| timm@ieee.org | +1-304-376-2859 papers | pre-prints
we want you

Help me find and fix problems in real-world AI/ML systems. NC State student or industrial partner? Join my research reading group (runs each semester).

I earned my Ph.D. in Computer Science from UNSW (1995) and am now a Full Professor at NC State University, where I direct the Irrational Research Lab (mad scientists 'r us)▌.

My work focuses on software engineering for AI, specifically building data-driven, explainable, and intelligent software systems. I am an ACM, IEEE, and ASE Fellow, and co-creator of the PROMISE repository, helping establish modern empirical software engineering by showing that small, interpretable AI models can often outperform larger, more complex ones.

I have published over 300 papers with more than 24,000 citations (h-index=74) and have advised 24 Ph.D. students. I serve as the Editor-in-Chief of the Automated Software Engineering journal and, from 2010 to 2026, as an Associate Editor for IEEE TSE.

My research is supported by $19M in competitive grants from government (NASA) and industry (LexisNexis, Microsoft, Meta). Recent highlights include over $2.5M from the NSF and $1M from the NSA to advance the science of trustworthy and compact AI.

Microsoft Meta
LexisNexis NSF NASA NSA

Research

AI, for Less

I strongly advocate for "AI, for Less". My research shows you do not always need massive computational power — simpler, optimized models can yield exceptional results while remaining transparent and trustworthy. We emphasize reproducibility and simplicity over "black box" deep learning, and show that lightweight active learning, co-training, and warm-starts can match or beat expensive alternatives.

LLMs & Deep Learning

Critical assessments of where LLMs and deep learning help — and where they hurt — in software engineering tasks. We study how to prompt, warm-start, and robustify LLM-based SE pipelines, and identify when simpler baselines still win.

Security & Vulnerability

Practical methods for vulnerability detection and cyberthreat intelligence, using active learning and adversarial ML to help teams prioritize security efforts without drowning in false positives.

Defect Prediction & Software Analytics

Building better predictors for software quality, project health, and technical debt — using smarter sampling, transfer learning, and hyperparameter optimization rather than brute-force compute. Includes benchmark repositories for reproducible research across hundreds of SE tasks.

Accountability & Trust

AI software must meet the same correctness and auditability standards as any safety-critical system. We develop methods to verify, audit, and correct AI outputs — ensuring decisions can be inspected, reproduced, and challenged — and probe the fragility of causal reasoning in high-stakes environments.

Other

Teaching

Current Ph.D. Students (3)

YearNameLocation
2028Srinath SrinivasanNC State
2027Kishan GangulyNC State
Amirali RayeganNC State

Completed Ph.D.s (24)

YearNameWhere Now
2024Andre LutosaRed Hat
Kewen PengMeta
Rahul YedidaLexis Nexis
2023Sherry (Xueqi) YangOracle
Xiao LingMeta
Suvodeep MajumderAWS
2021Rui ShuHong Kong
Shrikanth ChandrasekaranOracle
Tianpei (Patrick) XiaNewsBreak
Huy TuLinkedIn
2020Joymallya ChakrabortyAmazon
2019Zhe YuRochester Institute of Technology
Rahul KrishnaIBM Research
Jianfeng ChenMeta
Amritanshu AgrawalTikTok
Vivek NairMeta
2018Wei FuMeta
2014Abdel Salam SayyadBirzeit University
Fayola PetersJohnson & Johnson
Joe KrallCloudvirga
2012Ekrem KocaguneliPinterest
2011Ashutosh NandeshwarCCS Fundraising
2007David OwenMessiah College
2004Scott ChenMasimo.com