Institute for Mathematics and its Applications University of Minnesota 400 Lind Hall 207 Church Street SE Minneapolis, MN 55455 
20112012 Program
See http://www.ima.umn.edu/20112012/ for a full description of the 20112012 program on Mathematics of Information.
2:30pm3:00pm  Coffee break  Lind Hall 400 
1:25pm2:25pm  Using POMDPs to understand and support human sequential decision making with uncertainty  Brian J. Stankiewicz (3M)  Lind Hall 305  IPS 
2:30pm3:00pm  Coffee break  Lind Hall 400 
2:30pm3:00pm  Coffee break  Lind Hall 400 
11:15am12:15pm  Sparse coding and object recognition  Arthur Szlam (University of Minnesota)  Lind Hall 305  PS 
2:30pm3:00pm  Coffee break  Lind Hall 400 
2:30pm3:00pm  Coffee break  Lind Hall 400 
2:30pm3:00pm  Coffee break  Lind Hall 400 
2:30pm3:00pm  Coffee break  Lind Hall 400 
2:30pm3:00pm  Coffee break  Lind Hall 400 
11:15am12:15pm  Derivatives of eigenvalue functions  Brendan P.W. Ames (University of Minnesota)  Lind Hall 401  PS 
2:30pm3:00pm  Coffee break  Lind Hall 400 
2:30pm3:00pm  Coffee break  Lind Hall 400 
2:30pm3:00pm  Coffee break  Lind Hall 400 
2:30pm3:00pm  Coffee break  Lind Hall 400 
2:30pm3:00pm  Coffee break  Lind Hall 400 
2:30pm3:00pm  Coffee break  Lind Hall 400 
2:30pm3:00pm  Coffee break  Lind Hall 400 
2:30pm3:00pm  Coffee break  Lind Hall 400 
2:30pm3:00pm  Coffee break  Lind Hall 400 
All Day  Christmas Day (observed). The IMA is closed. 
All Day  University Holiday. The IMA is closed. 
2:30pm3:00pm  Coffee break  Lind Hall 400 
2:30pm3:00pm  Coffee break  Lind Hall 400 
2:30pm3:00pm  Coffee break  Lind Hall 400 
Event Legend: 

IPS  Industrial Problems Seminar 
PS  IMA Postdoc Seminar 
Brendan P.W. Ames (University of Minnesota)  Derivatives of eigenvalue functions 
Abstract: The eigenvalues of a symmetric operator play a significant role in many areas of mathematics and engineering. As such, it is important to understand the behaviour of the spectrum of a matrix under small perturbations. This talk is divided into two parts. In the first, I will review several important results from the literature regarding the sensitivity of the eigenvalues of a symmetric matrix. In particular, I will provide formulas for the directional derivatives of the spectrum of a symmetric matrix, and discuss how these formulas can be extended to obtain a series approximation of the spectrum of a symmetric matrix under small perturbations. The second half of the talk will focus on the sensitivity of functions of the spectrum of a symmetric matrix. If F(X) is a function that depends smoothly on the eigenvalues of X, I will provide conditions for when this smoothness is inherited by F. As examples, I will provide conditions for differentiability of spectral functions and primary matrix functions, as well as formulas for their gradients.  
Brian J. Stankiewicz (3M)  Using POMDPs to understand and support human sequential decision making with uncertainty 
Abstract: Humans possess the remarkable ability to make thousands of decision every day under conditions of incredible uncertainty. Furthermore the outcomes of a decisions may not be felt for hours, days, weeks or even years and there may even have been many decisions made before there is any cost or reward generated. Developing a robust decision making system for these conditions remains a computational challenge due to the combinatoric nature of these problem along with being able to dynamically formulate an estimate of the system. However, you and I do it every day hundreds if not thousands of times. Thus we remain existence proof that such a computational system can exist. To better understand how humans accomplish this task we leverage the work in Partially Observable Markov Decision Processes (POMDPs) to provide us with a framework for providing optimal decision policies when making sequential decision under uncertainty. By comparing human performance to the optimal policy we have developed methods to dissect and identify which aspects of human cognition are optimal and suboptimal. By identifying the computational strengths and limits of the human mind we can then identify the necessary computations to support and improve the human decision making process.  
Arthur Szlam (University of Minnesota)  Sparse coding and object recognition 
Abstract: I will review a standard object recognition architecture, and discuss each of the components: SIFT features, sparse coding, pooling, and linear classifiers. I will then mention recent work of myself and collaborators on a fast version of this machine. 
Brendan P.W. Ames  University of Minnesota  8/31/2011  8/30/2012 
Bubacarr Bah  University of Edinburgh  9/15/2011  12/15/2011 
Arindam Banerjee  University of Minnesota  9/1/2011  6/30/2012 
Andrew John Beveridge  Macalester College  9/1/2011  5/15/2012 
Sergey G Bobkov  University of Minnesota  9/1/2011  6/30/2012 
Luca Capogna  University of Minnesota  8/15/2011  6/10/2012 
Aycil Cesmelioglu  University of Minnesota  9/30/2010  8/30/2012 
Paolo Codenotti  University of Minnesota  9/1/2011  8/30/2012 
Jintao Cui  University of Minnesota  8/31/2010  8/30/2012 
Isabel K. Darcy  University of Iowa  9/1/2011  6/30/2012 
Dainius Dzindzalieta  Vilnius State University  9/1/2011  12/22/2011 
Leonardo Espin  NONE  9/1/2011  6/30/2012 
Carlos Andres GaravitoGarzon  University of Minnesota  9/8/2011  6/30/2012 
Yulia Hristova  University of Minnesota  9/1/2010  8/31/2012 
Gilad Lerman  University of Minnesota  9/1/2011  6/30/2012 
Wenbo Li  University of Delaware  9/1/2011  5/30/2012 
Xin Liu  University of Minnesota  8/31/2011  8/30/2012 
Shiqian Ma  University of Minnesota  8/31/2011  8/30/2013 
Yu (David) Mao  University of Minnesota  8/31/2010  8/30/2012 
Gabriela MartÃnez  University of Minnesota  8/31/2011  8/30/2013 
Saurabh Mishra  Eagan High School  8/22/2011  12/31/2011 
Dimitrios Mitsotakis  University of Minnesota  10/27/2010  8/31/2012 
Luke Olson  University of Illinois at UrbanaChampaign  9/1/2011  12/31/2011 
Mary Therese Padberg  University of Iowa  8/16/2011  1/31/2012 
Candice Renee Price  University of Iowa  8/1/2011  7/31/2012 
Weifeng (Frederick) Qiu  University of Minnesota  8/31/2010  8/30/2012 
Guillermo R. Sapiro  University of Minnesota  9/1/2011  5/31/2012 
Brian J. Stankiewicz  3M  12/2/2011  12/2/2011 
Arthur Szlam  University of Minnesota  8/31/2011  8/30/2012 
Jared Tanner  University of Edinburgh  9/19/2011  12/5/2011 
Divyanshu Vats  University of Minnesota  8/31/2011  8/30/2012 
Lan Wang  University of Minnesota  9/1/2011  5/12/2012 
Ke Wei  University of Edinburgh  10/10/2011  12/10/2011 
Elisabeth Werner  Case Western Reserve University  9/1/2011  12/3/2011 
Lingzhou Xue  University of Minnesota  9/1/2011  6/30/2012 
Ofer Zeitouni  University of Minnesota  9/1/2011  12/9/2011 
Teng Zhang  University of Minnesota  8/31/2011  8/30/2012 