• (Ongoing) Enrolled in Software Engineering Ph.D. Program in Institute for Software Research (ISR), Carnegie Mellon University (CMU)

• Master of Science in Software Engineering, Institute of Information Technology (IIT), University of Dhaka. Year: 2016, CGPA : 4.00 out of 4.00 (Gold Medalist for Highest CGPA)

• Bachelor of Science in Software Engineering, Institute of Information Technology (IIT), University of Dhaka. Year: 2014, CGPA : 3.99 out of 4.00 (Gold Medalist for Highest CGPA)

• Software Engineer, WebXT Search & Content Services Platform, Microsoft, Summer Internship 2022.

• (On Study Leave) Lecturer, Institute of Information Technology, University of Dhaka, Dhaka, Bangladesh, Since May 2017

• Software Engineer, Streams Tech, Inc., Dhaka, Bangladesh, March 2017 - July 2017

• Software Engineer, Icebreakers, Dhaka, Bangladesh, February 2016 - January 2017

• Software Engineer, KAZ Software, Dhaka, Bangladesh, January 2014 - June 2014

Software Engineering, SE4AI/SE4ML, Software Design, Software Quality Assurance

Visit my Google Scholar for Publications.

Collaboration Challenges in Building Production Machine Learning (ML) Systems: Interdisciplinary collaboration has always been considered challenging which stands true for modern ML projects as well. From the literature, it has been understood that collaboration in data science projects are not the same as the traditional software development teams due to several factors like more "exploration" process than "engineering" process, distinct skills and knowledge of involved roles, difficulty in testing, need of continuous support after deployment, data drifts, special requirements of fairness or explainability, etc. To better understand collaboration challenges and avenues toward better practices, we conducted interviews with 45 participants contributing to the development of ML-enabled systems for production use. Participants come from 28 organizations, from small startups to large big tech companies, and have diverse roles in these projects, including data scientists, software engineers, and managers. During our interviews, we explored organizational structures, interactions of project members with different technical backgrounds, and where conflicts arise between teams. We report our findings in this paper. A summarized description can be found here. The paper received the ACM-SIGSOFT Distinguished Paper Award in ICSE 2022.

Mining Machine Learning Production Systems in Open Source: As Machine Learning (ML) has been receiving massive attention for incredible advances and surpassing human-level cognition in many applications, the significance of analyzing the ML repositories have become necessary in many different contexts like evaluating maintainability, identifying common practices around ML components, etc. However, mining open-source to find the ML production systems is a challenge in itself, because of lack of indicators to distinguish production systems from ML tools or toy projects. Thus, our target in this research project is to define a set of indicators to find such projects and report a dataset of ML production systems.