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Department of Mathematics

Colloquium Series

Upcoming Colloquia

Thursday, December 1, 2022 - William Godoy, PhD (Oak Ridge National Laboratory (ORNL))

To attend the talk you will first need to register in advance at https://us06web.zoom.us/j/88406041576

Title: Pursuing graduate school and career paths for PhDs

Abstract: In this presentation we will talk about career paths for those planning to pursue a graduate school degree. We will provide exposure to the audience from my experiences on different career paths environments: academia, national labs and industry. In particular, we would point out the importance of having a diverse network for collaboration, being part of a scientific community, and the role of professional societies. We will also discuss the different kind of expectations as people progress in their professional career levels and uptake on early, mid and senior roles. Overall, we hope students will get exposure to these topics to get a grasp of the landscape in science and help them navigate these environments.

Short Bio: Dr. William F Godoy joined Oak Ridge National Laboratory in 2016, currently a Senior Computer Scientist in the Computational Science and Mathematics Division. A native of Peru, his background is in Mechanical Engineering with a strong research and development focus on the computational aspects of large-scale scientific modeling and simulation using high performance
computing (HPC) He obtained his PhD in 2009 from the University at Buffalo, The State University of New York. Prior experiences include a Senior Software Engineer position at Intel Corporation, 2012-2016, and a postdoctoral fellow at NASA Langley Research Center, 2009-2012. https://www.ornl.gov/staff-profile/william-f-godoy


Recent Colloquia

Thursday, November 3, 2022 - Earvin Balderama, Ph.D. (Fresno State)

By Zoom at 12 PM

Title: Statistical Models for Count Data

Abstract:  Count data are a type of data that takes on non-negative integer values {0, 1, 2, ...}. The Poisson and negative binomial distributions are commonly used as the basis in a generalized linear model for such data. The negative binomial is used in situations where the Poisson fit is inadequate due to over-dispersion. When there is an excess amount of zeros in the data, zero-inflated models and hurdle models are used, again typically specified by Poisson and negative binomial. What if negative binomial is still inadequate? In this talk, I will discuss the double-hurdle model, an extension of the single-hurdle model, to describe count data that contain both an excessive amount of zeros and some extremely large counts that cause so much over-dispersion that even a negative binomial  specification is inadequate. The double-hurdle model was applied to counts of marine birds observed along the US Atlantic coast. A Bayesian MCMC framework was used for estimation and validation. An R package, hurdlr, was created to implement hurdle and double hurdle models to zero-inflated data.


Friday, October 28, 2022 - Nathan Urban, Ph.D. (Brookhaven National Laboratory (BNL))

By Zoom at 9 AM

Title:  PROJECTING THE FUTURE: QUANTIFYING UNCERTAINTIES IN HOW THE CLIMATE WILL CHANGE

Abstract:  As carbon dioxide emissions to the atmosphere continue from fossil fuel consumption, the Earth's climate will change considerably over the course of this century due to the resulting greenhouse effect. Societies and ecosystems will feel these global changes in the form of extreme weather, recurrent flooding and droughts, and other regional impacts. But how certain are we in projections about the distant future? Every prediction of a physical theory comes with error bars arising from limitations in data and approximations that are made for the sake of computational  tractability. The scientific question is not whether the climate will change, but how likely it is to change by a given amount. This talk will discuss at a conceptual level the general mathematical formalism of statistical uncertainty quantification, and how it is being applied within the U.S. Department of Energy and other research institutions to estimate the plausible range of possible climate futures. The mathematical methods discussed may include Bayesian parameter estimation for nonlinear regression, Monte Carlo sampling, sensitivity analysis, model averaging, and surrogate modeling or emulation of expensive computer simulations. Applications may include probabilistic predictions of global warming and climate feedbacks, sea level rise from Antarctic ice sheet disintegration, and coastal flooding, among others. As time permits, I will also discuss educational training and career paths into this research field at the forefront of interdisciplinary science.

Short Bio:  Dr. Nathan Urban leads the Applied Mathematics group in the Computational Science Initiative at Brookhaven National Laboratory, on Long Island, New York. He received undergraduate degrees in physics, computer science, and mathematics from Virginia Tech, and a Ph.D. and M.Ed. in physics (computational statistical mechanics) from Penn State. After graduating, he moved into climate uncertainty quantification with postdoctoral appointments at Penn State and Princeton, and a staff position at Los Alamos National Laboratory. He received a DOE Office of Science Early Career Research award for multi-model climate uncertainties, and has led major projects involving coastal resilience planning under uncertainty and "in-situ" methods for embedding scalable statistical inference algorithms within exascale simulations. At Brookhaven he develops methodologies for uncertainty quantification, decision making under uncertainty and optimal experimental design, model reduction, scientific machine learning, and integrated computational frameworks for decision support, applied to problems in climate science, biomedicine, materials science, and others.


Friday, October 7, 2022 - Dr. Mario Bencomo

In PB 013 at 3 PM

Title: Topics in inverse problems

Abstract:  Inverse problems are a classification of mathematical problems in engineering and science that involve estimating model parameters/inputs relative to a “forward/direct” problem. In this talk I will present several examples of inverse problems to elucidate the definition and highlight typical mathematical challenges. I will also discuss in depth applications in the field of exploration seismology where such inverse problems require mathematical tools from numerical optimization and differential equations.

Short Bio: Mario J. Bencomo joined the Department of Mathematics at Fresno State as an Assistant Professor this Fall semester. Prior to coming to California, he was a Pfeiffer Postdoctoral instructor at Rice University, in the Department of Computational Applied Mathematics and Operations Research (CMOR), where he received his Ph.D. in 2017. His research interests include numerical methods for wave propagation problems, inverse  problems, optimal control with a focus on nonlinear conservation laws.


If you need a disability-related accommodation or wheelchair access information, please contact the Mathematics Department  at 559.278.2992 or e-mail  mathsa@csufresno.edu. Requests should be made at least one week in advance of the event.

Archived Colloquia