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 youโ€™re excited to push the frontiers of LLMs, letโ€™s talk.

Publications

In 47th International Conference on Software Engineering (ICSE), Ottawa, Canada, 2025.
In 46th International Conference on Software Engineering (ICSE), Lisbon, Portugal, 2024.
In 45th International Conference on Software Engineering (ICSE), Melbourne, Australia, 2023.
In 45th International Conference on Software Engineering (ICSE), Melbourne, Australia, 2023.
In 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), San Francisco, California, 2023.
International Workshop on Dependability and Trustworthiness of Safety-Critical Systems with Machine Learned Components at (ESEC/FSE), San Francisco, California, 2023.
In 44th International Conference on Software Engineering (ICSE), Pittsburgh, Pennsylvania, 2022.
In 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), Singapore, 2022.
In 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), Athens, Greece, 2021.

Services

Grants

  • Served in the National Science Foundation (NSF) Panel for grant proposal review.

Journal Reviewer

Conference Program Committees (PC)

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

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Long-Term Risks in ML Systems

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

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

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

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

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

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

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

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 principlesโ€”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: ๐Ÿ”˜ introduction to responsible AI and AI engineering ๐Ÿ”˜ fairness (metrics, bias mitigation, verification) ๐Ÿ”˜ robustness (design and testing for resilient AI) ๐Ÿ”˜ safety (requirements for safety-critical AI, production safety) ๐Ÿ”˜ explainability (black-box vs. white-box analysis, trustworthiness) ๐Ÿ”˜ 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: ๐Ÿ”˜ software development processes (Waterfall, Agile) ๐Ÿ”˜ requirements engineering and design principles ๐Ÿ”˜ implementation best practices and version control ๐Ÿ”˜ testing strategies and quality assurance ๐Ÿ”˜ maintenance, evolution, and professional ethics

Others

  • 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.

  • COMS 327: Advanced Programming Techniques: 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++) ๐Ÿ”˜ Design and build large programs from specification ๐Ÿ”˜ Memory management in C and C++ ๐Ÿ”˜ Templates and standard library ๐Ÿ”˜ 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.

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