I am currently a CS PhD student at Stanford University in the Scaling Intelligence Lab. My research interests center on scalable self-improving machine learning systems, also broadly interested in empirical ML and performance engineering.

Before Stanford, I graduated from Massachusetts Institute of Technology (MIT) with B.S. and M.Eng degrees (coursework here) and worked on the NVIDIA cuDNN team where I wrote CUDA kernels to accelerate deep learning workloads on GPUs.

Some other things I've done in the past:

• MIT Han Lab: developed high-performance computer vision kernels for TinyML on edge devices
• MIT-IBM Watson AI Lab: accelerated distributed training on HPC clusters for large scale graph neural networks [MLSys 2022 paper]
• Prod Cohort 2.0: attempted doing startups and an AI newsletter
• Cadence Design Systems: developed a machine learning workflow for faster and more accurate VLSI circuit timing analysis
• Praxis: benchmarked diffusion models and reviewed the torch.distributed package
• Matrix Capital Management: venture scout for crypto/web3 companies and projects
• Trail of Bits: performed formal verification studies on the Ethereum 2.0 CBC Casper protocols
• MIT Media Lab Digital Currency Initiative: implemented simulations of consensus algorithms to study 51% attack mitigation and symmetry breaking
• MIT CSAIL Decentralized Information Group: analyzed statistical properties of transactions on the bitcoin blockchain empirically
• MIT Bitcoin Club: vice president; content and marketing director of the MIT Bitcoin Expo
• Alphastar Academy: taught USA Computing Olympiad preparation courses

Outside of CS, I enjoy history of art and architecture, photography, and classical music.

Resume available upon request. If you have any questions or would like to chat, feel free to reach out (social links below).