This is the OR calendar for upcoming seminars, brown bags, guest lectures, and events of interest.
Tensors and their Eigenvectors
Speaker: Bernd Sturmfels (UC Berkeley and MPI Leipzig)
Operations Research Seminar Friday 1/27 at 1200 in Glasgow 286
Abstract: Eigenvectors of square matrices are central to linear algebra. Eigenvectors of tensors are a natural generalization. The spectral theory of tensors was pioneered by Lim and Qi over a decade ago, and it has found numerous applications. This lecture offers a first introduction, with emphasis on algebraic aspects.
Bernd Sturmfels received doctoral degrees in Mathematics in 1987 from the University of Washington, Seattle, and the Technical University Darmstadt, Germany. After postdoctoral years in Minneapolis and Linz, Austria, he taught at Cornell University, before joining UC Berkeley in 1995, where he is Professor of Mathematics, Statistics and Computer Science. Since 2017 he is a director at the Max-Planck Institute for Mathematics in the Sciences, Leipzig. His honors include a Sloan Fellowship, a David and Lucile Packard Fellowship, a Clay Senior Scholarship, an Alexander von Humboldt Senior Research Prize, the SIAM von Neumann Lecturership, the Sarlo Distinguished Mentoring Award, and an Einstein Fellowship in Berlin. He served as Vice President of the American Mathematical Society, and he was awarded an honorary doctorate from Goethe University Frankfurt in 2015. A leading experimentalist among mathematicians, Sturmfels has authored ten books and 240 research articles, in the areas of combinatorics, algebraic geometry, symbolic computation, and their applications. He has mentored 41 doctoral students and numerous postdocs. His current research addresses questions in algebra that are inspired by statistics, optimization, and biology.
Distinguished Lecture Series
Bayesian Search for Missing Aircraft
Lawrence D. Stone
20 April 2017
Glasgow Hall 109 at 1500
In recent years there have been a number of highly publicized searches for missing aircraft such as the ones for Air France flight AF 447 and Malaysia Airlines flight MH 370.
Bayesian search theory provides a well-developed method for planning searches for missing aircraft, ships lost at sea, or people missing on land. The theory has been applied successfully to searches for the missing US nuclear submarine Scorpion, the SS Central America (ship of gold), and the wreck of AF 447. It is used routinely the by U. S. Coast Guard to find people and ships missing at sea.
This talk presents the basic elements of the theory. It describes how Bayesian search theory was used to locate the wreck of AF 447 after two-years of unsuccessful search and discusses how it was applied to the search for MH 370. A crucial feature of Bayesian search theory is that it provides a principled method of combining all the available information about the location of a search object. This is particularly important in one-of-a-kind searches such as the one for AF 447 where there is little or no statistical data to rely upon.
Applied Risk Analytics: Making Advanced Analytics More Useful
Dr. Tony Cox
Traditional operations research emphasizes finding a feasible decision that maximizes an objective function. In practice, how decisions affect the objective function, and even what decisions are feasible, are often initially unknown. Managing risks effectively usually requires using available data, however limited, to answer the following questions, and then improve the answers in light of experience:
- DESCRIPTIVE ANALYTICS: What’s happening? What has changed recently? What should we be worrying about?
- PREDICTIVE ANALYTICS: What will (probably) happen if if we do nothing?
- CAUSAL ANALYTICS: What will (probably) happen if we take different actions or implement different policies? How soon are the consequences likely to occur, and how sure can we be?
- PRESCRIPTIVE ANALYTICS: What should we do next? How should we allocate available resources to explore, evaluate, and implement different actions or policies in different locations?
- EVALUATION ANALYTICS: How well are our risk management policies and decisions working? Are they producing (only) their intended effects? For what conditions or sub-populations do they work or fail?
- LEARNING ANALYTICS: How might we do better, taking into account value of information and opportunities to learn from small trials before scaling up?
- COLLABORATIVE ANALYTICS: How can we manage uncertain risks more effectively together?
This talk discusses recent advances in these areas and suggests how they might be integrated into a single decision support framework, which we call risk analytics, and applied to important policy questions such as whether, when, and how to revise risk management regulations or policies. Current technical methods of risk analytics, including change point analysis, quasi-experimental design and analysis, causal graph modeling, Bayesian Networks and influence diagrams, Granger causality and transfer entropy methods for time series, causal analysis and modeling, and low-regret learning provide a valuable toolkit for using data to assess and improve the performance of risk management decisions and policies by actively discovering what works, what does not, and how to improve over time.