research focus ▸ .
I'm a PhD researcher in Computer Science at Arizona State University. My core research is on LLM agents — how they're trained, how they reason and act at test time, and how we evaluate and keep them safe. My work spans these stages, with papers at ICLR, ACL, NAACL, EMNLP and NeurIPS workshops.
Selected work
✓ ICLR 2026 Conference
TL;DR Makes inference-time sampling produce genuinely diverse solutions — +21.6% pass@50.
✓ NeurIPS 2025 Workshop (Reliable ML from Unreliable Data) Workshop
TL;DR Stronger reasoning makes LLMs easier to jailbreak — via custom ciphers (ACE/LACE).
Preprint arXiv
TL;DR Uses multi-agent shopper simulation as a reward to optimize e-commerce query rewriting.
✓ ICLR 2025 Conference
TL;DR A diagnostic benchmark for reasoning about actions, change and ramifications.
Core research · LLM agents
Path
Building a multi-agent system that turns any uploaded document into an interactive webpage, and an optimization pipeline that autonomously improves multi-agent systems — tuning their context, topology, harness, cost, and latency.
Designed an agentic framework for e-commerce query rewriting via a genetic algorithm (OptAgent), using a multi-agent shopper simulation for offline semantic evaluation — a 3.28% gain over test-time baselines. Scaled data collection and analysis over large query logs with BigQuery.
Built an LLM system translating decompiler output into high-level source code for reverse engineering; fine-tuned with SFT + RL (GRPO) and a graph-edit-distance reward for structural fidelity, achieving a 2.75× improvement on held-out repositories.
Teaching assistant for graduate machine-learning coursework.
Built and maintained enterprise web applications.
Full-stack web development internship.
Updates