Peter Richtarik Explained

Peter Richtarik
Fields:Mathematics, Computer Science, Machine Learning
Workplaces:KAUST
Birth Place:Nitra, Slovakia
Nationality:Slovak
Alma Mater:Comenius University Cornell University
Thesis Title:Some algorithms for large-scale convex and linear minimization in relative scale
Thesis Url:https://ecommons.cornell.edu/xmlui/handle/1813/8155
Thesis Year: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.

Education

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]

Career

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]

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 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.

Awards and distinctions

Bibliography

External links

Notes and References

  1. Web site: Richtarik's DBLP profile . December 23, 2020.
  2. Web site: Richtarik's CV . August 21, 2016.
  3. Web site: Mathematics Genealogy Project . August 20, 2016.
  4. Web site: Cornell PhD Thesis . August 22, 2016.
  5. Web site: Postdoctoral Fellows at CORE . August 22, 2016.
  6. Web site: Simons Institute for the Theory of Computing, UC Berkeley . August 22, 2016.
  7. Web site: Alan Turing Institute Faculty Fellows . August 22, 2016.
  8. Web site: Optimization and Big Data 2012 . August 20, 2016.
  9. Web site: Optimization and Big Data 2015 . August 20, 2016.
  10. Book: Doing Data Science: Straight Talk from the Frontline . O'Reilly . Cathy O'Neil . Rachel Schutt . amp . 2013 . Modeling and Algorithms at Scale . August 21, 2016. 9781449358655 .
  11. Book: Convex Optimization: Algorithms and Complexity . Foundations and Trends in Machine Learning . Now Publishers . Sebastien Bubeck . 2015 . 978-1601988607 .
  12. Web site: Google Scholar. December 28, 2020.
  13. Web site: The h Index for Computer Science. December 28, 2020.
  14. Web site: SIGEST Award . August 20, 2016.
  15. Web site: EPSRC Fellowship . August 21, 2016.
  16. Web site: EUSA Awards 2015 . August 20, 2016.
  17. Web site: 46th Conference of Slovak Mathematicians . August 22, 2016.

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