Research Experiences For Undergraduates (REU)

REU 2007 Projects

Modeling Stock Returns and Optimizing Bond Portfolios

Sponsor: State Street Global Advisors (SSgA)
Advisor: Prof. Bogdan Doytchinov

Naomi Brownstein    Patrick Crutcher    Yu-Jay Huoh    Sean Skwerer    Grant Weller
Naomi Brownstein    Patrick Crutcher    Yu-Jay Huo    Sean Skwerer    Grant Weller

State Street Global Advisors (SSgA) is the largest institutional asset manager in the world. As such, SSgA is interested in modeling monthly stock returns and managing optimal bond portfolios. We will all be working on two related projects:

Mathematical Model of the Self-Tapping Screw Insertion Process

Sponsor: Bose Corporation
Advisor: Prof. Suzanne Weekes

Jonathan Adler    Matthew Bader
Jonathan Adler    Matthew Bader

A self-tapping screw is a high-strength one-piece fastener that is driven into preformed holes. The goal of the Major Qualifying Project (MQP) completed by A. Leo, et al. and the Research Experience for Undergraduates (REU2006) completed by Miller, et al. was to create a mathematical model that allows users to input data about their self-tapping screw and the material it is entering and output a torque curve which models the fastening process. We improve the algorithm to include the modeling of the failure of the joint as well as a model for the clamp load of the joint. We will also develop a model of heat dynamics in the screw that considers the rate of screw insertion, i.e. RPM.

Quantifying Uncertainty in Predictions of Hepatic Clearance

Sponsor: Pfizer
Advisor: Prof. Jayson D. Wilbur

Paul Bernhardt    Jaye Bupp    Morgan Gieseke    Nathan Langholz    Christopher Steiner   
Paul Bernhardt    Jaye Bupp    Morgan Gieseke    Nathan Langholz    Christopher Steiner

In the process of drug development, it is important to understand the pharmacokinetics of candidate compounds. Among the pharmacokinetic parameters of interest is hepatic clearance, which is the rate at which blood is cleared of the drug by the liver. Generally, point estimates for hepatic clearance are obtained from preclinical data and used in clinical models without accounting for the uncertainty in these estimates. The goal of this project is to construct a Bayesian model for hepatic clearance which can be used to quantify the uncertainty in terms of a posterior distribution and evaluate the sensitivity of the distribution to various model assumptions.

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Last modified: Jun 20, 2010, 23:04 EDT
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