Colloquium - Julie Bessac
Institution: Argonne National Laboratory
Title: Statistical Methods for Modeling and Prediction of Space-Time Environmental Data
Date: April 13, 2023
Location: C405 Wells Hall, Simulcast to Zoom (Click here for meeting details)
Time: 10:20 AM - 11:10 AM Eastern Time
Abstract:
We will discuss the context and challenges of statistical modeling for multidimensional
processes, and in particular, but not restricted to, environmental data. The methods
presented aim at characterizing, predicting and simulating complex phenomena by reproducing
target quantities such as probabilistic distributions, extremes, space-time dependence,
and interaction among variables, as well as multiscale aspects of processes at stake.
We focus on several applications of such statistical models: 1) the modeling of the
bulk and both tails of temperature distribution used in power-grid long-term planning,
2) stochastic enhancement of subgrid-scale variability of unresolved wind-related
quantities in weather and climate models, and 3) the use of statistical methods to
estimate compression ratios in lossy compression for scientific data.
Bio:
Julie Bessac received the B.Sc. degree in fundamental Mathematics and the M.S. degree
in Probability and Statistics, respectively in 2008 and 2011 from the University of
Rennes 1, France. She received the Ph.D. degree in 2014 in applied Mathematics from
the University of Rennes 1, France. Between 2014 and 2017, she was a post-doctoral
appointee in the Mathematics and Computer Science Division at Argonne National Laboratory,
Argonne, IL. Between 2017 and 2023, she has been a Computational Statistician at Argonne
National Laboratory. She recently joined the National Renewable Energy Laboratory
as a computational statistician as a remote employee and she is an adjunct professor
at the Mathematics Department of Virginia Tech where she is located. Her research
focuses on the statistical modeling, forecasting and uncertainty quantification for
diverse applications, as for instance geophysical processes and their applications
to energy systems, computer science and nuclear physics.