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.
(July 2021) New animated video (4 min) on Batch Optimization for DNA Synthesis for ISIT 2021, showing how to print synthetic DNA as efficiently as possible, with some nice upper and lower bounds based on non-trivial concentration inequalities and combinatorial arguments about random walks.
(June 2021) Complete rewrite of our paper on Robustness and Generalization to Nearest Categories. Very excited about this work on understanding robustness and OOD classification, uncovering the phenonmena that robust networks behave like 1-NN.
(May 2021) One paper accepted to COLT 2021 on the Average-case Communication Complexity of Statistical Problems.
(May 2021) Excited (really!) to serve as an Area Chair for NeurIPS 2021!
(Apr 2021) Check out our blog post on Costis Daskalakis' ALT 2021 keynote talk
(Mar 2021) New 3 minute animated video on an Introduction to Explainable Clustering.
(Jan 2021) Happy new year! Our paper on reconstructing trees from traces has been substantially updated with new figures and algorithms, and it has been officially accepted to the Annals of Applied Probability!
(Dec 2020) New paper on Approximate Trace Reconstruction, where we study the complexity of approximately reconstructing a string in edit distance, given a small number of samples from the deletion channel. Check it out for new upper and lower bounds!
(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!
(March 2020) My first deep learning paper! On the connection between robustness and local smoothness for separated data (e.g., images). Accepted to NeurIPS 2020.
(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!