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

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.