About Me

I’m a Postdoctoral Researcher at School of Computer Science, Carnegie Mellon University, where I work in the Institute for Software Research (ISR). I’m working with Dr. Eunsuk Kang on ensuring fairness and safety of high-assurance AI based software systems. I received 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 at the intersection of SE and AI focuses on identification, design, and verification of fairness and safety properties. At CMU, I’m working on the DARPA project Verified Security & Performance Enhancement of Large Legacy Software (V-SPELLS), to model and verify safety of Unmanned Aerial Vehicles (UAV). Prior to joining CMU, I worked in the NSF TRIPODS Institute D4 (Dependable Data-Driven Discovery), where I led the research on fairness engineering. I also worked on the Boa compiler to enable large-scale static analysis and mining of ML software.

I am on the academic job market! Feel free to reach out. Find my CV and statements (research, teaching, diversity).

  • Software Engineering (SE)
  • Artificial Intelligence (AI)
  • Programming Languages (PL)
  • Ph.D. in Computer Science, 2022

    Iowa State University

  • M.S. in Computer Science, 2021

    Iowa State University

  • B.S. in Information Technology, 2015

    Jahangirnagar University


I’m broadly interested in the intersection of SE and AI with particular focus in the SE for AI area. Unlike traditional software systems, AI based software is data-intensive, uncertain, and posing significant ethical and safety risks for society. My research in the area established fairness as a non-functional SE property for AI based software. By adopting both formal and empirical SE approaches, I aim to achieve safety and fairness through analysis of software abstractions and real-world implementations.

  • Fairness verification and reasoning: fairness verification of DNN (ICSE'23), causal reasoning in ML (FSE'22), compositional fairness (ICSE'23)
  • Design fair and safe AI: root cause analysis of bias (FSE'21), fair AutoML for trade-off (FSE'23), long-term analysis for safety (FSE-SE4SafeML'23)
  • AI engineering and static analysis: AI software architecture (ICSE'22), ML technical debts (FSE'22), Repay debts using LLMs (ICSE'24*), large-scale ML code mining (MSR'19)

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


Software and Societal Systems Department (S3D), Carnegie Mellon University
Postdoctoral Researcher
May 2022 – Present Pittsburgh, PA, United States
Laboratory for Software Design, Iowa State University
Research Assistant
May 2018 – May 2022 Ames, Iowa, United States
Dept. of Computer Science, Iowa State University
Teaching Assistant
Aug 2016 – May 2018 Ames, Iowa, United States


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.
In 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), Singapore, 2022.
In 44th International Conference on Software Engineering (ICSE), Pittsburgh, Pennsylvania, 2022.
In 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), Athens, Greece, 2021.
In 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), Sacramento, California, 2020.
In 16th International Conference on Mining Software Repositories (MSR), Montreal, Canada, 2019.


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 Excellence Award
Awarded by ISU recognizing outstanding research for the Ph.D. dissertation.
CAPS Award
Awarded by ACM to attend ICSE 2022 conference in-person at Pittsburgh, PA.
Publication Award
Awarded by ISU Computer Science Department in consecutive years 2021-22 for publication in the top-tier venues.
CAPS Award
Awarded by ACM to attend ESEC/FSE 2021 conference.
PLMW Scholar @ PLDI
ACM SIG Scholarships, awarded by Programming Language Mentoring Workshop (PLMW) at PLDI'19 (Programming Language Design and Implementation), Phoenix, AZ.
ACM Travel Grant
Awarded to attend Institute for Mathematics & its Applications (IMA) Workshop, University of Minnesota, June 18-29, 2019.
Professional Advancement Grants
Awarded by Graduate and Professional Student Senate (GPSS) at Iowa State University
NST Fellowship
National Science and Technology (NST) Fellowship Awarded by Ministry of Science & Technology, Bangladesh for Research Excellence.

Research Projects

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.

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.


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

August 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.

Recent Posts