Experience

Education

I finished my PhD in Computational and Mathematical Engineering at Stanford University in June 2023. My advisor was Professor Eric Darve.
I received my BS from Yale in 2016, double majoring in Applied Mathematics and Mechanical Engineering.

Work Experience

I currently work at Cerebras Systems, as part of their Advanced Technology Group. Some projects I have worked on include:
- Extremely long context attention (10M+ tokens) for real-time processing of huge (1B+ scalar) inputs
- Game-playing reinforcement learning pipeline, running on 16x Cerebras’ WSE-2 hardware (~30 petaFLOPs of compute) with 128 supporting CPUs
- Training massive (up to 2T params) transformer models for natural language processing (NLP) tasks on Cerebras’ WSE-2 hardware, using high (up to 100x) parameter sparsity and reversible computations to extract maximum performance
During the summer of 2021, I interned at NVIDIA as a deep learning research intern on the AV Perception team, investigating neural network pruning to speedup inference for image classification and object detection tasks.
During the summer of 2020, I interned at the SLAC National Accelerator Laboratory, working on anomaly detection algorithms to detect failures in the LCLS beam.
From 2016-2018, I was a full-time software engineer at Microsoft at their headquarters in Redmond, WA.
During undergrad, I also interned at SIG, GE Digital (formerly GE Intelligent Platforms), and CERN’s ATLAS detector.

Computer Skills

I have at time or another coded in: C, C++, C#, Java, Fortran77, Python 2 & 3, Julia, CUDA, Javascript/Typescript, R, PHP, SQL, MATLAB, and several Cerebras-specific languages. I (perhaps optimistically) believe that I can pick any of these back up and hit the ground running.

See LinkedIn for a more complete profile, or contact me for my resume.