Institute for Mathematics and its Applications University of Minnesota 114 Lind Hall 207 Church Street SE Minneapolis, MN 55455 
The 20082009 IMA thematic program will be on "Mathematics and Chemistry."
This program is broadly related to computational chemistry which has reached a stage of development where many chemical properties of both simple and complex systems may now be computed more accurately, more economically, or more speedily than they can be measured. Further advances in accuracy and practicality will depend on the development of both new theory and new algorithms, and mathematical techniques will play an important role in both of these areas. The advances in chemical theory and computations have built on interfaces with a number of areas of mathematics, including differential equations, linear and nonlinear algebra, optimization theory, probability theory, stochastic analysis, sampling theory, complex analysis, geometry, group theory, and numerical analysis. Further progress in computational chemistry will require that the ties between chemistry and mathematics be strengthened. This IMA program will provide a setting for the chemistry and mathematics communities to examine some of these problems together. The year will focus on issues in electronic structure, dynamics, and statistical mechanics, including both the mathematical underpinnings of modern molecular modeling and simulation and practical issues in stateoftheart applications. Applications areas will include organic and inorganic chemistry, biochemistry, solidstate chemistry, nanochemistry, advanced materials, photochemistry, catalysis, and environmental chemistry. Emphasis will be placed on mingling applied mathematicians with theoretical and computational chemists in each workshop. Limited financial support is available for the workshops. Detailed information about this program can be found at "Mathematics and Chemistry."
Markus Keel has accepted the position of Deputy Director of the IMA effective July 21, 2008. He is Professor in the School of Mathematics at the University of Minnesota. His research interests lie in partial differential equations and harmonic analysis. He is very familiar with the institute having participated in several IMA programs.
The outgoing associate director of the IMA, Cheri Shakiban will complete her two years of service at the end of August and will return to her position as professor of mathematics at University of St. Thomas. Chun Liu of Penn State University will start his new position as the IMA Associate Director effective September 1, 2008.
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  MM8.615.08  
9:00a9:30a  Coffee and Registration  EE/CS 3176  MM8.615.08  
9:30a9:40a  Welcome and Introduction  Richard J. Braun (University of Delaware) Fadil Santosa (University of Minnesota)  EE/CS 3180  MM8.615.08 
9:40a10:00a  Team 1: Modeling, simulation, and the analysis of a financial derivative  Christopher Bemis (Whitebox Advisors)  EE/CS 3180  MM8.615.08 
10:00a10:20a  Team 2: Stability of extending films  Olus N. Boratav (Corning)  EE/CS 3180  MM8.615.08 
10:20a10:40a  Team 3: Ribbon formation for electrical interconnection  J. Michael Gray (Medtronic) Robert Shimpa (Medtronic)  EE/CS 3180  MM8.615.08 
10:40a11:00a  Break  EE/CS 3176  MM8.615.08  
11:00a11:20a  Team 4: Loftfree unlofting methods for geometric design  Thomas Grandine (Boeing)  EE/CS 3180  MM8.615.08 
11:20a11:40a  Team 5: Optimal calibration in chemical spectroscopy  Anthony José Kearsley (National Institute of Standards and Technology)  EE/CS 3180  MM8.615.08 
11:40a12:00p  Team 6: Performance and robustness study of peertopeer networks  Chai Wah Wu (IBM)  EE/CS 3180  MM8.615.08 
12:00p1:30p  Lunch  MM8.615.08  
1:30p4:30p  Afternoon  start work on projects  Breakout Rooms  MM8.615.08  
Team 1  LindH 403 Team 2  LindH 217 Team 3  LindH 409 Team 4  LindH 215 Team 5  LindH 436 Team 6  LindH 401 
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 403  Breakout Rooms  MM8.615.08 
All Day  Students work on the projects. Mentors available for
consultation.
Team 1  LindH 403  Breakout Rooms  MM8.615.08 
All Day  Students work on the projects.
Team 1  LindH 403  Breakout Rooms  MM8.615.08 
All Day  Students work on the projects.
Team 1  LindH 403  Breakout Rooms  MM8.615.08 
9:00a9:30a  Coffee  
9:00a9:30a  Coffee  EE/CS 3176  MM8.615.08  
9:30a9:50a  Team 4 progress report  EE/CS 3180  MM8.615.08  
9:50a10:10a  Team 2 progress report  EE/CS 3180  MM8.615.08  
10:10a10:30a  Team 5 progress report  EE/CS 3180  MM8.615.08  
10:30a11:00a  Break  EE/CS 3176  MM8.615.08  
11:00a11:20a  Team 1 progress report  EE/CS 3180  MM8.615.08  
11:00a11:20a  Team 1 progress report  EE/CS 3180  
11:20a11:40a  Team 6 progress report  EE/CS 3180  MM8.615.08  
11:40a12:00p  Team 3 progress report  EE/CS 3180  MM8.615.08  
12:00p1:30p  Picnic  UofM East River Flats Park  
12:00p1:30p  Picnic at Cooke Hall Fields Picnic area map  Cooke Hall Fields Picnic area  MM8.615.08  
2:00p5:00p  Remainder of the day
Students work on projects. Mentors available for consultation.
Team 1  LindH 403  Breakout Rooms  MM8.615.08 
All Day  Students work on the projects. Mentors available for
consultation.
Team 1  LindH 403  Breakout Rooms  MM8.615.08 
All Day  Students work on the projects. Mentors available for
consultation.
Team 1  LindH 403  Breakout Rooms  MM8.615.08 
All Day  Students work on the projects. Mentors available for
consultation.
Team 1  LindH 403  Breakout Rooms  MM8.615.08 
8:30a9:00a  Coffee  EE/CS 3176  MM8.615.08  
9:00a9:30a  Team 3 final report  EE/CS 3180  MM8.615.08  
9:30a10:00a  Team 6 final report  EE/CS 3180  MM8.615.08  
10:00a10:30a  Team 1 final report  EE/CS 3180  MM8.615.08  
10:30a11:00a  Break  EE/CS 3176  MM8.615.08  
11:00a11:30a  Team 5 final report  EE/CS 3180  MM8.615.08  
11:30a12:00p  Team 2 final report  EE/CS 3180  MM8.615.08  
12:00p12:30p  Team 4 final report  EE/CS 3180  MM8.615.08  
12:30p2:00p  Pizza party  Lind Hall 400  MM8.615.08 
Event Legend: 

MM8.615.08  Mathematical Modeling in Industry XII  A Workshop for Graduate Students 
Team 1 progress report  
Abstract: No Abstract  
Christopher Bemis (Whitebox Advisors)  Team 1: Modeling, simulation, and the analysis of a financial derivative 
Abstract:
Project Description: Due to the complexity of financial markets, financial derivative modeling requires both an ability to understand and implement theoretical mathematical objects as well as a reliance on simulation techniques. A well known economist, Eugene Fama, once said, “We know all models are false.” This notwithstanding, an approximate model allows the practitioner to understand her position in terms of widely used market parameters such as volatility or correlation. Additionally, insight may be gained into the approximate distribution of payoffs as a function of such parameters once a model has been designated. This project will present and model a financial instrument dubbed a ‘dispersion option’. Such an option has a payoff structure contingent on how much individual stock returns within a basket diverge from the average return of the basket. As a first step, we will simulate such an option with a variety of real market data, and examine the distribution of payoffs, thereby gaining insight into the historical behavior of such instruments. We will then attempt to examine the distribution of payoffs of such an option based on multiple models of the underlying names. This may be done using simulation techniques or via a mathematical proof depending on the complexity of the model assumed. Of primary interest would be to understand the payoff structure of the option as a function of easily identifiable parameters. Reference: Options, Futures, and Other Derivatives, J. C. Hull, Prentice Hall. Especially chapters titled "Numerical Procedures", and "More on Models and Numerical Procedures" in the sixth edition. Prerequisites: Knowledge of options pricing theory (especially RiskNeutral Valuation), statistics, some numerical analysis, and ability to write simulation code. Desired: Coursework in mathematical finance and statistics, Matlab programming, and a familiarity with model selection techniques and evaluation. 

Olus N. Boratav (Corning)  Team 2: Stability of extending films 
Abstract:
Project Description: The goal of this research is to revisit the stability results of Yeow (1974) on extending flows with free surfaces. The eigenvalue problem will be formulated and solved for the flow of a Newtonian film such as the one encountered in film casting. The stable and unstable region boundaries will be obtained. The analysis will be extended to a nonisothermal case similar to the work by Shah and Pearson (1972). Stability boundaries for different draw velocity (at the inlet and the exit of the process), and viscosity ratios will be sought. For the solutions which are unstable (or marginally unstable), timedependent solutions (oscillating or growing in time) describing the free surface motion will be obtained. References:
Y. L., Yeow: On the stability of extending films: a model for the film
casting process (J. Fluid Mech. 1974 v66 (3) 613622.
Y. T. Shah & J. R. A. Pearson: On the stability of nonisothermal fiber
spinning  general case. Industrial & Engineering Chemistry
Fundamentals. 1972 v11 (2) 150153.
Additional References: 

Richard J. Braun (University of Delaware), Fadil Santosa (University of Minnesota)  Welcome and Introduction 
Abstract: No Abstract  
Thomas Grandine (Boeing)  Team 4: Loftfree unlofting methods for geometric design 
Abstract:
Project Description: The process of laying out the curves and surfaces
needed to describe free form shapes in mechanical design is called
lofting. Examples of lofting include shapes such as ship hulls,
airplane wings and bodies, automobile exteriors, and so on. The best
lofting procedures take a vector of inputs, which can contain items like
wing span, wing sweep angle, aspect ratios, wing leading edge
curvatures, etc., and produce a mathematical model of the geometric
shape. Good lofting procedures necessarily have to process the input
data nonlinearly in order to produce acceptable shapes.
Additionally, it is frequently important to solve the inverse problem.
Specifically, one is given a mathematical model of a geometric shape
and, with any luck, a lofting code and wants to know what vector of
inputs to the lofting code will produce the given shape. This problem
has been called the unlofting problem, and it can usually be solved with
with standard techniques in nonlinear least squares and nonlinear
parameter estimation. Just as frequently, though, the unlofting problem
arises in contexts where no lofting code exists, requiring such a code
to be produced as part of the solution. So far, the requirement to
produce a lofting code as part of the solution to the unlofting problem
has ruined all attempts to produce a fully automatic solution.
This project will attempt to construct a prototype unlofting code given
only a final geometric shape with no accompanying lofting code. Some
recent developments in multiresolution modeling have suggested a
promising approach to this problem that we will explore during the
workshop, focusing initially on 2D curves and then migrating to simple
3D shapes if time permits.
References:
"Multiresolution morphing for planar curves," by S. Hahmann, G.P. Bonneau, M. Cornillac, and B. Caramiaux. Computing 79 (24), pp. 197209 (2007) Prerequisites: Required: 1 semester of numerical analysis and computing skills. Desired: Knowledge of nonlinear least squares, splines, and Python programming. Keywords: Lofting, geometric morphing, inverse problems, multiresolution modeling, nonlinear parameter estimation. 

J. Michael Gray (Medtronic), Robert Shimpa (Medtronic)  Team 3: Ribbon formation for electrical interconnection 
Abstract: Project Description: Some electrical interconnections in medical devices are made by
forming and welding piece of thin flat ribbon (or wire) between
two electrical terminals. Current equipment for forming the
ribbon allows for a virtually an infinite set of motions
between the two terminals to be programmed. Currently the only
method for determining what the resulting shape of the ribbon
will be from a set of machine motions is to program the
machine, form a ribbon, visually observe the resultant shape,
and iterate until the "desired shape" is obtained. The
problems proposed are 1) Given some data regarding the ribbon
shapes that result from a very limited set of tool motions, can
a more general model be developed that can predict the shape of
the loop based on the machine motions, 2) Can this model be
improved by incorporating the material response behavior of the
ribbon or other physical relationships that govern ribbon
formation, 3) Can this model be inverted so that if a
particular ribbon shape is desired, a corresponding set of
machine parameters can be identified, and 4) If only the
spacing, positioning, and clearance around two terminals are
known, can an optimal shape be identified that minimizes the
stress induced in the ribbon from relative motion between the
terminals while avoiding interference with any of the
surrounding geometric constraints.
References:
1. "WireBonding Loop Profiles" http://www.siliconfareast.com/wirebondloopprofiles.htm 2. "Apparatus and method for laser welding of ribbons" US Patent 6,717,100
Prerequisites:
Familiarity with mechanics of materials, plastic deformation of
thin metal, curve fitting, data analysis, optimization, &
machine control would all be helpful.
Keywords:
wire bonding, interconnect ribbon, tool path control, plastic
deformation of thin wire or ribbon
Images: 

Anthony José Kearsley (National Institute of Standards and Technology)  Team 5: Optimal calibration in chemical spectroscopy 
Abstract: Project Description:
Instruments for chemical spectroscopy are finding key application in fields of homeland security, healthcare and manufacturing of chemicals and machine parts [1]. The need to automatically analyze large amounts of data quickly and to calibrate these instruments in an unbiased way is thus becoming ever more important. In many applications, for example healthcare and law enforcement, both calibration [2] and data analysis ([3,4]) should be performed with as little operator input as possible.
One of the most important chemical spectroscopy instruments is the Matrix Assisted Laser Desorption Absorption Time of Flight (MALDITOF) mass spectrometer. A schematic of the instrument is shown above, and sample data output is shown below. The MALDITOF produces a collection of 2tuples (usually between 50,000100,000 pairs of data points), from which one should identify peaks and then integrate the area underneath each peak. A major challenge is the development of an automated peak peaking and peak integration algorithm requiring no operator input. A second and closely related challenge is the development of an operator independent calibration scheme. I will outline an approach to the data analysis problem and present some very precursory work involving Standard Reference Materials (SRM). I will also present a first attempt at automatic instrument calibration. Data from larger molecules will be used as a litmus test. If time permits, I will will present at least one other spectroscopy instrument. References: [1] Introduction to Mass Spectrometry, J. T. Watson, LippencottRaven, 1997. [2] Wallace, W. E., Guttman, C. M., Flynn, K. M., Kearsley, A. J., `Numerical optimization of matrixassisted laser desorption/ionization timeofflight mass spectrometry: Application to synthetic polymer molecular mass distribution measurement’ ANALYTICA CHIMICA ACTA Volume: 604 Issue: 1 Special Issue: Pages: 6268 NOV 26 2007 [3] Wallace, W. E., Kearsley, A. J., Guttman, C. M., `An operatorindependent approach to mass spectral peak identification and integration’ ANALYTICAL CHEMISTRY. Volume: 76 Issue: 9 Pages: 24462452. MAY 1 2004 [4] Wallace, W. E., Kearsley, A. J., Guttman, C. M., `MassSpectator: Fully automated peak picking and integration  A Webbased tool for locating mass spectral peaks and calculating their areas without user input. ‘ ANALYTICAL CHEMISTRY Volume: 76 Issue: 9 Pages: 183A184A MAY 1 2004 Prerequisites: A programming language, (Fortran 90, C, C++, or Matlab); a course in optimization or signal processing is helpful but not necessary. 

Chai Wah Wu (IBM)  Team 6: Performance and robustness study of peertopeer networks 
Abstract:
Project description: Peertopeer networks are decentralized computing
architectures that promise to deliver scalability in data sharing and
streaming applications under dynamic network conditions. In these
architectures peers are connected to the network and contribute resources
in return for some useful services delivered by the network. Some
questions that determine the performance and robustness of the
peertopeer network are: what is the capacity of the network? How robust
is the network behavior with respect to flashcrowds and random peer
failures and departures? In this project we study the performance and
robustness of various peertopeer networks by studying various algorithms
for constructing the overlay network and for determining the data packets
that are transmitted . We study the properties of the complex network
resulting from these algorithms in order to identify peertopeer networks
which are both robust and efficient.
Prerequisites: computer programming (C, Matlab or Python), discrete
mathematics.

Kapil Ahuja  Virginia Polytechnic Institute and State University  8/5/2008  8/15/2008 
Donald G. Aronson  University of Minnesota  9/1/2002  8/31/2009 
Richard Barnard  Louisiana State University  8/5/2008  8/16/2008 
Daniel J. Bates  University of Minnesota  9/1/2006  8/15/2008 
Christopher Bemis  Whitebox Advisors  8/6/2008  8/15/2008 
Yermal Sujeet Bhat  University of Minnesota  9/1/2006  8/12/2008 
Olus N. Boratav  Corning  8/5/2008  8/16/2008 
Richard J. Braun  University of Delaware  8/5/2008  8/15/2008 
Hannah Callender  University of Minnesota  9/1/2007  8/31/2009 
Lyrial Marie Chism  University of Mississippi  8/5/2008  8/15/2008 
Sohhyun Chung  University of Michigan  8/5/2008  8/16/2008 
Holly Clark  University of Tennessee  8/5/2008  8/16/2008 
Ludovica Cecilia CottaRamusino  University of Minnesota  10/1/2007  8/30/2009 
Yilin Dai  Michigan Technological University  8/5/2008  8/15/2008 
Christina Dekany  Southern Methodist University  8/5/2008  8/16/2008 
Yuan Dong  Northern Illinois University  8/5/2008  8/15/2008 
Olivier Dubois  University of Minnesota  9/3/2007  8/31/2009 
Daniel Flath  Macalester College  8/27/2008  12/20/2008 
Christopher Fraser  University of Chicago  8/27/2008  6/30/2009 
Yutheeka Gadhyan  University of Houston  8/5/2008  8/16/2008 
Simon Gemmrich  McGill University  8/5/2008  8/16/2008 
Jerome Goddard II  Mississippi State University  8/5/2008  8/16/2008 
Jason E. Gower  University of Minnesota  9/1/2006  8/31/2008 
Thomas Grandine  Boeing  8/5/2008  8/15/2008 
J. Michael Gray  Medtronic  8/6/2008  8/15/2008 
Huaiying Gu  University of Michigan  8/5/2008  8/16/2008 
Shiyuan Gu  Louisiana State University  8/5/2008  8/15/2008 
Xiaoqing He  University of Minnesota  8/6/2008  8/15/2008 
Milena Hering  University of Minnesota  9/1/2006  8/22/2008 
Peter Hinow  University of Minnesota  9/1/2007  8/31/2009 
Junming Huang  University of Pittsburgh  8/5/2008  8/16/2008 
Liquan Huang  University of Delaware  8/5/2008  8/16/2008 
Ashraf Ibrahim  Texas A & M University  8/5/2008  8/15/2008 
Christopher Jones  University of Pittsburgh  8/5/2008  8/16/2008 
Anthony José Kearsley  National Institute of Standards and Technology  8/5/2008  8/16/2008 
Markus Keel  University of Minnesota  7/21/2008  6/30/2009 
Taebeom Kim  University of Houston  8/5/2008  8/15/2008 
Jill Klentzman  Southern Methodist University  8/5/2008  8/16/2008 
Dias Kurmashev  University of Memphis  8/5/2008  8/15/2008 
Jiyung Lois Kwon  Washington State University  8/5/2008  8/16/2008 
ChiunChang Lee  National Taiwan University  8/26/2008  7/31/2009 
Anton Leykin  University of Minnesota  8/16/2006  8/15/2008 
Xingjie Li  University of Minnesota  8/6/2008  8/15/2008 
TaiChia Lin  National Taiwan University  8/23/2008  7/31/2009 
Youzuo Lin  Arizona State University  8/5/2008  8/15/2008 
Zhongyi Nie  University of Kentucky  8/5/2008  8/16/2008 
Mauricio Osorio  University of Cincinnati  8/5/2008  8/15/2008 
Gregory Richards  Kent State University  8/5/2008  8/15/2008 
Fadil Santosa  University of Minnesota  7/1/2008  6/30/2010 
Deena Schmidt  University of Minnesota  9/1/2007  8/28/2008 
Chehrzad Shakiban  University of Minnesota  9/1/2006  8/31/2008 
Qiling Shi  University of Central Florida  8/5/2008  8/16/2008 
Robert Shimpa  Medtronic  8/6/2008  8/15/2008 
Yan Shu  Georgia Institute of Technology  8/5/2008  8/15/2008 
Andrew M. Stein  University of Minnesota  9/1/2007  8/31/2009 
Lin Tong  Iowa State University  8/5/2008  8/15/2008 
Erkan Tüzel  University of Minnesota  9/1/2007  8/31/2009 
Jon Van Laarhoven  University of Iowa  8/5/2008  8/15/2008 
Jiabin Wang  Rutgers University  8/5/2008  8/16/2008 
Zhian Wang  University of Minnesota  9/1/2007  8/31/2009 
Jia Wei  Texas A & M University  8/5/2008  8/15/2008 
Benjamin Weitz  University of Minnesota  7/2/2008  8/30/2008 
Chai Wah Wu  IBM  8/5/2008  8/15/2008 
Mohammad Zaki  University of Illinois at UrbanaChampaign  8/9/2008  8/15/2008 
Hongchao Zhang  University of Minnesota  9/1/2006  8/15/2008 
Guangjin Zhong  Michigan Technological University  8/4/2008  8/15/2008 
Qinghua Zhu  University of Delaware  8/5/2008  8/16/2008 