Stella Offner has been selected as the 2024 recipient of the Peter O’Donnell Distinguished Researcher Award by the Oden Institute for Computational Engineering and Sciences.
This award recognizes Oden Institute core faculty who have demonstrated a sustained record of distinguished research in computational engineering and sciences. Recipients are chosen for their outstanding research record, the significant contributions they have made to the Oden Institute and its CSEM graduate program, and the distinction their work and reputation brings to the Oden Institute and the University of Texas at Austin.
The Distinguished Researcher Award provides discretionary funds of $25,000 a year, for up to four years in support of its awardee’s research within the Institute.
As an associate professor of astronomy and co-director of the Center for Scientific Machine Learning, Offner’s research focuses on understanding how stars like the Sun form by combining numerical simulations, observations and observational modeling.
“Flexible funding like this is excellent to explore new research directions -- to start new projects and get preliminary results, which can open up additional grant funding opportunities,” she explained. “I will likely use the funds to support student projects.”
Offner's recent work reflects two key areas of focus. Firstly, she has been actively integrating machine learning techniques into her studies while collaborating with fellow faculty members across campus. This interdisciplinary approach, particularly within the realms of astronomy, machine learning, and AI, represents an emerging field where researchers are pioneering innovative methodologies. Offner's efforts in this area have been instrumental in simplifying processes that were previously reliant on manual intervention, utilizing simulations as a training set to identify features and derive insights from observational data.
Additionally, Offner's research extends to large-scale simulations aimed at modeling the intricate processes underlying star formation. Within these simulations, encompassing thousands of forming stars, she incorporates various physical phenomena such as gravity, turbulence, radiation, and magnetic fields. This endeavor poses a formidable challenge due to the complexity of interactions among these factors and the vast dynamic range involved. Nevertheless, she remains dedicated to advancing computational modeling techniques, aiming to create simulations that accurately represent the diverse aspects of stellar formation.