About Me
Ph.D. candidate in Computer Science at Arizona State University, ranked 1st in my cohort with a perfect 4.0 GPA. Recipient of the Outstanding Thesis Award and IEEE-HKN Honor Society member. Expected graduation: May 2026.
My work is grounded in a strong systems and networking foundation and is broadening into reinforcement learning and applied machine learning. It spans reliable, low-latency networked systems, distributed systems, and hardware acceleration. I designed, implemented, and deployed the MLED distributed system framework on the FABRIC national testbed across 30+ geographically distributed sites, achieving a 14,000× reduction in undetected errors for petabyte-scale data transfers.
On the ML side, I develop reinforcement learning for prognostics (joint remaining-useful-life estimation and failure-mode classification) and Graph Neural Networks for spatiotemporal forecasting. My systems work includes cloud-scale performance optimization, FPGA-based hardware acceleration (50× speedup in checksum computation), and ML infrastructure for TB-scale datasets. I have published in POMACS (ACM SIGMETRICS) and IEEE Communications Letters, with work in NeurIPS 2026 (under review) and presentations at ACM SIGMETRICS 2025 and IEEE FCCM 2025. I also co-founded Expeditise LLC, an AI startup building intelligent tools for everyday workflows. I'm eager to apply a systems and real-time perspective to interactive and robotic applications.
