| Institute for Mathematics and its Applications University of Minnesota 400 Lind Hall 207 Church Street SE Minneapolis, MN 55455 |
Web: http://www.ima.umn.edu |
Email: ima-staff@ima.umn.edu |
Telephone: (612) 624-6066 |
Fax: (612) 626-7370
Additional newsletters available at http://www.ima.umn.edu/newsletters
Additional newsletters available at http://www.ima.umn.edu/newsletters
IMA Newsletter #418
August 2011
IMA Events
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)Mathematical Modeling in Industry XV — A Workshop for Graduate Students
August 3-12, 2011
Organizers: Richard J. Braun (University of Delaware), Gilad Lerman (University of Minnesota Twin Cities)
Schedule
Wednesday, August 3
| All Day | Workshop Outline: Posing of problems by the 6 industry mentors. Half-hour 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. | MM8.3-12.11 | ||
| 9:00am-9:30am | Coffee and Registration | Keller Hall 3-176 | MM8.3-12.11 | |
| 9:30am-9:40am | Welcome to the IMA | Fadil Santosa (University of Minnesota) | Keller Hall 3-180 | MM8.3-12.11 |
| 9:40am-10:00am | 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 |
| 10:00am-10:20am | Team 2: Modeling aircraft hoses and flexible conduits | Thomas Grandine (Boeing) | Keller Hall 3-180 | MM8.3-12.11 |
| 10:20am-10:40am | 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 |
| 10:40am-11:00am | Break | Keller Hall 3-176 | MM8.3-12.11 | |
| 11:00am-11:20am | Team 4: Diffraction by photomasks | Apo Sezginer (KLA - Tencor) | Keller Hall 3-180 | MM8.3-12.11 |
| 11:20am-11:40am | Team 5: Optimizing power generation and delivery in smart electrical grids | Chai Wah Wu (IBM) | Keller Hall 3-180 | MM8.3-12.11 |
| 11:45am-1:30pm | Lunch | Lind Hall 400 | MM8.3-12.11 | |
| 1:30pm-4:30pm |
Afternoon - start work on projects
Team 1- 4-125A Mechanical Engineering Team 2- 401 Lind Hall Team 3- 305 Lind Hall Team 4- 340 Lind Hall Team 5- 321 Mechanical Engineering | MM8.3-12.11 |
Thursday, August 4
| 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- 4-125A Mechanical Engineering Team 2- 401 Lind Hall Team 3- 305 Lind Hall Team 4- 340 Lind Hall Team 5- 321 Mechanical Engineering | MM8.3-12.11 |
Friday, August 5
| All Day |
Students work on the projects. Mentors available for consultation. Team 1- 4-125A Mechanical Engineering Team 2- 401 Lind Hall Team 3- 305 Lind Hall Team 4- 340 Lind Hall Team 5- 321 Mechanical Engineering | MM8.3-12.11 |
Saturday, August 6
| All Day |
Students work on the projects.
Team 1- 4-125A Mechanical Engineering Team 2- 401 Lind Hall Team 3- 305 Lind Hall Team 4- 340 Lind Hall Team 5- 321 Mechanical Engineering | MM8.3-12.11 |
Sunday, August 7
| All Day |
Students work on the projects.
Team 1- 4-125A Mechanical Engineering Team 2- 401 Lind Hall Team 3- 305 Lind Hall Team 4- 340 Lind Hall Team 5- 321 Mechanical Engineering | MM8.3-12.11 |
Monday, August 8
| 9:00am-9:30am | Coffee | Keller Hall 3-176 | MM8.3-12.11 | |
| 9:30am-9:50am | Team 4 progress report | Keller Hall 3-180 | MM8.3-12.11 | |
| 9:50am-10:10am | Team 2 progress report | Keller Hall 3-180 | MM8.3-12.11 | |
| 10:10am-10:30am | Team 5 progress report | Keller Hall 3-180 | MM8.3-12.11 | |
| 10:30am-11:00am | Break | MM8.3-12.11 | ||
| 11:00am-11:20am | Team 1 progress report | Keller Hall 3-180 | MM8.3-12.11 | |
| 11:20am-11:40am | Team 3 progress report | Keller Hall 3-180 | MM8.3-12.11 | |
| 12:00pm-1:30pm | Picnic at Lind Hall court yard area | MM8.3-12.11 | ||
| 2:00pm-5:00pm |
Students work on the projects. Mentors available for consultation. Team 1- 4-125A Mechanical Engineering Team 2- 401 Lind Hall Team 3- 305 Lind Hall Team 4- 340 Lind Hall Team 5- 321 Mechanical Engineering | MM8.3-12.11 |
Tuesday, August 9
| All Day |
Students work on the projects. Mentors available for consultation. Team 1- 4-125A Mechanical Engineering Team 2- 401 Lind Hall Team 3- 305 Lind Hall Team 4- 340 Lind Hall Team 5- 321 Mechanical Engineering | MM8.3-12.11 |
Wednesday, August 10
| All Day |
Students work on the projects. Mentors available for consultation. Team 1- 4-125A Mechanical Engineering Team 2- 401 Lind Hall Team 3- 305 Lind Hall Team 4- 340 Lind Hall Team 5- 321 Mechanical Engineering | MM8.3-12.11 |
Thursday, August 11
| All Day |
Students work on the projects. Mentors available for consultation. Team 1- 4-125A Mechanical Engineering Team 2- 401 Lind Hall Team 3- 305 Lind Hall Team 4- 340 Lind Hall Team 5- 321 Mechanical Engineering | MM8.3-12.11 |
Friday, August 12
| 8:30am-9:00am | Coffee | Keller Hall 3-176 | MM8.3-12.11 | |
| 9:00am-9:30am | Team 3 final report | Keller Hall 3-180 | MM8.3-12.11 | |
| 9:30am-10:00am | Team 1 final report | Keller Hall 3-180 | MM8.3-12.11 | |
| 10:00am-10:30am | Team 4 final report | Keller Hall 3-180 | MM8.3-12.11 | |
| 10:30am-11:00am | Break | MM8.3-12.11 | ||
| 11:00am-11:30am | Team 5 final report | Keller Hall 3-180 | MM8.3-12.11 | |
| 11:30am-12:00pm | Team 2 final report | Keller Hall 3-180 | MM8.3-12.11 | |
| 12:00pm-1:30pm | Pizza party | MM8.3-12.11 |
Event Legend: |
|
| MM8.3-12.11 | Mathematical Modeling in Industry XV — A Workshop for Graduate Students |
Abstracts
| 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 X-ray 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 cross-sections 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 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):
- 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 year, that dimension will shrink to 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. An optical image of a photomask is projected on to a silicon wafer on which the integrated circuits will be formed. Presently, 193 nm wavelength deep UV light is used to project the image. Photomasks are inspected for defects by deep UV microscopy, which is subject to the same resolution limit as lithography. Manufacturing next generation integrated circuits and inspecting their photomasks require higher-resolution imaging. The leading candidate for next generation lithography, EUV (extreme ultraviolet) lithography uses 13.5 nm wavelength. At that wavelength, no material is highly reflective or transparent. The photomask and mirrors of the projector are coated with a multi-layer Bragg reflector to achieve sufficient reflectance. The photomask has a patterned absorber film over the Bragg reflector. The thickness of the absorber and the lateral dimensions of the mask pattern are on the order of four EUV wavelengths. Rapid yet accurate simulation of EUV image formation is essential to optimizing the photomask pattern and interpreting the images of an inspection microscope. This project concerns the key step of image simulation, which is calculating the diffracted near-field of the EUV photomask. 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 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
| Baha Alzalg | Washington State University | 8/2/2011 - 8/12/2011 |
| Brendan P.W. Ames | University of Minnesota | 8/31/2011 - 8/30/2012 |
| Catalina Anghel | University of Toronto | 8/2/2011 - 8/14/2011 |
| Stefan E. Atev | ViTAL Images, Inc. | 8/3/2011 - 8/12/2011 |
| Qichuan Bai | Pennsylvania State University | 8/2/2011 - 8/12/2011 |
| Nusret Balci | University of Minnesota | 9/1/2009 - 8/31/2011 |
| Jorge Humberto Banuelos | Macalester College | 8/2/2011 - 8/12/2011 |
| Richard J. Braun | University of Delaware | 8/2/2011 - 8/8/2011 |
| Luca Capogna | University of Arkansas | 8/15/2011 - 6/10/2012 |
| Aycil Cesmelioglu | University of Minnesota | 9/30/2010 - 8/30/2012 |
| Chi Hin Chan | University of Minnesota | 9/1/2009 - 8/31/2011 |
| Weitao Chen | Ohio State University | 8/2/2011 - 8/12/2011 |
| Paolo Codenotti | University of Minnesota | 8/31/2011 - 8/30/2012 |
| Miles Crosskey | Duke University | 8/2/2011 - 8/13/2011 |
| Jintao Cui | University of Minnesota | 8/31/2010 - 8/30/2012 |
| Randy H. Ewoldt | Massachusetts Institute of Technology | 9/1/2009 - 8/15/2011 |
| Brittan Farmer | University of Michigan | 8/2/2011 - 8/12/2011 |
| Oscar E. Fernandez | University of Michigan | 8/31/2010 - 8/22/2011 |
| Eric Thomas Foxall | University of Victoria | 8/2/2011 - 8/12/2011 |
| Xing (Margaret) Fu | Stanford University | 8/2/2011 - 8/12/2011 |
| Wenying Gan | University of California, Los Angeles | 8/2/2011 - 8/13/2011 |
| Thomas Grandine | Boeing | 8/2/2011 - 8/13/2011 |
| Ke Han | Pennsylvania State University | 8/2/2011 - 8/12/2011 |
| Yulia Hristova | University of Minnesota | 9/1/2010 - 8/31/2012 |
| Huiyi Hu | University of California, Los Angeles | 8/2/2011 - 8/13/2011 |
| Boqiang Huang | Universität Paderborn | 8/24/2011 - 9/10/2011 |
| Qing Huang | Arizona State University | 8/2/2011 - 8/12/2011 |
| Jens Christian Jorgensen | New York University | 8/2/2011 - 8/12/2011 |
| Sunnie Joshi | Texas A & M University | 8/2/2011 - 8/12/2011 |
| Eunkyung Ko | Mississippi State University | 8/2/2011 - 8/12/2011 |
| Pawel Konieczny | University of Minnesota | 9/1/2009 - 8/31/2011 |
| Sanjiv Kumar | Google Inc. | 8/2/2011 - 8/12/2011 |
| Angela Kunoth | Universität Paderborn | 7/23/2011 - 9/12/2011 |
| Rosemonde Lareau-Dussault | University of Sherbrooke | 8/2/2011 - 8/12/2011 |
| Gilad Lerman | University of Minnesota | 8/3/2011 - 8/12/2011 |
| Hengguang Li | Syracuse University | 8/16/2010 - 8/16/2011 |
| Zhi (George) Lin | University of Minnesota | 9/1/2009 - 8/31/2011 |
| Xin Liu | University of Minnesota | 8/31/2011 - 8/30/2012 |
| Shiqian Ma | University of Minnesota | 8/31/2011 - 8/30/2013 |
| Kara Lee Maki | University of Delaware | 9/1/2009 - 8/19/2011 |
| Yu (David) Mao | University of Minnesota | 8/31/2010 - 8/30/2012 |
| Oumar Mbodji | McMaster University | 8/2/2011 - 8/13/2011 |
| Dimitrios Mitsotakis | University of Minnesota | 10/27/2010 - 8/31/2012 |
| Dennis Moore | University of Kentucky | 8/2/2011 - 8/12/2011 |
| Cecilia Ortiz-Duenas | University of Minnesota | 9/1/2009 - 8/31/2011 |
| Ahmet Ozkan Ozer | Iowa State University | 8/2/2011 - 8/12/2011 |
| Mary Therese Padberg | University of Iowa | 8/1/2011 - 6/1/2012 |
| Candice Renee Price | University of Iowa | 8/1/2011 - 7/31/2012 |
| Weifeng (Frederick) Qiu | University of Minnesota | 8/31/2010 - 8/30/2012 |
| Mustazee Rahman | University of Toronto | 8/2/2011 - 8/12/2011 |
| Anton Sakovich | McMaster University | 8/2/2011 - 8/13/2011 |
| Fadil Santosa | University of Minnesota | 7/1/2008 - 8/30/2011 |
| Shirin Sardar | Rice University | 8/2/2011 - 8/13/2011 |
| Apo Sezginer | KLA - Tencor | 8/2/2011 - 8/13/2011 |
| Shuanglin Shao | Institute for Advanced Study | 9/1/2009 - 8/17/2011 |
| Alex Shum | University of Waterloo | 8/2/2011 - 8/12/2011 |
| Cory Simon | University of British Columbia | 8/2/2011 - 8/13/2011 |
| Arthur Szlam | University of Minnesota | 8/31/2011 - 8/30/2012 |
| Changhui Tan | University of Maryland | 8/2/2011 - 8/12/2011 |
| Dimitar Trenev | Texas A & M University | 9/1/2009 - 8/4/2011 |
| Hsiao-Chieh Tseng | University of California, Davis | 8/2/2011 - 8/12/2011 |
| Caroline Uhler | University of Minnesota | 8/31/2011 - 10/30/2011 |
| Divyanshu Vats | University of Minnesota | 8/31/2011 - 8/30/2012 |
| Chai Wah Wu | IBM | 8/2/2011 - 8/13/2011 |
| Haley Yaple | Northwestern University | 8/2/2011 - 8/12/2011 |
| Kidist Zeleke | University of Houston | 8/2/2011 - 8/13/2011 |
| Teng Zhang | University of Minnesota | 8/31/2011 - 8/30/2012 |
| Zhou Zhou | University of Michigan | 8/2/2011 - 8/13/2011 |
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