Hi! I’m a Senior Applied Scientist at Microsoft, where I work on AI-driven transformation in software engineering. I explore how AI is reshaping software development and how we can help engineers harness AI to build better products. My work combines research and practical problem-solving to understand how AI changes engineering practices, workflows, and the way software products are built.
I received my PhD from the Software and Societal Systems Department (S3D) at Carnegie Mellon University (CMU), where I was advised by Christian Kästner. My doctoral research focused on the intersection of software engineering (SE) and machine learning (ML). Using primarily empirical research methods, I investigated the challenges of developing ML products and explored ways to improve collaboration, engineering practices, and development processes. I also enjoy building tools that help practitioners address real-world challenges.
Throughout my career, I’ve been passionate about bridging the gap between research and practice, collaborating across disciplines, and translating research insights into practical solutions. I also enjoy teaching, mentoring, and sharing knowledge with others.
If you’re interested in collaborating or chatting about AI, software engineering, AI-powered developer tools, or the future of software development, feel free to reach out!
Email: nadianahar (at) microsoft.com
PhD Dissertation
Facilitating Collaboration in Building Machine Learning Products
PhD Dissertation — Carnegie Mellon University (CMU), Software and Societal Systems Department (S3D).
My doctoral dissertation investigates how interdisciplinary teams collaborate to develop machine learning products, identifying challenges and opportunities to improve communication, documentation, engineering practices, and development processes.
Advisor: Christian Kästner
Publications
Regulating Explainability in Machine Learning Applications–Observations from a Policy Design Experiment
N Nahar, J Rowlett, M Bray, ZA Omar, X Papademetris, A Menon, C Kästner
In ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT), Rio de Janeiro, Brazil, 2024
News & Activities
- August 2026 — 🏢 Joined Microsoft as a Senior Applied Scientist, working on AI-driven transformation in software engineering and helping engineers build better products.
- May 2026 — 🎓 Celebrated my PhD commencement at Carnegie Mellon University (CMU).
- April 2026 — 🎓 Successfully defended my PhD dissertation, Facilitating Collaboration in Building Machine Learning Products, at Carnegie Mellon University.
- April 2026 — 🏆 Received a Best Paper Award and presented our work at the ACM CHI Conference on Human Factors in Computing Systems in Barcelona, Spain.
- June 2025 — 📣 Invited talk at SEMLA -- Software Engineering for Machine Learning Applications Symposium in Montreal.
- May 2025 — 📣 Attended and gave a talk on my ongoing project on model requirement at Beyond the Model Meeting (BTMM) in Toronto.
- May 2025 — 📣 Presented both of my accepted papers at ICSE 2025 in Ottawa.
- March 2025 — 📣 Invited Talk at Microsoft AI Integrator Engineer Workshop.
- January 2025 — 📖 I am thrilled to co-teach CMU 17-313: Foundations of Software Engineering in Spring 2025, with Michael Hilton, and Austin Henley.
- December 2024 — 📝 Our paper from my summer internship research at Microsoft Research on solutions and challenges of integrating LLMs into software products got accepted at the Software Engineering in Practice (SEIP) track of ICSE 2025.
- July 2024 — 📝 My research paper on mining ML products from open source got accepted at the First Cycle of ICSE 2025.
- June 2024 — 🏢 I am excited to join Microsoft Research as a Research Intern. Excited to work with my superstar mentors Christian Bird, Jenna Butler and Tom Zimmermann.
- May 2024 — 📝 I proposed my thesis, Facilitating Collaboration in Building Machine Learning Products. Check out the proposal document.
- July 2024 — 👩🏫 I am mentoring two awesome REU students for summer 2024.
- March 2024 — 📝 Our paper on explainable AI policy design has been accepted at FAccT 2024.
- Sept 2023 — 👩🏫 I joined as the Publicity Chair for the 3rd International Conference on AI Engineering – Software Engineering for AI (CAIN), 2024.
- June 2023 — 📣 I gave an invited talk at 2023 ISaCTN Technical Symposium about collaboration challenges in building machine learning systems.
- May 2023 — 🏆 I received Ivica Crnkovic ‘Early Career Researcher Award’, at the 2nd International Conference on AI Engineering – Software Engineering for AI (CAIN), 2023.
- May 2023 — 👩🏫 I am mentoring two awesome REU students for summer 2023.
- March 2023 — 📝 Our meta-summary paper was accepted in CAIN 2023.
- March 2023 — 📝 Our collaborative model cards paper got accepted in CHI 2023.
- March 2023 — 🎙️ I talked on a podcast arranged by DataTalksClub, about my past and ongoing projects: https://datatalks.club/podcast/s13e05-se4ml-software-engineering-for-machine-learning.html.
- Oct 2022 — 📖 I gave guest lectures on Software Engineering for Machine Learning (SE4ML) in two sections of CMU 17-313: Foundations of Software Engineering.
- Oct 2022 — 📖 I gave a lecture on Data Quality in CMU 17-645: Machine Learning in Production course.
- Oct 2022 — 📣 I gave an invited talk to iterative.ai: https://www.youtube.com/watch?v=FKdVSNfnD_M.
- Nov 2022 — 📣 I gave an invited talk to data circles, a platform for empowering women data scientists.
- April 2022 — 📖 I have a guest lecture at CMU 17-645: Machine Learning in Production course.
- May 2022 — 🏆 I received the ACM SIGSOFT Distinguished Paper Award at ICSE 2022.
- Dec 2021 — 📝 My paper on collaboration challenges got accepted at ICSE 2022.
Projects
Identified challenges of developing ML products based on an interview study with .... See more
Interdisciplinary collaboration has always been considered challenging which stands true for modern ML products as well. To better understand collaboration challenges and avenues toward better practices, I have conducted interviews with 45 participants contributing to the development of MLenabled systems for production use. We report our findings in this paper, published in ICSE 2022 and received ACM SIGSOFT Distinguished Paper Award.
Unique characteristics of LLMs force developers, who are accustomed to traditional .... See more
Unique characteristics of LLMs force developers, who are accustomed to traditional software development and evaluation, out of their comfort zones as the LLM components shatter standard assumptions about software systems. This study explores the emerging solutions that software developers are adopting to navigate the encountered challenges. I leveraged mixed-method research, including 26 interviews and a survey with 332 responses, and identified 19 emerging solutions regarding quality assurance. We report our findings in this paper, published in ICSE 2025-SEIP track.
This is an ongoing study, which aims to design and evaluate an intervention to enhance .... See more
This is an ongoing study, which aims to design and evaluate an intervention to enhance effective negotiations among cross-disciplinary development teams. It focuses on using a boundary object to elicit and agree on actionable model requirements. My proposed solution involves developing an assistant that leverages LLMs for knowledge translation, contextual explanation, and conflict resolution, enabling data scientists and software engineers to reach consensus on model specifications.
This study emerged from a collaboration with an industry partner facing challenges in adopting .... See more
This study emerged from a collaboration with an industry partner facing challenges in adopting responsible AI (RAI). We observed tensions, particularly when data scientists, despite company mandates, remained resistant or indifferent to ethical considerations in ML product development. Recognizing the need for a transformative and politically nuanced approach, I designed and evaluated an intervention to align interests and foster agreements. This approach leverages LLMs to create compelling stories about the potential harm ML products can inflict on end-users, encouraging data scientists and software engineers to engage meaningfully with RAI principles.
Despite the rising use of machine learning (ML), developers still struggle to transition .... See more
Despite the rising use of machine learning (ML), developers still struggle to transition from ML prototypes to final products. Academic researchers often find it difficult to suggest solutions or assess these challenges due to their lack of access to the industry’s closed-source ML products. In this study, I have defined, identified, and analyzed open-source ML products. I have curated and analyzed a dataset of 262 repositories from GitHub, which resulted in 21 findings related to various development activities. The paper has been published in ICSE 2025.
In collaborationwith Yale University’s Medical and Social Science researchers .... See more
In collaborationwith Yale University’s Medical and Social Science researchers, we designed a policy for governing and guiding Explainable AI (XAI) implementation through an experimental study involving an interdisciplinary team of ML and policy researchers. The observations and lessons from the policy design experiment are published in FAccT 2024 and AIES 2024. We are currently evaluating the policy’s effectiveness through a largescale controlled experiment in an educational setting.
Achievements
Ivica Crnkovic ‘Early Career Researcher Award’, at the 2nd International Conference on AI Engineering
Software Engineering for AI (CAIN).
ACM SIGSOFT Distinguished Paper Award
ICSE 2022.
IIT Academic Excellence Gold Medal Award (MSSE), University of Dhaka
Received from Honorable President of Bangladesh.
IIT Academic Excellence Gold Medal Award (BSSE), University of Dhaka
Received from Honorable President of Bangladesh.
Champion of BASIS Code Warriors Challenge
National Software Development Competition.
Talks & Presentations
- Invited Talk at Microsoft AI Integrator Engineer Workshop — 📣 at Microsoft, March 2025.
- Internal Research and Workshop Talks within Microsoft (HCAIX, NFoW, Demo day, etc) — 📝 at Microsoft, July 2024.
- Research Paper Presentation — 📝 at FAccT, June 2024.
- Invited Guest Talk — 📣 at ISaCTN Technical Symposium, June 2023.
- Invited Podcast: Season 13, episode 5, ”SE4ML - Software Engineering for Machine Learning” (Video, Podcast Available in: Apple, Spotify, Anchor) — 🎙️ with DataTalks.Club, March 2023.
- Research Paper Presentation — 📝 at CAIN, May 2023.
- Invited Guest Talk at November Tech Talk — 📣 at Data Circles, November 2022.
- Invited Guest Talk at Iterative AI October Meetup, ”Collaboration Challenges in Building ML-Enabled Systems with Nadia Nahar” (Video) — 📣 Iterative AI, October 2022.
- Research Paper Presentation — 📝 at ICSE, May 2022.
Teaching
- Instructor on Record — CMU 17-313 Foundations of Software Engineering, Spring 2025.
- Guest Lecture on Software Engineering for Machine Learning (SE4ML) — CMU 17-313 Foundations of Software Engineering, Spring 2023.
- Guest Lecture on Software Engineering for Machine Learning (SE4ML) — CMU 17-313 Foundations of Software Engineering, Fall 2023.
- Guest Lecture on Data Quality — CMU 17-645: Machine Learning in Production, Fall 2022.
- Teaching Assistant — CMU 17-645: Machine Learning in Production, Spring 2022.
- Guest Lecture on Collaboration in SE4ML — CMU 17-645: Machine Learning in Production, Spring 2022.
- Lecturer: Taught 15 different courses for software engineering (e.g, Design Patterns, Software Quality Assurance, and Software Design) undergraduate and graduate classes — University of Dhaka, Bangladesh, from May 2017 to August 2020.