Highlights
Contribution beyond the day job: conference talks, hackathon judging, peer review and artifact evaluation, and mentoring across the engineering and AI community.
49 entries
Reviewer for REALM 2026, the 2nd Workshop for Research on Agent Language Models at EMNLP 2026 (Budapest, Hungary), reviewing one submitted paper on language agents.
Area Chair for GroundLM 2026, the workshop on grounding and retrieval-augmented language models at EMNLP 2026 (Budapest, Hungary), overseeing five submissions and writing meta-review recommendations.
Reviewer for the EMNLP 2026 Industry Track (Budapest, Hungary), reviewing one submitted paper on deploying natural-language-processing systems in real-world settings.
Peer reviewer for the 2nd Workshop on Efficient Reasoning at COLM 2026, reviewing three submitted papers.
Judged the 6th Global AI Hackathon, Hack-Nation's largest to date (5,500+ applications, some 2,500 builders across 12 hubs), run with the MIT Club of Northern California and the MIT Club of Germany. Scored seven projects on the jury.
Reviewer for GroundLM 2026, the workshop on grounding and retrieval-augmented language models at EMNLP 2026 (Budapest, Hungary).
Artifact evaluator for ASE 2026 (IEEE/ACM International Conference on Automated Software Engineering).
Technical Program Committee member for IEEE SoutheastCon 2027, IEEE Region 3's annual conference, held in Daytona Beach, Florida, reviewing submissions across its technical tracks.
AI Engineer World's Fair 2026 talk: agents need feature flags. We ship the most behavior-changing systems we have ever built, agents that send email, move money, and modify databases, with none of the canary, segment-targeting, and kill-switch discipline web teams settled a decade ago. The talk lays out six flag types for the six behavior surfaces an agent has, a rollout playbook with the numbers to track, and why a stop button is now required by law.
AI Engineer World's Fair 2026 talk: your coding agent is creating review debt, the accumulating gap between the code an agent produces and the code humans have actually reviewed, trusted, and understood. Five signal families and ten deterministic checks turn that gap into a single defensible score, validated across 524 real pull requests.
