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 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! For more information please contact:
Darinka Dentcheva
Professor
Peirce
Room 302
Phone: 201.216.8640
Fax: 201.216.8321
ddentche@stevens.edu |
| December 12, 2009
Vice-Director of LaBRI visits Mathematical Sciences DeptDr. Pascal Weil, Vice-Director of the Laboratoire Bordelais de Recherche en Informatique (LaBRI), visited the Mathematical Sciences Department and the Algebraic Cryptography Center (ACC) at Stevens October 19-31. While at Stevens, Dr. Weil consulted on collaborations related to postquantum cryptology, robotics, and parallel computations. Dr. Weil also presented a talk at the New York Algebra and Cryptography Seminar, a series sponsored jointly by the ACC and City College of New York. LaBRI is a well-known computer science laboratory at the University of Bordeaux with over 300 research members and numerous collaborative ties throughout the world. For more information please contact:
Alexei Miasnikov
Professor, Department Director
Kidde
Room 371
Phone: 201.216.8598
Fax: 201.216.8321
amiasnik@stevens.edu |  December 10, 2009
IP Training Seminar - Protecting SoftwareThe creation, protection and commercialization of Intellectual Property is central to the implementation of Academic Entrepreneurship. It is therefore, essential that you attend the Intellectual Property (IP) Awareness Seminar, sponsored by the Office of Academic Entrepreneurship, (OAE). The seminar will be held on December 10th, 12:00pm until 2:00 pm.
This Seminar will focus on protecting intellectual property in Software.
David Peacock - will begiving an overview of the patent process and patent pipeline at Stevens, and our Patent Attorneys, from Greenberg Traurig will be addressing Legal Requirements.
This event is open to Stevens faculty, staff and students. For more information please contact:
David Peacock
Director - Intellectual Property Management
EAS Building
Room 210
Phone: 201.216.5242
Fax: 201.216.8185
dpeacock@stevens.edu |
|  May 28, 2009
Stevens Hosting Joint Conference on High Frequency Data Modeling in July 2009Stevens Institute of Technology will be co-hosting a joint conference to explore intersection of physics and data modeling with New Mexico State University.
The Joint Conference on High Frequency Data Modeling, July 10-12, 2009 on the Stevens campus in Hoboken, NJ, is an example of interdisciplinary research within our Financial Engineering and Mathematics programs, who reached out to thought leaders in complementary areas to encourage synergy between the fields of Econophysics and High Frequency Data Modeling. Emphasis will be placed on models for high frequency data and applications of statistics and statistical mechanics to tackle modeling problems within a complex system and systems of systems framework. Mathematicians, physicists, economists, industry representatives, and graduate students will meet to collaborate, with the goal of advancing the quality of research currently under development in the field of high frequency data modeling, and to open doors for future collaboration and networking. The Conference is being funded in part by the National Science Foundation (NSF), the International Mathematical Union, American Statistical Association, Stevens Institute of Technology, and New Mexico State University.
Conference speakers from industry, government and academia, including representatives from Columbia University, JP Morgan, New York State Banking Department, New York University, and City University of London, UK, will present and lead discussions related to the complex challenges of High Frequency Data Modeling. The conference will focus on three interrelated topics:
• Stochastic modeling and statistical analysis of high-frequency data
• Models in econophysics and application to the analysis of high-frequency data
• Systems and complex adaptive systems in finance
To register and learn more, please visit the conference website at www.stevens.edu/FEconference2009. For more information please contact:
Khaldoun Khashanah, PhD
Distinguished Service Professor and Program Director, Financial Engineering and Director of FE
Babbio Center
Phone: 201 216 5446
Fax: 201.216.5541
kkhashan@stevens.edu |
| May 10, 2007
Macroscopic equations for forest dynamics: scaling up from individual trees to forest.Stochastic Systems Seminar Forest simulation models have been proven remarkably effective at capturing the dynamics of real forests. In mathematical terms, individual-based simulators are spatial stochastic processes that predict properties of populations and communities by simulating the fate of every plant throughout its life cycle. Unfortunately, non-linear spatial stochastic processes are notoriously intractable, which limits the usefulness of forest simulators to basic scientists, and, also, they require too much computer power to be used at large scale, such as in global models; one cannot simulate every tree on the Earth. To solve the twin problems of computational intensiveness and mathematical intractability, what is needed is a way to predict a forest's community dynamics using only individual-level information, but without simulating every plant. This requires so-called macroscopic equations for variables of interest to ecologists, such as the mean density and size structure of each species and how these change though time.
In physical systems, macroscopic equations for the dynamics of fluids can be derived from stochastic models of the random collisions and transformations of individual molecules. Using similar approach we have developed a new spatial individual-based forest model that is based on a new approximation for the plasticity of crown shape. Its structure allows us to derive an accurate approximation to the individual-based model for the means of the stochastic process in a forest simulator that predicts the mean densities and size structures in the simulator using the same parameter values and functional forms, and, also, is analytically tractable. The approximation is represented by a system of Von Foerster partial differential equations coupled with an integral equation that we call the Perfect Plasticity Approximation (PPA). We have derived a series of analytical results including equilibrium abundances for trees of different crown shapes, stability conditions, transient behaviors, and coexistence conditions. For more information please contact:
Nicolai Strigul
Research Assistant Professor
Kidde
Room 221
Phone: 201.216.8763
Fax: 201.216.8321
nstrigul@stevens.edu |
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