Foundations of Data Science Boot Camp

Simons Institute "Foundations of Data Science" Program

Training

August 27, 2018 to August 31, 2018
9:00am to 4:30pm
UC Berkeley

This training workshop is intended to acquaint program participants with the key themes of the Simons Institute's Foundations of Data Science Program being offered during the Fall 2018 semester. BIDS Senior Fellow Michael Mahoney is co-organizing and presenting.

Foundations of Data Science Boot Camp
Dates: August 27-31, 2018
Location: Calvin Hall, Simons Institute, UC Berkeley

Five days of tutorial presentations will be presented, including the following speakers and topics:

  • Ravi Kannan (Microsoft Research India) - Foundations of Data Science
  • David Woodruff (CMU) - Sketching for Linear Algebra I & II: Basics of Dimensionality Reduction & CountSketch
  • Ken Clarkson (IBM Almaden) - Sketching for Linear Algebra III: Randomized Hadamard, Kernel Methods
  • Rachel Ward (UT Austin) - First Order Stochastic Optimization
  • Michael Mahoney (ICSI & UC Berkeley) - Sampling for Linear Algebra and Optimization
  • Fred Roosta (University of Queensland) - Stochastic Second Order Optimization Methods I
  • Will Fithian (UC Berkeley) - Statistical Interference
  • Santosh Vempala (Georgia Tech) - High Dimensional Geometry and Concentration
  • Ilias Diakonikolas (USC) - High Dimensional Robust Statistics
  • Ilya Razenshteyn (Microsoft Research) - Nearest Neighbor Methods
  • Michael Kapralov (EPFL) - Data Streams

Speaker(s)

Michael Mahoney

Associate Professor, Department of Statistics

Michael Mahoney works on algorithmic and statistical aspects of modern large-scale data analysis. Much of his recent research has focused on large-scale machine learning including randomized matrix algorithms and randomized numerical linear algebra; geometric network analysis tools for structure extraction in large informatics graphs; scalable implicit regularization methods; and applications in genetics, astronomy, medical imaging, social network analysis, and internet data analysis.