Institute for Mathematics and its Applications University of Minnesota 114 Lind Hall 207 Church Street SE Minneapolis, MN 55455 
20092010 IMA Participating Institutions Conferences
All Day  Workshop Outline: Posing of problems by the 6 industry mentors. Halfhour introductory talks in the morning followed by a welcoming lunch. In the afternoon, the teams work with the mentors. The goal at the end of the day is to get the students to start working on the projects.  EE/CS 3180  MM8.514.09  
9:00am9:30am  Coffee and Registration  EE/CS 3176  MM8.514.09  
9:30am9:40am  Welcome to the IMA  Fadil Santosa (University of Minnesota)  EE/CS 3180  MM8.514.09 
9:40am10:00am  Team 1: Tensor tomography of stressinduced birefringence in commercial glasses  Douglas C. Allan (Corning Incorporated)  EE/CS 3180  MM8.514.09 
10:00am10:20am  Team 2: Robust portfolio optimization using a simple factor model  Christopher Bemis (Whitebox Advisors)  EE/CS 3180  MM8.514.09 
10:20am10:40am  Team 3: Social and communication networks  Eric van den Berg (Telcordia)  EE/CS 3180  MM8.514.09 
10:40am11:00am  Break  EE/CS 3176  MM8.514.09  
11:00am11:20am  Team 4: Problems associated with remotely sensing wind speed  John R. Hoffman (Lockheed Martin)  EE/CS 3180  MM8.514.09 
11:20am11:40am  Team 5: Fast computational methods for reservoir flow models  Robert Shuttleworth (ExxonMobil)  EE/CS 3180  MM8.514.09 
11:40am12:00pm  Team 6: Visual words: Text analysis concepts for computer vision  Brendt Wohlberg (Los Alamos National Laboratory)  EE/CS 3180  MM8.514.09 
12:00pm1:30pm  Lunch  MM8.514.09  
1:30pm4:30pm  Afternoon  start work on projects
Team 1  LindH 401  Breakout Rooms  MM8.514.09 
All Day  Students work on the projects. Mentors guide their groups through the modeling process, leading discussion sessions, suggesting references, and assigning work.
Team 1  LindH 401  Breakout Rooms  MM8.514.09 
All Day  Students work on the projects. Mentors available for consultation.
Team 1  LindH 401  Breakout Rooms  MM8.514.09 
All Day  Students work on the projects.
Team 1  LindH 401  Breakout Rooms  MM8.514.09 
All Day  Students work on the projects.
Team 1  LindH 401  Breakout Rooms  MM8.514.09 
9:00am9:30am  Coffee  EE/CS 3176  MM8.514.09  
9:30am9:50am  Team 4 progress report  EE/CS 3180  MM8.514.09  
9:50am10:10am  Team 2 progress report  EE/CS 3180  MM8.514.09  
10:10am10:30am  Team 5 progress report  EE/CS 3180  MM8.514.09  
10:30am11:00am  Break  EE/CS 3176  MM8.514.09  
11:00am11:20am  Team 1 progress report  EE/CS 3180  MM8.514.09  
11:20am11:40am  Team 6 progress report  EE/CS 3180  MM8.514.09  
11:40am12:00pm  Team 3 progress report  EE/CS 3180  MM8.514.09  
12:00pm1:30pm  Picnic at Cooke Hall Fields Picnic area map  Cooke Hall Fields Picnic area  MM8.514.09  
2:00pm5:00pm  Remainder of the day: Students work on projects. Mentors available for consultation.
Team 1  LindH 401  Breakout Rooms  MM8.514.09 
All Day  Students work on the projects. Mentors available for consultation.
Team 1  LindH 401  Breakout Rooms  MM8.514.09 
All Day  Students work on the projects. Mentors available for consultation.
Team 1  LindH 401  Breakout Rooms  MM8.514.09 
All Day  Students work on the projects. Mentors available for consultation.
Team 1  LindH 401  Breakout Rooms  MM8.514.09 
8:30am9:00am  Coffee  EE/CS 3176  MM8.514.09  
9:00am9:30am  Team 3 final report  EE/CS 3180  MM8.514.09  
9:30am10:00am  Team 6 final report  EE/CS 3180  MM8.514.09  
10:00am10:30am  Team 1 final report  EE/CS 3180  MM8.514.09  
10:30am11:00am  Break  EE/CS 3176  MM8.514.09  
11:00am11:30am  Team 5 final report  EE/CS 3180  MM8.514.09  
11:30am12:00pm  Team 2 final report  EE/CS 3180  MM8.514.09  
12:00pm12:30pm  Team 4 final report  EE/CS 3180  MM8.514.09  
12:30pm2:00pm  Pizza party  Lind Hall 400  MM8.514.09 
10:45am11:15am  Coffee break  Lind Hall 400 
Event Legend: 

MM8.514.09  Mathematical modeling in industry XIII  A workshop for graduate students 
Douglas C. Allan (Corning Incorporated)  Team 1: Tensor tomography of stressinduced birefringence in commercial glasses 
Abstract:
Project Description:
Birefringence refers to
a different index of refraction for orthogonal light polarizations in
a transparent material. In stressfree glasses (which are isotropic
and can be made homogeneous) the birefringence is zero by symmetry.
When such a glass is subjected to stress, even by squeezing with your
fingers, stressinduced birefringence is readily observed. In real
glasses a certain amount of stress is unavoidably frozen in during
glass forming. It is of interest in a number of applications needing
low or nearly zero birefringence to control and minimize the level of
frozenin stress birefringence.
The goal of this
project is to develop computational tools in Matlab to read limited
sets of birefringence measurements and approximately reconstruct a
stress distribution within the glass part that would be consistent
with the measured birefringence scans. The general mathematical
jargon for this procedure is "tensor tomography," but we are not
trying to solve the problem at its most exact and sophisticated
level. Instead we seek to make the absolutely simplest model for
stresses within a sample that is approximately or adequately
representative of the real stresses in the sample. Such an
approximate reconstruction of stress would be useful to understand
what stresses have developed in the sample and also how the
birefringence would be altered if glass were removed, changing the
stress boundary conditions. The model stress would have to obey the
usual requirements of material continuity and force balance as well
as the forcefree boundary condition on the surface. Part of our
goal is to achieve an adequate approximate description of stress
using the fewest birefringence measurements possible.
We have in mind a
reallife application where reconstruction of the stress field from
limited birefringence measurements would be useful. The application
is in the manufacture of lens blanks, or blocks of extremely pure and
highly homogeneous glass used to make the diffractionlimited optics
for computer chip manufacture. Here the problem is fully
threedimensional, and at minimum several directions of birefringence
measurement will be required.
I am interested in
possibly using Green function methods to solve for a stress
distribution based on a set of initial strains. The strain field
would constitute the unknown degrees of freedom for which we solve.
This would automatically satisfy material continuity and force
balance within the interior, and can be arranged also to satisfy the
boundary conditions on faces. However, we may elect to pursue finite
element methods or other choices depending on student interests and
experience.
References:
Background on linear
elastic theory and stressinduced birefringence can be found in many
sources, including the web or textbooks in your university library.
Note that we will work only in the linear regime and only with
perfectly isotropic and homogeneous samples (when in their
stressfree condition), so much of the mathematics is simplified.
1. One useful set of notes on linear elastic theory can be found at
http://www.engin.brown.edu/courses/en224.
See the Lecture Notes and especially the "Kelvin solution" of
section 3.2 which is the basis of the Green function method.
2. Some basics of
birefringence are included in the IMA Mathematical Modeling in
Industry Workshop 2006 report found at
http://www.ima.umn.edu/20052006/MM8.918.06/abstracts.html.
See the link to the "Team 1 report" pdf .
Prerequisites:
Required:
computing skills, numerical analysis skills, familiarity with Fourier
analysis and convolution, ability to manipulate data arrays.
Desired: some optics,
some physics, familiarity with continuum elastic theory (stress and
strain); the needed optical and glassforming background will be
supplied.
Keywords: stressinduced birefringence, optical properties of glass, data analysis algorithms, tensor tomography, linear elastic theory 

Christopher Bemis (Whitebox Advisors)  Team 2: Robust portfolio optimization using a simple factor model 
Abstract: Project Description: Active portfolio management has developed substantially since the formulation of the Capital Asset Pricing Model (CAPM). While the original methodology of portfolio optimization has been lauded, it is essentially an academic exercise, with practitioners eschewing the suggested weightings. There are myriad reasons for this: nonstationarity of data, insufficiency of modeling parameters, sensitivity of optimization to small perturbations, and assumption of uniform investor utility all indicate potential failures in the model. We will follow the work of Goldfarb and Iyengar and address some of the issues raised above. In particular, we will consider robust portfolio selection problems. These, still, suffer from the features of nonstationarity and potential misalignment of true investor risk aversion. However, they add flexibility and attempt to remove parameter specification sensitivity. Under this framework, we will also consider how a factor model may enhance our desired results. To be consistent with current conceptions and literature, we will attempt to assimilate the work of Fama and French into our model. References: Goldfarb, D. and Iyengar, G. 2003. Robust portfolio selection problems. Mathematics of Operations Research 28: 138 Goldfarb, D., Erdogan, E., and Iyengar, G. 2007. Robust portfolio management. Computational Finance 11: 7198 Fama, E. and French, K. 1993. Common Risk Factors in the Returns on Stocks and Bonds. Journal of Financial Economics 33: 3–56 Nocedal, J. and Wrigth, S. 1999. Numerical Optimization. SpringerVerlag, New York. Prerequisites: Familiarity with meanvariance optimization, constrained optimization methods, and regression. Desired: Coursework in mathematical finance, statistics and optimization; Matlab programming; and some work with second order cone programs.  
John R. Hoffman (Lockheed Martin)  Team 4: Problems associated with remotely sensing wind speed 
Abstract: Projection Description:
The earth’s atmosphere is a swirling ball of gas. The cause of
the swirling, especially near the surface, is due to different
temperatures of the air. These different air temperatures
change the index of refraction for the air in the atmosphere.
Thus when light travels through this turbulent/random medium
the light ends up getting speckled. It is these speckles,
caused by the turbulent atmosphere that limited the resolution
of earthbound astronomical observations until the invention of
adaptive optics. You have observed this phenomenon any time
you’ve looked at a star. It is the motion of these speckles
over our eyes that causes the stars to twinkle. The graphic
below illustrates how light from a source ends up distorted by
the atmosphere resulting in a specular image.
Our problem focuses on a particular aspect of imaging through
turbulence. In the early 1970’s it was shown by Lawrence,
Clifford and Oochs and Lee and Harp that the primary source of
the variation of the intensity of light on a pair of photo
detector was from the wind. This observation can be used to
create a poorly posed inverse problem that if one can solve,
permits one to compute the cross wind profile along the path of
the light beam. The specific relationship relating timelagged
cross covariance and wind speed is given by:
where: τ – is the time lag between adjacent pixels L – is the length of the flight path. k – is wave number of the light used (the light is assumed to be monochromatic.) K – has units of 1 / length, is the reciprocal of the size of a turbulent eddy ball. ρ – spacing between detectors v(z) – wind speed parallel to the line connecting the detectors C_{n}^{2} (z) – scintillation coefficient Several different authors since then have advertised an ability to measure the gross average wind over long periods of time. (10 minute intervals is a common metric.) Here are several questions that I currently have on this phenomenology. The team will answer any questions that I don’t answer between now and this summer.


Fadil Santosa (University of Minnesota)  Welcome to the IMA 
Abstract: No Abstract  
Robert Shuttleworth (ExxonMobil)  Team 5: Fast computational methods for reservoir flow models 
Abstract:
Project Description:
Reservoir simulations are used in the oil industry for field development and for production forecast. The heart of a simulator is a computer program that solves for the fluid flow within the reservoirs. The flow of fluid is modeled by a system of coupled, nonlinear partial differential equations (PDEs). These equations are then discretized in space and time. When using an implicit time discretization, a system of nonlinear algebraic equations needs to be solved at each time step. This is typically done using Newton’s method on a set of linearized state equations. At each Newton iteration, a linear system must be solved to update the set of state variables.
The challenge of performing accurate and realistic simulation is that the number of unknowns can be large, requiring the solution of a large system of nonlinear algebraic equations at each time step. The task in this project is to understand the bottleneck in the calculation and find ways to speed it up.
We will conduct our research using a MATLAB based, 2phase flow simulator with fixed spatial discretization and adaptive time stepping. We consider two different time discretization schemes. The first scheme is fully implicit, while the second is based on an operator splitting method.
References:
Fundamentals of Numerical Reservoir Simulation Donald W. Peaceman Elsevier Science Inc. New York, NY, USA Finite Volume Methods for Hyperbolic Problems Randall J. LeVeque Cambridge University Press Prerequisites: Background in numerical analysis, numerical linear algebra, scientific computation, and numerical methods for partial differential equations. Experience in MATLAB programming. 

Brendt Wohlberg (Los Alamos National Laboratory)  Team 6: Visual words: Text analysis concepts for computer vision 
Abstract:
Large collections of image and video data are becoming increasingly common in a diverse range of applications, including consumer multimedia (e.g. flickr and YouTube), satellite imaging, video surveillance, and medical imaging. One of the most significant problems in exploiting such collections is in the retrieval of useful content, since the collections are often of sufficient size to make a manual search impossible. These problems are addressed in computer vision research areas such as contentbased image retrieval, automatic image tagging, semantic video indexing, and object detection. A sample of the exciting work being done in these areas can be obtained by visiting the websites of leading research groups such as Caltech Computational Vision, Carnegie Mellon Advanced Multimedia Processing Lab, LEAR, MIT CSAIL Vision Research, Oxford Visual Geometry Group, and WILLOW. One of the most promising ideas in this area is that of visual words, constructed by quantizing invariant image features such as those generated by SIFT. These visual word representations allow text document analysis techniques (Latent Semantic Analysis, for example) to be applied to computer vision problems, an interesting example being the use of Probabilistic Latent Semantic Analysis or Latent Dirichlet allocation to learn to recognize categories of objects (e.g. car, person, tree) within an image, using a training set which is only labeled to indicate the object categories present in each image, with no indication of the location of the object in the image. In this project we will explore the concept of visual words, understand their properties and relationship with text words, and consider interesting extensions and new applications. References:[1] Lowe, David G., Distinctive Image Features from ScaleInvariant Keypoints, International Journal of Computer Vision, vol. 60, no. 2, pp. 91110, 2004. doi: 10.1023/b:visi.0000029664.99615.94 [2] Leung, Thomas K. and Malik, Jitendra, Representing and Recognizing the Visual Appearance of Materials using Threedimensional Textons, International Journal of Computer Vision, vol. 43, no. 1, pp. 2944, 2001. doi: 10.1023/a:1011126920638 [3] Liu, David and Chen, Tsuhan, DISCOV: A Framework for Discovering Objects in Video, IEEE Transactions on Multimedia, vol. 10, no. 2, pp. 200208, 2008. doi: 10.1109/tmm.2007.911781 [4] Fergus, Rob, Perona, Pietro and Zisserman, Andrew, Weakly Supervised ScaleInvariant Learning of Models for Visual Recognition, International Journal of Computer Vision, vol. 71, no. 3, pp. 273303, 2007. doi: 10.1007/s112630068707x [5] Philbin, James, Chum, Ondřej, Isard, Michael, Sivic, Josef and Zisserman, Andrew, Object retrieval with large vocabularies and fast spatial matching, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2007. doi: 10.1109/CVPR.2007.383172 [6] Yang, Jun, Jiang, YuGang, Hauptmann, Alexander G. and Ngo, ChongWah, Evaluating bagofvisualwords representations in scene classification, Proceedings of the international workshop on multimedia information retrieval (MIR '07), pp. 197206, 2007. doi: 10.1145/1290082.1290111 [7] Yuan, Junsong, Wu, Ying and Yang, Ming, Discovery of Collocation Patterns: from Visual Words to Visual Phrases, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 18, 2007. doi: 10.1109/cvpr.2007.383222 [8] Fergus, Rob, FeiFei, Li, Perona, Pietro and Zisserman, Andrew, Learning object categories from Google's image search, IEEE International Conference on Computer Vision (ICCV), vol. 2, pp. 18161823, 2005. doi: 10.1109/iccv.2005.142 [9] Quelhas, P., Monay, F., Odobez, J.M., GaticaPerez, D., Tuytelaars, T. and Van Gool, L., Modeling scenes with local descriptors and latent aspects, IEEE International Conference on Computer Vision (ICCV), pp. 883890, 2005. doi: 10.1109/iccv.2005.152 [10] Sivic, Josef, Russell, Bryan C., Efros, Alexei A, Zisserman, Andrew and Freeman, William T., Discovering objects and their location in images, IEEE International Conference on Computer Vision (ICCV), pp. 370377, 2005. doi: 10.1109/iccv.2005.77 Prerequisites:A strong computational background is essential, preferably with significant experience in Matlab programming. (While experience with other programming languages such as C, C++, or Python may be useful, Matlab is likely to be the common language when individual team member contributions need to be integrated into a joint code.) Some background in areas such as image/signal processing, optimization theory, or statistical inference would be highly beneficial. 

Eric van den Berg (Telcordia)  Team 3: Social and communication networks 
Abstract: Project Description: In recent years, the structure of complex networks has become object of intense study by scientists from various disciplines; see e.g. [1], [2] and [3], or the bookform paper collection [4]. One often studied mechanism of growth and evolution in such networks, e.g. social networks, is preferential attachment [2]. In communications network engineering, network protocols have been modeled mathematically using tools from optimization [5] and game theory [6]. A picture has emerged of layered networks (modeled as graphs) where each layer of the whole acts noncooperatively, implicitly optimizing its own objective, treating other network layers largely as a black box. The network layers interact dynamically, and implicit cooperation towards a common overall objective is achieved by a suitable, modular decomposition of tasks to the individual layers. In this project, we will focus on the interaction between social networks and communication networks. Given the communication network, how do social networks grow and evolve? Does preferential attachment account for the structure observed? How do communication networks and their (often protocolinduced) ‘preferences’ affect the structure of social networks, and vice versa? We will use mathematics (optimization, game theory, graph theory) and computer simulation to investigate these questions. Prerequisites: Background: Optimization, Probability, Differential Equations. Computer skills: Matlab, R, Python. References: [1] M.E. Newman, "The Structure and Function of Complex Networks," SIAM Review, Vol. 45, No. 2, pp. 167256, 2003. [2] L.A. Barabasi, R. Albert, "Emergence of Scaling in Random Networks," Science, Vol. 286, No. 5439, pp. 509512, 1999. [3] D.J. Watts, "The ‘New’ Science of Networks," Ann. Rev. Sociology Vol. 30, pp. 243270, 2004. [4] M.E. Newman, L.A. Barabasi, D.J. Watts, "The Structure and Dynamics of Networks," Princeton, 2006. [5] M. Chiang, S.H. Low, A.R. Calderbank and J.C. Doyle, "Layering as Optimization Decomposition: A Mathematical Theory of Network Architectures," Proceedings of the IEEE, Vol. 95, No. 1, pp. 255312, January 2007 [6] E. Altman, T. Boulogne, R. ElAzouzi, T. Jimenez and L. Wynter, "A Survey of Networking Games in Telecommunications," Computers and Operations Research, Vol. 33, No. 2, pp. 286311, 2006. 
Douglas C. Allan  Corning Incorporated  8/4/2009  8/15/2009 
Fabio Ancona  Università di Padova  7/23/2009  8/1/2009 
Deepak Aralumallige Subbarayappa  Wichita State University  8/4/2009  8/14/2009 
Donald G. Aronson  University of Minnesota  9/1/2002  8/31/2009 
Christopher Bemis  Whitebox Advisors  8/5/2009  8/14/2009 
Brian Bies  Washington University  6/28/2009  8/1/2009 
Michael Blaser  Eidgenössische TH Hönggerberg  7/12/2009  8/1/2009 
Chris Bonnell  University of Illinois at UrbanaChampaign  8/4/2009  8/15/2009 
Richard J. Braun  University of Delaware  8/4/2009  8/15/2009 
MariaCarme T. Calderer  University of Minnesota  8/5/2009  8/14/2009 
Hannah Callender  University of Minnesota  9/1/2007  8/14/2009 
Lingyan Cao  University of Maryland  8/4/2009  8/14/2009 
Teng Chen  University of Central Florida  8/4/2009  8/14/2009 
WangJuh Chen  Arizona State University  8/4/2009  8/14/2009 
Xianjin Chen  University of Minnesota  9/1/2008  8/31/2010 
Rinaldo Mario Colombo  Università di Brescia  7/12/2009  8/2/2009 
Gianluca Crippa  Università di Parma  7/11/2009  8/1/2009 
Charles Doering  University of Michigan  8/15/2009  6/15/2010 
Olivier Dubois  University of Minnesota  9/3/2007  8/31/2009 
Carlos Andres GaravitoGarzon  University of Puerto Rico  8/4/2009  8/14/2009 
Nicholas Gewecke  University of Tennessee  8/4/2009  8/14/2009 
G.D. Veerappa Gowda  Tata Institute of Fundamental Research  7/12/2009  8/1/2009 
Peter Hinow  University of Minnesota  9/1/2007  8/21/2009 
Luan Thach Hoang  Texas Tech University  7/12/2009  8/1/2009 
John R. Hoffman  Lockheed Martin  8/5/2009  8/14/2009 
Yulia Hristova  Texas A & M University  8/4/2009  8/14/2009 
Xueying Hu  University of Michigan  8/4/2009  8/14/2009 
Yunkyong Hyon  University of Minnesota  9/1/2008  8/31/2010 
Mark Iwen  University of Minnesota  9/1/2008  8/31/2010 
Srividhya Jeyaraman  University of Minnesota  9/1/2008  8/31/2010 
Lijian Jiang  University of Minnesota  9/1/2008  8/31/2010 
Kayyunnapara Thomas Joseph  Tata Institute of Fundamental Research  7/12/2009  8/1/2009 
Hoi Tin Kong  University of Georgia  8/4/2009  8/14/2009 
ChiunChang Lee  National Taiwan University  8/26/2008  8/15/2009 
Yachun Li  Shanghai Jiaotong University  7/12/2009  8/1/2009 
Yongfeng Li  University of Minnesota  9/1/2008  8/31/2010 
Zhen Li  Iowa State University  8/4/2009  8/14/2009 
Weihua Lin  University of Oklahoma  8/4/2009  8/14/2009 
William Lindsey  Purdue University  8/4/2009  8/14/2009 
Chun Liu  University of Minnesota  9/1/2008  8/31/2010 
Sijia Liu  Iowa State University  8/4/2009  8/14/2009 
Maria Lukacova  Technische Universität HamburgHarburg  7/20/2009  8/1/2009 
Vasileios Maroulas  University of Minnesota  9/1/2008  8/31/2010 
Catherine (Katy) A. Micek  University of Minnesota  8/5/2009  8/14/2009 
David K. Misemer  3M  8/3/2009  8/14/2009 
Somayeh Moazeni  University of Waterloo  8/4/2009  8/15/2009 
Linh Viet Nguyen  Texas A & M University  8/4/2009  8/14/2009 
Truyen V Nguyen  University of Akron  7/12/2009  8/1/2009 
Shinya Nishibata  Tokyo Institute of Technology  7/17/2009  8/1/2009 
Minah Oh  University of Florida  8/4/2009  8/15/2009 
Arshad Ahmud Iqbal Peer  University of Mauritius  7/12/2009  8/1/2009 
Tomasz Piotr Piasecki  Polish Academy of Sciences  7/12/2009  8/1/2009 
Juan Mario Restrepo  University of Arizona  8/10/2009  6/15/2010 
Roger Robyr  Universität Zürich  7/11/2009  8/1/2009 
Andrea Catalina Rubiano  Purdue University  8/4/2009  8/14/2009 
Patrick Sanan  California Institute of Technology  8/4/2009  8/14/2009 
Fadil Santosa  University of Minnesota  7/1/2008  6/30/2010 
David Seal  University of Wisconsin  8/4/2009  8/14/2009 
Tsvetanka Sendova  University of Minnesota  9/1/2008  8/31/2010 
Lu Shu  University of Delaware  8/4/2009  8/14/2009 
Robert Shuttleworth  ExxonMobil  8/4/2009  8/15/2009 
Scott Small  University of Iowa  8/4/2009  8/14/2009 
Laura Valentina Spinolo  Scuola Normale Superiore  7/11/2009  8/1/2009 
ChungKai Sun  University of California, San Diego  8/4/2009  8/14/2009 
Huan Sun  Pennsylvania State University  8/4/2009  8/14/2009 
Huan Sun  Pennsylvania State University  8/15/2009  12/15/2009 
Eugene Trofimov  University of Pittsburgh  8/4/2009  8/14/2009 
Lev Truskinovsky  École Polytechnique  7/16/2009  8/6/2009 
Toni Kathleen Tullius  Rice University  8/4/2009  8/14/2009 
Erkan Tüzel  University of Minnesota  9/1/2007  8/7/2009 
Eric van den Berg  Telcordia  8/4/2009  8/15/2009 
Alexis Frederic Vasseur  University of Texas  7/26/2009  8/1/2009 
Li Wang  University of Wisconsin  8/4/2009  8/14/2009 
Ting Wang  University of Michigan  8/3/2009  8/14/2009 
Ying Wang  Ohio State University  7/11/2009  8/1/2009 
Ying Wang  Ohio State University  8/2/2009  8/14/2009 
Yu Wang  University of Delaware  8/4/2009  8/14/2009 
Zhian Wang  University of Minnesota  9/1/2007  8/31/2009 
Brendt Wohlberg  Los Alamos National Laboratory  8/4/2009  8/15/2009 
Wei Xiong  University of Minnesota  9/1/2008  8/31/2010 
Bo Yang  University of Minnesota  8/5/2009  8/14/2009 
Haijun Yu  Purdue University  7/12/2009  8/1/2009 
Yanni Zeng  University of Alabama at Birmingham  7/19/2009  8/1/2009 
Jingyan Zhang  Pennsylvania State University  8/4/2009  8/14/2009 
Weigang Zhong  University of Minnesota  9/8/2008  8/31/2010 
Xinghui Zhong  Brown University  8/4/2009  8/14/2009 
Kun Zhou  Pennsylvania State University  8/4/2009  8/14/2009 