Ilya Razenshteyn

GENERAL INFORMATION

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

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 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.

Email: ilyaraz@mit.edu, ilyaraz@microsoft.com
My CV (as of August 22, 2017)
My research statement (as of December 2016)

I do Theoretical Computer Science. My research interests are revolving around efficient algorithms for massive datasets with geometric structure.

I help organizing TCS+: a series of online seminars in Theory. Watch some of the past talks: the speaker list can easily compete with any offline seminar I know of! If you want to suggest a speaker, fill a form.

My wife does Theory as well!

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

VIDEOS OF MY TALKS

“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.

SELECTED PUBLICATIONS

A. Andoni, H. Nguyen, A. Nikolov, IR and E. Waingarten, Approximate Near Neighbors for General Symmetric Norms, STOC, 2017 (arXiv).
A. Andoni, T. Laarhoven, IR and E. Waingarten, Optimal Hashing-based Time–Space Trade-offs for Approximate Near Neighbors, SODA, 2017 (arXiv).
Invited to the special issue of ACM Transactions on Algorithms
A. Andoni, P. Indyk, T. Laarhoven, IR and L. Schmidt, Practical and Optimal LSH for Angular Distance, NIPS, 2015 (arXiv).
A. Andoni, R. Krauthgamer and IR, Sketching and Embedding are Equivalent for Norms, STOC, 2015 (arXiv).
Accepted to the special issue of SIAM Journal on Computing
Invited to Highlights of Algorithms (HALG 2016)

See the full list of papers (with slides, posters, videos etc).

TEACHING

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