
Michał Dereziński
Email: mderezin at berkeley edu I am a research fellow at the Simons Institute for the Theory of Computing (Fall 2018, Foundations of Data Science program). I am also a postdoc at the Foundations of Data Analysis (FODA) Institute, UC Berkeley. 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 largescale distributed optimization. 
Education and Research 
Ph.D. in Computer Science, University of California, Santa Cruz.
Leveraged volume sampling for linear regression
Reverse iterative volume sampling for linear regression
Subsampling for Ridge Regression via Regularized Volume Sampling
BatchExpansion Training: An Efficient Optimization Framework
Discovering Surprising Documents with ContextAware Word Representations
Unbiased estimates for linear regression via volume sampling
Anticipating Concept Drift in Online Learning
The limits of squared Euclidean distance regularization
Active SemiSupervised Concept Hierarchy Refinement 
Experience 
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. 