• Professor Patricia B. Cerrito
• Professor Kevin Clancey
• Professors Ryan Gill and Kiseop Lee
• Professor André Kézdy
• Professor Greg A. Rempala

I. Professor Patricia B. Cerrito

1. An examination of the quality-of-life for patients with melanoma. Data were
collected at baseline, during, and after chemotherapy. It was found that during
treatment, many patients suffered both physical and emotional symptoms. After
 treatment, the physical symptoms disappeared but the emotional symptoms
continued. The data were analyzed using traditional statistical techniques but also
data mining techniques of neural network analysis and genetic programming.

2. An examination of the occurrence of exercise-induced asthma in college athletes.
 This project is ongoing. Athletes had to complete a screening survey as part of a
 mandatory physical. Athletes who indicated some breathing difficulties during
 practice were referred to a physician for follow-up and management of the
 breathing problems. It was discovered early on that athletes in some sports were
 reluctant to respond honestly to the questions because of a perceived stigma
 against athletes with asthma. An education program was developed to reduce or
 eliminate this stigma as a barrier to treatment. The data will also be compared to
 geographic information using data mining techniques to determine the relationship
 between asthma symptoms and environmental hazards.

3. An examination of the compliance of patients undergoing physical therapy for
 shoulder injuries. All patients are given home exercises to perform regularly during
 physical therapy. Each patient will be surveyed weekly to determine the level of
 compliance. This will be related to the recovery of their shoulder injuries, and the
 ability to avoid surgical repair. Categorical data analysis will be used to examine
 the data

II. Professor Kevin Clancey

Modeling Ohio River Barge Waves
The mathematical theory of shallow water waves dates back to 1849 and the
Scottish Engineer, John Scott Russell. It is known that shallow water waves satisfy a
certain nonlinear differential equation - the so called Korteveg - de Vries equation. It
develops that classical special functions called theta functions also satisfy such
equations. As a consequence, it is natural to try to use theta functions to
simulate wave motions. More specifically, computer software will be used with linear
sums of theta functions to mathematically model water waves behind barges on the
Ohio river. The coefficients in such sums and quasi-periods of theta functions will be
related to river and barge parameters.

Prerequisites: Some experience with mathematical software such as Maple and
elementary differential equations.

III. Professors Ryan Gill and Kiseop Lee

Objective: To provide students with various tools of statistical data analysis. They
learn how to analyze real data sets and how to write a report in a scientific format.


Description: This project provides various methods of statistical data analysis,
especially with linear models. Students are asked to collect data sets from ‘real
world settings’ such as sports, medicine, the climate, population demographics,
stock markets, etc. They are required to complete a report with the goal of study,
methods of data collection, tools of data analysis, conclusions and discussion.

Plan:

1. Study the following topics :collection of data, sampling survey, t-test, F-test,paired t-test, two sample t-test, simple linear regression, multiple linearregression, regression diagnostics, experimental design, generalized linearmodel, categorical data analysis
2. Learn the statistical software: ex) R, SAS, S-plus, Microsoft Excel]
3. Collect data sets and analyze them.
4. Write a report.

It will be roughly a two semester project. Students do steps 1 and 2 in the first] semester and steps 3 and 4 in the second semester.  Students with a background] in statistics can complete a project as a summer research experience.]

IV. Professor André Kézdy

Determinants and Rotations      SOLVED! (September, 2003)
Consider an n x n matrix  A=[a_ij], whose entries are n² independent variables. For
an arbitrary selection r₁,r₂,...,r_n from {0,...n-1} (repetition allowed), let A[r₁,r₂,...,r_n]
denote the matrix obtained from A by, for each i=1,...,n, rotating row i a total of r_i
cells to the right with wrap around.  For example when n= 4, by definition the matrix
A is

a11	a12	a13	a14
a21	a22	a23	a24
a31	a32	a33	a34
a41	a42	a43	a44

while the matrix A[1,2,0,1] is

a14	a11	a12	a13
a23	a24	a21	a22
a31	a32	a33	a34
a44	a41	a42	a43

Let det(A[r₁,r₂,...,r_n]) be the determinant of the matrix A[r₁,r₂,...,r_n].  Observe that this
determinant is a multilinear function of the variables a_ij.  We focus on the collection
of all of the determinants we can obtain from A this way:  

S = { det(A[r₁,r₂,...,r_n]) : for all r₁,r₂,...,r_n from {0,...n-1} }.

Naturally S contains nⁿ determinants, since there are n independent choices for
values of each of the r_i's.   Next consider the special subset of n! of these
determinants:

T  = { det(A[r₁,r₂,...,r_n]) : for all r₁,r₂,...,r_n, where 0 ≤ r_i ≤ n - i}.

OPEN PROBLEM: Prove that every element in S can be written as an integer
 linear combination of the elements in T.  

For more information on this problem see Professor Kézdy.

V. Professor Greg A. Rempala

National Center for Disease Control and Prevention has compiled on its web site 
the extensive national mortality tables by different mortality causes. The data is  
organized by the county of residence. Preliminary analysis  indicated that Jefferson 
county Kentucky occupies (unfortunately) a very prominent place in these statistics 
with respect to several different causes of death including suicide auto-accidents 
and various cancers (most notably brain and lung). 


This project would be concerned with developing a precise mathematical model of 
the geographical mortality pattern for the parts of the states of Kentucky and Indiana 
neighboring Jefferson county which could possibly provide an answer to the 
question whether indeed the residents of Jeff county are at a higher risk of death for 
cancer and auto-accident, comparing to their neighbors.  The model would also 
compare Jeff county to similar  socio-economic areas in the different parts of the 
US.  Most of the data is available on the internet. Methodological papers, textbooks 
and other material on geo-spatial mathematical  models in epidemiology are 
available from the library and from myself. gr