Email: mderezin at berkeley edu
I am a postdoc at the Foundations of Data Analysis (FODA) Institute at UC Berkeley.
Previously, I was a research fellow at the Simons Institute for the Theory of Computing (Fall 2018, Foundations of Data Science program). I obtained my Ph.D. in Computer Science at the University of California, Santa Cruz, advised by professor Manfred Warmuth. In my research, I develop efficient data sampling techniques with applications to learning theory and optimization. Prior to UCSC, I completed Master's degrees in mathematics and computer science at the University of Warsaw. I also interned at a variety of Silicon Valley research labs (e.g. Microsoft, Yahoo, eBay), working on projects ranging from online learning to large-scale distributed optimization.Short overview of my thesis work:
Unbiased estimates for linear regression via volume sampling
Ph.D. in Computer Science, University of California, Santa Cruz, 2018.
Determinantal Point Processes in Randomized Numerical Linear Algebra
Debiasing Distributed Second Order Optimization with Surrogate Sketching
and Scaled Regularization
Sampling from a k-DPP without looking at all items
Precise expressions for random projections: Low-rank approximation and
Improved guarantees and a multiple-descent curve for the
Column Subset Selection Problem and the Nyström method
Exact expressions for double descent and implicit regularization via surrogate random design
Isotropy and Log-Concave Polynomials: Accelerated Sampling and High-Precision Counting of Matroid Bases
Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling
Bayesian experimental design using regularized determinantal point processes
Unbiased estimators for random design regression
Exact sampling of determinantal point processes with sublinear time preprocessing
Distributed estimation of the inverse Hessian by determinantal averaging
Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression
Fast determinantal point processes via distortion-free intermediate sampling
Correcting the bias in least squares regression with volume-rescaled sampling
Leveraged volume sampling for linear regression
Reverse iterative volume sampling for linear regression
Anticipating Concept Drift in Online Learning
Active Semi-Supervised Concept Hierarchy Refinement
Research Intern, Microsoft Research, Sunnyvale, CA
Intern Research Scientist, Yahoo Inc., Sunnyvale, CA
Intern Research Scientist, eBay Inc., San Jose, CA
Software engineer Intern, Nvidia, Santa Clara, CA.
Intern, Interdisciplinary Centre for Mathematical and Computational Modelling, Warsaw, Poland.