Prof. Tim Menzies is a Full Professor of Computer Science at NC State University and the director of the Irrational Research Lab. His work focuses on software engineering for AI, specifically building data-driven, explainable, and intelligent software systems. As an ACM, IEEE, and ASE Fellow, and co-creator of the PROMISE repository, he helped establish modern empirical software engineering by demonstrating that small, interpretable AI models can often outperform larger, complex ones.
Dr. Menzies has published over 300 papers with more than 24,000 citations and has advised 24 Ph.D. students. He serves as the Editor-in-Chief of the Automated Software Engineering journal and, from 2010 to 2026, as an Associate Editor for IEEE TSE.
His research is supported by $19M in competitive grants from both government and industry. Recent highlights include over $2.5M from the NSF and $1M from the NSA to advance the science of trustworthy and compact AI. For more information, visit timm.fyi.
News
- Mar'26: Invited, Co-chair, Future of SE track, ICSE 2028
- Mar'26: Invited, PC Member, ICSE'27 NIER
- Feb'26: Invited, IEEE Fellows Review Committee
- Jan'26: Invited Keynote, VerifAI, France
- Jan'26: Invited Keynote, SSBSE'26
- Dec'25: Invited, Area Chair: ASE'26, ICSE'26
- Oct'15: Invited, PC Member: ISSTA'26, FSE'26, ICSE NIER'26, CAIN'26
- Sep'25: Invited Talk, Valencia SE Group
- Aug'25: New letter, CACM, The Case for Compact AI
- Aug'25: Invited Keynote, Airbnb Dev Conference, San Francisco
- July'25: PC Invites: AAAI Social Impact'26, CAIN'26
- July'25: Invited Keynote, Future Advanced Testing Workshop, Paris
- June'25: Invited Keynote, ESEM'25, Hawaii
- May'25: Co-chair, EMSE 2025 Doctoral Symposium
- Apr'25: Andre Motta completes his Ph.D.
Current Research
AI, for Less
I strongly advocate for "AI, for Less". My research demonstrates that you do not always need massive computational power; simpler, optimized models can yield exceptional results while remaining fair, transparent, and trustworthy. We emphasize reproducibility and simplicity over "black box" deep learning.
AI for SE & SE for AI
AI software is still software, so faults in SE mean faults in AI. SE teams often race to deliver AI-based solutions without first checking for bias, optimality, or explainability. I apply decades of Software Engineering wisdom to address these problems in AI, utilizing analytics for defect prediction and effort estimation.
Trust, Causality
Ensuring AI systems in high-stakes environments are explainable, and trustable. We use causal graphs and rigorous optimization to ensure AI can be trusted by end-users and institutions.
Students & Alumni
I seek talented grad students & industrial partners to find + fix the problems in real-world AI/ML. If you want to be a leader in AI (and not just another follower), we should talk.
I have advised 24 Ph.D. students. Below is a list of my current students and alumni:
Current Ph.D. Students
| Year | Name | Location |
|---|---|---|
| 2028 | Srinath Srinivasan | NC State |
| 2027 | Kishan Ganguly | NC State |
| Amirali Rayegan | NC State |
Completed Ph.D. Students
| Year | Name | Where Now |
|---|---|---|
| 2024 | Andre Lutosa | Red Hat |
| Kewen Peng | Meta | |
| Rahul Yedida | Lexis Nexis | |
| 2023 | Sherry (Xueqi) Yang | Oracle |
| Xiao Ling | Meta | |
| Suvodeep Majumder | AWS | |
| 2021 | Rui Shu | Hong Kong |
| Shrikanth Chandrasekaran | Oracle | |
| Tianpei (Patrick) Xia | NewsBreak | |
| Huy Tu | ||
| 2020 | Joymallya Chakraborty | Amazon |
| 2019 | Zhe Yu | Rochester Institute of Technology |
| Rahul Krishna | IBM Research | |
| Jianfeng Chen | Meta | |
| Amritanshu Agrawal | TikTok | |
| Vivek Nair | Meta | |
| 2018 | Wei Fu | Meta |
| 2014 | Abdel Salam Sayyad | Birzeit University |
| Fayola Peters | Johnson & Johnson | |
| Joe Krall | Cloudvirga | |
| 2012 | Ekrem Kocaguneli | |
| 2011 | Ashutosh Nandeshwar | CCS Fundraising |
| 2007 | David Owen | Messiah College |
| 2004 | Scott Chen | Masimo.com |