Prior to this, I was a visiting Research Scientist at Facebook Reality Labs, exploring the intersection of Distributed Computing, Social Networks, and Augmented Reality. In June 2018, I graduated with my PhD in Computer Science & Engineering from the University of Washington (UW). My advisor was Paul Beame, and I was a part of both the Theory and MISL groups. I also spent time at Microsoft Research, working with the DNA Storage and the Theory groups. Previously, I obtained a BS from the Department of Computer Science at the University of Illinois Urbana-Champaign (UIUC).
My research explores the algorithmic foundations of fundamental data science problems. Throughout my PhD and industry research, I have worked on efficient clustering, similarity search, graph algorithms, and parameter estimation. I have also proven new lower bounds for distributed and sequential models. Overall, my goal is to provide deep insight into complex tasks, invoking diverse tools from theoretical computer science, statistics, and discrete mathematics.
- New paper on the Equivalence of Systematic Linear Data Structures and Matrix Rigidity, including new cell probe lower bounds for the vector-matrix-vector problem
- New paper on Edge Isoperimetric Inequalities for Powers of the Hypercube, including a new bound on the Kleitman-West Problem.
- New paper on Adversarial Examples for kNN and Random Forests.
- Sanjoy Dasgupta, Ilya Razenshteyn, and I are organizing a workshop on Data Science Through a Geometric Lens at STOC 2019.
- Our paper on tree trace reconstruction will appear in COLT 2019.
- We started the MAD Science Seminar at UCSD, a weekly collaborative talk+lunch series.
- In January, I started as a Data Science Fellow at UCSD, and I love it here so far!