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This is an exciting time in engineering and mathematics.  The complexity of the world requires the ability to think quantitatively and handle large amounts of data.

 

Click on the images below to learn more.

Internet of Things

With R. S. Sreenivas (UIUC ISE), we are trying to understand how IoT can enable both sensing and control in the modern world.

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Traffic Patterns

Manhattan Traffic Patterns

We started working with Professor Dan Work (CEE at UIUC) on big data approaches to traffic.  Who likes to sit in a traffic jam?

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Virtual Reality and Visual Cliffs

Virtual Reality and Visual Cliffs

This is an ongoing project with M. Hernandez (Kinesiology at UIUC) to understand anxiety and visual cliffs

 

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Optimal Transport in Hand-Picked Crops

Optimal Transport in Hand-Picked Crops

A number of hIgh-Value hand-picked specialty crops are very susceptible to spoilage after they have been picked and while they are waiting to be refrigerated.   We built a model around this problem.  Preprint.

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Uncertainty Quantification

Uncertainty Quantification

Uncertainty is a crucial factor in financial stability, but it is notoriously difficult to measure. This working paper extends techniques from engineering to quantify fundamental economic uncertainty, and applies the method to an example of portfolio stress testing. By this measure, uncertainty peaked in late 2008.   Preprint.

Video as a Sensor

Video as a SensorCars are becoming more sensor-enabled as autonomy draws closer. We decided to see what we could learn by treating video as a sensor.

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Dynamics of Bankrupt Stocks

Bankrupt Stocks

Bankrupt stocks are still traded while the court winds the company down.  This creates some interesting dynamics as traders short a finite number of shares of the stock. Preprint.

Stochastic Moving Boundary Problem

Stochastic moving boundary value problem

The dynamics of many multiphase systems are described by moving boundary problems.  What happens if we add noise to these PDE’s? Preprint.

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