Research Experiences For Undergraduates (REU)

REU 2014 Projects

Predictive Power of a Generalized Preventive Care Segmentation Model

Industrial Sponsor: Dr. Jack Newsom and Kathleen Durant - Silverlink Communications, Inc.

Faculty Advisor: Prof. Matthew Willyard


Ciaran Evans		Parker Hund		Eric Varley		Devin Wang

Abstract: Healthcare costs in America have risen sharply in recent years. One of the most effective ways to combat this trend is via preventive health care measures. We explore a dataset consisting of insurance claims provided by our sponsor, Silverlink, in order to pinpoint patterns that will enable insurance providers to identify and contact their customers who are most likely to respond to some form of preventive health care intervention. To detect these patterns, we use a variety of data mining techniques such as clustering, association analysis, decision trees, and regression.

Performance sensitivity to installation methods in vertical U-Tube geothermal energy harvesting systems.

Industrial Sponsor: Paul Ormond, P.E., C.G.D. - Haley & Aldrich, Inc and NEGPA

Faculty Advisors: Prof. Burt Tilley and Prof. Suzanne L. Weekes


Keenan Hawekotte		Samuel Naden		Sophia Novitzky		Mahalia Sapp

Residential geothermal energy harvesting systems have the potential to provide a cost-effective, low carbon footprint technology for heating and cooling. These systems use piping systems for coolant that either collects energy from the subsurface in the winter, or transports energy from the residence to the subsurface in the summer. We consider here the most common ground source heat pump (GSHP) installations, where a single flexible tubing is inserted in to a vertical bore with a single bend at the bottom of the well. The bore is then filled with grout, and the two ends of the tube at the surface provide the inlet and outlet to the GSHP. The focus of this project is on how the performance of the system depends on the distance between the two vertical portions of the pipe as a function of depth. We consider the temperature evolution in the exchanger and the surrounding soil as a function of time when the coolant volumetric flow rate is fixed, and see how the temperature rise over time depends on the piping geometry and spacing. Time permitting, we shall also consider the case for a time-dependent volumetric flow rate of the fluid and how the net temperature rise is affected by different design choices.

Estimating Liquidity Risk Using Exchange Traded Funds

Industrial Sponsor: Dr. Luis Roman and Dr. Ritripua Samanta - State Street Global Advisors

Faculty Advisor: Prof. Marcel Blais


Hannah Li		Evan Witz		Claire Kelling		Rachel Crowell

When valuing a portfolio of risky assets, portfolio managers face the issue that the market value of a portfolio may differ significantly from the short-term liquidation value of the portfolio. In short, a collection of assets may not be worth its quoted market value if the portfolio manager has to liquidate the portfolio over a short-term time horizon. This is a type of liquidity risk, one that many financial institutions, such as State Street Global Advisors, face. The financial crisis of 2008 illustrated that this is type of risk is dangerous to ignore, which has lead to the current need in industry to be able to model and predict liquidity premiums and their fluctuations over time. The metrics currently used in financial industry for liquidity employ proxies which may involve other risk factors, making a liquidity premium difficult to isolate. The proposed model isolates liquidity risk from other factors by forming a portfolio based on buying Exchange Traded Funds (ETFs), which trade alongside stocks on exchanges, and shorting the underlying basket of securities, which are typically less liquid than the ETFs, using the weighting system employed by the ETF issuer. Essentially we are buying and selling the same set of securities, and the difference between the price of the ETF and the value of the basket of underlying securities should be purely due to a difference in liquidity. We work with the ETF called JNK, which is based on bonds with poor credit ratings as issued by Fitch, Moody's, and Standard & Poor's. We assess and implement the model provided by Chacko et al. in An Index Based Measure of Liquidity by analyzing empirical data of the JNK and the underlying securities. We will then have the opportunity to suggest extensions or modifications of the model and compare our models with other common methods of liquidity modeling.

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Last modified: Jan 02, 2015, 23:30 UTC