Institute for Mathematics and its Applications University of Minnesota 400 Lind Hall 207 Church Street SE Minneapolis, MN 55455 
9:00am10:30am  Other attendees speak  Lind Hall 305  ND6.207.1.11  
10:30am11:00am  Break  4th floor Lind  ND6.207.1.11  
11:00am12:30pm  Plus open problems  Lind Hall 305  ND6.207.1.11  
2:30pm3:00pm  Coffee break  Lind Hall 400 
All Day  Independence Day. The IMA is closed. 
8:30am9:00am  Registration and coffee  Keller Hall 3176  SW7.1316.11  
8:30am9:00am  Registration and coffee  Keller Hall 3176  SWb7.1316.11  
9:00am9:15am  Welcome; Introductions  Fadil Santosa (University of Minnesota)  Keller Hall 3176  SW7.1316.11 
9:00am9:15am  Welcome; Introductions  Fadil Santosa (University of Minnesota)  Keller Hall 3176  SWb7.1316.11 
9:15am10:30am  Project Descriptions/Formation of Breakout Groups  Keller Hall 2172  SWb7.1316.11  
9:15am10:15am  Why Wavelets?  Keller Hall 2170  SW7.1316.11  
10:15am10:30am  Break  Keller Hall 3176  SW7.1316.11  
10:30am11:45am  Individual Group Work  Keller Hall 2172  SWb7.1316.11  
10:30am11:45am  Digital Images  Keller Hall 2170  SW7.1316.11  
11:45am1:30pm  Lunch  SW7.1316.11  
11:45am1:30pm  Lunch  SWb7.1316.11  
1:30pm2:45pm  Individual Group Work  Keller Hall 2172  SWb7.1316.11  
1:30pm2:45pm  The Haar Wavelet Transformation (HWT)  Keller Hall 2170  SW7.1316.11  
2:45pm3:00pm  Break  Keller Hall 3176  SW7.1316.11  
2:45pm3:00pm  Break  Keller Hall 3176  SWb7.1316.11  
3:00pm4:15pm  Individual Group Work  Keller Hall 2172  SWb7.1316.11  
3:00pm4:15pm  Coding the HWT, Edge Detection Application  Keller Hall 2170  SW7.1316.11  
4:15pm4:30pm  Group photo  SW7.1316.11  
4:15pm4:30pm  Group photo  SWb7.1316.11 
8:30am9:00am  Coffee  Keller Hall 3176  SW7.1316.11  
8:30am9:00am  Coffee  Keller Hall 3176  SWb7.1316.11  
9:00am10:15am  Individual Group Work  Keller Hall 2172  SWb7.1316.11  
9:00am10:15am  Cumulative Energy, Entropy, and PSNR  Keller Hall 2170  SW7.1316.11  
10:15am10:30am  Break  Keller Hall 3176  SW7.1316.11  
10:15am10:30am  Break  Keller Hall 3176  SWb7.1316.11  
10:30am11:45am  Individual Group Work  Keller Hall 2172  SWb7.1316.11  
10:30am11:45am  Huffman Coding  Keller Hall 2170  SW7.1316.11  
11:45am1:15pm  Lunch  SW7.1316.11  
11:45am1:15pm  Lunch  SWb7.1316.11  
1:15pm2:30pm  Status Report from All Groups  Keller Hall 2172  SWb7.1316.11  
1:15pm2:30pm  Putting It All Together: Image Compression  Keller Hall 2170  SW7.1316.11  
2:30pm2:45pm  Break  Keller Hall 3176  SW7.1316.11  
2:30pm2:45pm  Break  Keller Hall 3176  SWb7.1316.11  
2:45pm4:00pm  Individual Group Work  Keller Hall 2172  SWb7.1316.11  
2:45pm4:00pm  Daubechies Wavelet Transformations  Keller Hall 2170  SW7.1316.11  
5:00pm10:30pm  Dinner Excursion  SWb7.1316.11  
5:00pm10:30pm  Dinner Excursion  TBA  SW7.1316.11 
8:30am9:00am  Coffee  Keller Hall 3176  SW7.1316.11  
8:30am9:00am  Coffee  Keller Hall 3176  SWb7.1316.11  
9:00am10:15am  Individual Group Work  Keller Hall 2172  SWb7.1316.11  
9:00am10:15am  Fourier Series and Filter Construction  Keller Hall 2170  SW7.1316.11  
10:15am10:30am  Break  Keller Hall 3176  SW7.1316.11  
10:15am10:30am  Break  Keller Hall 3176  SWb7.1316.11  
10:30am11:45am  Individual Group Work  Keller Hall 2172  SWb7.1316.11  
10:30am11:45am  Biorthogonal Wavelet Filters  Keller Hall 2170  SW7.1316.11  
11:45am1:30pm  Lunch  SW7.1316.11  
11:45am1:30pm  Lunch  SWb7.1316.11  
1:30pm5:00pm  Excursion: TBA  TBA  SW7.1316.11  
1:30pm5:00pm  Excursion: TBA  SWb7.1316.11 
8:30am9:00am  Coffee  Keller Hall 3176  SW7.1316.11  
8:30am9:00am  Coffee  Keller Hall 3176  SWb7.1316.11  
9:00am10:15am  Presentations to Introductory Workshop Participants  Keller Hall 3180  SWb7.1316.11  
9:00am10:15am  Presentations from Projects Workshop Participants  Keller Hall 3180  SW7.1316.11  
10:15am10:30am  Break  Keller Hall 3176  SW7.1316.11  
10:15am11:30am  Break  Keller Hall 3176  SWb7.1316.11  
10:30am11:45am  Presentations to Introductory Workshop Participants  Keller Hall 3180  SWb7.1316.11  
10:30am11:45am  Presentations from Projects Workshop Participants  Keller Hall 3180  SW7.1316.11 
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: 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 Xray dose images, and pathology such as stenosis and calcifications. 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 crosssections vary too rapidly. The goal of this project is to design a method for refining a vessel segmentation based on the following general approach:
The project will use real clinical data and many different types of vessels. References:
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 2point boundary value problems, Matlab 

Sanjiv Kumar (Google Inc.)  Team 3: Fast nearest neighbor search in massive highdimensional 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). Treebased methods scale poorly with dimensionality, typically reducing to worst case linear search. Hashing based methods are popular for largescale search but learning accurate and fast hashes for highdimensional 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 stateoftheart hashing methods, developing the formulation for learning datadependent 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, Highdimensional hashing References: For a quick overview of ANN search, review the following tutorials (more references are given at the end of the tutorials):
 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 gatelength. 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 xrays, 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 electronbeam 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 realtime. Both lithography and inspection use partial coherence imaging, which means the photomask is illuminated from many directions by spatially coherent timeharmonic planewaves that are temporally incoherent with each other. Accurately simulating partial coherent imaging requires solving Maxwell’s equations for many planewaves 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 solvers^{3} are used to study at most a micronbymicron portion of a circuit. Simulating an entire photomask in that manner would take millions of years using a supercomputer. Fast approximations due to Kirchhoff^{1} and Hopkins^{2} are used to handle an entire chip or photomask. These approximations have been extended to include edge diffraction^{4}. An approach called domain decomposition (different than the domain decomposition method in PDEs) estimates the diffracted nearfield as a collage of easiertosolve diffracted fields^{5}. Kirchhoff+Hopkins approximation and some of its extensions provide an estimate of the diffracted nearfield 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 subwavelength 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:
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 plugin 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. 
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. DiazEspinosa  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 UrbanaChampaign  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 
GuangTsai 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 WenYu 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 
JoseMaria 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 OrtizDuenas  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 