Antigoni Georgiadou

Bio

Antigoni Georgiadou is an Applied Mathematician at the Science Engagement Section, Algorithms & Performance Analysis (APA) Group. Dr. Georgiadou obtained her Ph.D. in Mathematics from Florida State University where she worked on optimization in stellar evolution applications. She received her bachelor’s degree in Applied Mathematics and Physical Sciences from the National Technical University of Athens, Greece in 2013. Dr. Georgiadou worked at the European Space Agency in Darmstadt, Germany, to apply machine learning (ML) methods for radiation belts profile predictions for the Integral and XMM-Newton satellites. In 2017, she joined the Theoretical Astrophysics Group at Fermilab to work on an analysis to develop a statistical framework with Gaussian Processes and ML for cosmological simulations. Before joining the APA Group, Antigoni Georgiadou was a postdoctoral research associate in OLCF’s Advanced Computing for Nuclear, Particles, & Astrophysics Group.

Astrophysics applications in the areas of cosmology and stellar evolution simulations have been motivating some of her research questions. As a member of the APA Group Dr. Georgiadou collaborates with scientists across domains to optimize performance and requirements of applications on OLCF supercomputers. Dr. Georgiadou is the lead of the Uncertainty Quantification Working Group in the Science Engagement section and has been supporting INCITE, ECP, and CAAR projects for current and emerging GPU-accelerated systems at the OLCF. Since 2023, Dr. Georgiadou is the Chair of the SC workshop on "Enabling Predictive Science with Optimization and Uncertainty Quantification in HPC" taking place at The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC'23, SC'24). Starting July 2023, she serves as a committee member of INCITS for Fortran and the parent Programming Languages (PL22). 

Awards

• 2024 Better Scientific Software (BSSw) Fellowship Program Honorable Mention
• 2017-2019 URA Visiting Scholarship at Fermi National Accelerator Laboratory (FNAL)
• DAAD Research Grant- German Academic Exchange Service

Publications