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. Recently, I've been very interested in adversarial robustness, edit distance, trace reconstruction, and explainable AI. Over the past few years, I have also worked on efficient clustering, similarity search, graph algorithms, and parameter estimation. Overall, my goal is to provide deep insight into complex tasks, invoking diverse tools from theoretical computer science, statistics, and discrete mathematics.
(Oct 2020) New survey with lots of new models and open questions: Trace Reconstruction Problems in Computational Biology, to appear in the Special Issue of IEEE Transactions on Information Theory Dedicated to the Memory of Vladimir I. Levenshtein.
(Aug 2020) Check out my new animated video about vector matrix vector queries! Watch the first minute and you'll be hooked!
(June 2020) Ever wondered what you could learn about matrix M by querying <u,Mv> for different vectors u,v? See our new paper for algorithms and lower bounds!
(May 2020) We started a new UCSD Machine Learning Blog. Stay tuned for many interesting posts this summer!
(April 2020) Two papers accepted to ISIT 2020! The first on making DNA storage more efficient, and the second on covering codes for insertions and deletions (see below).
(March 2020) My first deep learning paper! On the connection between robustness and local smoothness for separated data (e.g., images).
(March 2020) Very excited about our new paper on Explainable k-means and k-medians clustering, taking a stab at explainable AI with provable guarantees!
(March 2020) New paper on Distributed All-Pairs Similarity Search, with real experiments on real data, handling skew (e.g., popular YouTube videos or Tweets) better than previous approaches! Accepted to WWW 2020.
Yet another new paper on Covering Codes for Insertions and Deletions, where we provide new upper and lower bounds for an asymmetric covering problem regarding words of different lengths. Accepted to IEEE Transactions on Information Theory.
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. Accepted to ITCS 2020.
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. Accepted to AISTATS 2020.
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!