About
PROFILE
I am a software engineer graduate student exploring AI-enabled systems, software architecture, and low-level systems programming.
Currently pursuing my Master of Science in Software Engineering at Carnegie Mellon University, I previously completed my Bachelor of Science in Computer Science degree with High Honor in Research at the University of Rochester. My coursework focus on ML systems, software architecture, and systems programming, as well as software engineering principles and techniques.
My prior research focuses on non-intrusive workload characterization of large language models (LLMs) using GPU telemetry and cache timing side-channels.
CURRENTLY
BEYOND
Benchmarking Intel AMX matrix acceleration on Sapphire Rapids, microarchitectural profiling, and hardware teardowns.
Reading emerging AI and machine learning literature alongside foundational systems papers, processor architectures, and computing history.
Self-hosting open-weight models, quantization experiments, and exploring high-efficiency local inference runtimes.
Motorsport, aviation, and nature photography.
FOCUS
ML SYSTEMS & TELEMETRY
Non-intrusive ML workload characterization: NVML GPU telemetry and Flush+Reload cache signals extracted as PyTorch tensors for inference-phase classification.
SYSTEMS PROGRAMMING
Low-level execution work in C and ASM, runtime optimization, and heterogeneous compute pipelines.
SOFTWARE ARCHITECTURE & SYSTEMS
Distributed backend systems: microservice architectures on gRPC/NATS, Docker, and Kubernetes, plus backend platforms.
SOFTWARE ENGINEERING
Rigorous software lifecycle practices, automated testing and CI/CD automation, API contract design, and engineering methodologies for AI-enabled systems.