I am a researcher interested in anomaly detection, numerical linear algebra, high performance computing, and several other topics. Currently I am a 5th year PhD student at Stanford’s ICME working with Professor Eric Darve.

Experience

Education

  • I am a 5th year PhD student in Computational and Mathematical Engineering at Stanford University. My advisor is Professor Eric Darve.
  • I received my BS from Yale in 2016, double majoring in Applied Mathematics and Mechanical Engineering.

Work Experience

  • I most recently interned at Cerebras Systems on the Advanced Technology Group, exploring how to train 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, Javascript/Typescript, R, PHP, SQL, MATLAB, and Visual Basic. 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.