
October
4, 2002
Ron Mahler (Lockheed Martin
Tactical Defense Systems)
This
talk addresses the problem of detecting and tracking large
numbers of non-cooperative targets in a cluttered background.
The usual approach, which is computationally intractable in
general, would be to attempt to detect and track each and
every target or potential target. The proposed approach uses
the opposite strategy: it attempts to track only what is knowable
(initially, geometrical shape and target density) and only
later attempting to resolve individual targets out of the
"multitarget background" as (and if) more data becomes available.
From a mathematical point of view the approach is novel because
the multitarget scenario is modeled as a random measure (specifically,
a multidimensional random point process) and the optimal (but
intractable) recursive Bayes filter is approximated by propagating
the first moment measure (more accurately, its density function)
instead of the full multitarget posterior density function.

October
11, 2002
Fred Hulting (General
Mills)
Statistics
in New Product Development
Taking
a new product from idea to reality requires many difficult
steps. Among them are the identification and optimization
of the concept and product, and the startup and refinement
of the associated manufacturing process. "Statistics" - including
statistical thinking, experimental design, statistical methods,
and statistical computing - plays a vital role in new product
development. Using a series of examples, this talk will highlight
the way in which statistics (and statisticians) can contribute
to the development of successful new consumer products.

November
1, 2002
Oleg Aleksandrov (University
of Minnesota)
Wave
Propagation in an Optical Fiber

November
22, 2002
Mihail M. Sigalas (Agilent
Technologies)
Photonic
Crystals in Optical Communications Slides:
html pdf
Recent trends in optical communications show an increase in
device integration along with a decrease in device size. Photonic
crystals (PC) may be the platform of future miniaturize optical
devices because they can control the light in sizes of the
order of the wavelength.
The theoretical tools needed to study PC will be presented.
Results for both two dimensional slab PC and three dimensional
PC will be shown and the advantages of each case will be discussed.

December
13, 2002
Ann E. DeWitt (3M Research
and Development) adewitt@mmm.com
Mathematics
Applied to Biological Systems in Drug Discovery Slides:
html pdf
Advances
in tools to probe biological phenomena such as combinatorial
chemistry, high-throughput screening, genomics and proteomics
have, in part, resulted in a rapid rise in the rate at which
information is collected. The corresponding increase in the
volume of information supplies a rich source for understanding
how biological systems operate, but appropriate methods for
placing each new piece of information into a larger context
must be developed. Certainly mathematics have been applied
to the investigation of biological systems in the past, and
further opportunities arise from the need to organize and
understand vast amounts of information, and to, furthermore,
systematically, quantitatively capture behavior for predictive
engineering.
This
presentation will focus on how mathematics is used as a data
analysis and predictive engineering tool to understand biological
processes (i.e. life!), including a general introduction to
the emerging discipline of "systems biology." Doctoral research
conducted at Massachusetts Institute of Technology will be
used for illustration along with examples from current research
conducted in 3M Pharmaceuticals.
Speaker
Information:
Senior Research Engineer
Software, Electric & Mechanical Systems Technology Center/Pharmaceuticals
3M Research and Development
Ph.D.
Chemical Engineering, 2001
Massachusetts Institute of Technology, Cambridge, MA
Thesis advisor: Douglas A. Lauffenburger
B.S.
Chemical Engineering, 1996
University of Illinois, Urbana-Champaign, IL
Speaker
Publications:
DeWitt, Ann E., T. Iida, H. Lam, V. Hill, H.S. Wiley, D.A.
Lauffenburger. Affinity Regulates Spatial Range of EGF Receptor
Autocrine Ligand Binding. Developmental Biology, 2002,
v250; pp. 305-316.
DeWitt,
Ann E., H. S. Wiley, D. A. Lauffenburger. Quantitative Analysis
of the EGF Receptor Autocrine System Reveals Cryptic Regulation
of Cell Response by Ligand Capture. Journal of Cell Science,
June 2001, v114; pp. 2301-13.

January
31, 2003
Stephen
Mildenhall (Kemper Insurance) Stephen.Mildenhall@kemperinsurance.com
The
Evolution of Property-Casualty Insurance Liabilities
Property-Casualty insurance liabilities, related to claims
from automobile accidents, house fires, liability claims,
etc., are characterized by reporting and settlement lags which
can be several years long. As a result, the liabilities and
loss payments from a given set of insurance policies evolve
over time, with payments gradually increasing to their ultimate
settlement values. Actuaries use aggregate loss distributions
(random sums) to model ultimate settlement values but there
is no established way of decomposing ultimate losses into
losses paid each year. This talk will explain how the negative
multinomial distribution can be used to decompose ultimate
losses into losses by year, and show that the resulting decomposition
has empirically desirable properties. Next, we will discuss
a Markov-chain model of claim complexity, which can be combined
with the decomposition result, in order to produce a model
with increasing average claim severity over time, a phenomenon
observed in most lines of insurance. The Markov-chain model
is an interesting departure from traditional actuarial analyses
because it uses detailed cross-sectional data rather than
long-term summary data.

February
7, 2003
Lawrence
C. Cowsar (Bell Laboratories, Lucent Technologies)
Raman
Amplified Optical Transport Systems
Optical
transport system capacity has outpaced Moore's law over the
past two decades. The pace continues unabated as a new generation
of commercial products based on Raman amplification are being
introduced. This talk will focus on some of the simulation
challenges that arise in the design, control and testing of
this next generation of optical transport.

February
21, 2003
Lili Ju (IMA Industrial
Postdoc) ju@ima.umn.edu
Cortical
Surface Flattening Using Discrete Conformal Mapping with Minimal
Metric Distortion
Although
flattening a cortical surface necessarily introduces metric
distortion due to the non-constant Gaussian curvature of the
surface, the Riemann Mapping Theorem states that continuously
differentiable surfaces can be mapped without angular distortion.
Several techniques have been proposed for flattening polygonal
representations of surfaces while substantially minimizing
metric distortion, and methods for conformal flattening of
polygonal surfaces have also been proposed. We describe an
efficient method for generating conformal flat maps of triangulated
surfaces while minimizing metric distortion within the class
of conformal maps. Our method, which controls both angular
and metric distortion, involves the solution of a linear system
and a small scale nonlinear minimization. It can be applied
to user-defined "patches" or to an entire cortical surface.

March
7, 2003
Wade S. Martinson (Process Solutions Technology
Development Center, Cargill Inc.) wade_martinson@cargill.com
The
Differentiation Index and Industrial Dynamic Simulation Slides:
pdf
The
differentiation index has become an important tool for understanding
systems of coupled differential and algebraic equations, referred
to as DAEs in the process simulation community. More recently,
this concept has been extended to coupled partial differential
and algebraic equations. In this talk, a simulation problem
from the chemical processing industry will be used to illustrate
how high index model formulations lead to practical problems
with dynamic simulation, and how index analysis can lead to
model reformulations that permit successful simulation.

April
18, 2003
Richard Y. Chiao, Ph.D.
(GE Medical Systems)
Diagnostic
Ultrasound: Technology and Applications
Ultrasound has developed over
the past 50 years into a major diagnostic imaging modality,
complementing CT, MRI and nuclear imaging. Major applications
of ultrasound today include cardiovascular, abdominal organs,
muskloskeletal, small parts, and OB/Gyn. Increased clinical
usage of ultrasound has been driven by technological advances
that exploit the following advantages compared to other modalities:
real-time (especially important for heart and blood flow),
safe due to non-ionizing radiation, portable, and low cost.
Basic ultrasound modes include B-mode that images the acoustic
reflectivity of tissue structures and Doppler that measures
blood velocity. Recent advances include harmonic imaging that
improves image quality by exploiting the nonlinear behavior
of high-amplitude ultrasound propagation in tissue or micro-bubble
contrast agents, and code technology that circumvents traditional
resolution / penetration tradeoffs. Future directions for
ultrasound are at the intersection of clinical needs (image
quality, new applications, and increased productivity) and
major technological trends (miniaturization, SW), which include
miniaturized systems and probe components, improved image
quality, new imaging parameters, and 4D imaging.

April
25, 2003
Nicholas Bennett (Schlumberger
Doll Research) nbennett@ridgefield.oilfield.slb.com
Posterior Uncertainty in Decimated
Wavelet Model Parameterizations
Solving a geophysical inverse
problem means determining the parameters of an earth model
given a set of measurements. In solving many practical inverse
problems, accounting for the uncertainty of the solution is
very important to aid in decision-making. In this work, we
address the problem of determining the posterior uncertainty
of the solution for models that arise from decimated wavelet
bases using a simple 1-dimensional seismic travel time inversion
problem.
Our
inversion methodology is to pick a model decimation, prepare
a prior mean and covariance matrix of the wavelet coefficients,
compute a posterior mean and covariance, and then to sample
from this posterior distribution. We also sample different
choices of model decimation in proportion to their posterior
probability. These samples span the uncertainty of the inverse
problem solution, accounting for both the uncertainty in the
choice of model decimation and of wavelet coefficients. We
note that a re-normalization of the decimated prior covariance
matrix of the wavelet coefficients is required to properly
account for the amount of variance in the prior distribution.
Further, we present a fast algorithm for computing this normalized
decimated prior covariance matrix.

May
2, 2003
Daniel R. Baker (General
Motors R&D Center, 30500 Mound Rd., MC 480-102-000, Warren,
MI 48090-9055) daniel.r.baker@gm.com
Impedance as a Diagnostic
Tool for Studying Fuel Cells
We
will start by showing some CFD simulations of current distribution
on a fuel cell. The current distribution behaves differently
under different operating conditions and we will try to explain
this behavior. This will lead us to consider impedance spectroscopy
as a tool to investigate some of the critical effects that
impact current distribution. A short explanation of impedance
methods will be given along with a discussion of how to interpret
impedance data in the context of current distribution. Special
emphasis will be given to the high frequency resistance (HFR)
as a tool for understanding membrane humidification. Other
impedance applications include assessing proton resistance
in the porous cathode, kinetic resistance of the cathode electrode,
and the relative contribution of gas transport resistance
to voltage losses.

May 13,
2003, 2:30 pm Tuesday, Rm 409 Lind Hall (note
special time and
place)
Andrew Mullhaupt (S.A.C.
Capital Management)
Cantelli's Lemma and
the Estimation of Transaction Costs

Industrial
Programs