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Projects in 2008–2009

Cardinal Health: Modeling Fluid Transport in Subcutaneous Tissue

Rachel Levy
Richard Haskell

The goal of this project is to produce a mathematical model of fluid flow in subcutaneous tissue. Two models have been developed: a compartment model that segregates the fluid into homogeneous regions, and a continuous model that describes the properties of the fluid at each point in space and time.


  • Steven Rosenthal (fall) (Project Manager)
  • Brian Stock (spring) (Project Manager)
  • Harry Dudley
  • Melissa Strait

Chicago Trading Company: Building a Multi-Agent Artificial Stock Market

Donald Williams

We design and build a multiagent, multiasset stock-market simulator using an object-oriented software development paradigm. The simulator employs a software-driven market mechanism, which handles transactions for instantiated agents based on market and limit orders for multiple correlated assets. Using evolving trading strategies within the simulator, we establish a behavioral model for decision analytics that facilitates statistical inferencing and insight into the dynamics of financial markets and the agents operating therein.


  • Edwin Lei
  • Rishad Manekia (Project Manager)
  • Kevin Oeltze
  • Jane Pan

Citadel Investment Group: Optimizing Pairs Trading Portfolios

Francis Su

We studied a method of statistical arbitrage known as pairs trading, and developed an automated strategy for quantitatively constructing a portfolio of pairs that attempts to minimize risk while maximizing expected returns. Our work builds on the research done by the 2007-2008 Pairs-Trading Clinic Team. While last year's focus was to implement a basic pairs-trading strategy and optimize parameters, this year's emphasis is on controlling risk.


  • Patrick Foley (Fall)
  • Maria Pavlovskia (fall) (Project Manager)
  • Brett Cooper (spring) (Project Manager)
  • Denis Aleshin
  • Chris Fox
  • Josh Klontz
  • Bryce Lampe

Laserfiche: Deblurring: Removing Image Distortion Induced by Camera Motion

Darryl Yong

Blurring is a major challenge preventing people from using digitial cameras in place of scanners to capture documents. To help Laserfiche offer this capability in their document management suite, we have created software that automatically deblurs images without any knowledge of the motion causing the blur. Our solution is a modified version of a recently published natural image deblurring algorithm that exploits underlying patterns in images of text. We have also improved the runtime performance of the original algorithm.


  • Aaron Abromowitz
  • Richard Bowen (Project Manager)
  • Donavion Huskey
  • Brett McLarnon