE conference, 2023. Melbourne |
| ICSE'23 | Presented the technical track paper “Towards Understanding Fairness and its Composition in Ensemble ML” at the 45th ACM/IEEE ICSE conference, 2023. Melbourne |
| Invited Talk | Invited by Dr. Wallapak Tavanapong to present “Fairness Verification and Sustainability of AI Systems”, 2023. Virtual |
| ICSE'22 | Presented technical track paper “The Art and Practice of Data Science Pipeline” at the 44th ACM/IEEE ICSE conference, 2022. Pittsburgh, PA |
| Invited Talk | Presented “Understanding and Reasoning Fairness of ML-Based Software” at the CREATE SE4AI group (Concordia, Polytechnique Montreal, Queen’s, University of Alberta), February 2022. Virtual |
Recent Posts
Contact
- sumon@case.edu
- 216.368.1494
- 10900 Euclid Ave, Cleveland, OH 44106
- DM Me