Nationality Slovak | Academic advisors Yurii Nesterov | |
 | ||
Fields Mathematician, Computer scientist | ||
Fellow short talks dr peter richtarik edinburgh university
Peter Richtarik is a Slovak mathematician working in the area of big data optimization and machine learning, known for his work on randomized coordinate descent algorithms. He is currently a reader in the School of Mathematics at the University of Edinburgh.
Contents
- Fellow short talks dr peter richtarik edinburgh university
- Peter richtarik accelerated parallel and proximal coordinate descent 12 02 2014
- Education
- Career
- Academic work
- Awards and distinctions
- References
Peter richtarik accelerated parallel and proximal coordinate descent 12 02 2014
Education
Richtarik earned a master's degree in mathematics from Comenius University, Slovakia, in 2001, graduating summa cum laude. In 2007, he obtained a PhD in operations research from Cornell University, advised by Michael Jeremy Todd. Between 2007 and 2009, he was a postdoctoral scholar in the Center for Operations Research and Econometrics and Department of Mathematical Engineering at Universite catholique de Louvain, Belgium, working with Yurii Nesterov.
Career
Since 2009, Richtarik has been working at the University of Edinburgh. He is a Faculty Fellow of the Alan Turing Institute and the Head of a big data optimization group at the University. Richtarik founded and organizes a conference series entitled "Optimization and Big Data".
Academic work
Richtarik's early research concerned gradient-type methods, optimization in relative scale, sparse principal component analysis and algorithms for optimal design. Since his appointment at Edinburgh, he has been working extensively on building algorithmic foundations of randomized methods in convex optimization, especially randomized coordinate descent algorithms. These methods are well suited for optimization problems described by big data, and have applications in fields such as machine learning, signal processing and data science. Richtarik is the co-inventor of an algorithm generalizing the randomized Kaczmarz method for solving a system of linear equations.