Max-Planck-Institut für Informatik
max planck institut
mpii logo Minerva of the Max Planck Society

MPI-INF or MPI-SWS or Local Campus Event Calendar

<< Previous Entry Next Entry >> New Event Entry Edit this Entry Login to DB (to update, delete)
What and Who
Title:Inexactness, geometry, and optimization for data analysis
Speaker:Suvrit Sra
coming from:Max-Planck Institute for Intelligent Systems, Tübingen, and Carnegie Mellon University (ML Dept), Pittsburgh
Speakers Bio:Suvrit Sra is a Sr. Research Scientist at the Max Planck Institute

for Intelligent Systems, in Tübingen, Germany. He obtained Ph.D. in
Computer Science from the University of Texas at Austin in 2007. In
Spring 2013 he was visiting faculty at UC Berkeley (EECS), and is
currently also a visiting faculty member in the Machine Learning
Department at Carnegie Mellon University.

His research is dedicated to bridging a number of mathematical areas
(such as geometry, analysis, noncommutative algebra, convex
analysis, matrix analysis, statistics, optimization, etc.) with
data-driven real-world applications.

His work has won several awards; most notably the "SIAM 2011
Outstanding Paper Prize". He regularly organizes a workshops on
"Optimization for Machine Learning" at the Neural Information
Processing Systems (NIPS) conference, and has recently (co)-edited a
book with the same title.

Event Type:Talk
Visibility:D1, D2, D3, D4, D5, RG1, SWS, MMCI
We use this to send out email in the morning.
Level:MPI Audience
Date, Time and Location
Date:Wednesday, 9 April 2014
Duration:30 Minutes
Building:E1 4
The current data-age is witnessing an unprecedented confluence of
disciplines. A single data analysis task can demand expertise in
computer science, statistics, functional analysis, optimization, or
more. But what aspects of data are driving this rich interaction? We
may single out at least two: size and form.

We hear a lot about "size" but less about "form". I will highlight
examples from my own research that touch both these aspects. In
particular, I mention progress on a framework for inexact
optimization, which subsumes numerous other algorithms and is first
of its kind for tackling nonconvex, nonsmooth problems that arise in
large-scale data analysis. Next, I will talk more about "form",
specifically the geometry of data. My motivation lies in a number of
applications where data are not merely vectors, but richer objects
such as matrices, strings, functions, graphs, trees, etc. Processing
such data in their "intrinsic representation" raises deep
mathematical and algorithmic concerns replete with open problems.

To add perspective, I ground the whole talk in applications from
computational imaging, computer vision, machine learning, and
statistics. Time permitting, I will mention some fascinating
connections of beyond machine learning and data analysis.
Name(s):Bernt Schiele
Video Broadcast
Video Broadcast:NoTo Location:
Tags, Category, Keywords and additional notes
Attachments, File(s):
  • Bernt Schiele, 04/07/2014 02:12 PM -- Created document.