I'm a Computer Science Ph.D. student at Stanford University advised by Christopher Ré. My research interests lie at the intersection of machine learning and systems. Recently, I have been excited about learning embeddings of data, including words, source code, and knowledge graph entities. I graduated with a BS in Electrical & Computer Engineering and a BS in Computer Science from Cornell University. At Cornell, I had the opportunity to do research in computer architecture with Christopher Batten. I am supported by the National Science Foundation Graduate Research Fellowship and the Stanford EDGE Fellowship.
[10/20/20] Our paper on Bootleg is accepted to CIDR 2021.
[09/21/20] Excited to be interning at Microsoft's applied research lab for Azure Data this fall!
[03/02/20] Presented our work on Embedding Stability at MLSys in Austin.
Bootleg, a self-supervised named entity disambiguation (NED) system for the tail. Bootleg improves over 50 F1 points over a BERT NED baseline on disambiguating tail (i.e rarely seen) entities in Wikipedia, while achieving state-of-the-art performance on standard, sentence-level NED benchmarks. [website] [code]
Embedding Stability, a study of the impact of word embedding memory on the downstream instability of NLP tasks. Embeddings must be continually re-trained on constantly changing data. However, training is inherently unstable, such that small changes in data can cause dramatically different results. We explore measures to estimate when this instability will impact downstream NLP tasks. [pdf] [slides]
High-Accuracy Low-Precision Training (HALP), a gradient descent variant which is able to theoretically converge to highly accurate solutions while using low-precision. We empirically verified HALP on linear regression and logistic regression problems, as well as LSTMs and CNNs. [blog] [pdf] [slides]
Publications and Preprints
Teaching ExperienceCornell University
- ECE 4750: Computer Architecture, Undergraduate Teaching Assistant (Fall 2016)
- CS 1110: Introduction to Python, Consultant (Fall 2014, Spring 2015, Fall 2015, Spring 2016)