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IMA Newsletter #417

July 2011

IMA Events

Invariant Objects in Dynamical Systems and their Applications

June 20 - July 1, 2011

Organizers: Peter W. Bates (Michigan State University), Rafael de la Llave (University of Texas)

IMA Workshop

Wavelets and Applications: Project Building Workshop

July 13-16, 2011

Organizers: Catherine Beneteau (University of South Florida), Caroline Haddad (College at Geneseo, SUNY), David Ruch (Metropolitan State College of Denver), Patrick Van Fleet (University of St. Thomas)

IMA Workshop

Wavelets and Applications: A Multi-Disciplinary Undergraduate Course with an Emphasis on Scienti fic Computing

July 13-16, 2011

Organizers: Catherine Beneteau (University of South Florida), Caroline Haddad (College at Geneseo, SUNY), David Ruch (Metropolitan State College of Denver), Patrick Van Fleet (University of St. Thomas)

IMA Workshop

Macaulay2

July 25-29, 2011

Organizers: Anton Leykin (Georgia Institute of Technology), Sonja Petrović (University of Illinois)

PI Summer Graduate Program

Summer Program for Graduate Students: Topological Methods in Complex Systems

July 25 - August 12, 2011

Organizers: Robert Ghrist (University of Pennsylvania), Robert MacPherson (Institute for Advanced Study), Konstantin Mischaikow (Rutgers University)
Schedule

Friday, July 1

9:00am-10:30am Other attendees speakLind Hall 305 ND6.20-7.1.11
10:30am-11:00am Break4th floor Lind ND6.20-7.1.11
11:00am-12:30pm Plus open problemsLind Hall 305 ND6.20-7.1.11
2:30pm-3:00pm Coffee breakLind Hall 400

Monday, July 4

All Day Independence Day. The IMA is closed.

Wednesday, July 13

8:30am-9:00am Registration and coffeeKeller Hall 3-176 SW7.13-16.11
8:30am-9:00am Registration and coffeeKeller Hall 3-176 SWb7.13-16.11
9:00am-9:15am Welcome; IntroductionsFadil Santosa (University of Minnesota)Keller Hall 3-176 SW7.13-16.11
9:00am-9:15am Welcome; IntroductionsFadil Santosa (University of Minnesota)Keller Hall 3-176 SWb7.13-16.11
9:15am-10:30am Project Descriptions/Formation of Breakout GroupsKeller Hall 2-172 SWb7.13-16.11
9:15am-10:15am Why Wavelets? Keller Hall 2-170 SW7.13-16.11
10:15am-10:30am BreakKeller Hall 3-176 SW7.13-16.11
10:30am-11:45am Individual Group WorkKeller Hall 2-172 SWb7.13-16.11
10:30am-11:45am Digital ImagesKeller Hall 2-170 SW7.13-16.11
11:45am-1:30pm Lunch SW7.13-16.11
11:45am-1:30pm Lunch SWb7.13-16.11
1:30pm-2:45pm Individual Group WorkKeller Hall 2-172 SWb7.13-16.11
1:30pm-2:45pm The Haar Wavelet Transformation (HWT)Keller Hall 2-170 SW7.13-16.11
2:45pm-3:00pm BreakKeller Hall 3-176 SW7.13-16.11
2:45pm-3:00pm BreakKeller Hall 3-176 SWb7.13-16.11
3:00pm-4:15pm Individual Group WorkKeller Hall 2-172 SWb7.13-16.11
3:00pm-4:15pm Coding the HWT, Edge Detection ApplicationKeller Hall 2-170 SW7.13-16.11
4:15pm-4:30pm Group photo SW7.13-16.11
4:15pm-4:30pm Group photo SWb7.13-16.11

Thursday, July 14

8:30am-9:00am CoffeeKeller Hall 3-176 SW7.13-16.11
8:30am-9:00am CoffeeKeller Hall 3-176 SWb7.13-16.11
9:00am-10:15am Individual Group WorkKeller Hall 2-172 SWb7.13-16.11
9:00am-10:15am Cumulative Energy, Entropy, and PSNRKeller Hall 2-170 SW7.13-16.11
10:15am-10:30am BreakKeller Hall 3-176 SW7.13-16.11
10:15am-10:30am BreakKeller Hall 3-176 SWb7.13-16.11
10:30am-11:45am Individual Group WorkKeller Hall 2-172 SWb7.13-16.11
10:30am-11:45am Huffman CodingKeller Hall 2-170 SW7.13-16.11
11:45am-1:15pm Lunch SW7.13-16.11
11:45am-1:15pm Lunch SWb7.13-16.11
1:15pm-2:30pm Status Report from All GroupsKeller Hall 2-172 SWb7.13-16.11
1:15pm-2:30pm Putting It All Together: Image CompressionKeller Hall 2-170 SW7.13-16.11
2:30pm-2:45pm BreakKeller Hall 3-176 SW7.13-16.11
2:30pm-2:45pm BreakKeller Hall 3-176 SWb7.13-16.11
2:45pm-4:00pm Individual Group WorkKeller Hall 2-172 SWb7.13-16.11
2:45pm-4:00pm Daubechies Wavelet TransformationsKeller Hall 2-170 SW7.13-16.11
5:00pm-10:30pm Dinner Excursion SWb7.13-16.11
5:00pm-10:30pm Dinner ExcursionTBA SW7.13-16.11

Friday, July 15

8:30am-9:00am CoffeeKeller Hall 3-176 SW7.13-16.11
8:30am-9:00am CoffeeKeller Hall 3-176 SWb7.13-16.11
9:00am-10:15am Individual Group WorkKeller Hall 2-172 SWb7.13-16.11
9:00am-10:15am Fourier Series and Filter ConstructionKeller Hall 2-170 SW7.13-16.11
10:15am-10:30am BreakKeller Hall 3-176 SW7.13-16.11
10:15am-10:30am BreakKeller Hall 3-176 SWb7.13-16.11
10:30am-11:45am Individual Group WorkKeller Hall 2-172 SWb7.13-16.11
10:30am-11:45am Biorthogonal Wavelet FiltersKeller Hall 2-170 SW7.13-16.11
11:45am-1:30pm Lunch SW7.13-16.11
11:45am-1:30pm Lunch SWb7.13-16.11
1:30pm-5:00pm Excursion: TBATBA SW7.13-16.11
1:30pm-5:00pm Excursion: TBA SWb7.13-16.11

Saturday, July 16

8:30am-9:00am CoffeeKeller Hall 3-176 SW7.13-16.11
8:30am-9:00am CoffeeKeller Hall 3-176 SWb7.13-16.11
9:00am-10:15am Presentations to Introductory Workshop ParticipantsKeller Hall 3-180 SWb7.13-16.11
9:00am-10:15am Presentations from Projects Workshop ParticipantsKeller Hall 3-180 SW7.13-16.11
10:15am-10:30am BreakKeller Hall 3-176 SW7.13-16.11
10:15am-11:30am BreakKeller Hall 3-176 SWb7.13-16.11
10:30am-11:45am Presentations to Introductory Workshop ParticipantsKeller Hall 3-180 SWb7.13-16.11
10:30am-11:45am Presentations from Projects Workshop ParticipantsKeller Hall 3-180 SW7.13-16.11

Wednesday, August 3

12:00pm-1:00pm Team 1: Geometric and appearance modeling of vascular structures in CT and MR Stefan E. Atev (ViTAL Images, Inc.)Keller Hall 3-180 MM8.3-12.11
12:00pm-1:00pm Team 2: Modeling aircraft hoses and flexible conduits Thomas Grandine (Boeing)Keller Hall 3-180 MM8.3-12.11
12:00pm-1:00pm Team 5: Optimizing power generation and delivery in smart electrical grids Chai Wah Wu (IBM)Keller Hall 3-180 MM8.3-12.11
12:00pm-1:00pm Team 3: Fast nearest neighbor search in massive high-dimensional sparse data sets  Sanjiv Kumar (Google Inc.)Keller Hall 3-180 MM8.3-12.11
12:00pm-1:00pm Team 4: Diffraction by photomasks Apo Sezginer (KLA - Tencor)Keller Hall 3-180 MM8.3-12.11
Abstracts
Why Wavelets?
Abstract: TBA
Stefan E. Atev (ViTAL Images, Inc.) Team 1: Geometric and appearance modeling of vascular structures in CT and MR
Abstract: Project Description:



Figure 1. Segmentation of the internal carotid artery (left). Vessel tree with the common, internal and external carotid arteries (right).


Accurate vessel segmentation is required in many clinical applications, such as identifying the degree of stenosis (narrowing) of a vessel to assess if blood flow to an organ is sufficient, quantification of plaque buildup (to determine the risk of stroke, for example), and in detecting aneurisms which pose severe risks if ruptured. Proximity to bone can pose segmentation challenges due to the similar appearance of bone and contrasted vessels in CT (Figure 1 – the internal carotid has to cross the skull base); other challenges are posed by low X-ray dose images, and pathology such as stenosis and calcifications.



Figure 2. Cross section of vessel segmentation from CT data, shown with straightened centerline.

A typical segmentation consists of a centerline that tracks the length of the vessel, lumen surface and vessel wall surface. Since for performance reasons most clinical applications use only local vessel models for detection, tracking and segmentation, in the presence of noise the results can become physiologically unrealistic – for example in the figure above, the diameter of the lumen and wall cross-sections vary too rapidly.


Figure 3. Vessel represented as a centerline with periodically sampled cross-sections in the planes orthogonal to the centerline. Note that some planes intersect, which makes this representation problematic. The in-plane cross-sections of the vessel are shown on the right.

The goal of this project is to design a method for refining a vessel segmentation based on the following general approach:
  1. Choose an appropriate geometric representations for vessel segmentation (e.g., generalized cylinders) and derive the equations and methods necessary to manipulate it as required and to convert to and from the representation. One common, but sometimes problematic representation is shown in Figure 3.

  2. Learn a geometric model for vessels based on the representation from a set of training data (for example segmentations obtained from low-noise clinical images). Example model parameters:

    • - Relative rate of vessel diameter change as a function of centerline curvature

      - Typical wall thickness as a function of lumen cross-section area

  3. Learn an appearance model for the vessels that captures details about how vessels appear in a clinical imaging modality such as CT. For example:

    • - Radial lumen intensity profile in Hounsfeld units

      - Rate of intensity change along the centerline

  4. Compute the most likely vessel representation given a starting segmentation and the learned geometric and appearance models.

The project will use real clinical data and many different types of vessels.

References:
  1. C. Kirbas and F. Quek. “A review of vessel extraction techniques and algorithms”. ACM Computing Surveys, vol. 36, pp. 81–121, 2000.

  2. T. McInerney and D. Terzopoulos. “Deformable models in medical image analysis: A survey”. Medical Image Analysis, vol. 1, pp. 91 – 108, 1996.
Prerequisites:
Optimization, Statistics and Estimation, Differential Equations and Geometry. MATLAB programming.

Keywords:
Vessel segmentation, shape statistics, appearance models
Thomas Grandine (Boeing) Team 2: Modeling aircraft hoses and flexible conduits
Abstract: Project Description:



Modern commercial airplanes are assembled out of millions of different parts.  While many of these parts are rigid, many of them are not.  For example, the hydraulic lines and flexible electrical conduits that supply an airplane's landing gear change their shape as the landing gear goes through its motion (you can see some of these lines in the accompanying photograph).  These shapes can be modeled by minimizing the potential energy of the rest state of one of these flexible lines as the ends of the lines are moved by the landing gear.  While this problem is amenable to solution through direct optimization of individual finite elements, the method often proves to be slow and unreliable.  In this investigation, we will explore the use of variational methods (i.e. the calculus of variations) in an attempt to discover a more elegant and robust approach to modeling these flexible airplane parts.

Reference: 

Any textbook on the calculus of variations.  My favorite is The Variational Principles of Mechanics, by Cornelius Lanczos. 

Keywords:
Geometrical modeling, calculus of variations, boundary value problems

Prerequisites:
Calculus of variations, optimization, numerical methods for ODEs and 2-point boundary value problems, Matlab
Sanjiv Kumar (Google Inc.) Team 3: Fast nearest neighbor search in massive high-dimensional sparse data sets 
Abstract: Project Description:



Driven by rapid advances in many fields including Biology, Finance and Web Services, applications involving millions or even billions of data items such as documents, user records, reviews, images or videos are not that uncommon. Given a query from a user, fast and accurate retrieval of relevant items from such massive data sets is of critical importance. Each item in a data set is typically represented by a feature vector, possibly in a very high dimensional space. Moreover, such a vector tends to be sparse for many applications. For instance, text documents are encoded as a word frequency vector. Similarly, images and videos are commonly represented as sparse histograms of a large number of visual features. Many techniques have been proposed in the past for fast nearest neighbor search. Most of these can be divided in two paradigms: Specialized data structures (e.g., trees), and hashing (representing each item as a compact code). Tree-based methods scale poorly with dimensionality, typically reducing to worst case linear search. Hashing based methods are popular for large-scale search but learning accurate and fast hashes for high-dimensional sparse data is still an open question.

In this project, we aim to focus on fast approximate nearest neighbor search in massive databases by converting each item as a binary code. Locality Sensitive Hashing (LSH) is one of the most prominent methods that uses randomized projections to generate simple hash functions. However, LSH usually requires long codes for good performance. The main challenge of this project is how to learn appropriate hash functions that take input data distribution into consideration. This will lead to more compact codes, thus reducing the storage and computational needs significantly. The project will focus on understanding and implementing a few state-of-the-art hashing methods, developing the formulation for learning data-dependent hash functions assuming a known data density, and experimenting with medium to large scale datasets.

Keywords:
Approximate Nearest Neighbor (ANN) search, Hashing, LSH, Sparse data, High-dimensional hashing 

References:
For a quick overview of ANN search, review the following tutorials (more references are given at the end of the tutorials):
  1. http://www.sanjivk.com/EECS6898/ApproxNearestNeighbors_1.pdf
  2. http://www.sanjivk.com/EECS6898/ApproxNearestNeighbors_2.pdf
Prerequisites: 
- Good computing skills (Matlab or C/C++)
- Strong background in optimization, linear algebra and calculus
- Machine learning and computational geometry background preferred but not necessary
Apo Sezginer (KLA - Tencor) Team 4: Diffraction by photomasks
Abstract: Project Description:

A PC sold in 2010 had billions of transistors with 32 nm gate-length.  In a few years, that dimension will become 22 nm.  Light is essential to fabrication and quality control of such small semiconductor devices.

Integrated circuits are manufactured by repeatedly depositing a film of material and etching a pattern in the deposited film.  The pattern is formed by a process called photo lithography, Greek for writing on stone with light.  Lithography optically projects a pattern that is present on a photomask, a master copy, using light of 193 nm wavelength, on to a silicon wafer on which the integrated circuits will be formed.  Writing 22 nm patterns using 193 nm wavelength is challenging and takes massive amount of calculation.  The pattern on the photomask is different from the pattern printed on the wafer, and it is obtained by solving an inverse problem.  Reducing the wavelength simplifies the mathematical problem but introduces physical challenges.  Extreme ultraviolet light (close to soft x-rays, 13.5 nm wavelength) lithography is being developed but 193 nm light will remain the work horse for many years.

A photomask is manufactured by electron-beam lithography and optically inspected, again using a wavelength that is larger than the features that are inspected.  The image of the photomask formed in the inspection microscope can be significantly different from the pattern on the photomask.  Determining whether the photomask is correctly written requires calculating the expected inspection image in real-time. 

Both lithography and inspection use partial coherence imaging, which means the photomask is illuminated from many directions by spatially coherent time-harmonic plane-waves that are temporally incoherent with each other.   Accurately simulating partial coherent imaging requires solving Maxwell’s equations for many plane-waves incident from different directions.  Approximate methods such as Born approximation are not applicable because photomask materials are strong scatterers.   Rigorous lithography simulators comprising Mawell’s Equations solvers3 are used to study at most a micron-by-micron portion of a circuit.  Simulating an entire photomask in that manner would take millions of years using a supercomputer.  Fast approximations due to Kirchhoff1 and Hopkins2 are used to handle an entire chip or photomask.  These approximations have been extended to include edge diffraction4. An approach called domain decomposition (different than the domain decomposition method in PDEs) estimates the diffracted near-field as a collage of easier-to-solve diffracted fields5. Kirchhoff+Hopkins approximation and some of its extensions provide an estimate of the diffracted near-field in O(n) operations for n field points at the exit plane of the photomask.  O(n) methods ignore multiple scattering which leads to waveguide effects, and Wood’s anomalies.  As we move deeper into the sub-wavelength domain, no O(n) method remains accurate, and no method that is more rigorous moves into the reach of computers as the computation complexity grows in proportion to the speed of computers. 

The goal of this project is to improve the accuracy of fast partial coherence image computation.

References:
  1. G. Kirchhoff, Vorlesungen uber Mathematiche Physik, Zweiter Band, Mathematiche Optik, Leipzig, Druck und Verlag, 1891, also books.google.com, p 80 states the Kirchhoff approximation.

  2. H. H. Hopkins, “On the diffraction theory of optical images,” Proc. Roy. SOC. London, Ser. A 217, pp. 408-432, 1953.

  3. C. A. Mack, Inside PROLITH: A Comprehensive Guide to Optical Lithography Simulation, FINLE Technologies (Austin, TX: 1997).

  4. K. Adam, A. R. Neureuther, “Domain decomposition methods for the rapid electromagnetic simulation of photomask scattering,” Journal of Micro/Nanolithography, MEMS, and MOEMS, Vol. 1, No. 3, p. 253-269, 2002, SPIE, Bellingham, WA.

  5. P. Evanschitzky, F. Shao, A. Erdmann, “Simulation of larger mask areas using the waveguide method with fast decomposition technique”, Proc. SPIE Vol. 6730, 2007, SPIE, Bellingham, WA.

Keywords:
Diffraction, Computational Lithography, Partial Coherence Imaging

Prerequisites:
Basic optics, electromagnetics, computational methods for wave equations
Chai Wah Wu (IBM) Team 5: Optimizing power generation and delivery in smart electrical grids
Abstract: Project Description:




In the next generation electrical grid, or "smart grid", there will be many heterogeneous power generators, power storage devices and power consumers.   This will include residential customers who traditionally are only part of the ecosystem as consumers, but will in the foreseeable future increasingly provide renewable energy generation through photovoltaics and wind energy and provide energy storage through plug-in hybrid vehicles.  What makes this electrical grid "smart" is the capability to insert a vast number of sensors and actuators into the system.  This allows a wide variety of information about all the constituents to be collected and various aspects of the electrical grid to be controlled via advanced electric meters, smart appliances, etc.   Information gathered consists of e.g. amount of energy use, planned energy consumption, efficiency and status of equipment, energy generation costs, etc  and this information is then used by all constituents to optimize certain objectives. This necessitates communication and information technology to transmit and process this information.  The goal of this project is to focus on the optimization of local objectives in a smart grid.  In particular, we study various centralized and decentralized optimization algorithms to determine the optimal matching and maintain stability between energy producers, energy storage, and energy consumers all connected in a complex and dynamic network.  

Technical prerequisites:
scripting languages (Matlab, python), optimization, linear and nonlinear programming.

Preferred but not necessary:
graph theory, combinatorics, computer programming, experience with CPLEX, R.
Visitors in Residence
Bruce Atwood Beloit College 7/12/2011 - 7/16/2011
Nusret Balci University of Minnesota 9/1/2009 - 8/31/2011
Brett Barwick University of South Carolina 7/24/2011 - 7/30/2011
Daniel J. Bates Colorado State University 7/24/2011 - 7/30/2011
Peter W. Bates Michigan State University 6/19/2011 - 7/1/2011
Catherine Beneteau University of South Florida 7/12/2011 - 7/16/2011
John Burke Boston University 6/19/2011 - 7/1/2011
Marta Canadell Cano University of Barcelona 6/18/2011 - 7/1/2011
Fatih Celiker Wayne State University 7/24/2011 - 7/31/2011
Aycil Cesmelioglu University of Minnesota 9/30/2010 - 8/30/2012
Chi Hin Chan University of Minnesota 9/1/2009 - 8/31/2011
Soumyadeep Chatterjee University of Minnesota 7/13/2011 - 7/16/2011
David Cook II University of Kentucky 7/24/2011 - 7/30/2011
Jintao Cui University of Minnesota 8/31/2010 - 8/30/2012
Rafael de la Llave University of Texas at Austin 6/19/2011 - 7/3/2011
Adrian Delgado University of Texas 7/12/2011 - 7/16/2011
Oliver R. Diaz-Espinosa Duke University 6/19/2011 - 7/1/2011
David Eisenbud Mathematical Sciences Research Institute 7/24/2011 - 7/30/2011
Mohamed Sami ElBialy University of Toledo 6/19/2011 - 7/2/2011
Daniel Erman University of Michigan 7/24/2011 - 7/30/2011
Randy H. Ewoldt University of Minnesota 9/1/2009 - 8/31/2011
Oscar E. Fernandez University of Michigan 8/31/2010 - 8/30/2011
Florian Geiß Universität des Saarlandes 7/24/2011 - 7/30/2011
Daniel R. Grayson University of Illinois at Urbana-Champaign 7/24/2011 - 7/30/2011
Alexander Grigo University of Toronto 6/19/2011 - 7/1/2011
Elizabeth Gross University of Illinois 7/24/2011 - 7/30/2011
Caroline Haddad College at Geneseo, SUNY 7/12/2011 - 7/16/2011
Àlex Haro Provinciale University of Barcelona 6/18/2011 - 7/1/2011
Gurgen (Greg) Hayrapetyan Michigan State University 6/19/2011 - 7/1/2011
Franziska Babette Hinkelmann Virginia Polytechnic Institute and State University 7/24/2011 - 7/30/2011
Yulia Hristova University of Minnesota 9/1/2010 - 8/31/2012
Gemma Huguet Centre de Recerca Matemàtica 6/19/2011 - 7/1/2011
Anders Nedergaard Jensen Aarhus University 7/24/2011 - 7/30/2011
Christine Jost University of Stockholm 7/24/2011 - 7/30/2011
Vishesh Karwa Pennsylvania State University 7/24/2011 - 7/30/2011
Changho Keem Seoul National University 7/24/2011 - 7/30/2011
Kimberly D. Kendricks Central State University 6/5/2011 - 7/6/2011
Gabor Kiss University of Exeter 6/19/2011 - 7/1/2011
Helmut Knaust University of Texas 7/12/2011 - 7/16/2011
Pawel Konieczny University of Minnesota 9/1/2009 - 8/31/2011
Robert Krone Georgia Institute of Technology 7/24/2011 - 7/30/2011
Angela Kunoth Universität Paderborn 7/23/2011 - 9/12/2011
Guang-Tsai Lei GTG Research 6/19/2011 - 7/1/2011
Anton Leykin Georgia Institute of Technology 7/24/2011 - 7/30/2011
Hengguang Li University of Minnesota 8/16/2010 - 8/15/2011
Ji Li Brigham Young University 6/19/2011 - 7/1/2011
Zeng Lian New York University 6/19/2011 - 7/2/2011
Yu Liang Michigan State University 6/19/2011 - 7/2/2011
Zhi (George) Lin University of Minnesota 9/1/2009 - 8/31/2011
Zhiwu Lin Georgia Institute of Technology 6/19/2011 - 7/1/2011
Martin Wen-Yu Lo National Aeronautics and Space Administration (NASA) 6/19/2011 - 7/1/2011
Nan Lu Georgia Institute of Technology 6/19/2011 - 7/1/2011
Kara Lee Maki University of Minnesota 9/1/2009 - 8/31/2011
Yu (David) Mao University of Minnesota 8/31/2010 - 8/30/2012
Abraham Martin del Campo Texas A & M University 7/24/2011 - 7/30/2011
John Conrad Merkel III Oglethorpe University 7/12/2011 - 7/17/2011
Dimitrios Mitsotakis University of Minnesota 10/27/2010 - 8/31/2012
Jose-Maria Mondelo Autonomous University of Barcelona 6/18/2011 - 7/1/2011
W. Frank Moore Cornell University 7/24/2011 - 7/30/2011
Charles Howard Morgan Jr. Lock Haven University 6/19/2011 - 7/1/2011
Benson Muite University of Michigan 6/19/2011 - 7/1/2011
David Murrugarra Tomairo Virginia Polytechnic Institute and State University 7/24/2011 - 7/30/2011
Zubin Olikara University of Colorado 6/19/2011 - 7/1/2011
Eduardo Ortiz University of Puerto Rico 7/12/2011 - 7/16/2011
Cecilia Ortiz-Duenas University of Minnesota 9/1/2009 - 8/31/2011
Bruce B. Peckham University of Minnesota 6/19/2011 - 7/1/2011
Nikola Petrov University of Oklahoma 6/20/2011 - 7/2/2011
Sonja Petrović University of Illinois 7/24/2011 - 7/30/2011
Tuoc Van Phan University of Tennessee 6/19/2011 - 7/1/2011
Weifeng (Frederick) Qiu University of Minnesota 8/31/2010 - 8/30/2012
Claudiu Raicu University of California, Berkeley 7/24/2011 - 7/30/2011
Ajaykumar Rajasekharan Seagate Technology 7/13/2011 - 7/16/2011
Narayanan Ramakrishnan Seagate Technology 7/13/2011 - 7/16/2011
David Ruch Metropolitan State College of Denver 7/12/2011 - 7/17/2011
Julio Cesar Salazar Ospina École Polytechnique de Montréal 6/19/2011 - 7/2/2011
Fadil Santosa University of Minnesota 7/1/2008 - 8/30/2011
Stephen Schecter North Carolina State University 6/28/2011 - 7/1/2011
George R Sell University of Minnesota 6/20/2011 - 7/1/2011
Shuanglin Shao University of Minnesota 9/1/2009 - 8/31/2011
Gregory G. Smith Queen's University 7/24/2011 - 7/30/2011
Bart Snapp Ohio State University 7/24/2011 - 7/30/2011
Dumitru Stamate University of Bucharest 7/24/2011 - 7/30/2011
Milena Stanislavova University of Kansas 6/19/2011 - 7/1/2011
Michael E. Stillman Cornell University 7/24/2011 - 7/30/2011
Stephen Sturgeon University of Kentucky 7/24/2011 - 7/30/2011
Seth Sullivant Harvard University 7/24/2011 - 7/29/2011
Kaisa Taipale St. Olaf College 7/25/2011 - 7/29/2011
Zach Teitler Boise State University 7/24/2011 - 7/30/2011
Dimitar Trenev University of Minnesota 9/1/2009 - 8/31/2011
Patrick Van Fleet University of St. Thomas 7/13/2011 - 7/16/2011
Jan Verschelde University of Illinois 7/24/2011 - 7/30/2011
Rachel Weir Allegheny College 7/12/2011 - 7/17/2011
Gwyneth Whieldon Cornell University 7/24/2011 - 7/30/2011
Alexander Wurm Western New England College 6/19/2011 - 7/1/2011
Zhifu Xie Virginia State University 6/19/2011 - 7/1/2011
Josephine Yu Massachusetts Institute of Technology 7/24/2011 - 7/30/2011
Ganghua Yuan Northeast (Dongbei) Normal University 4/27/2011 - 7/27/2011
YI Zhang University of Minnesota 7/25/2011 - 7/29/2011
Legend: Postdoc or Industrial Postdoc Long-term Visitor

IMA Affiliates:
Arizona State University, Boeing, Corning Incorporated, ExxonMobil, Ford, General Motors, Georgia Institute of Technology, Honeywell, IBM, Indiana University, Iowa State University, Korea Advanced Institute of Science and Technology (KAIST), Lawrence Livermore National Laboratory, Lockheed Martin, Los Alamos National Laboratory, Medtronic, Michigan State University, Michigan Technological University, Mississippi State University, Northern Illinois University, Ohio State University, Pennsylvania State University, Portland State University, Purdue University, Rice University, Rutgers University, Sandia National Laboratories, Schlumberger Cambridge Research, Schlumberger-Doll, Seoul National University, Siemens, Telcordia, Texas A & M University, University of Central Florida, University of Chicago, University of Delaware, University of Houston, University of Illinois at Urbana-Champaign, University of Iowa, University of Kentucky, University of Maryland, University of Michigan, University of Minnesota, University of Notre Dame, University of Pennsylvania, University of Pittsburgh, University of Tennessee, University of Wisconsin-Madison, University of Wyoming, US Air Force Research Laboratory, Wayne State University, Worcester Polytechnic Institute