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February 2020

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14
Feb

Applied Mathematics Seminar: Magic, Gandhi, and balding: matrix methods for stochastic dynamic programming

Conference/Seminar

Speaker: Jody Reimer, University of Utah
Abstract: What unites all of the things listed in this title? You'll have to show up to find out. This work is based on the concept of tradeoffs, a central idea in ecology and evolutionary biology. For example, the evolution of life history strategies is often framed in the language of tradeoffs. Behavioural ecologists may be interested in the tradeoffs inherent in the allocation of time (e.g., between foraging and vigilance) and resources (e.g., how much energy to invest in a reproductive attempt). Conservationists and wildlife managers must also consider tradeoffs between cost, political pressures, and management goals. Stochastic dynamic programming (SDP) is a powerful and flexible method for exploring optimal tradeoffs and has been used in a broad range of applications. In the last 30 years, concomitant with the development of SDP methods in ecology and evolution, matrix methods have emerged as another powerful tool for analyzing ecological systems.

3:30 pm - 4:30 pm | Animal Science |
20
Feb

Data, Models, Errors, And Uncertainty

Conference/Seminar

Talk by Eric Kostelich, President's Professor, School of Mathematical and Statistical Sciences - ASU

3:30 pm - 4:30 pm | Animal Science |
28
Feb

Applied Mathematics Seminar: Stochastic Approximate Model-Checker for Infinite-State Analysis

Conference/Seminar

Speaker: Zhen Zhang, Department of E & CE, Utah State University

Abstract: There has been an increasing demand for providing formal guarantees in the design process of safety-critical synthetic genetic circuits. As a prominent formal verification technique, probabilistic model checking has demonstrated significant potential in formally analyzing the intrinsic probabilistic behaviors of complex genetic circuit designs. However, its scalability is limited as probabilistic models of real-world applications typically have very large or infinite state space. In this talk, I will present a new infinite state CTMC model checker, STAMINA, with improved scalability. It uses a novel state space approximation method to reduce large and possibly infinite state CTMC models to finite state representations that are amenable to existing probabilistic model checkers. It is integrated with a new property-guided state expansion approach that improves the analysis accuracy. This method is evaluated on a design of a genetic toggle switch and several other benchmarking examples. Comparisons with another state-of- the-art tool demonstrate both accuracy and efficiency of the presented method.

3:30 pm - 4:30 pm | Animal Science |
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