Peter Richtarik | |
|---|---|
| Born | |
| Nationality | Slovak |
| Alma mater | Comenius University Cornell University |
| Scientific career | |
| Fields | Mathematics, Computer Science, Machine Learning |
| Institutions | KAUST |
| Thesis | Some algorithms for large-scale convex and linear minimization in relative scale (2007) |
| Academic advisors | Yurii Nesterov |
| Website | https://richtarik.org |
Peter Richtarik is a Slovak mathematician and computer scientist [1] working in the area of big data optimization and machine learning, known for his work on randomized coordinate descent algorithms, stochastic gradient descent and federated learning. He is currently a Professor of Computer Science at the King Abdullah University of Science and Technology.
Richtarik earned a master's degree in mathematics from Comenius University, Slovakia, in 2001, graduating summa cum laude. [2] In 2007, he obtained a PhD in operations research from Cornell University, advised by Michael Jeremy Todd. [3] [4]
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. [5] [6] Between 2009 and 2019, Richtarik was a Lecturer and later Reader in the School of Mathematics at the University of Edinburgh. He is a Turing Fellow. [7] Richtarik founded and organizes a conference series entitled "Optimization and Big Data". [8] [9]
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 and stochastic gradient descent methods. 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. [10] [11] Richtarik is the co-inventor of an algorithm generalizing the randomized Kaczmarz method for solving a system of linear equations, contributed to the invention of federated learning, and co-developed a stochastic variant of the Newton's method.
