Christos Boutsidis

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Associate

Goldman Sachs

New York, New York

christos.boutsidis@gmail.com

Research

My research focuses on solving important problems in numerical linear algebra and machine learning; specifically, I am interested in developing fast approximation algorithms for such problems. Those algorithms are often used in place of the more traditional exact algorithms when one wants to trade the accuracy of the solution with the running time. Additionally, I am exploring solutions to those problems in different models of computation such as distributed, streaming, and online. The main tool that I m using to design new algorithms is ‘‘sketching", which offers powerful randomized and/or sampling techniques for matrices. Besides the design and the theoretical analysis of efficient approximation algorithms, I am interested to understand what are the limits of each model (lower bounds) and how the algorithms perform in practice on large matrices.

Experiences

  • July 2015 - present : Associate, Goldman Sachs, New York, New York.

  • Apr 2014 - Jun 2015: Research Scientist, Yahoo Labs, New York, New York.

  • Aug 2011 - Mar 2014: Research Staff Member, Math Sciences, IBM Watson.

  • June 2010 - Aug 2010: Internship - Quantitative analyst, WorldQuant.

  • May 2009 - Nov 2009: Internship, Math Sciences Department, IBM Zurich.

  • May 2008 - Aug 2008: Internship, Services Engineering Dept., IBM Watson.

  • Sept 2008 - Dec 2008: Visiting Student, University of California, Los Angeles.

  • Aug 2006 - May 2011: PhD Student, Computer Science Department, RPI.

  • Sept 2001 - July 2006: Undergrad student, University of Patras, Greece