Applied AI, with people in the loop
Undergraduate Researcher in ASU's CoRAL Lab. Both of the threads below sit at the intersection of NLP/applied AI and a genuinely human problem — how people work together, and how to protect people who don't have technical defenses of their own.
Behavioral Team Formation
CoRAL Lab, Arizona State University · Undergraduate Researcher
Automated or algorithmically-assisted team formation — in education, hackathons, or workplace settings — typically optimizes for skills or availability, but tends to ignore behavioral compatibility signals that strongly predict whether a team actually functions well together. This project investigates behavioral signals as an input to automated team-formation algorithms.
Findings from this work are informing a paper in progress.
On-device scam detection (SentinelEdge)
Built out of a 1st-place hackathon project — full case study →
A paper describing SentinelEdge's approach to real-time, privacy-preserving scam detection — a compressed, fine-tuned small language model performing transcript analysis entirely on-device, with federated learning updates and zero data leaving the device — has been submitted to EMNLP 2026.
Abstract and link will be added once the paper is public.
Research interests
Applied NLP, on-device/privacy-preserving ML, and the human-behavior side of team and system design — how technical constraints (privacy, compute) can be the design driver rather than an afterthought.
Future directions
Extending the behavioral-signal approach beyond team formation to other collaborative-AI settings, and continuing to explore where on-device inference changes what's possible for privacy-sensitive applications.