Institute for Mathematics and its Applications University of Minnesota 114 Lind Hall 207 Church Street SE Minneapolis, MN 55455 
20082009 Program
See http://www.ima.umn.edu/20082009 for a full description of the 20082009 program on Mathematics and Chemistry.
Professor Hans Weinberger is Professor Emeritus in Department
of Mathematics
of University of Minnesota. He obtained his Sc.D.
http://en.wikipedia.org/wiki/Sc.D. on the thesis
/Fourier Transforms of Moebius Series/ advised by Richard
Duffin
Professor Ingrid Daubechies of Princeton University will give
the IMA
Math Matters public lecture
on October 29, 2008:
http://www.ima.umn.edu/20082009/PUB10.29.08/
Professor Daubechies obtained her Ph.D. from Vrije Universiteit
Brussel
The Institute for Mathematics and its Applications (IMA) in conjunction with the National Science Foundation Division of Math Sciences is organizing a oneday workshop on the new initiative called SOLAR http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=503298. The goal of the workshop is to bring together a mixed audience of mathematicians, chemists and materials scientists to discuss scientific challenges in high efficiency harvesting, conversion and storage of solar energy. The workshop will aim to plant the seeds for interdisciplinary collaborations necessary to address the grand challenges in solar energy conversion and storage. It will aim to educate the mixed audience of attendees about the issues and provide a framework for detailed discussions on what needs to be done to surpass the grand challenges. The workshop will begin with four talks by speakers who are experts in fundamental science of solar energy conversion and culminate in the afternoon with panel discussions. For detail, see: http://www.ima.umn.edu/20082009/SW11.1.08/.
All Day  9:00am12:05pm Density Functional Theory for Physics and Chemistry Session (continued)
2:00pm DFT Math Session  W9.2910.3.08  
8:30am9:00am  Coffee  EE/CS 3176  W9.2910.3.08  
9:00am9:50am  TBA  Eberhard K. U. Gross (Freie Universität Berlin)  EE/CS 3180  W9.2910.3.08 
9:50am10:20am  Coffee  EE/CS 3176  W9.2910.3.08  
10:20am11:10am  Van der Waals density functional: theory, implementations, and applications  David Langreth (Rutgers University)  EE/CS 3180  W9.2910.3.08 
11:15am12:05pm  New density functionals with broad applicability for thermochemistry, thermochemical kinetics, noncovalent interactions, transition metals, and spectroscopy  Donald G. Truhlar (University of Minnesota)  EE/CS 3180  W9.2910.3.08 
12:05pm2:00pm  Lunch  W9.2910.3.08  
2:00pm2:50pm  Open mathematical issues in quantum chemistry: a personal perspective  Claude Le Bris (CERMICS)  EE/CS 3180  W9.2910.3.08 
2:50pm3:20pm  Coffee  EE/CS 3176  W9.2910.3.08  
3:20pm4:10pm  Exact embedding of local defects in crystals  Mathieu Lewin (Université de CergyPontoise)  EE/CS 3180  W9.2910.3.08 
All Day 
9:00am11:55am DFT Math Session (continued)
Chair: Heinz Siedentop (LudwigMaximiliansUniversität München) 2:00pm Algorithms Session  W9.2910.3.08  
8:30am9:00am  Coffee  EE/CS 3176  W9.2910.3.08  
9:00am9:50am  A linear scaling subspace iteration algorithm with optimally localized nonorthogonal wave functions for KohnSham density functional theory  Carlos J. GarciaCervera (University of California, Santa Barbara)  EE/CS 3180  W9.2910.3.08 
9:50am10:20am  Coffee  EE/CS 3176  W9.2910.3.08  
10:20am11:10am  Construction of exponentially localized Wannier functions  Gianluca Panati (Università di Roma "La Sapienza")  EE/CS 3180  W9.2910.3.08 
11:15am11:55am  Second chances: The chair of the day will deliver a 30 minutes overview of the field followed by a discussion.  Heinz Siedentop (LudwigMaximiliansUniversität München)  EE/CS 3180  W9.2910.3.08 
11:55am2:00pm  Lunch  W9.2910.3.08  
2:00pm2:50pm  A direct constrained minimization algorithm for solving the KohnSham equations  Chao Yang (Lawrence Berkeley National Laboratory)  EE/CS 3180  W9.2910.3.08 
2:50pm3:20pm  Coffee  EE/CS 3176  W9.2910.3.08  
3:20pm4:10pm  Augmented basis sets in finite cluster DFT  James W. Davenport (Brookhaven National Laboratory)  EE/CS 3180  W9.2910.3.08 
6:30pm8:30pm  Workshop dinner at Caspian Bistro  Caspian Bistro 2418 University Ave SE Minneapolis, MN 55414 6126231133 
W9.2910.3.08 
All Day  Algorithms Session (continued) Chair: François Gygi (University of California, Davis)  W9.2910.3.08  
8:30am9:00am  Coffee  EE/CS 3176  W9.2910.3.08  
9:00am9:50am  Firstprinciples molecular dynamics for petascale computers  François Gygi (University of California, Davis)  EE/CS 3180  W9.2910.3.08 
9:50am10:20am  Coffee  EE/CS 3176  W9.2910.3.08  
10:20am11:10am  Modern optimization tools and electronic structure calculations  José Mario Martínez (State University of Campinas (UNICAMP))  EE/CS 3180  W9.2910.3.08 
11:15am12:05pm  Partitionofunity finiteelement approach for large, accurate ab initio electronic structure calculations  John E. Pask (Lawrence Livermore National Laboratory)  EE/CS 3180  W9.2910.3.08 
12:05pm1:45pm  Lunch  W9.2910.3.08  
1:25pm2:25pm  Dealing with stiffness in lowMach number flows  Caroline GattiBono (Lawrence Livermore National Laboratory)  Vincent Hall 570  IPS 
1:45pm2:35pm  Mathematical and algorithmic challenges in the simulation of electronic structure and dynamics on quantum computers  Alán AspuruGuzik (Harvard University)  EE/CS 3180  W9.2910.3.08 
2:35pm3:05pm  Coffee  EE/CS 3176  W9.2910.3.08  
3:05pm3:45pm  Second chances: The chair of the day will deliver a 30 minutes overview of the field followed by a discussion.  François Gygi (University of California, Davis)  EE/CS 3180  W9.2910.3.08 
3:45pm3:55pm  Closing remark  EE/CS 3180  W9.2910.3.08 
All Day  A SYMPOSIUM IN HONOR OF HANS
WEINBERGER'S 80th BIRTHDAY:
DIFFERENTIAL EQUATIONS: ANALYSIS, APPLICATIONS &
COMPUTATION
Jianzhong Su (University of Texas), Chair, Morning Session Roger Lui (Worcester Polytechnic Institute) Chair, Afternoon Session  SW10.4.08  
9:00am9:30am  Registration and Coffee  EE/CS 3176  SW10.4.08  
9:30am9:40am  Welcome  Peter J. Olver (University of Minnesota)  EE/CS 3180  SW10.4.08 
9:40am10:30am  Population spread and the dynamics of biological invasions  Mark Lewis (University of Alberta)  EE/CS 3180  SW10.4.08 
10:30am10:40am  Group photo  SW10.4.08  
10:40am11:10am  Coffee  EE/CS 3176  SW10.4.08  
11:10am12:00pm  Spectral properties, regularity and optimal bounds for solutions of elliptic boundary value problems  Howard A. Levine (Iowa State University)  EE/CS 3180  SW10.4.08 
12:00pm2:00pm  Lunch  SW10.4.08  
2:00pm2:50pm  Numerical work of Hans F. Weinberger  John E. Osborn (University of Maryland)  EE/CS 3180  SW10.4.08 
2:50pm3:20pm  Coffee  EE/CS 3176  SW10.4.08  
3:20pm4:10pm  Speed selection for traveling waves  Donald G. Aronson (University of Minnesota)  EE/CS 3180  SW10.4.08 
6:00pm9:30pm  Banquet at the Carlson Private Dining Room (Carlson
School of Management) 321 19th Avenue South Minneapolis, MN 55455  Carlson Private Dining Room (Carlson School of Management)  SW10.4.08 
10:45am11:15am  Coffee break  Lind Hall 400  
2:30pm3:30pm  Math 8994: Topics in classical and
quantum mechanics Electronic structure calculations and molecular simulation: A mathematical initiation  Eric Cances (CERMICS) Claude Le Bris (CERMICS)  Lind Hall 305 
10:45am11:15am  Coffee break  Lind Hall 400  
11:15am12:15pm  The Mathematical basis for molecular van der Waals forces  Ridgway Scott (University of Chicago)  Lind Hall 305  PS 
3:00pm4:00pm  Reading group for Professor Ridgway Scott's book "Digital Biology"  Ridgway Scott (University of Chicago)  Lind Hall 401 
10:00am11:00am  A view of outstanding problems in density functional theory  Steven M. Valone (Los Alamos National Laboratory)  Lind Hall 409  SMC 
10:45am11:15am  Coffee break  Lind Hall 400  
2:30pm3:30pm  Math 8994: Topics in classical and
quantum mechanics Electronic structure calculations and molecular simulation: A mathematical initiation  Eric Cances (CERMICS) Claude Le Bris (CERMICS)  Lind Hall 305 
10:45am11:15am  Coffee break  Lind Hall 400  
11:15am12:15pm  Panagiotis Stinis, University of Minnesota TBA  Vincent Hall 570  AMS 
10:45am11:35am  Coffee break  Lind Hall 400 
10:45am11:15am  Coffee break  Lind Hall 400  
2:30pm3:30pm  Math 8994: Topics in classical and
quantum mechanics Electronic structure calculations and molecular simulation: A mathematical initiation  Eric Cances (CERMICS) Claude Le Bris (CERMICS)  Lind Hall 305 
10:45am11:15am  Coffee break  Lind Hall 400  
11:15am12:15pm  Model reference control in the biological systems  Yongfeng Li (University of Minnesota)  Lind Hall 409  PS 
12:15pm1:30pm  postdoc lunch meeting  Donald G. Aronson (University of Minnesota)  Lind Hall 409  
3:00pm4:00pm  Reading group for Professor Ridgway Scott's book "Digital Biology." Chapter 3 discussion  Ridgway Scott (University of Chicago)  Lind Hall 401 
10:45am11:15am  Coffee break  Lind Hall 400  
4:00pm5:00pm  New efficient algorithms for a general blood tissue transportmetabolism model and stiff differential equations  Dexuan Xie (University of Wisconsin)  Lind Hall 409  SMC 
10:45am11:15am  Coffee break  Lind Hall 400  
11:15am12:15pm  Modeling dispersive fluid flow  Ridgway Scott (University of Chicago)  Vincent Hall 570  AMS 
10:45am11:15am  Coffee break  Lind Hall 400  
1:25pm2:25pm  Virtual prototyping of hearing aids using numerical modeling and supercomputing  Thomas H. Burns (Starkey Laboratories)  Vincent Hall 570  IPS 
3:45pm4:45pm  Seminar: Quantum vacuum energy in rectangular geometries and the problems of moving beyond  Stephen Fulling (Texas A & M University)  Lind Hall 409 
10:45am11:15am  Coffee break  Lind Hall 400 
10:45am11:15am  Coffee break  Lind Hall 400  
11:15am12:15pm  BornOppenheimer corrections near a RennerTeller crossing  Mark S. Herman (University of Minnesota)  Lind Hall 305  PS 
3:00pm4:00pm  Reading group for Professor Ridgway Scott's book "Digital Biology"  Ridgway Scott (University of Chicago)  Lind Hall 401 
10:45am11:15am  Coffee break  Lind Hall 400 
10:45am11:15am  Coffee break  Lind Hall 400  
2:30pm3:30pm  Waves and mixing  Roman Schubert (University of Bristol)  Vincent Hall 570  DSS 
10:45am11:15am  Coffee break  Lind Hall 400 
All Day  Morning Session Chair: Irina Rish (IBM) Afternoon Session Chair: Richard Souvenir (University of North Carolina  Charlotte)  SW10.2730.08  
8:00am8:45am  Registration and coffee  EE/CS 3176  SW10.2730.08  
8:45am9:00am  Welcome to the IMA  Fadil Santosa (University of Minnesota)  EE/CS 3180  SW10.2730.08 
9:00am9:50am  The best lowrank Tucker approximation of a tensor  Lars Eldén (Linköping University)  EE/CS 3180  SW10.2730.08 
9:55am10:45am  Detecting mixed dimensionality and density in noisy point clouds  Gloria Haro Ortega (Universitat Politecnica de Catalunya)  EE/CS 3180  SW10.2730.08 
10:45am11:15am  Coffee  EE/CS 3176  SW10.2730.08  
11:15am12:05pm  A Geometric perspective on machine Learning  Partha Niyogi (University of Chicago)  EE/CS 3180  SW10.2730.08 
12:05pm2:00pm  Lunch  SW10.2730.08  
2:00pm2:50pm  Manifold models for signal acquisition, compression, and processing  Richard G. Baraniuk (Rice University)  EE/CS 3180  SW10.2730.08 
2:55pm3:45pm  Harmonic and multiscale analysis on lowdimensional data sets in highdimensions  Mauro Maggioni (Duke University)  EE/CS 3180  SW10.2730.08 
3:30pm4:30pm  Fulldimensional potential energy surfaces for small molecules  Bastiaan J. Braams (Emory University)  283 Kolthoff Hall  
3:45pm4:00pm  Group Photo  SW10.2730.08  
4:00pm4:30pm  Coffee  EE/CS 3176  SW10.2730.08  
4:30pm5:30pm  Large group discussion on What have we learned about manifold learning — what are its implications for machine learning and numerical analysis? What are open questions? What are successes? Where should we be optimistic and where should we be pessimistic?  Partha Niyogi (University of Chicago)  EE/CS 3180  SW10.2730.08 
5:30pm7:00pm  Poster Session and Reception: 5:307:00 Poster submissions welcome from all participants  Lind Hall 400  SW10.2730.08  
Compressive sampling reconstruction by subspace refinement (poster)  Bradley K. Alpert (National Institute of Standards and Technology)  
Analysis of scalar fields over point cloud data (poster)  Frédéric Chazal (INRIA Saclay  ÎledeFrance )  
Joint manifold models for collaborative inference (poster)  Mark Andrew Davenport (Rice University)  
The smashed filter for compressive classification(poster)  Marco F. Duarte (Rice University)  
3D motion segmentation via robust subspace separation (poster)  Ehsan Elhamifar (Johns Hopkins University)  
teratively reweighted least squares and vector valued data restoration from lower dimensional samples (poster)  Massimo Fornasier (Johann Radon Institute for Computational and Applied Mathematics )  
Clustering on Riemannian manifolds (poster)  Alvina Goh (Johns Hopkins University) René Vidal (Johns Hopkins University)  
Random projections for manifold learning (poster)  Chinmay Hegde (Rice University)  
Representing and manipulating implicitly defined manifolds (poster)  Michael E. Henderson (IBM)  
Fast multiscale clustering and manifold identification (poster)  Dan Kushnir (Yale University)  
Supervised dictionary learning (poster)  Julien Mairal (INRIA )  
A supervised dimensionality reduction framework for exponentialfamily variables (poster)  Irina Rish (IBM)  
Tensor approximation  structure and methods (poster)  Berkant Savas (Linköping University)  
Structure determination of proteins using cryoelectron microscopy (poster)  Yoel Shkolnisky (Yale University) Amit Singer (Princeton University)  
High order consistency relations for classification and denoising of CryoEM images (poster)  Yoel Shkolnisky (Yale University) Amit Singer (Princeton University)  
kplanes for classification (poster)  Arthur Szlam (University of California, Los Angeles)  
Manifold models for single and multisignal recovery (poster)  Michael Wakin (Colorado School of Mines)  
Using persistent homology to recover spatial information from encounter traces (poster)  Brenton Walker (Laborartory For Telecommunications Sciences)  
Mixed data segmentation via lossy data compression (poster)  John Wright (University of Illinois at UrbanaChampaign)  
Orthantwise gradient projection method for sparse reconstruction (poster)  Qiu Wu (University of Texas)  
Highdimensional multimodel estimation – its Algebra, statistics, and sparse representation (poster)  Allen Yang Yang (University of California, Berkeley)  
Approximate nearest subspace search with applications to pattern recognition (poster)  Lihi ZelnikManor (TechnionIsrael Institute of Technology) 
All Day  Morning Session Chair: Lihi ZelnikManor (TechnionIsrael Institute of Technology) Afternoon Session Chair: Michael E. Henderson (IBM)  SW10.2730.08  
8:30am9:00am  Coffee  EE/CS 3176  SW10.2730.08  
9:00am9:50am  Multilinear (tensor) manifold data modeling  M. Alex O. Vasilescu (SUNY)  EE/CS 3180  SW10.2730.08 
9:55am10:45am  Recovering sparsity in high dimensions  Ronald DeVore (Texas A & M University)  EE/CS 3180  SW10.2730.08 
10:45am11:15am  Coffee  EE/CS 3176  SW10.2730.08  
11:15am12:05pm  Clustering linear and nonlinear manifolds  René Vidal (Johns Hopkins University)  EE/CS 3180  SW10.2730.08 
12:05pm2:00pm  Lunch  SW10.2730.08  
2:00pm2:50pm  Instance optimal adaptive regression in high dimensions  Wolfgang Dahmen (RWTH Aachen)  EE/CS 3180  SW10.2730.08 
2:55pm3:45pm  Spectral and geometric methods in learning  Mikhail Belkin (Ohio State University)  EE/CS 3180  SW10.2730.08 
3:45pm4:15pm  Coffee  EE/CS 3176  SW10.2730.08  
4:15pm5:15pm  Large group discussion on:
1. The representation of highlevel information and lowlevel data 2. The symbiotic linkage between information and data 3. The need to transform qualitative information into quantitative data sets and vice versa 4. The need to think beyond the learning for classification.  Tristan Nguyen (Office of Naval Research)  EE/CS 3180  SW10.2730.08 
6:30pm8:30pm  Workshop dinner  Kikugawa at Riverplace 43 Main Street SE Minneapolis MN 55414 6123783006 
SW10.2730.08 
All Day  Morning Session Chair: Stacey E. Levine (Duquesne University) Afternoon Session Chair: Ramesh Natarajan (IBM Research Division)  SW10.2730.08  
8:30am9:00am  Coffee  EE/CS 3176  SW10.2730.08  
9:00am9:50am  Interpolation of functions on R^{n}  Charles L. Fefferman (Princeton University)  EE/CS 3180  SW10.2730.08 
9:55am10:45am  Multimanifold data modeling via spectral curvature clustering  Gilad Lerman (University of Minnesota)  EE/CS 3180  SW10.2730.08 
10:45am11:15am  Coffee  EE/CS 3176  SW10.2730.08  
11:15am12:05pm  Visualization & matching for graphs and data  Tony Jebara (Columbia University)  EE/CS 3180  SW10.2730.08 
12:05pm2:00pm  Lunch  SW10.2730.08  
2:00pm2:50pm  Topology and data  Gunnar Carlsson (Stanford University)  EE/CS 3180  SW10.2730.08 
2:55pm3:45pm  Dense error correction via L1 minimization  Yi Ma (University of Illinois at UrbanaChampaign)  EE/CS 3180  SW10.2730.08 
3:45pm4:15pm  Coffee  EE/CS 3176  SW10.2730.08  
4:15pm5:15pm  Large group discussion on Manifold Clustering 1) What have have been recent advances on manifold clustering? a) Algebraic approaches b) Spectral approaches c) Probabilistic approaches 2) What have been successful applications of manifold clustering?
3) What is the role of topology, geometry, and statistics, in
manifold learning, i.e., 3) What are the open problems in manifold clustering?  René Vidal (Johns Hopkins University)  EE/CS 3180  SW10.2730.08 
5:15pm6:30pm  Math matters public lecture reception  Lind Hall 400  SW10.2730.08  
7:00pm8:15pm  Math matters public lecture: Surfing with wavelets  Ingrid Daubechies (Princeton University)  Willey Hall 125  SW10.2730.08 
All Day  Chair: LekHeng Lim (University of California, Berkeley)  SW10.2730.08  
8:30am9:00am  Coffee  EE/CS 3176  SW10.2730.08  
9:00am9:50am  CPOPT: Optimization for fitting CANDECOMP/PARAFAC models  Tamara G. Kolda (Sandia National Laboratories)  EE/CS 3180  SW10.2730.08 
9:55am10:45am  Semisupervised learning by multimanifold separation  Xiaojin Zhu (University of Wisconsin)  EE/CS 3180  SW10.2730.08 
10:45am11:15am  Coffee  EE/CS 3176  SW10.2730.08  
11:15am12:05pm  Mathematical problems suggested by AnalogtoDigital conversion  Ingrid Daubechies (Princeton University)  EE/CS 3180  SW10.2730.08 
12:05pm12:10pm  Closing remark  EE/CS 3180  SW10.2730.08  
12:10pm2:00pm  Conference lunch at Loring Pasta Bar in Dinkytown  Loring Pasta Bar in Dinkytown  SW10.2730.08  
2:30pm3:30pm  Waves and mixing (part II)  Roman Schubert (University of Bristol)  Vincent Hall 570  DSS 
10:45am11:15am  Coffee break  Lind Hall 400  
4:00pm5:00pm  Reading group for Professor Ridgway Scott's book "Digital Biology"  Ridgway Scott (University of Chicago)  Lind Hall 401 
Event Legend: 

AMS  Applied Mathematics Seminar 
DSS  Dynamical Systems Seminar 
IPS  Industrial Problems Seminar 
PS  IMA Postdoc Seminar 
SMC  IMA Seminar on Mathematics and Chemistry 
SW10.2730.08  MultiManifold Data Modeling and Applications 
SW10.4.08  Differential Equations: Analysis, Applications & Computation 
W9.2910.3.08  Mathematical and Algorithmic Challenges in Electronic Structure Theory 
Bradley K. Alpert (National Institute of Standards and Technology)  Compressive sampling reconstruction by subspace refinement (poster) 
Abstract: Spurred by recent progress in compressive sampling methods, we develop a new reconstruction algorithm for the Fourier problem of recovering from noisy samples a linear combination of unknown frequencies embedded in a very large dimensional ambient space. The approach differs from both L1norm minimization and orthogonal matching pursuit (and its variants) in that no basis for the ambient space is chosen a priori. The result is improved computational complexity. We provide numerical examples that demonstrate the method's robustness and efficiency. Joint work with Yu Chen.  
Donald G. Aronson (University of Minnesota)  Speed selection for traveling waves 
Abstract: No Abstract  
Alán AspuruGuzik (Harvard University)  Mathematical and algorithmic challenges in the simulation of electronic structure and dynamics on quantum computers 
Abstract: The exact simulation of quantum mechanical systems on classical computers generally scales exponentially with the size of the system N. Using quantum computers, the computational resources required to carry out the simulation are polynomial. Our group has been working in the development and characterization of quantum computational algorithms for the simulation of chemical systems. We will give a tutorial on our algorithms for the simulation of molecular electronic structure, molecular properties and quantum dynamics, and will discuss the opportunities, open questions and challenges in the field of simulation of physical systems using quantum computers or dedicated quantum devices.  
Richard G. Baraniuk (Rice University)  Manifold models for signal acquisition, compression, and processing 
Abstract: Joint work with Mark Davenport, Marco Duarte, Chinmay Hegde, and Michael Wakin. Compressive sensing is a new approach to data acquisition in which sparse or compressible signals are digitized for processing not via uniform sampling but via measurements using more general, even random, test functions. In contrast with conventional wisdom, the new theory asserts that one can combine "lowrate sampling" (dimensionality reduction) with an optimizationbased reconstruction process for efficient and stable signal acquisition. While the growing compressive sensing literature has focused on sparse or compressible signal models, in this talk, we will explore the use of manifold signal models. We will show that for signals that lie on a smooth manifold, the number of measurements required for a stable representation is proportional to the dimensionality of the manifold, and only logarithmic in the ambient dimension — just as for sparse signals. As an application, we consider learning and inference from manifoldmodeled data, such as detecting tumors in medical images, classifying the type of vehicle in airborne surveillance, or estimating the trajectory of an object in a video sequence. Specific techniques we will overview include compressive approaches to the matched filter (dubbed the "smashed filter"), intrinsic dimension estimation for point clouds, and manifold learning algorithms. We will also present a new approach based on the joint articulation manifold (JAM) for compressive distributed learning, estimation, and classification.  
Mikhail Belkin (Ohio State University)  Spectral and geometric methods in learning 
Abstract: In recent years a variety of spectral and geometrybased methods have become popular for various tasks of machine learning, such as dimensionality reduction, clustering and semisupervised learning. These methods use a model of data as a probability distribution on a manifold, or, more generally a mixture of manifolds. In the talk I will discuss some of these methods and recent theoretical results on their convergence. I will also talk about how spectral methods can be used to learn mixtures of Gaussian distributions, which may be considered the simplest case of multimanifold data modeling.  
Bastiaan J. Braams (Emory University)  Fulldimensional potential energy surfaces for small molecules 
Abstract: (This will be an informal talk in Donald Truhlar's group meeting.) Studies of molecular dynamics and molecular spectroscopy generally start from the BornOppenheimer approximation and require some form of analytical potential energy surface fitted to ab initio electronic structure calculations. We have used computational invariant theory and the MAGMA computer algebra system as an aid to develop representations for the potential energy and dipole moment surfaces that are fully invariant under permutations of like nuclei. We express the potential energy surface in terms of internuclear distances using basis functions that are manifestly invariant. A dipole moment is represented with use of effective charges at positions of the nuclei, which must transform as a covariant, rather than as an invariant, under permutations of like nuclei. Malonaldehyde (CHOHCHCHO) provides an illustrative application. The associated molecular permutational symmmetry group is of order 288 (4!3!2!) and the use of full permutational symmetry makes it possible to obtain a compact representation for the surface.  
Thomas H. Burns (Starkey Laboratories)  Virtual prototyping of hearing aids using numerical modeling and supercomputing 
Abstract: In an effort to efficiently manufacture quality products, numerical models and empirical measurements are used to predict (virtually) the performance of a hearing aid. Finite element analysis is used to study multiphysics processes such as thermomechanically induced stress due to heat flow from soldering, acoustic and structural interactions due to transducer vibration, and mechanical shock failure due to drop testing. Following a synopsis of hearingaid anatomy, the presentation will show numerous animations depicting results from the virtual prototypes. Dr. Burns received a Ph.D. in engineering acoustics from Penn State, specializing in signal processing of acoustical holography measurements. He joined Starkey Labs in November of 1999, following periods as a consultant in concert hall acoustics at Kirkegaard Associates, and a senior design engineer of condenser microphones at Shure. Currently, he is the Director of Starkey’s Applied Technology and Research Group, and serves on the Hearing Aid Measurement Standards committee for ANSI Bioacoustics (S3/WG48). By day, he directs an advanced development team of engineers at Starkey. By night, he changes diapers and lulls his kids to sleep by playing Chopin Nocturnes on his concert grand.  
Eric Cances (CERMICS), Claude Le Bris (CERMICS)  Math 8994: Topics in classical and
quantum mechanics Electronic structure calculations and molecular simulation: A mathematical initiation 
Abstract: Meeting time: Mondays and Wednesdays 2:30 ‐ 3:30 pm Room 305 Lind Hall. The course will present the basics of the quantum theory commonly used in computational chemistry for electronic structure calculations, and the basics of molecular dynamics simulations. The perspective is definitely mathematical. After the presentation of the models, the mathematical properties will be examined. The state of the art of the mathematical knowledge will be mentioned. Numerical analysis and scientific computing questions will also be thoroughly investigated. The course is intended for students and researchers with a solid mathematical background in mathematical analysis and numerical analysis. Familiarity with the models in molecular simulation in the broad sense is not needed. The purpose of the course to introduce the audience to the field. This is a 1‐3 credit course offered through the School of Mathematics. Non‐student participants are welcome to audit without registering. Note that no particular knowledge of quantum mechanics or classical mechanics will be necessary: the basic elements will be presented. For additional information and course registration, please contact: Markus Keel (keel@math.umn.edu).  
Gunnar Carlsson (Stanford University)  Topology and data 
Abstract: The nature and quantity of data arising out of scientific applications requires novel methods, both for exploratory analysis as well as analysis of significance and validation. One set of new methods relies on ideas and methods from topology. The study of data sets requires an extension of standard methods, which we refer to as persistent topology. We will survey this area, and show that algebraic methods can be applied both to the exploratory and the validation side of investigations, and show some examples.  
Frédéric Chazal (INRIA Saclay  ÎledeFrance )  Analysis of scalar fields over point cloud data (poster) 
Abstract: (Joint work with L. Guibas, S. Oudot and P. Skraba  to appear in proc. SODA'09). Given a realvalued function f defined over some metric space X, is it possible to recover some structural information about f from the sole information of its values at a finite subset L of sample points, whose pairwise distances in X are given? We provide a positive answer to this question. More precisely, taking advantage of recent advances on the front of stability for persistence diagrams, we introduce a novel algebraic construction, based on a pair of nested families of simplicial complexes built on top of the point cloud L, from which the persistence diagram of f can be faithfully approximated. We derive from this construction a series of algorithms for the analysis of scalar fields from point cloud data. These algorithms are simple and easy to implement, have reasonable complexities, and come with theoretical guarantees. To illustrate the generality and practicality of the approach, we also present experimental results obtained in various applications, ranging from clustering to sensor networks.  
Wolfgang Dahmen (RWTH Aachen)  Instance optimal adaptive regression in high dimensions 
Abstract: Joint work with Peter Binev, Ron DeVore, and Philipp Lamby. This talk addresses the recovery of functions of a large number of variables from point clouds in the context of supervised learning. Our estimator is based on two conceptional pillars. First, the notion of sparse occupancy trees is shown to warrant efficient computations even for a very large number of variables. Second, a properly adjusted adaptive treeapproximation scheme is shown to ensure instance optimal performance. By this we mean the rapid decay (with increasing sample size) of the probability that the estimator deviates from the regression function (in a certain natural norm) by more than the error of best nterm approximation in the sparse tree setting.  
Ingrid Daubechies (Princeton University)  Math matters public lecture: Surfing with wavelets 
Abstract: Wavelets are used in the analysis of sounds and images, as well as in many other applications. The wavelet transform provides a mathematical analog to a music score: just as the score tells a musician which notes to play when, the wavelet analysis of a sound takes things apart into elementary units with a well defined frequency (which note?) and at a well defined time (when?). For images wavelets allow you to first describe the coarse features with a broad brush, and then later to fill in details. This is similar to zooming in with a camera: first you can see that the scene is one of shrubs in a garden, then you concentrate on one shrub and see that it bears berries, then, by zooming in on one branch, you find that this is a raspberry bush. Because wavelets allow you to do a similar thing in more mathematical terms, the wavelet transform is sometimes called a "mathematical microscope." Wavelets are used by many scientists for many different applications. Outside science as well, wavelets are finding their uses: wavelet transforms are an intergral part of the image compression standard JPEG2000. The talk will start by explaining the basic principles of wavelets, which are very simple. Then they will be illustrated with some examples, including an explanation of image compression.  
Ingrid Daubechies (Princeton University)  Mathematical problems suggested by AnalogtoDigital conversion 
Abstract: In AnalogtoDigital conversion, continuously varying functions (e.g. the output of a microphone) are transformed into digital sequences from which one then hopes to be able to reconstruct a close approximation to the original function. The functions under consideration are typically bandlimited (i.e. their Fourier transform is zero for frequencies higher than some given value, called the bandwidth); such functions are completely determined by samples taken at a rate determined by their bandwidth. These samples then have to be approximated by a finite binary representation. Surprisingly, in many practical applications one does not just replace each sample by a truncated binary expansion; for various technical reasons, it is more attractive to sample much more often and to replace each sample by just 1 or 1, chosen judicously. In this talk, we shall see what the attractions are of this quantization scheme, and discuss several interesting mathematical questions suggested by this approach. This will be a review of work by many others as well as myself. It is also a case study of how continuous interaction with engineers helped to shape and change the problems as we tried to make them more precise.  
James W. Davenport (Brookhaven National Laboratory)  Augmented basis sets in finite cluster DFT 
Abstract: Density functional theory provides a systematic approach to the electronic structure of atoms, molecules and solids. It requires the repeated solution of single particle Schrodinger equations in a self consistent loop. Most techniques involve some sort of basis set, the most common ones being plane waves or Gaussians. In crystalline materials the most accurate solutions involve augmented basis sets. These combine numerical solutions of the Schrodinger equation in regions near the atomic nucleii with so called ‘tail functions’ in more distant regions. In the linear augmented plane wave (LAPW) method the tail functions are plane waves. This formulation has been incorporated into the WIEN2k code. With the current interest in nanoscale clusters, biomolecules, and other finite systems it is desirable to have a comparably accurate method for these. While it is always possible to build supercells, it is often convenient to have completely localized functions which eliminate interaction between periodic images. We recently proposed a finite cluster version of the linear augmented Slatertype orbital (LASTO) method [1]. STO’s have the correct behavior at large distances and possess an addition theorem – they can be reexpanded about other sites with analytic coefficients. We solve the Poisson equation by replacing the spherical part of the density near the nucleii with a smooth pseudodensity. The full potential, including the nonsphrical piece is then solved on a grid. Examples of small clusters and comparison with the Gaussian based program NWChem will be given. [1] K. S. Kang, J. W. Davenport, J. Glimm, D. E. Keyes, and M. McGuigan, submitted to J. Computational Chemistry.  
Mark Andrew Davenport (Rice University)  Joint manifold models for collaborative inference (poster) 
Abstract: We introduce a new framework for collaborative inference and efficient fusion of manifoldmodeled data. We formulate the notion of a joint manifold model for signal ensembles, and using this framework we demonstrate the superior performance of joint processing techniques for a range of tasks including detection, classification, parameter estimation, and manifold learning. We then exploit recent results concerning random projections of lowdimensional manifolds to develop an efficient framework for distributed data fusion. As an example, we extend the smashed filter – a maximum likelihood, modelbased estimation and classification algorithm that exploits random measurements – to a distributed setting. Bounds for the robustness of this scheme against measurement noise are derived. We demonstrate the utility of our framework in a variety of settings, including large scale camera networks, networks of acoustic sensors, and multimodal sensors. This is joint work with Richard Baraniuk, Marco Duarte, and Chinmay Hegde.  
Ronald DeVore (Texas A & M University)  Recovering sparsity in high dimensions 
Abstract: We assume that we are in $R^N$ with $N$ large. The first problem we consider is that there is a function $f$ defined on $Omega:=[0,1]^N$ which is a function of just $k$ of the coordinate variables: $f(x_1,dots,x_N)=g(x_{j_1},dots,x_{j_k})$ where $j_1,dots,j_k$ are not known to us. We want to approximate $f$ from some of its point values. We first assume that we are allowed to choose a collection of points in $Omega$ and ask for the values of $f$ at these points. We are interested in what error we can achieve in terms of the number of points when we assume some smoothness of $g$ in the form of Lipschitz or higher smoothness conditions. We shall consider two settings: adaptive and nonadaptive. In the adaptive setting, we are allowed to ask for a value of $f$ and then on the basis of the answer we get we can ask for another value. In the nonadaptive setting, we must prescribe the $m$ points in advance. A second problem we shall consider is when $f$ is not necessarily only a function of $k$ variables but it can be approximated to some tolerance $epsilon$ by such a function. We seek again sets of points where the knowledge of the values of $f$ at such points will allow us to approximate $f$ well. Our main consideration is to derive results which are not severely effected by the size of $N$, i.e. are not victim of the curse of dimensionality. We shall see that this is possible.  
Marco F. Duarte (Rice University)  The smashed filter for compressive classification(poster) 
Abstract: We propose a framework for exploiting the same measurement techniques used in emerging compressive sensing systems in the new setting of compressive classification. The first part of the framework maps traditional maximum likelihood hypothesis testing into the compressive domain; we find that the number of measurements required for a given classification performance level does not depend on the sparsity or compressibility of the signal but only on the noise level. The second part of the framework applies the generalized maximum likelihood method to deal with unknown transformations such as the translation, scale, or viewing angle of a target object. Such a set of transformed signals forms a lowdimensional, nonlinear manifold in the highdimensional image space. We exploit recent results that show that random projections of a smooth manifold result in a stable embedding of the manifold in the lowerdimensional space. Nondifferential manifolds, prevalent in imaging applications, are handled through the use of multiscale random projections that perform implicit regularization. We find that the number of measurements required for a given classification performance level grows linearly in the dimensionality of the manifold but only logarithmically in the number of pixels/samples and image classes. This is joint work with Mark Davenport, Michael Wakin and Richard Baraniuk.  
Lars Eldén (Linköping University)  The best lowrank Tucker approximation of a tensor 
Abstract: The problem of computing the best multilinear lowrank approximation of a tensor can be formulated as an opimization problem on a product of Grassmann manifolds (by multilinear lowrank approximation we understand an approximation in the sense of the Tucker model). In the Grassmann approach we want to find (bases of) subspaces that represent the lowrank approximation. We have recently derived a Newton algorithm for this problem, where a quadratic model on the tangent space of the manifold is used. From the Grassmann Hessian we derive conditions for a local optimum. We also discuss the sensitivity of the subspaces to perturbations of the tensor elements.  
Ehsan Elhamifar (Johns Hopkins University)  3D motion segmentation via robust subspace separation (poster) 
Abstract: We consider the problem of segmenting multiple rigidbody motions in a video sequence from tracked feature point trajectories. Using the affine camera model, this motion segmentation problem can be cast as the problem of segmenting samples drawn from a union of linear subspaces of dimension two, three or four. However, due to limitations of the tracker, occlusions and the presence of nonrigid objects in the scene, the obtained motion trajectories may contain grossly mistracked features, missing entries, or not correspond to any valid motion model. In this poster, we present a combination of robust subspace separation schemes that can deal with all of these practical issues in a unified framework. For complete uncorrupted trajectories, we examine approaches that try to harness the subspace structure of the data either globally (Generalized PCA) or by minimizing a lossy coding length (Agglomerative Lossy Coding). For incomplete or corrupted trajectories, we develop methods based on PowerFactorization or L1minimization. The former method fills in missing entries using a linear projection onto a lowdimensional space. The latter method draws strong connections between lossy compression, rank minimization, and sparse representation. We compare our approach to other methods on a database of 167 motion sequences with full motions, independent motions, degenerate motions, partially dependent motions, missing data, outliers, etc. Our results are on par with stateoftheart results, and in many cases exceed them.  
Charles L. Fefferman (Princeton University)  Interpolation of functions on R^{n} 
Abstract: The talk explains joint work with Bo'az Klartag, solving the following problem and several variants. Let f be a realvalued function on an Npoint set E in R^{n}. Compute efficiently a function F on the whole R^{n} that agrees with f on E and has C^{m} norm close to least possible.  
Massimo Fornasier (Johann Radon Institute for Computational and Applied Mathematics )  teratively reweighted least squares and vector valued data restoration from lower dimensional samples (poster) 
Abstract: We present the analysis of a superlinear convergent algorithm for L1minimization based on an iterative reweighted least squares. We show improved performances in compressed sensing. A similar algorithm is then applied for the efficient solution of a system of singular PDEs for image recolorization in a relevant reallife problem of art restoration.  
Carlos J. GarciaCervera (University of California, Santa Barbara)  A linear scaling subspace iteration algorithm with optimally localized nonorthogonal wave functions for KohnSham density functional theory 
Abstract: We present a new linear scaling method for electronic structure computations in the context of KohnSham density functional theory (DFT). The method is based on a subspace iteration, and takes advantage of the nonorthogonal formulation of the KohnSham functional, and the improved localization properties of nonorthogonal wave functions. We demonstrate the efficiency of the algorithm for practical applications by performing fully threedimensional computations of the electronic density of alkane chains. This is joint work with Jianfeng Lu, Yulin Xuan, and Weinan E, at Princeton University.  
Caroline GattiBono (Lawrence Livermore National Laboratory)  Dealing with stiffness in lowMach number flows 
Abstract: Numerical simulation of lowMach number flows presents challenges because of the stiffness introduced by the disparity of time scales between acoustic and convective motions. In particular, the acoustic, highspeed modes often contain little energy but determine the time step for explicit schemes through the CFL condition. A natural idea is therefore to separate the acoustic modes from the rest of the solution and to treat them implicitly, while the advective motions are treated explicitly or semiimplicitly. In this talk, we present a numerical allspeed algorithm that respects lowMach number asymptotics but is suitable for any Mach number. We use a splitting method based on a Hodge/Helmholtz decomposition of the velocities to separate the fast acoustic dynamics from the slower anelastic dynamics. The acoustic waves are treated implicitly, while the advection is treated semiimplicitly. The splitting mechanism is demonstrated on two applications. The first application is a combustive flow, where Euler equations are completed by an enthalpy evolution equation. Then, we present a stratified atmospheric flow where the presence of gravity waves adds one more degree of complexity. Benchmark results are presented that compare well with the literature.  
Alvina Goh (Johns Hopkins University), René Vidal (Johns Hopkins University)  Clustering on Riemannian manifolds (poster) 
Abstract: Over the past few years, various techniques have been developed for learning a lowdimensional representation of a nonlinear manifold embedded in a highdimensional space. Unfortunately, most of these techniques are limited to the analysis of a single connected nonlinear submanifold of a Euclidean space and suffer from degeneracies when applied to linear manifolds (subspaces). This work proposes a novel algorithm for clustering data sampled from multiple submanifolds of a Riemannian manifold. First, we learn a representation of the data using generalizations of local nonlinear dimensionality reduction algorithms from Euclidean to Riemannian spaces. Such generalizations exploit geometric properties of the Riemannian space, particularly its Riemannian metric. Then, assuming that the data points from different groups are separated, we show that the null space of a matrix built from the local representation gives the segmentation of the data. However, this method can fail when the data points are drawn from a union of linear manifolds, because M contains additional vectors in its null space. In this case, we propose an alternative method for computing M, which avoids the aforementioned degeneracies, thereby resulting in the correct segmentation. The final result is a simple linear algebraic algorithm for simultaneous nonlinear dimensionality reduction and clustering of data lying in multiple linear and nonlinear manifolds. We present several applications of our algorithm to computer vision problems such as texture clustering, segmentation of rigid body motions, segmentation of dynamic textures, segmentation of diffusion MRI. Our experiments show that our algorithm performs on par with stateoftheart techniques that are specifically designed for such segmentation problems.  
Eberhard K. U. Gross (Freie Universität Berlin)  TBA 
Abstract: No Abstract  
François Gygi (University of California, Davis)  Firstprinciples molecular dynamics for petascale computers 
Abstract: Firstprinciples molecular dynamics (FPMD) is a simulation method that combines molecular dynamics with the accuracy of a quantum mechanical description of electronic structure. It is increasingly used to address problems of structure determination, statistical mechanics, and electronic structure of solids, liquids and nanoparticles. The high computational cost of this approach makes it a good candidate for use on largescale computers. In order to achieve high performance on terascale and petascale computers, current FPMD algorithms have to be reexamined and redesigned. We present new, largescale parallel algorithms developed for FPMD simulations on computers including O(10^{3}) to O(10^{4}) CPUs. Examples include the problem of simultaneous diagonalization of symmetric matrices used in the calculation of Maximally Localized Wannier Functions (MLWFs), and the Orthogonal Procrustes problem that arises in the context of BornOppenheimer molecular dynamics simulations. Supported by NSFOCI PetaApps through grant 0749217.  
François Gygi (University of California, Davis)  Second chances: The chair of the day will deliver a 30 minutes overview of the field followed by a discussion. 
Abstract: No Abstract  
Gloria Haro Ortega (Universitat Politecnica de Catalunya)  Detecting mixed dimensionality and density in noisy point clouds 
Abstract: We present a statistical model to learn mixed dimensionalities and densities present in stratifications, that is, mixture of manifolds representing different characteristics and complexities in the data set. The basic idea relies on modeling the high dimensional sample points as a process of translated Poisson mixtures, with regularizing restrictions, leading to a model which includes the presence of noise. The translated Poisson distribution is useful to model a noisy counting process, and it s derived from the noiseinduced translation of a regular Poisson distribution. By maximizing the loglikelihood of the process counting the points falling into a local ball, we estimate the local dimension and density. We show that the sequence of all possible local countings in a point cloud formed by samples of a stratification can be modeled by a mixture of different Translated Poisson distributions, thus allowing the presence of mixed dimensionality and densities in the same data set. A partition of the points in different classes according to both dimensionality and density is obtained, together with an estimation of these quantities for each class.  
Chinmay Hegde (Rice University)  Random projections for manifold learning (poster) 
Abstract: We propose a novel method for linear dimensionality reduction of manifoldmodeled data. First, we show that given only a small number random projections of sample points in R^N belonging to an unknown Kdimensional Euclidean manifold, the intrinsic dimension (ID) of the sample set can be estimated to high accuracy. Second, we prove that using only this set of random projections, we can estimate the structure of the underlying manifold. In both cases, the number of random projections (M) required is linear in K and logarithmic in N, meaning that K < M << N. To handle practical situations, we develop a greedy algorithm to estimate the smallest size of the projection space required to perform manifold learning. Our method is particularly relevant in distributed sensing systems and leads to significant potential savings in data acquisition, storage and transmission costs. (Joint work with Michael Wakin and Richard Baraniuk.)  
Michael E. Henderson (IBM)  Representing and manipulating implicitly defined manifolds (poster) 
Abstract: This poster illustrates an algorithm which was developed to compute implicitly defined manifolds in engineering applications, where the manifold is of low (14) dimension, but is embedded in a very high dimensional space (100 and up). Though the computational details (finding a point and the tangent space of the manifold) are different than in manifold learning, and the embedding is explicit instead of one of the unknowns, there are significant issues in common when computing any manifold. The representation used closely follows the definition of a manifold. A set of spherical balls (with differing radii)serve as the chart domains, with the embedding mapping limited to the embedded center and the tangent space. In addition, polyhedra are associated with each chart so that overlapping charts correspond to faces of the polyhedra (common to the polyhedra of the overlapping charts). Boundary charts are represented in the same manner, beginning with a polyhedral cone, and adding faces for each overlapping chart. The polyhedra of interior charts are LaguerreVoronoi cells, and so it is very easy to locate points on the boundary of a partially represented manifold (if all vertices are closer to the origin than the radius of the ball the chart is completely surrounded by other charts). This provides a basic "continuation" or "extension" algorithm for creating a set of covering charts on a manifold. In terms of data structures, the atlas of charts is a simple list. The polyhedra are in the same simple list, but also form cell complex whose dual is a LaguerreDelaunay "triangulation" of the manifold. Interestingly, the fine structure is a cell complex, but so is the gross structure of the manifold. Manifolds with boundary are represented by another cell complex. In this one the faces are manifolds which share boundaries which are the boundary cells of the face. So far this approach has been applied to three different kinds of manifolds which are common in dynamical systems. I hope to find similar applications in manifold learning.  
Mark S. Herman (University of Minnesota)  BornOppenheimer corrections near a RennerTeller crossing 
Abstract: We perform a rigorous mathematical analysis of the bending modes of a linear triatomic molecule that exhibits the RennerTeller effect. Assuming the potentials are smooth, we prove that the wave functions and energy levels have asymptotic expansions in powers of epsilon, where the fourth power of epsilon is the ratio of an electron mass to the mass of a nucleus.To prove the validity of the expansion, we must prove various properties of the leading order equations and their solutions. The leading order eigenvalue problem is analyzed in terms of a parameter b, which is equivalent to the parameter originally used by Renner. For 0 < b < 1, we prove selfadjointness of the leading order Hamiltonian, that it has purely discrete spectrum, and that its eigenfunctions and their derivatives decay exponentially. Perturbation theory and finite difference calculations suggest that the ground bending vibrational state is involved in a level crossing near b = 0.925. We also discuss the degeneracy of the eigenvalues. Because of the crossing, the ground state is degenerate for 0 < b < 0.925 and nondegenerate for 0.925 < b < 1.  
Tony Jebara (Columbia University)  Visualization & matching for graphs and data 
Abstract: Given a graph between N highdimensional nodes, can we faithfully visualize it in just a few dimensions? We present an algorithm that improves the stateofthe art in dimensionality reduction by extending the Maximum Variance Unfolding method. Visualizations are shown for social networks, species trees, image datasets and human activity. If we are only given a dataset of N samples, how should we link the samples to build a graph? The space to explore is daunting with 2^(N2) choices but two interesting subfamilies are tractable: matchings and bmatchings. We place distributions over these families and recover the optimal graph or perform Bayesian inference over graphs efficiently using belief propagation algorithms. Higher order distributions over matchings can also be handled efficiently via fast Fourier algorithms. Applications are shown in tracking, network reconstruction, classification, and clustering. Biography Tony Jebara is Associate Professor of Computer Science at Columbia University and director of the Columbia Machine Learning Laboratory. His research intersects computer science and statistics to develop new frameworks for learning from data with applications in vision, networks, spatiotemporal data, and text. Jebara is also cofounder and head of the advisory board at Sense Networks. He has published over 50 peerreviewed papers in conferences and journals including NIPS, ICML, UAI, COLT, JMLR, CVPR, ICCV, and AISTAT. He is the author of the book Machine Learning: Discriminative and Generative (Kluwer). Jebara is the recipient of the Career award from the National Science Foundation and has also received honors for his papers from the International Conference on Machine Learning and from the Pattern Recognition Society. He has served as chair and program committee member for many learning conferences. Jebara's research has been featured on television (ABC, BBC, New York One, TechTV, etc.) as well as in the popular press (New York Times, Slash Dot, Wired, Scientific American, Newsweek, etc.). He obtained his PhD in 2002 from MIT. Jebara's lab is supported in part by the Central Intelligence Agency, the National Science Foundation, the Office of Naval Research, the National Security Agency, and Microsoft.  
Tamara G. Kolda (Sandia National Laboratories)  CPOPT: Optimization for fitting CANDECOMP/PARAFAC models 
Abstract: Joint work with Evrim Acar, and Daniel M. Dunlavy (Sandia National Laboratories). Tensor decompositions (e.g., higherorder analogues of matrix decompositions) are powerful tools for data analysis. In particular, the CANDECOMP/PARAFAC (CP) model has proved useful in many applications such as chemometrics, signal processing, and web analysis. The problem of computing the CP decomposition is typically solved using an alternating least squares (ALS) approach. We discuss the use of optimizationbased algorithms for CP, including how to efficiently compute the derivatives necessary for the optimization methods. Numerical studies highlight the positive features of our CPOPT algorithms, as compared with ALS and GaussNewton approaches.  
Dan Kushnir (Yale University)  Fast multiscale clustering and manifold identification (poster) 
Abstract: We present a novel multiscale clustering algorithm inspired by algebraic multigrid techniques. Our method begins with assembling data points according to local similarities. It uses an aggregation process to obtain reliable scaledependent global properties, which arise from the local similarities. As the aggregation process proceeds, these global properties affect the formation of coherent clusters. The global features that can be utilized are for example density, shape, intrinsic dimensionality and orientation. The last three features are a part of the manifold identification process which is performed in parallel to the clustering process. The algorithm detects clusters that are distinguished by their multiscale nature, separates between clusters with different densities, and identifies and resolves intersections between clusters. The algorithm is tested on synthetic and real datasets, its running time complexity is linear in the size of the dataset.  
David Langreth (Rutgers University)  Van der Waals density functional: theory, implementations, and applications 
Abstract: The van der Waals density functional of Dion, Rydberg, Schroder, Langreth, and Lundqvist [Phys. Rev. Lett. 92, 246401 (2004)] will be reviewed, discussing implementations and applications by our group and others. New results relevalent for hydrogen storage in metalorganic framework (MOF) materials, as well for the intercalation of drug molecules in DNA will be presented.  
Claude Le Bris (CERMICS)  Open mathematical issues in quantum chemistry: a personal perspective 
Abstract: I will overview some open mathematical questions related to the models and techniques of computational quantum chemistry. The talk is based upon a recent article coauthored with E. Cances and PL. Lions, and published in Nonlinearity, volume 21, T165T176, 2008.  
Gilad Lerman (University of Minnesota)  Multimanifold data modeling via spectral curvature clustering 
Abstract: We propose a fast multiway spectral clustering algorithm for multimanifold data modeling. We describe the supporting theory as well as the practical choices guided by it. We emphasize the case of hybrid linear modeling, i.e., when the manifolds are affine subspaces in a Euclidean space, and then extend this setting to more general manifolds and other embedding metric spaces. We exemplify applications of the algorithm to several realworld problems while comparing it with other methods.  
Howard A. Levine (Iowa State University)  Spectral properties, regularity and optimal bounds for solutions of elliptic boundary value problems 
Abstract: No Abstract  
Mathieu Lewin (Université de CergyPontoise)  Exact embedding of local defects in crystals 
Abstract: By means of rigorous thermodynamic limit arguments, we derive a new variational model providing exact embedding of local defects in insulating or semiconducting crystals. A natural way to obtain variational discretizations of this model is to expand the perturbation of the periodic density matrix generated by the defect in a basis of precomputed maximally localized Wannier functions of the host crystal. This approach can be used within any semiempirical or Density Functional Theory framework. This is a joint work with Eric Cancès and Amélie Deleurence (Ecole Nationale des Ponts et Chaussées, France).  
Mark Lewis (University of Alberta)  Population spread and the dynamics of biological invasions 
Abstract: Classical models for the growth and spread of introduced species track the front of an expanding wave of population density. Models are typically parabolic partial differential equations and related integral formulations. One method to infer the speed of the expanding wave is to equate the speed of spread of the nonlinear system with the speed of spread of a related linear system. When these two speeds coincide we say that the spread rate is linearly predictable. While many spread rates are linearly predictable, some notable cases are not, such as those involving competition between multiple species. Hans Weinberger's work has impacted the theory of linear predictability, both for singlespecies and for multispecies models. I will review some of this theory, from the perspective of a mathematical ecologist interested in applying the theory to biology. In my talk I will apply some of the results to real biological problems, including species competition, spread of disease and population dynamics of stream ecosystems.  
Yongfeng Li (University of Minnesota)  Model reference control in the biological systems 
Abstract: Motivated by the engineering control technique, model reference control(MRC) is introduced for controlling biological systems. Mathematical framework of MRC is provided. And numerical simulation of controlling SIRS disease models and BZ oscillatory reaction is employed to test its validity.  
Yi Ma (University of Illinois at UrbanaChampaign)  Dense error correction via L1 minimization 
Abstract: It is know that face images of different people lie on multiple lowdimensional subspaces. In this talk, we will show that these subspaces are tightly bundled together as a "bouquet". Precisely due to this unique structure, it allows extremely robust reconstruction and recognition of faces despite severe corruption or occlusion. We will show that if the image resolution and the size of the face database grow in proportion to infinity, computer can correctly and efficiently recover or recognize a face image with almost 100% of its pixels randomly and arbitrarily corrupted, a truly magic ability of L1minimization. This is joint work with John Wright of UIUC.  
Mauro Maggioni (Duke University)  Harmonic and multiscale analysis on lowdimensional data sets in highdimensions 
Abstract: We discuss recent advances in harmonic analysis ideas and algorithms for analyzing data sets in highdimensional spaces which are assumed to have lowerdimensional geometric structure. They enable the analysis of both the geometry of the data and of functions on the data, and they can be broadly subdivided into local, global and multiscale techniques, roughly corresponding to PDE techniques, Fourier and wavelet analysis ideas in lowdimensional Euclidean signal processing. We discuss applications to machine learning tasks, image processing, and discuss current research directions.  
Julien Mairal (INRIA )  Supervised dictionary learning (poster) 
Abstract: It is now well established that sparse signal models are well suited to restoration tasks and can effectively be learned from audio, image, and video data. Recent research has been aimed at learning discriminative sparse models instead of purely reconstructive ones. This work proposes a new step in that direction, with a novel sparse representation for signals belonging to different classes in terms of a shared dictionary and multiple classdecision functions. The linear variant of the proposed model admits a simple probabilistic interpretation, while its most general variant admits an interpretation in terms of kernels. An optimization framework for learning all the components of the proposed model is presented, along with experimental results on standard handwritten digit and texture classification tasks. This is a joint work with F. Bach (INRIA), J. Ponce (Ecole Normale Supérieure), G. Sapiro (University of Minnesota) and A. Zisserman (Oxford University).  
José Mario Martínez (State University of Campinas (UNICAMP))  Modern optimization tools and electronic structure calculations 
Abstract: Optimization concepts will be reviewed with an eye on their proved or potential application in Electronic Structure Calculations and other Chemical Physics problems. We will discuss the role of trustregion schemes, line searches, linearly and nonlinearly constrained optimization, Inexact Restoration and SQP methods and the type of convergence theories that may be useful in order to explain the practical behavior of the methods. Emphasis will be given on general principles instead of algorithmic details.  
Tristan Nguyen (Office of Naval Research)  Large group discussion on:
1. The representation of highlevel information and lowlevel data 2. The symbiotic linkage between information and data 3. The need to transform qualitative information into quantitative data sets and vice versa 4. The need to think beyond the learning for classification. 5. How mathematics can be useful to the aforementioned domains of interest in conjunction with information integration and data fusion. 
Abstract: No Abstract  
Partha Niyogi (University of Chicago)  Large group discussion on What have we learned about manifold learning — what are its implications for machine learning and numerical analysis? What are open questions? What are successes? Where should we be optimistic and where should we be pessimistic? 
Abstract: No Abstract  
Partha Niyogi (University of Chicago)  A Geometric perspective on machine Learning 
Abstract: Increasingly, we face machine learning problems in very high dimensional spaces. We proceed with the intuition that although natural data lives in very high dimensions, they have relatively few degrees of freedom. One way to formalize this intuition is to model the data as lying on or near a low dimensional manifold embedded in the high dimensional space. This point of view leads to a new class of algorithms that are "manifold motivated" and a new set of theoretical questions that surround their analysis. A central construction in these algorithms is a graph or simplicial complex that is dataderived and we will relate the geometry of these to the geometry of the underlying manifold. Applications to data analysis, machine learning, and numerical computation will be considered.  
John E. Osborn (University of Maryland)  Numerical work of Hans F. Weinberger 
Abstract: In this talk we will survey several papers (listed
below) by
Hans Weinberger dealing with numerical and approximation
issues. We have
divided them into three categories: (i) approximation of
eigenvalues; (ii)
approximation theory issues; and (iii) error bounds for
iterative methods for
matrix inversion.
The seven papers listed are only a small part of Hans’ work—but
they
were very influential. We, of course, cannot discuss any of
these papers
in detail, but will instead concentrate on those results that
are especially
insightful and elegant.


Gianluca Panati (Università di Roma "La Sapienza")  Construction of exponentially localized Wannier functions 
Abstract: The exponential localization of Wannier functions in two or three dimensions is proven for all insulators that display timereversal symmetry, settling a longstanding conjecture. The proof make use of geometric techniques, which also imply that Chern insulators cannot display exponentially localized Wannier functions. Finally, a new algorithm to explicitly construct the exponentially localized Wannier functions is exhibited.  
John E. Pask (Lawrence Livermore National Laboratory)  Partitionofunity finiteelement approach for large, accurate ab initio electronic structure calculations 
Abstract: Principle Collaborator: Natarajan Sukumar (University of California, Davis) Over the past few decades, the planewave (PW) pseudopotential method has established itself as the dominant method for large, accurate, densityfunctional calculations in condensed matter. However, due to its global Fourier basis, the PW method suffers from substantial inefficiencies in parallelization and applications involving highly localized states, such as those involving 1strow or transitionmetal atoms, or other atoms at extreme conditions. Modern realspace approaches, such as finitedifference (FD) and finiteelement (FE) methods, can address these deficiencies without sacrificing rigorous, systematic improvability but have until now required much larger bases to attain the required accuracy. Here, we present a new realspace FE based method which employs modern partitionofunity FE techniques to substantially reduce the number of basis functions required, by building known atomic physics into the Hilbert space basis, without sacrificing locality or systematic improvability. We discuss pseudopotential as well as allelectron applications. Initial results show orderofmagnitude improvements relative to current stateoftheart PW and adaptivemesh FE methods for systems involving localized states such as d and felectron metals and/or other atoms at extreme conditions. This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DEAC5207NA27344.  
Irina Rish (IBM)  A supervised dimensionality reduction framework for exponentialfamily variables (poster) 
Abstract: Dimensionality reduction (DR) is a popular dataprocessing technique that serves the following two main purposes: it helps to provide a meaningful interpretation and visualization of the data, and it also helps to prevent overfitting when the number of dimensions greatly exceeds the number of samples, thus working as a form of regularization. However, dimensionality reduction should be driven by a particular data analysis goal. When our goal is prediction rather than an (unsupervised) exploratory data analysis, supervised dimensionality reduction (SDR) that combines DR with learning a predictor can significantly outperform a simple combination of unsupervised DR with a subsequent learning of a predictor on the resulting lowdimensional representation. The problem of supervised dimensionality reduction can be also viewed as finding a predictive lowdimensional representation, which captures the information about the class label contained in the highdimensional feature vector while ignoring the highdimenional noise. We propose a general SDR framework that views both features and class labels as exponentialfamily random variables (PCAlike dimensionality reduction is included as a particular case of Gaussian data). We learn data and classappropriate generalized linear models (GLMs), thus handling both classification and regression, with both discrete and realvalued data. Our SDRGLM optimization problem can be viewed as discriminative learning based on minimization of conditional probability of class given thehidden variables, while using as a regularizer the conditional probability of the features given the lowdimensional (hiddenvariable) representation. The main advantage of our approach, besides being more general, is using simple closedform update rules when performing its alternate minimization procedure. This method yields a short Matlab code, fast performance, and is guaranteed to converge. The convergence property, as well as closed form update rules, result from using appropriate auxiliary functions bounding each part of the objective function (i.e., reconstruction and prediction losses). We exploit the additive property of auxiliary functions in order to combine bounds on multiple loss functions. Promising empirical results are demonstrated on a variety of highdimensional datasets. Particularly, our results on simulated datasets convincingly demonstrate that SDRGLM approach can discover underlying lowdimensional structure in highdimensional noisy data, while outperforming SVM and SVDM, often by far, and practically always beating the unsupervised DR followed by learning a predictor. On reallife datasets, SDR approaches outperfrom the unsupervised DR by far, while matching or sometimes outperforming SVM and SVDM.  
Berkant Savas (Linköping University)  Tensor approximation  structure and methods (poster) 
Abstract: We consider the tensor approximation problem. Given a tensor we want to approximate it by another tensor of lower multilinear rank. It turns out this problem is defined on a product of Grassmann manifolds. We describe the structure of various parts the approximation problem and present convergence plots for Newton and quasiNewton methods (with BFGS and Limited memory BFGS updates) that solve the problem. All algorithms are applicable to both general and symmetric tensors and incorporate the Grassmann manifold structure of the problem. The benefits of these algorithms compared to traditional alternating least squares approaches are higher convergence rates and the existence of rigorous theory establishing convergence to a stationary point. This is joint work with Lars Eldén (Linköping University) and LekHeng Lim (University of California, Berkeley).  
Roman Schubert (University of Bristol)  Waves and mixing 
Abstract: Part I: Wave equations have the property that in the limit of short wavelength the propagation of waves is driven by an underlying dynamical system. Two standard examples are quantum mechanics, which in the semiclassical limit is governed by classical mechanics, and the theory of light, which for short wavelength is accurately described by the rays of geometric optics. A natural question is how the properties of the underlying dynamical system are reflected in the propagation of waves and in the possible wave patterns that can emerge. In this talk we will focus on the case that the dynamical system is chaotic, in particular mixing, and discuss the classical conjectures and some rigorous results on the consequences for wave propagation and the behavior of eigenfunctions.  
Roman Schubert (University of Bristol)  Waves and mixing (part II) 
Abstract: Part II: In the second part we will focus on the major open problem in the field and the current approaches to deal with it. In many applications one is interested in wave propagation for small wavelength and large times and this poses serious problems. Currently we understand the theory only for times up to the Ehrenfest time, which is related to the Liapunov exponents of the underlying dynamical system, and which is unfortunately rather short. We will discuss the Ehrenfest time and its relation to a number of important open problems, and then present a recent approach to explore larger times.  
Ridgway Scott (University of Chicago)  The Mathematical basis for molecular van der Waals forces 
Abstract: We show how van der Waals forces can be explained based on induced polarization of molecules. We derive an exact expression for the limiting behavior in the case of two induced dipoles that is faster than the usual LennardJones potential.  
Yoel Shkolnisky (Yale University), Amit Singer (Princeton University)  Structure determination of proteins using cryoelectron microscopy (poster) 
Abstract: The goal in CryoEM structure determination is to reconstruct 3D macromolecular structures from their noisy projections taken at unknown random orientations by an electron microscope. Resolving the CryoEM problem is of great scientific importance, as the method is applicable to essentially all macromolecules, as opposed to other existing methods such as crystallography. Since almost all large proteins have not yet been crystallized for 3D Xray crystallography, CryoEM seems the most promising alternative, once its associated mathematical challenges are solved. We present an extremely efficient and robust solver for the CryoEM problem that successfully recovers the projection angles in a globally consistent manner. The simple algorithm combines ideas and principles from spectral graph theory, nonlinear dimensionality reduction, geometry and computed tomography. The heart of the algorithm is a unique construction of a sparse graph followed by a fast computation of its eigenvectors. Joint work with Ronald Coifman and Fred Sigworth.  
Yoel Shkolnisky (Yale University), Amit Singer (Princeton University)  High order consistency relations for classification and denoising of CryoEM images (poster) 
Abstract: In order for biologists to exploit the full potential embodied in the CryoEM method, two major challenges must be overcome. The first challenge is the extremely low signaltonoise ratio of the projection images. Improving the signaltonoise by averaging sameview projections is an essential preprocessing step for all algorithms. The second challenge is the heterogeneity problem, in which the observed projections belong to two or more different molecules or different conformations. Traditional methods assume identical particles, therefore failing to distinguish the different particle species. This results in an inaccurate reconstruction of the molecular structure. For the first problem, we present two different high order consistency relations between triplets of images. The inclusion of such high order information leads to improvement in the classification and the denoising of the noisy images compared to existing methods that use only pairwise similarities. We also present a generalization of Laplacian eigenmaps to utilize such higher order affinities in a data set. This generalization is different from current tensor decomposition methods. For the second challenge, we describe a spectral method to establish two or more ab initio reconstructions from a single set of images. Joint work with Ronald Coifman and Fred Sigworth.  
Arthur Szlam (University of California, Los Angeles)  kplanes for classification (poster) 
Abstract: The kplanes method is the generalization of kmeans where the representatives of each cluster are affine linear sets. We will describe some possible modifications of this method for discriminative learning problems.  
Donald G. Truhlar (University of Minnesota)  New density functionals with broad applicability for thermochemistry, thermochemical kinetics, noncovalent interactions, transition metals, and spectroscopy 
Abstract: This lecture reports on work carried out in collaboration with
Yan Zhao.
We have developed a suite of density functionals. All four
functionals are accurate for noncovalent interactions and
mediumrange correlation energy. The functional with broadest
capability, M06, is uniquely well suited for good performance
on both transitionmetal and main groupchemistry; it also
gives good results for barrier heights. Another functional,
M06L has no HartreeFock exchange, which allows for very fast
calculations on large systems, and it is especially good for
transitionmetal chemistry and NMR chemical shieldings. M082X
and an earlier version, M062X, have the very best performance
for maingroup thermochemistry, barrier heights, and
noncovalent interactions. M06HF has no oneelectron
selfinteraction error and is the best functional for charge
transfer spectroscopy. A general characteristic of the whole
suite is the optimized inclusion of kinetic energy density and
higher separate accuracy of mediumrange exchange and
correlation contributions with less cancellation of errors than
previous functionals [14]; for example, the functionals are
compatible with a range of HartreeFock exchange and, although
one or another of them may be more highly recommended for one
or another property or application, all four are better on
average than the very popular B3LYP functional. A few sample
applications, including catalytic systems [5,6] and
nanomaterials [7], will also be discussed. Recent work on
lattice constants, band gaps, and an improved version of M062X
will be summarized if time permits.
[1] "Design of Density Functionals by Combining the Method of
Constraint Satisfaction with Parametrization for
Thermochemistry, Thermochemical Kinetics, and Noncovalent
Interactions," Zhao, Y. ; Schultz, N. E.; Truhlar, D. G.; J.
Chem. Theory Comput. 2006, 2, 364382.
[2] "A New Local Density Functional for Main Group Thermochemistry, Transition Metal Bonding, Thermochemical Kinetics, and Noncovalent Interactions," Zhao, Y.; Truhlar, D. G. J. Chem. Phys. 2006, 125, 194101/118. [3] “The M06 Suite of Density Functionals for Main Group Thermochemistry, Thermochemical Kinetics, Noncovalent Interactions, Excited States, and Transition Elements: Two New Functionals and Systematic Testing of Four M06Class Functionals and 12 Other Functionals,” Zhao, Y.; Truhlar, D. G. Theor. Chem. Acc. 2008, 120, 215241. [4] "Density Functionals with Broad Applicability in Chemistry," Zhao, Y.; Truhlar, D. G. Acc. Chem. Res. 2008 41, 157167. [5] “Attractive Noncovalent Interactions in Grubbs SecondGeneration Ru Catalysts for Olefin Metathesis," Zhao, Y.; Truhlar, D. G. Org. Lett. 2007, 9, 19671970. [6] "Benchmark Data for Interactions in Zeolite Model Complexes and Their Use for Assessment and Validation of Electronic Structure Methods," Zhao, Y.; Truhlar, D. G. J. Phys. Chem. C 2008, 112, 68606868. [7] "SizeSelective Supramolecular Chemistry in a Hydrocarbon Nanoring," Zhao, Y.; Truhlar, D. G. J. Am. Chem. Soc.2007, 129, 84408442. 

Steven M. Valone (Los Alamos National Laboratory)  A view of outstanding problems in density functional theory 
Abstract: Constrainedsearch density functional theory (DFT) pioneered by Levy [1] poses the problem of the theory as one of searching over subsets of Hilbert space. As such it provides a hypothetical means of constructing densitybased energy functionals for use in electronic structure applications. I will illustrate the constrainedsearch form with simple examples [2]. Early results on continuity of the energy functional [3] and the advent of "opensystem" DFT [4] will be reviewed. The construction of energy functionals will discussed in the context of the ColleSalvetti functionals [5] that played a subtle, but important, role in the 1998 Nobel Prize in Chemistry [6]. Alternative constructions based on constrainedsearch DFT will be discussed. Finally topics pertaining to excitations in homogeneous electron gases and from the introduction of other constraints to DFT calculations [7,8] will be entertained. [1] M Levy, Proc Natl Acad Sci USA 76, 6062 (1979). [2] SM Valone, "Vignette on ConstrainedSearch Density Functional Theory," private communication, August (2008). [3] SM Valone, J Chem Phys 73, 1344 (1980). [4] JP Perdew, RG Parr, M Levy, and JL Balduz, Jr, Phys Rev Lett 49, 1691 (1982). [5] R Colle and O Salvetti, Theoret Chim Acta (Berl) 37, 329 (1975). [6] C Lee, W Yang, and RG Parr, Phys Rev B 37, 785 (1988). [7] XY Pan, V Sahni, and L Massa, Phys Rev Lett 93, 130401 (2004). [8] Q Wu and T van Voorhis, Phys Rev A 72, 024502 (2005); J Behler, B Delley, K Reuter, and M Scheffler, Phys Rev B 75, 115409 (2007).  
M. Alex O. Vasilescu (SUNY)  Multilinear (tensor) manifold data modeling 
Abstract: Most observable data such as images, videos, human motion capture
data, and speech are the result of multiple factors (hidden
variables) that are not directly measurable, but which are of
interest in data analysis. In the context of computer vision and
graphics, we deal with natural images, which are the consequence
of multiple factors related to scene structure, illumination, and
imaging. Multilinear algebra offers a potent mathematical
framework for extracting and explicitly representing the
multifactor structure of image datasets.
I will present two multilinear models for learning (nonlinear)
manifold representations of image ensembles in which the multiple
constituent factors (or modes) are disentangled and analyzed
explicitly. Our nonlinear models are computed via a tensor
decomposition, known as the Mmode SVD, which is an extension to
tensors of the conventional matrix singular value
decomposition (SVD), or through a generalization of
conventional (linear) ICA called Multilinear Independent
Components Analysis (MICA).
I will demonstrate the potency of our novel statistical learning
approach in the context of facial image biometrics, where the
relevant factors include different facial geometries,
expressions, lighting conditions, and viewpoints. When applied to
the difficult problem of automated face recognition, our
multilinear representations, called TensorFaces (Mmode PCA) and
Independent TensorFaces (MICA), yields significantly improved
recognition rates relative to the standard PCA and ICA
approaches. Recognition is achieved with a novel Multilinear
Projection Operator.
Bio: M. Alex O. Vasilescu is an Assistant Professor of Computer Science at Stony Brook University (SUNY). She received her education at MIT and the University of Toronto. She was a research scientist at the MIT Media Lab from 200507 and at New York University's Courant Insitute from 200105. She has also done research at IBM, Intel, Compaq, and Schlumberger corporations, and at the MIT Artificial Intelligence Lab. She has published papers in computer vision and computer graphics, particularly in the areas of face recognition, human motion analysis/synthesis, imagebased rendering, and physicsbased modeling (deformable models). She has given several invited talks about her work and has several patents pending. Her face recognition research, known as TensorFaces, has been funded by the TSWG, the Department of Defense's Combating Terrorism Support Program. She was named by MIT's Technology Review Magazine to their 2003 TR100 List of Top 100 Young Innovators. http://www.cs.sunysb.edu/~maov 

René Vidal (Johns Hopkins University)  Clustering linear and nonlinear manifolds 
Abstract: Over the past few years, various techniques have been developed for learning a lowdimensional representation of data lying in a nonlinear manifold embedded in a highdimensional space. Unfortunately, most of these techniques are limited to the analysis of a single submanifold of a Euclidean space and suffer from degeneracies when applied to linear manifolds (subspaces). The simultaneous segmentation and estimation of a collection of submanifolds from sample data points is a challenging problem that is often thought of as "chickenandegg". Therefore, this problem is usually solved in two stages (1) data clustering and (2) model fitting, or else iteratively using, e.g. the Expectation Maximization (EM) algorithm. The first part of this talk will show that for a wide class of segmentation problems (mixtures of subspaces, mixtures of fundamental matrices/trifocal tensors, mixtures of linear dynamical models), the "chickenandegg" dilemma can be tackled using an algebraic geometric technique called Generalized Principal Component Analysis (GPCA). In fact, it is possible to eliminate the data segmentation step algebraically and then use all the data to recover all the models without first segmenting the data. The solution can be obtained using linear algebraic techniques, and is a natural extension of classical PCA from one to multiple subspaces. The second part of this talk will present a novel algorithm for clustering data sampled from multiple submanifolds of a Riemannian manifold, e.g. the space of probability density functions. The algorithm, called Locally Linear Manifold Clustering (LLMC) is based on clustering a lowdimensional representation of the data learned using generalizations of local nonlinear dimensionality reduction algorithms from Euclidean to Riemannian spaces. The talk will also present a few motivating applications of manifold clustering to computer vision problems such as texture clustering, segmentation of rigid body motions, segmentation of dynamic textures, segmentation of diffusion MRI.  
Michael Wakin (Colorado School of Mines)  Manifold models for single and multisignal recovery (poster) 
Abstract: The emerging theory of Compressive Sensing states that a signal obeying a sparse model can be reconstructed from a small number of random linear measurements. In this poster, we will explore manifoldbased models as a generalization of sparse representations, and we will discuss the theory and applications for use of these models in single and multisignal recovery from compressive measurements.  
Brenton Walker (Laborartory For Telecommunications Sciences)  Using persistent homology to recover spatial information from encounter traces (poster) 
Abstract: In order to better understand human and animal mobility and its potential effcts on Mobile AdHoc networks and DelayTolerant Networks, many researchers have conducted experiments which collect encounter data. Most analyses of these data have focused on isolated statistical properties such as the distribution of node interencounter times and the degree distribution of the connectivity graph. On the other hand, new developments in computational topology, in particular persistent homology, have made it possible to compute topological invariants from noisy data. These homological methods provide a natural way to draw conclusions about global structure based on collections of local information. We use persistent homology techniques to show that in some cases encounter traces can be used to deduce information about the topology of the physical space the experiment was conducted in, and detect certain changes in the space. We also show that one can distinguish between simulated encounter traces generated on a bounded rectangular grid from traces generated on a grid with the opposite edges wrapped (a toroidal grid). Finally, we have found that nontrivial topological features also appear in real experimental encounter traces.  
John Wright (University of Illinois at UrbanaChampaign)  Mixed data segmentation via lossy data compression (poster) 
Abstract: We consider the problem of clustering mixed data drawn sampled from distributions of (possibly) varying intrinsic dimensionality, e.g., degenerate Gaussians or linear subspaces. We approach this problem from the perspective of lossy data compression, seeking a segmentation that minimizes the number of bits needed to code the data up to a prespecified distortion. The coding length is minimized via an agglomerative algorithm that merges the pair of groups such that the resulting coding length is minimal. This simple algorithm converges globally for a wide range of simulated examples. It also produces stateoftheart experimental results on applications in image segmentation from texture features and motion segmentation from tracked point features.  
Qiu Wu (University of Texas)  Orthantwise gradient projection method for sparse reconstruction (poster) 
Abstract: Many problems in signal processing and statistics involve in finding sparse solutions by solving the l_{1} regularized least square problem. The orthantwise method exploits that fact that the l_{1} term is a linear function over any given orthant. Hence, the objective function is differentiable orthantwise. We implement two variants of the orthantwise gradient projection method: one is based on steepestdescent search direction and the other one is based a quasiNewton search direction. Experimental results with compressive sampling problems demonstrate that the performance of our method compares favorably with that of alternative methods in literatures.  
Dexuan Xie (University of Wisconsin)  New efficient algorithms for a general blood tissue transportmetabolism model and stiff differential equations 
Abstract: Fast algorithms for simulating mathematical models of coupled bloodtissue transport and metabolism are critical for the analysis of data on transport and reaction in tissue. This talk will introduce a general blood tissue transportmetabolism model governed by a large system of onedimensional hyperbolic partial differential equations, and then present a new parallel algorithm for solving it. The key part of the new algorithm is to approximate the model as a group of independent ordinary differential equation (ODE) systems such that each ODE system has the same size as the model and can be integrated independently. The accuracy of the algorithm is demonstrated for solving a simple bloodtissue transport model with an analytical solution. Numerical experiments were made for a largescale coupled blood tissue transportmetabolism model on a distributedmemory parallel computer and a sharedmemory parallel computer, showing the high parallel efficiency of the algorithm. In the second part of this talk, a wellknown implicit RungeKutta algorithm called the Radau IIA method will be discussed, which is a favorite stiff ODE solver for the new parallel algorithm. The most time consuming part of the Radau IIA method is to solve a large scale nonlinear algebraic system of stage values. Currently, the widelyused nonlinear solver was still a simplified Newton method proposed by Liniger & Willoughby in 1970. In practice, it may suffer poor convergence problems, forcing the Radau IIA method to select too small step sizes in order to guarantee the convergence. To provide the Radau IIA method with a robust nonlinear solver, this talk will present a new simplified Newton algorithm and discuss its convergence and performance. Numerical results confirm that the new algorithm can have better convergence properties than the current one and can significantly improve the performance of the Radau IIA method.  
Allen Yang Yang (University of California, Berkeley)  Highdimensional multimodel estimation – its Algebra, statistics, and sparse representation (poster) 
Abstract: Recent advances in information technologies have led to unprecedented large amounts of highdimensional data from many emerging applications. The need for more advanced techniques to analyze such complex data calls for shifting research paradigms. In this presentation, I will overview and highlight several results in the area of estimation of mixture models in highdimensional data spaces. Applications will be presented in problems such as motion segmentation, image segmentation, face recognition, and human action categorization. Through this presentation, I intend to emphasize the confluence of algebra and statistics that may lead to more advanced solutions in analyzing complex singular data structures such as mixture linear subspaces and nonlinear manifolds.  
Chao Yang (Lawrence Berkeley National Laboratory)  A direct constrained minimization algorithm for solving the KohnSham equations 
Abstract: I will present a direct constrained minimization (DCM) algorithm for solving the KohnSham equations. The key ingredients of this algorithm involve projecting the KohnSham total energy functional into a sequences of subspaces of small dimensions and seeking the minimizer of total energy functional within each subspace. The minimizer of a subspace energy functional not only provides a search direction along which the KS total energy functional decreases but also gives an optimal ``steplength" to move along this search direction. I will provide some numerical examples to demonstrate the efficiency and accuracy of this approach and compare it with the widely used method of selfconsistent field (SCF) iteration. I will also discuss a few other numerical issues in algorithms designed to solve the KohnSham equations.  
Lihi ZelnikManor (TechnionIsrael Institute of Technology)  Approximate nearest subspace search with applications to pattern recognition (poster) 
Abstract: Linear and affine subspaces are commonly used to describe the appearance of objects under different lighting, viewpoint, articulation, and even identity. A natural problem arising from their use is  given a query image portion represented as a point in some high dimensional space  find a subspace near to the query. This talk presents an efficient solution to the approximate nearest subspace problem for both linear and affine subspaces. Our method is based on a simple reduction to the problem of nearest point search, and can thus employ treebased search or localitysensitive hashing to find a near subspace. Further performance improvement is achieved by using random projections to lower the dimensionality of the problem. We provide theoretical proofs of correctness and error bounds of our construction, and demonstrate its capabilities on synthetic and real data. Our experiments demonstrate that an approximate nearest subspace can be located significantly faster than the exact nearest subspace, while at the same time it can find better matches compared to a similar search on points, in the presence of variations due to viewpoint, lighting, and so forth.  
Xiaojin Zhu (University of Wisconsin)  Semisupervised learning by multimanifold separation 
Abstract: Semisupervised learning on a single manifold has been the subject of intense study. We consider the setting of multiple manifolds, in which it is assumed that the target function is smooth within each manifold, yet the manifolds can intersect and partly overlap. We discuss our recent work to separate these manifolds from unlabeled data, and perform a 'mild' form of semisupervised learning which is hopefully robust to the model assumption. 
Rigoberto Advincula  University of Houston  10/31/2008  11/2/2008 
Iman Aganj  University of Minnesota  10/27/2008  10/30/2008 
Alina Alexeenko  Purdue University  10/31/2008  11/2/2008 
Wesley D. Allen  University of Georgia  9/28/2008  10/1/2008 
Bradley K. Alpert  National Institute of Standards and Technology  10/26/2008  10/30/2008 
Arnaud Natesh Anantharaman  Ecole Nationale des Ponts et Chaussees  10/6/2008  10/12/2008 
Ery AriasCastro  University of California, San Diego  10/26/2008  10/31/2008 
Donald G. Aronson  University of Minnesota  9/1/2002  8/31/2009 
Donald G. Aronson  University of Minnesota  10/4/2008  10/4/2008 
Alán AspuruGuzik  Harvard University  10/2/2008  10/3/2008 
Alán AspuruGuzik  Harvard University  10/31/2008  11/2/2008 
Gregory L Baker  Michigan State University  10/31/2008  11/2/2008 
Amartya Sankar Banerjee  University of Minnesota  9/26/2008  10/3/2008 
Gang Bao  Michigan State University  10/31/2008  11/2/2008 
Leah Bar  University of Minnesota  10/27/2008  10/30/2008 
Richard G. Baraniuk  Rice University  10/26/2008  10/27/2008 
Rodney J. Bartlett  University of Florida  9/28/2008  10/1/2008 
Axel D. Becke  Dalhousie University  9/28/2008  10/3/2008 
Mikhail Belkin  Ohio State University  10/26/2008  10/30/2008 
Peter Binev  University of South Carolina  10/25/2008  10/30/2008 
Francisco BlancoSilva  University of South Carolina  10/26/2008  10/30/2008 
Edward Howard Bosch  National Geospatial Intelligence Agency  10/26/2008  10/31/2008 
Khalid Boushaba  Iowa State University  10/3/2008  10/5/2008 
Bastiaan J. Braams  Emory University  9/28/2008  11/8/2008 
James Joseph Brannick  Pennsylvania State University  10/30/2008  11/2/2008 
Michael P. Brenner  Harvard University  10/11/2008  10/13/2008 
Maila Brucal  University of Kansas  10/3/2008  10/5/2008 
Peter Brune  University of Chicago  9/8/2008  6/30/2009 
Felipe Alfonso Bulat  Duke University  9/28/2008  10/4/2008 
Kieron J. Burke  University of California, Irvine  9/29/2008  10/2/2008 
Thomas H. Burns  Starkey Laboratories  10/17/2008  10/17/2008 
SunSig Byun  University of Iowa  9/26/2008  10/4/2008 
Wei Cai  University of North Carolina  Charlotte  10/31/2008  11/2/2008 
MariaCarme T. Calderer  University of Minnesota  9/1/2008  6/30/2009 
Hannah Callender  University of Minnesota  9/1/2007  8/31/2009 
Eric Cances  CERMICS  9/1/2008  12/23/2008 
Steve Cantrell  University of Miami  10/3/2008  10/5/2008 
Gunnar Carlsson  Stanford University  10/28/2008  10/30/2008 
Pete George Casazza  University of Missouri  10/26/2008  10/31/2008 
William Austin Casey  Pacific Northwest National Laboratory  10/26/2008  10/30/2008 
Isabelle Catto  Université de Paris IX (ParisDauphine)  9/26/2008  10/3/2008 
Alessandro Cembran  University of Minnesota  9/26/2008  10/3/2008 
Arindam Chakraborty  Pennsylvania State University  9/28/2008  10/3/2008 
Frédéric Chazal  INRIA Saclay  ÎledeFrance  10/25/2008  10/30/2008 
Guangliang Chen  University of Minnesota  10/27/2008  10/30/2008 
Xianjin Chen  University of Minnesota  9/1/2008  8/31/2010 
Xianjin Chen  University of Minnesota  10/4/2008  10/4/2008 
Daniel M. Chipman  University of Notre Dame  9/14/2008  12/13/2008 
Hi Jun Choe  University of Iowa  9/28/2008  10/4/2008 
Matteo Cococcioni  University of Minnesota  9/29/2008  10/3/2008 
Aron J. Cohen  Duke University  9/28/2008  10/3/2008 
Chris Cosner  University of Miami College of Arts and Sciences  10/3/2008  10/5/2008 
Ludovica Cecilia CottaRamusino  University of Minnesota  10/1/2007  8/30/2009 
Nathan R. M. Crawford  University of California, Irvine  9/27/2008  10/4/2008 
Wolfgang Dahmen  RWTH Aachen  10/26/2008  10/29/2008 
Steven Benjamin Damelin  Georgia Southern University  10/26/2008  10/30/2008 
Ingrid Daubechies  Princeton University  10/29/2008  10/30/2008 
James W. Davenport  Brookhaven National Laboratory  9/29/2008  10/3/2008 
Mark Andrew Davenport  Rice University  10/26/2008  10/30/2008 
Ajitha Devarajan  Iowa State University  9/28/2008  10/3/2008 
Ronald DeVore  Texas A & M University  10/26/2008  10/29/2008 
Kadir Diri  University of Southern California  9/28/2008  10/3/2008 
David C. Dobson  University of Utah  10/31/2008  11/2/2008 
Charles Doering  University of Michigan  10/11/2008  10/13/2008 
Dan Dougherty  North Carolina State University  10/31/2008  11/2/2008 
Qiang Du  Pennsylvania State University  10/31/2008  11/5/2008 
Julio Duarte  Eastman Kodak Company  10/27/2008  10/30/2008 
Marco F. Duarte  Rice University  10/26/2008  10/30/2008 
Olivier Dubois  University of Minnesota  9/3/2007  8/31/2009 
Olivier Dubois  University of Minnesota  10/4/2008  10/4/2008 
Phillip Duxbury  Michigan State University  10/31/2008  11/2/2008 
Weinan E  Princeton University  9/28/2008  10/2/2008 
Weinan E  Princeton University  10/31/2008  11/6/2008 
Lars Eldén  Linköping University  10/26/2008  10/30/2008 
Ehsan Elhamifar  Johns Hopkins University  10/26/2008  10/30/2008 
Ahmel ElMawas  University of Minnesota  10/27/2008  10/30/2008 
Maria Esteban  Université de Paris IX (ParisDauphine)  9/27/2008  11/15/2008 
Kai Fan  North Carolina State University  9/25/2008  10/4/2008 
Charles L. Fefferman  Princeton University  10/26/2008  10/29/2008 
Jay P. Fillmore  University of San Diego  10/4/2008  10/4/2008 
Heather Lyn Finotti  University of Tennessee  10/31/2008  11/2/2008 
Daniel Flath  Macalester College  8/27/2008  12/20/2008 
Andrea Floris  Freie Universität Berlin  9/28/2008  10/3/2008 
Massimo Fornasier  Johann Radon Institute for Computational and Applied Mathematics  10/26/2008  10/30/2008 
Roger Fosdick  University of Minnesota  10/4/2008  10/4/2008 
Stephen Foster  Mississippi State University  10/31/2008  11/2/2008 
Christopher Fraser  University of Chicago  8/27/2008  6/30/2009 
Christopher Ray Fredregill  University of Minnesota  10/4/2008  10/4/2008 
Mituhiro Fukuda  Tokyo Institute of Technology  9/25/2008  10/4/2008 
Stephen Fulling  Texas A & M University  10/1/2008  10/30/2008 
Alexander Gaenko  Iowa State University  9/28/2008  10/3/2008 
Irene M. Gamba  University of Texas  10/31/2008  11/2/2008 
Weiguo Gao  Fudan University  9/27/2008  12/13/2008 
Carlos J. GarciaCervera  University of California, Santa Barbara  9/2/2008  12/12/2008 
Caroline GattiBono  Lawrence Livermore National Laboratory  10/2/2008  10/4/2008 
Anna Gilbert  University of Michigan  10/11/2008  10/13/2008 
Peter M.W. Gill  Australian National University  9/28/2008  10/3/2008 
Benjamin David Goddard  University of Warwick  9/29/2008  10/10/2008 
Alvina Goh  Johns Hopkins University  10/26/2008  10/30/2008 
Guillermo Hugo Goldsztein  Georgia Institute of Technology  10/31/2008  11/2/2008 
Jay Gopalakrishnan  University of Florida  9/1/2008  2/28/2009 
Jay Gopalakrishnan  University of Florida  10/4/2008  10/4/2008 
Andreas Görling  FriedrichAlexanderUniversität ErlangenNürnberg  9/28/2008  10/3/2008 
John Greer  National Geospatial Intelligence Agency  10/26/2008  10/30/2008 
Bella Grigorenko  M.V. Lomonosov Moscow State University  9/28/2008  10/3/2008 
Eberhard K. U. Gross  Freie Universität Berlin  9/28/2008  10/3/2008 
Yujin Guo  University of Minnesota  10/4/2008  10/4/2008 
François Gygi  University of California, Davis  9/30/2008  10/3/2008 
George A. Hagedorn  Virginia Polytechnic Institute and State University  9/28/2008  10/3/2008 
Gloria Haro Ortega  Universitat Politecnica de Catalunya  10/25/2008  11/1/2008 
Theodore Hatcher  Andrews University  10/3/2008  10/5/2008 
Timothy F. Havel  Massachusetts Institute of Technology  9/28/2008  10/3/2008 
Timothy F. Havel  Massachusetts Institute of Technology  10/31/2008  12/12/2008 
Martin HeadGordon  University of California, Berkeley  9/29/2008  10/3/2008 
Chinmay Hegde  Rice University  10/26/2008  10/31/2008 
Michael E. Henderson  IBM  10/26/2008  10/31/2008 
William Henry  Mississippi State University  10/31/2008  11/2/2008 
Mark S. Herman  University of Minnesota  9/1/2008  8/31/2010 
Lotfi Hermi  University of Arizona  10/3/2008  10/5/2008 
Gaston Hernandez  University of Connecticut  10/3/2008  10/5/2008 
Jan S. Hesthaven  Brown University  10/31/2008  11/2/2008 
Masahiro Higashi  University of Minnesota  9/26/2008  10/3/2008 
Peter Hinow  University of Minnesota  9/1/2007  8/31/2009 
Peter Hinow  University of Minnesota  10/4/2008  10/4/2008 
Mark R. Hoffmann  University of North Dakota  9/28/2008  10/3/2008 
Mary Ann Horn  Vanderbilt University  10/12/2008  10/14/2008 
Dirk Hundertmark  University of Illinois at UrbanaChampaign  9/28/2008  10/10/2008 
Yunkyong Hyon  University of Minnesota  9/1/2008  8/31/2010 
Yunkyong Hyon  University of Minnesota  10/4/2008  10/4/2008 
Olexandr Isayev  Jackson State University  9/28/2008  10/4/2008 
Mark Iwen  University of Minnesota  9/1/2008  8/31/2010 
Alexander Izzo  Bowling Green State University  9/1/2008  6/30/2009 
Naresh Jain  University of Minnesota  10/4/2008  10/4/2008 
Tony Jebara  Columbia University  10/27/2008  10/30/2008 
Samson A. Jenekhe  University of Washington  10/31/2008  11/2/2008 
Srividhya Jeyaraman  University of Minnesota  10/4/2008  10/4/2008 
Srividhya Jeyaraman  University of Minnesota  9/1/2008  8/31/2010 
Lijian Jiang  University of Minnesota  9/1/2008  8/31/2010 
Shi Jin  University of Wisconsin  10/31/2008  11/1/2008 
Max A. Jodeit  University of Minnesota  10/3/2008  10/4/2008 
Erin R. Johnson  Duke University  9/28/2008  10/3/2008 
Daniel D. Joseph  University of Minnesota  10/4/2008  10/4/2008 
Ajay Joshi  University of Minnesota  10/27/2008  10/30/2008 
Markos A. Katsoulakis  University of Massachusetts  10/31/2008  11/2/2008 
Markus Keel  University of Minnesota  7/21/2008  6/30/2009 
John Kemper  University of St. Thomas  10/4/2008  10/4/2008 
Harvey B Keynes  University of Minnesota  10/4/2008  10/4/2008 
Yongho Kim  University of Minnesota  9/26/2008  10/3/2008 
Rollin A. King  Bethel University  9/29/2008  10/3/2008 
Robert V. Kohn  New York University  10/11/2008  10/13/2008 
Robert V. Kohn  New York University  10/31/2008  11/13/2008 
Tamara G. Kolda  Sandia National Laboratories  10/26/2008  10/30/2008 
Mario Koppen  TU München  9/28/2008  10/3/2008 
Karol Kowalski  Pacific Northwest National Laboratory  9/28/2008  10/3/2008 
Aliaksandr Krukau  Rice University  9/28/2008  10/3/2008 
Anna Krylov  University of Southern California  9/25/2008  12/25/2008 
Dan Kushnir  Yale University  10/26/2008  10/30/2008 
Diane Lambert  Google Inc.  10/11/2008  10/13/2008 
Arie Landau  University of Southern California  10/12/2008  10/28/2008 
David Langreth  Rutgers University  9/29/2008  10/2/2008 
Triet Minh Le  Yale University  10/26/2008  10/30/2008 
Claude Le Bris  CERMICS  9/11/2008  5/30/2009 
Federico Lecumberry  University of the Republic  10/27/2008  10/30/2008 
ChiunChang Lee  National Taiwan University  10/4/2008  10/4/2008 
ChiunChang Lee  National Taiwan University  8/26/2008  7/31/2009 
Hijin Lee  Korea Advanced Institute of Science and Technology (KAIST)  10/4/2008  10/4/2008 
Hijin Lee  Korea Advanced Institute of Science and Technology (KAIST)  9/29/2008  10/3/2008 
Hijin Lee  Korea Advanced Institute of Science and Technology (KAIST)  10/27/2008  10/30/2008 
Long Lee  University of Wyoming  10/31/2008  11/2/2008 
Gilad Lerman  University of Minnesota  10/26/2008  10/30/2008 
Howard A. Levine  Iowa State University  10/3/2008  10/5/2008 
Stacey E. Levine  Duquesne University  10/25/2008  10/31/2008 
Melvyn P. Levy  Duke University  9/28/2008  10/4/2008 
Mathieu Lewin  Université de CergyPontoise  9/26/2008  10/25/2008 
Mark Lewis  University of Alberta  10/3/2008  10/4/2008 
Bingtuan Li  University of Louisville  10/3/2008  10/5/2008 
Jichun Li  University of Nevada  10/31/2008  11/2/2008 
Tianjiang Li  Pennsylvania State University  10/26/2008  10/30/2008 
Yongfeng Li  University of Minnesota  9/1/2008  8/31/2010 
Yongfeng Li  University of Minnesota  10/4/2008  10/4/2008 
Hstau Y Liao  Columbia University  10/26/2008  10/30/2008 
LekHeng Lim  University of California, Berkeley  10/26/2008  10/30/2008 
Florence J. Lin  University of Southern California  9/30/2008  10/2/2008 
TaiChia Lin  National Taiwan University  8/23/2008  7/31/2009 
Roland Lindh  Lund University  9/28/2008  10/3/2008 
Walter Littman  University of Minnesota  10/4/2008  10/4/2008 
Chun Liu  University of Minnesota  9/1/2008  8/31/2010 
Chun Liu  University of Minnesota  10/4/2008  10/4/2008 
Di Liu  Michigan State University  10/31/2008  11/2/2008 
Kevin Long  Sandia National Laboratories  10/31/2008  11/2/2008 
Carlos Silva Lopez  University of Minnesota  9/26/2008  10/3/2008 
Gang Lu  California State University  9/28/2008  10/4/2008 
Gang Lu  California State University  10/31/2008  11/2/2008 
Jianfeng Lu  Princeton University  9/25/2008  10/4/2008 
Roger Lui  Worcester Polytechnic Institute  10/3/2008  10/5/2008 
Russell Luke  University of Delaware  9/28/2008  10/3/2008 
Mitchell Luskin  University of Minnesota  9/1/2008  6/30/2009 
Yi Ma  University of Illinois at UrbanaChampaign  10/26/2008  10/30/2008 
Taylor Joseph Mach  Bethel University  9/29/2008  10/3/2008 
Mauro Maggioni  Duke University  10/26/2008  10/30/2008 
Julien Mairal  INRIA  10/26/2008  11/2/2008 
Albert Marden  University of Minnesota  10/1/2008  10/1/2008 
Alex Marker  Schott North America, Inc.  10/31/2008  11/2/2008 
Laurence D. Marks  Northwestern University  9/28/2008  10/3/2008 
Vasileios Maroulas  University of Minnesota  9/1/2008  8/31/2010 
José Mario Martínez  State University of Campinas (UNICAMP)  9/28/2008  10/3/2008 
Hiroshi Matano  University of Tokyo  10/4/2008  10/4/2008 
James McCusker  Michigan State University  10/31/2008  11/2/2008 
Juan C. Meza  Lawrence Berkeley National Laboratory  9/25/2008  10/4/2008 
Steven L. Mielke  University of Minnesota  9/26/2008  10/3/2008 
Willard Miller Jr.  University of Minnesota  10/1/2008  10/1/2008 
Willard Miller Jr.  University of Minnesota  10/27/2008  10/30/2008 
Washington Mio  Florida State University  10/26/2008  10/30/2008 
Peter Monk  University of Delaware  10/31/2008  11/1/2008 
Yoichiro Mori  University of Minnesota  10/4/2008  10/4/2008 
Paula MoriSánchez  Duke University  9/28/2008  10/3/2008 
Dmitriy Morozov  Duke University  10/26/2008  10/30/2008 
Zuhair Nashed  University of Central Florida  10/31/2008  11/2/2008 
Ramesh Natarajan  IBM Research Division  10/26/2008  10/30/2008 
Junalyn NavarraMadsen  Texas Woman's University  9/25/2008  10/3/2008 
Alexander V. Nemukhin  Moscow State University  9/25/2008  10/3/2008 
Tristan Nguyen  Office of Naval Research  10/26/2008  10/30/2008 
Olalla Nieto Faza  University of Minnesota  9/26/2008  10/3/2008 
Yasunori Nishimori  National Institute of Advanced Industrial Science and Technology  10/26/2008  10/30/2008 
Partha Niyogi  University of Chicago  10/26/2008  10/30/2008 
Arthur J. Nozik  Department of Energy  10/31/2008  11/2/2008 
Duane Nykamp  University of Minnesota  10/4/2008  10/4/2008 
Andrew Odlyzko  University of Minnesota  10/4/2008  10/4/2008 
Peter J. Olver  University of Minnesota  10/4/2008  10/4/2008 
Peter J. Olver  University of Minnesota  10/12/2008  10/12/2008 
John E. Osborn  University of Maryland  10/3/2008  10/5/2008 
Biao Ou  University of Toledo  10/3/2008  10/5/2008 
MiaoJung Yvonne Ou  Oak Ridge National Laboratory  9/25/2008  10/3/2008 
Victor Padron  Normandale Community College  10/4/2008  10/4/2008 
Igor Pak  University of Minnesota  10/27/2008  10/30/2008 
Gianluca Panati  Università di Roma "La Sapienza"  9/24/2008  10/4/2008 
Stephen D Pankavich  Indiana University  10/31/2008  11/7/2008 
John E. Pask  Lawrence Livermore National Laboratory  9/30/2008  10/4/2008 
George Pau  Lawrence Berkeley National Laboratory  9/28/2008  10/3/2008 
Larry Payne  Cornell University  10/1/2008  10/5/2008 
John P. Perdew  Tulane University  9/26/2008  10/3/2008 
Arlie O. Petters  Duke University  10/11/2008  10/13/2008 
Peter Polacik  University of Minnesota  10/4/2008  10/4/2008 
Craig T. Poling  Lockheed Martin  10/11/2008  10/13/2008 
Matej Praprotnik  Max Planck Institute for Polymer Research  10/8/2008  11/8/2008 
Oleg Prezhdo  University of Washington  10/31/2008  11/2/2008 
Emil Prodan  Yeshiva University  9/28/2008  10/10/2008 
Emil Prodan  Yeshiva University  10/31/2008  11/2/2008 
Keith Promislow  Michigan State University  10/31/2008  11/6/2008 
Ignacio Ramirez  University of Minnesota  10/27/2008  10/28/2008 
Shankar Rao  University of Illinois at UrbanaChampaign  10/27/2008  10/28/2008 
Peter Rejto  University of Minnesota  10/4/2008  10/4/2008 
Donald Richards  Pennsylvania State University  10/11/2008  10/13/2008 
Christian Ringhofer  Arizona State University  10/31/2008  11/2/2008 
Irina Rish  IBM  10/26/2008  10/28/2008 
Marielba Rojas  Technical University of Denmark  9/28/2008  10/4/2008 
Adrienn Ruzsinszky  Tulane University  9/26/2008  10/3/2008 
Murti Sacapaka  University of Minnesota  10/27/2008  10/30/2008 
Paul E. Sacks  Iowa State University  10/3/2008  10/5/2008 
Fadil Santosa  University of Minnesota  7/1/2008  6/30/2010 
Fadil Santosa  University of Minnesota  10/4/2008  10/4/2008 
Guillermo R. Sapiro  University of Minnesota  10/26/2008  10/30/2008 
Duane Sather  University of Colorado  10/3/2008  10/5/2008 
Berkant Savas  Linköping University  10/26/2008  10/30/2008 
Andreas Savin  Université de Paris VI (Pierre et Marie Curie)  10/8/2008  11/7/2008 
Arnd Scheel  University of Minnesota  9/1/2008  6/30/2009 
Arnd Scheel  University of Minnesota  10/4/2008  10/4/2008 
Roman Schubert  University of Bristol  10/5/2008  11/8/2008 
Ridgway Scott  University of Chicago  9/1/2008  6/30/2009 
Gustavo E. Scuseria  Rice University  9/29/2008  10/1/2008 
George R Sell  University of Minnesota  10/4/2008  10/4/2008 
Tsvetanka Sendova  University of Minnesota  9/1/2008  8/31/2010 
Tsvetanka Sendova  University of Minnesota  9/1/2008  10/31/2008 
James Serrin  University of Minnesota  10/4/2008  10/4/2008 
Chehrzad Shakiban  University of Minnesota  10/4/2008  10/4/2008 
Chehrzad Shakiban  University of Minnesota  10/12/2008  10/12/2008 
Yuk Sham  University of Minnesota  9/1/2008  6/30/2009 
David H. Sharp  Los Alamos National Laboratory  10/10/2008  10/14/2008 
David C. Sherrill  Georgia Institute of Technology  9/29/2008  10/1/2008 
Stephen Shipman  Louisiana State University  10/31/2008  11/2/2008 
Yoel Shkolnisky  Yale University  10/26/2008  10/30/2008 
ChiWang Shu  Brown University  10/31/2008  11/2/2008 
Heinz Siedentop  LudwigMaximiliansUniversität München  9/22/2008  12/19/2008 
Amit Singer  Princeton University  10/26/2008  10/30/2008 
Ravishankar Sivalingam  University of Minnesota  10/27/2008  10/28/2008 
Lyudmila V. Slipchenko  Iowa State University  9/25/2008  10/2/2008 
Richard Souvenir  University of North Carolina  Charlotte  10/26/2008  10/30/2008 
Andrew M. Stein  University of Minnesota  9/1/2007  8/31/2009 
Andrew M. Stein  University of Minnesota  10/4/2008  10/4/2008 
Marvin Stein  University of Minnesota  10/4/2008  10/4/2008 
Panagiotis Stinis  University of Minnesota  10/27/2008  10/30/2008 
Gabriel Stoltz  École Nationale des PontsetChaussées (ENPC)  9/23/2008  10/2/2008 
Bernd Sturmfels  University of California, Berkeley  10/12/2008  10/13/2008 
Jianzhong Su  University of Texas  10/3/2008  10/5/2008 
Jianwei Sun  Tulane University  9/28/2008  10/4/2008 
Qiyu Sun  University of Central Florida  10/31/2008  11/2/2008 
Vladimir Sverak  University of Minnesota  10/11/2008  10/13/2008 
Arthur Szlam  University of California, Los Angeles  10/26/2008  10/30/2008 
Jianmin Tao  Los Alamos National Laboratory  9/28/2008  10/3/2008 
P. Craig Taylor  Colorado School of Mines  10/31/2008  11/1/2008 
William Toczyski  University of Minnesota  10/27/2008  10/30/2008 
Carl Toews  Duquesne University  10/26/2008  10/30/2008 
David J. Tozer  University of Durham  9/27/2008  10/4/2008 
Sergei Tretiak  Los Alamos National Laboratory  10/31/2008  11/1/2008 
Donald G. Truhlar  University of Minnesota  9/1/2008  6/30/2009 
Birkan Tunc  Istanbul Technical University  10/25/2008  11/1/2008 
Erkan Tüzel  University of Minnesota  9/1/2007  8/31/2009 
George Vacek  Hewlett Packard  9/28/2008  10/3/2008 
Rosendo Valero  University of Minnesota  9/26/2008  10/3/2008 
Steven M. Valone  Los Alamos National Laboratory  9/8/2008  11/30/2008 
Steven M. Valone  Los Alamos National Laboratory  10/4/2008  10/4/2008 
Mark van Schilfgaarde  Arizona State University  10/31/2008  11/2/2008 
M. Alex O. Vasilescu  SUNY  10/26/2008  10/30/2008 
René Vidal  Johns Hopkins University  10/26/2008  10/30/2008 
Oleg A. Vydrov  Massachusetts Institute of Technology  9/28/2008  10/4/2008 
Michael Wakin  Colorado School of Mines  10/26/2008  10/29/2008 
Brenton Walker  Laborartory For Telecommunications Sciences  10/26/2008  11/1/2008 
Homer Walker  Worcester Polytechnic Institute  9/28/2008  10/3/2008 
Hong Wang  University of South Carolina  10/31/2008  11/2/2008 
LinWang Wang  Lawrence Berkeley National Laboratory  10/31/2008  11/2/2008 
Qi Wang  University of South Carolina  10/31/2008  11/2/2008 
Yi Wang  University of Minnesota  10/27/2008  10/30/2008 
Zhian Wang  University of Minnesota  9/1/2007  8/31/2009 
Zhian Wang  University of Minnesota  10/4/2008  10/4/2008 
Henry A. Warchall  National Science Foundation  9/29/2008  10/1/2008 
Henry A. Warchall  National Science Foundation  10/31/2008  11/2/2008 
Hans Weinberger  University of Minnesota  10/4/2008  10/4/2008 
Jonathan Tyler Whitehouse  University of Minnesota  10/27/2008  10/30/2008 
Colin Wolden  Colorado School of Mines  10/31/2008  11/2/2008 
John Wright  University of Illinois at UrbanaChampaign  10/26/2008  10/30/2008 
Margaret H. Wright  New York University  10/11/2008  10/13/2008 
Qiu Wu  University of Texas  10/26/2008  10/31/2008 
Dexuan Xie  University of Wisconsin  9/4/2008  12/15/2008 
Wei Xiong  University of Minnesota  9/1/2008  8/31/2010 
Wei Xiong  University of Minnesota  10/4/2008  10/4/2008 
Zhenli Xu  University of North Carolina  Charlotte  9/25/2008  10/3/2008 
Jue Yan  Iowa State University  10/31/2008  11/1/2008 
Allen Yang Yang  University of California, Berkeley  10/26/2008  10/30/2008 
Chao Yang  Lawrence Berkeley National Laboratory  9/8/2008  11/8/2008 
Fei Yang  University of Minnesota  10/27/2008  10/30/2008 
Ke Yang  University of Minnesota  9/26/2008  10/3/2008 
Weitao Yang  Duke University  9/28/2008  10/1/2008 
Xingzhou Yang  Mississippi State University  10/31/2008  11/2/2008 
Ahmad S Yasamin  Indiana University  10/26/2008  10/30/2008 
Luping Yu  University of Chicago  10/31/2008  11/1/2008 
Ofer Zeitouni  University of Minnesota  10/1/2008  10/1/2008 
Lihi ZelnikManor  TechnionIsrael Institute of Technology  10/26/2008  10/30/2008 
Teng Zhang  University of Minnesota  10/27/2008  10/30/2008 
Weigang Zhong  University of Minnesota  9/1/2008  8/31/2010 
Xiaojin Zhu  University of Wisconsin  10/26/2008  10/30/2008 
Yu Zhuang  Texas Tech University  10/30/2008  11/2/2008 