KAUST2017-2020PhD, Optimization and machine learning

Thesis title: Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters

Thesis advisor: Peter Richtárik

Dissertation committee: Stephen Wright, Tong Zhang, Raúl Tempone, and Bernard Ghanem

University of Edinburgh2016-2017MSc by Research, Mathematics and Statistics

Thesis title: Extending the Reach of Big Data Optimization-Randomized Algorithms for Minimizing Relatively Smooth Functions

Thesis advisor: Peter Richtárik

Comenius University, Bratislava2013-2016Bc. Ecomomics and Financial mathematics (Honours)

Thesis title: Analysis of causal relationships in reconstructed phase space

Thesis advisor: Anna Krakovská

Google Research, New York2019 (summer)Research intern

I worked under Sashank Reddi with a goal to speed up neural network training. We got some interesting results; the project is still in progress. I have also collaborated with Sanjiv Kumar and Srinadh Bhojanapalli.

Amazon, Berlin2018 (summer)Applied Science Intern

I worked under Rodolphe Jenatton on speeding up log likelihood optimization for ABLR model (Bayesian Optimization). I had a chance to work with Matthias Seeger, Cedric Archambeau and others great researchers. 

Slovak Academy of Sciences2015-2016Research assistant

Designing new methods for causality detection in reconstructed phase space. Ended up with this paper