If you happen to look at my presentation slides or papers and see some typos or have questions, feel free to email me. I am very interested to know about them.:)

Quick links:

Presentation slides on my research:

Modewise methods for tensor dimension reduction, Online Asymptotic Geometric Analysis Seminar, 06/2020, video of the talk
Sketching for Motzkin's method, Asilomar conference, 11/2019
Iterative linear solvers and random matrices: Block Gaussian sketch and project method, SOCAMS, 02/2019
Constructive regularization of the random matrix norm, GeorgiaTech, 03/2019
Regularization of the random matrix norm: local and global obstructions, NYU, 03/2017
Coverings of random ellipsoids, and invertibility of matrices with i.i.d. heavy-tailed entries, AMS, 03/2016

Invited research talks:

June 2020
Online Asymptotic Geometric Analysis Seminar, List of abstracts
April 2020
Graduate Seminar California State University Channel Islands, online
November 2019
Combinatorics and Probability seminar, UCIrvine, CA Abstract
November 2019
Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA
October 2019
Probability seminar, Stanford, CA Abstract
October 2019
Probability seminar, USC, CA List of abstract
March 2019
High-dimensional seminar, GeorgiaTech, Atlanta, GA Abstract
March 2019
University of Alberta, Edmonton, Canada
February 2019
Southern California Applied Mathematics Symposium, Caltech, Pasadena, CA List of abstracts
September 2018
Structural Inference in High-Dimensional Models worksop, HSE, Moscow, Russia Abstract
August 2018
SUMIFRAS workshop, TAMU University, College Station, TX Abstract
June 2017
Probability seminar, Universite-Paris-Est Marne-la-Vallee, France Abstract
June 2017
Probability seminar, University of Alberta, Edmonton, Canada
May 2017
Probability seminar, Universite Paris Diderot, France Abstract
March 2017
MIC seminar, Center for Data Science, NYU, New York Abstract
June 2016
CMS Summer Sectional Meeting University of Alberta, Edmonton, Canada Abstract
March 2016
AMS Spring Southeastern Sectional Meeting University of Georgia, Athens, GA Abstract

Expository Machine Learning talks at Berkeley Lab:

In July and August 2017 I led an introductory learning seminar in Machine Learning at Berkeley Lab.
The majority of the material that we covered was based on the slides and literature for this Stanford class.
Some additional materials I prepared for the convenience of presentation:
Unsupervised learning techniques: beyond clustering and PCA
Slides
Regularizations in linear regression: ridge, lasso, bridge, elastic net etc
Handwritten notes

Random matrices reading seminar at UCLA:


Since January 2019, I co-organize Random matrices reading seminar with Palina Salanevich.
Topics included:
  • Uncertainty principles on graphs (Winter term 2019)
  • Mathematics of neural networks (Spring term 2019)
  • Delocalization of eigenvectors of random matrices and graphs (Fall 2019, Reading list)

Some (old) expository talks:

(January 2017) Structured Random Matrices tutorial (on Ramon van Handel's survey)
Analysis/Probability Learning Seminar, abstracts: Part 1 and Part 2
(September 2016) Matrix regularizing effects of Gaussian perturbations
On Aizenman, Peled, Schenker, Shamis and Sodin's paper. Analysis/Probability Learning Seminar, abstract
(October 2015) Geometric random walks
Short exposition of Santosh Vempala survey. Student Analysis Seminar, abstract
(March 2015) The smallest singular value of random rectangular matrices with no moment assumptions on entries
On Konstantin Tikhomirov's paper. Analysis/Probability Learning Seminar, abstract
(March 2015) Small ball probabilities and an introduction to the Littlewood-Offord problem
Student Analysis Seminar, abstract
(November 2014) Cube slicings in R^n
Student Analysis Seminar, abstract

Geometric analysis reading seminar at UMichigan:

During 2014 and 2015, I gave several of talks on Geometric analysis reading seminar (topics included: contact points and John's theorem; Milman's quotient of subspace theorem and Bourgain-Milman inequality; Paouris’ deviation inequality, Banach-Mazur distance to the cube estimates)