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.**