# Ilya Razenshteyn

## GENERAL INFORMATION

I am a researcher in the Machine Learning and Optimization group at Microsoft Research Redmond.

## TRIVIA

During Fall 2017, I was a Simons Junior Fellow at Columbia University hosted by Alexandr Andoni.
In August 2017 I graduated with Ph.D. in Computer Science from MIT Computer Science and AI Laboratory, where
I was privileged to be advised by
Piotr Indyk.
Check out my award-winning thesis **“High-Dimensional Similarity Search and Sketching: Algorithms and Hardness”**.
I graduated in June 2012 from Moscow State University with B.S. in Mathematics;
my advisors were great Maxim Babenko and Sasha Shen.

## NEWS

*06/12/19:*New paper**“Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers”***06/11/19:*I have given a talk**“Adversarial Examples from Computational Constraints”**at ICML 2019*05/26/19:*I have given a talk**“Hölder Homeomorphisms and Approximate Nearest Neighbors”**at Workshop on Metric Embeddings and Dimensionality Reduction at Northwestern University*05/17/19:*I have given a talk**“Extension theorems, dimension reduction, and clustering”**at Simons Collaboration on Algorithms & Geometry Annual Meeting*04/21/19:*One paper accepted to ICML 2019*04/18/19:*One paper accepted to COLT 2019*04/03/19:*New paper**“SANNS: Scaling Up Secure Approximate $k$-Nearest Neighbors Search”***02/11/19:*One paper accepted to STOC 2019*02/07/19:*New paper**“On Mean Estimation for General Norms with Statistical Queries”***01/26/19:*New paper**“Learning Sublinear-Time Indexing for Nearest Neighbor Search”**

## MISCELLANEOUS

- Email:
**ilyaraz@mit.edu**,**ilyaraz@microsoft.com** - My CV (as of July 2, 2018)
- My Google Scholar profile
- My research statement (as of December 2016)

My wife does Theory as well!

**Program Committees:** STOC 2019, SODA 2019, CSR 2018, SEA 2016.

## VIDEOS OF MY TALKS

- “Hölder Homeomorphisms and Approximate Nearest Neighbors” (Simons Institute)
- A tutorial on the nearest neighbor search: part 1, part 2 (Foundations of Data Science Bootcamp @ Simons Institute)
- “Beyond John's Ellipsoid” (Learning and Optimization workshop at MSR)
- “Optimal Hashing-based Time-Space Trade-offs for Approximate Near Neighbors” (CMU Theory Lunch)
- “Sketching and Embedding are Equivalent for Norms” (TCS+)
- “Locality-Sensitive Hashing and Beyond” (Microsoft Research)

## CODE

- FALCONN: a highly optimized C++ library (with Python bindings) for similarity search based on LSH.
- QuadSketch: a simple and fast algorithm for compressing Euclidean metrics.
- pytorch_kmeans: implementation of the k-means algorithm in PyTorch that works for large datasets.

## TEACHING

- Fall 2018: The second incarnation of Algorithms through Geometric Lens, University of Washington.
- Fall 2017: co-teaching (with Alexandr Andoni) a class Algorithms through Geometric Lens, Columbia University.
- May 2017: mini-course Algorithms for high-dimensional data at the school Recent Advances in Algorithms, Saint Petersburg.
- January 2016: mini-course From “compact presentations” to algorithms for big data at PDMI CS Club (me and students who took the class)
- Fall 2015: TA for Introduction to Algorithms (6.006) at MIT (taught by Piotr Indyk and Ron Rivest)
- Spring 2014: TA for the Piotr Indyk's course Geometric Computing (6.850) at MIT
- Spring 2011: TA for the Andrew Goldberg's mini-course Path and Flow Algorithms at Yandex Data Analysis School

© 2015–2019