Research Topics

Below are detailed descriptions of some themes in our recent research projects

Financial engineering and
risk management

Financial data is noisy -- data is missing, data becomes outdated, models are wrong -- and yet one wants to compute “optimal” decisions using this noisy data. What is the impact of the noise in the data on the decisions? Can one correct for data errors within the optimization procedure? We have explored many applications of robust optimization in financial decision making.

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Large scale analytics

Good solutions for large scale analytics problem are built on the ability to couple amounts of data, with appropriate models, and scalable optimization techniques -- and somewhere in the mix is also good visualization. The key in developing algorithms is to identify features of the problem that can be exploited. We are forever on the quest for models and methods that can have significant practical impact.

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Resilient and sustainable infrastructure

Infrastructure is being stressed from various sources -- high demand, unreliable supply, extreme weather, accidents. Traditionally, infrastructure planners design with very little reserve in order to keep costs low. However, there is a growing interest in designing systems with resilience and sustainability as an equally important goal. We are working on developing new power flow models for optimally using demand response programs to prevent grid instability in the face of uncertain supply from renewable sources such as wind and solar power.

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Information and optimization
in biological systems

Biological systems have to sense and react to the information available from the environment. The underlying assumption of this research is that the millions of years of would have optimized the information aggregation systems modulo some path dependence having to do with early mistakes! Given this assumption, can one rationalize the information processing structures seen in individual cells as well as multicellular organisms.

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