I’m interested in performance-critical systems for modern ML—especially attention and sequence models—where algorithmic choices show up directly in memory traffic, kernel design, and end-to-end latency. I like working at the boundary between architecture and software: profiling bottlenecks, forming clear hypotheses about where time/data is going, and turning that into measurable speedups.

More broadly, I’m drawn to computer architecture (memory systems, interconnects, accelerators, HW/SW tradeoffs) and to the mathematical foundations that make good engineering decisions possible (probability/statistics, linear algebra, signal processing).

I keep the theory grounded through hands-on accelerated computing work. For example, in my final semester at UBC I took CPEN 512 (Parallel and Configurable Computer Architecture), where I implemented dense linear algebra kernels (matmul, LU decomposition) across multiple parallel programming models (OpenMPI, pthreads, CUDA, Bluespec, Vectorblox). For the final project, I implemented a Cooley–Tukey FFT using CUDA and OpenMPI.