|May 11, 2010 |
Dr. Dentcheva Receives Multi-Year NSF Grant for Risk-Averse Optimization
Professor Darinka Dentcheva of the Department of Mathematical Sciences at Stevens Institute of Technology has received her 4th NSF award for continuing research on Risk-Averse Optimization. As one of the initiators in this field of study, Dr. Dentcheva's work is increasingly important as a factor in developing methods for solving stochastic dynamic optimization problems that involve risk-averse preferences.
"Successive Risk-Neutral Approximations of Dynamic Risk-Averse Optimization Problems" is a three-year collaborative effort with Andrzej Ruszczynski of Rutgers University for the total amount of $350,000.
Risk-Averse Optimization is essentially a methodology that incorporates probabilistic models into a decision process with special attention paid to events of small probability and high consequences. Typical examples of stochastic optimization can be seen in supply chain management, military planning, energy production and distribution, telecommunications, insurance and finance and medicine. Her research will benefit the graduate education of both Rutgers University and Stevens Institute.
The project will concentrate on multistage stochastic optimization problems and on Markov decision processes incorporating dynamic risk measures and dynamic stochastic ordering constraints. The proposed numerical approach integrates modern theories of risk measures and stochastic orders with decomposition techniques for large-scale optimization problems, methods of non-smooth optimization, and stochastic control methods.
"This award is a reflection of Professor Dentcheva's continued national leadership in this critical area of research. Her work will have important ramifications to the work of many researchers at Stevens and beyond in the area of risk assessment," said Dr. Michael Bruno, Feiler Chair Professor & Dean, School of Engineering & Science at Stevens.
Dr. Dentcheva's work will also provide qualitative advances in areas involving multi-stage decision-making in stochastic systems under high uncertainty and risk. It will provide modeling and algorithmic tools to formalize and solve long-term planning problems in which risk is an important issue and average performance criteria are insufficient.
To learn more about Dr. Dentcheva's research, please visit the Department of Mathematical Sciences and her research profile!