Who I Am
I’m a senior HPC and cluster infrastructure engineer at a quantitative trading firm with 8+ years building and operating large-scale distributed systems — Kubernetes, SLURM, GPU clusters, and cloud platforms.
I’m pursuing an MS in Computer Science with an ML specialization at Georgia Tech, alongside the Certificate in Quantitative Finance (CQF). This gives me production infrastructure expertise, ML systems depth, and the mathematical foundations for derivatives pricing and risk modeling.
What I Build
I work on the infrastructure that makes ML systems run fast and reliably at scale: GPU cluster operations, inference optimization (vLLM, TensorRT-LLM), distributed training orchestration, and the emerging agentic AI infrastructure layer — deploying autonomous systems that reason, decide, and act on production workloads.
What I Write About
This site has two main content streams:
Blog — Technical deep dives on ML infrastructure, GPU inference, distributed training, and the systems engineering behind quantitative platforms. Written from a practitioner’s perspective.
Lab Notes — My public lab notebook. Shorter field reports on deploying new tools, running benchmarks, and documenting what I find before anyone else does.
I also maintain project pages for my portfolio work, notes for quick technical observations, and a reading list of books I recommend.
Current Focus
- Production: Expanding ML infrastructure scope — vLLM inference optimization, GPU cluster operations
- Academic: MS in CS (ML specialization, Georgia Tech, May 2027) + CQF final project (July 2026)
- Portfolio: Three projects at the intersection of HPC inference, distributed training, and quantitative finance
- Learning: Agentic AI infrastructure (LangGraph, MCP, NemoClaw/OpenClaw), hardware-agnostic inference (vLLM, Ray, JAX)
Outside of engineering, I play chess and poker — both sharpen the strategic and probabilistic thinking I apply to systems architecture and risk modeling.