CV

AMAI Research Lab, advised by Dr. Haoxin Wang. Expected May 2028.

• Advised by Dr. Haoxin Wang.
• Designing PoseBench, a benchmark that isolates interaction reasoning from single-person motion modelling in multi-agent pose prediction; in preparation.
• Developing a frequency-domain method that separates interaction effects from individual motion in multi-agent pose prediction for human–robot teaming; under review, 2026.
• Co-authored EpiFormer (arXiv, June 2026), an early-fusion geometric deep learning model for antibody epitope prediction that lifts F1 on the AsEP benchmark from 0.337 to 0.482.
• Co-authored LM-Meter (ACM/IEEE SEC, December 2025), a lightweight on-device profiler that exposes phase- and kernel-level inference latency of language models at under 3% throughput cost.

• Designed and built an AI proof of concept that generates PowerPoint decks automatically, streamlining content creation and lifting team productivity.
• Kept the prototype current with advances in AI and emerging technology, and proposed the next round of capabilities.

• Led a student consulting team for a leading mining company through data collection, preparation, and analysis, delivering five analyses and two new KPIs.
• Built a Power BI drill-down KPI for haul-cycle productivity and a detractor model that traces the causes of low productivity.
• Recommended KPIs to raise operating efficiency and lower operating costs.

• Analysed and visualised cancer-cell life-cycle data in Python, R, and Tableau, surfacing trends that lab researchers built on in their own work.
• Collected and cleaned research data across several lab projects.

• Ran weekly club activities, study groups, online Q&A sessions, and networking events that connected students with professionals.
• Represented the University of Arizona at the WSIA Actuarial Symposium at Arizona State University, fall 2019.