News
What's new.
Workshops, talks, new papers, and updates from the lab.
- Oct 2026
Chairing the LEAF workshop at IEEE MILCOM 2026
I'm in the Washington, DC area co-chairing LEAF (Lightweight Edge-Adaptive Foundation Models for Intelligence and Autonomous Reasoning), Workshop 4 at IEEE MILCOM 2026, with Venkat Dasari and Peng Wang of DEVCOM ARL. The program has eleven papers on compressing and deploying foundation models at the tactical edge, including two posters from our group: GOE and Measure, Then Compress.
- Sep 2026
Accepted to the NeurIPS 2026 AI for Science Workshop
Venkat Dasari and I will present “Your Research Loop Is Measuring Noise: Accept Rules for Autonomous Experimentation” as a poster at the AI for Science Workshop at NeurIPS 2026 in Sydney, Australia, on December 12. The work asks when an AI agent running its own experiments should trust an improvement.
- Sep 2026
CUSP preprint on arXiv
Johnny Yu, David Garcia, and I released CUSP, which splits a multi-agent vision-language system's uncertainty into each agent's own doubt and the disagreement between agents, so the system can catch errors and know when to abstain.
- Jun 2026
Lightning talk at the Mayo Clinic AI Research Summit
Huynh Tran’s abstract “Uncertainty-Aware Detection of Rare Leukemia Cells in Flow Cytometry Using an Entropy-Enhanced Predictive Meta-Model”, our collaboration with Mayo Clinic hematopathology, was selected for a lightning talk at the 2026 AI Research Summit in Rochester, Minnesota.
- Apr 2026
SCoOP at the ICLR 2026 workshop on Agentic AI in the Wild
Johnny Yu presented SCoOP, a training-free way to measure how uncertain a team of vision-language models is, as a poster at the ICLR 2026 workshop on Agentic AI in the Wild: From Hallucinations to Reliable Autonomy.
- Oct 2025
Two student-led papers at IEEE MILCOM 2025
Jake Sander presented fine-tuning vs. distillation for compressing LLMs onto edge hardware, and Huynh Tran showed that neurosymbolic transfer learning improves network intrusion detection.