Collaborative Research Projects

Model Reduction and Robustness Analysis for Computationally Efficient, High Fidelity Inference in Complex Physical Processes

Statistical Physics for Computing and Communications

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Energy Infrastructure: Smart Grids

Grid Design - develop efficient and robust integration of geographically distributed renewable generation by solving cutting-edge optimization problems that incoporate difficult constraints imposed by the the transmission of electrical power.
Grid Control - develop distributed control techniques and algorithms based on queuing theory to enable high penetration of small-scale distributed generation and plug-in hybrid vehicles.
Grid Stability - develop a toolbox capable of detecting instabilities and failures for preventing costly outages.

Munther Dahleh
MIT Instructor

Sanjoy Mitter
MIT Instructor

David Gamarnik
MIT Instructor

Michael Chertkov
Mentor

Smart Grids Homepage

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Large-Scale Inverse Problems

Our research focus is on Monte-Carlo methodology for large systems of equations. These may arise in inverse problems, DP, or other ... but we are focusing on inverse problems and (to a lesser extent) DP for the moment.

Mengdi Wang
MIT Student

Dimitri Bertsekas
MIT Instructor

John Tsitsiklis
MIT Instructor

Frank Alexander
Mentor

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Model Reduction and Robustness Analysis for Computationally Efficient, High Fidelity Inference in Complex Physical Processes

Modern tools of semi-definite programming (in particular, the public domain SeDuMe program) can be used successfully in black-box identification of significantly nonlinear circuits, providing performance superior to the existing methods. A Polynomial Optimization Toolbox, was successfully designed and implemented to enable efficient realization of nonlinear system optimization algorithms. An analytical study of highly structured dynamic programming tasks associated with the design of reduced complexity digital signal processing systems was performed.

Mitra Osqui
MIT Student

Bradley Bond
MIT Student

Alexandre Megretski
MIT Instructor

Frank Alexander
Mentor

Michael Wall
Mentor

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Statistical Physics for Computing and Communications

This project exploits methods from statistical physics to provide fundamental advances in computing and communication systems. The intersection of computer science, information theory and statistical physics has seen a recent explosion of activity, resulting in new algorithms and new methods of analysis. Discrete computational challenges including constraint satisfaction, error correction and communication network performance have benefited from techniques and insights offered by statistical physics. Physics, at the same time, has been significantly enriched by approaches from discrete computation, such as message-passing algorithms.