Bio
Dr. Ghosh's research focuses on data-driven and machine learning approaches combined with the state-of-the-art first principles methods to study complex material systems. In particular, she has interests in developing physics-based machine learning frameworks to investigate causal mechanisms in a wide range of materials ranging from inorganic perovskites, actinides, 2D systems to organic crystals and polymers. Bridging the gap between appropriately utilizing data generated by simulations and experiments require a great deal of understanding the nuances present in both fields, which is the ultimate goal of her efforts.
Awards
Special Performance Award (SPA), CSED, ORNL, 2023, 2022, 2021
ORNL LDRD SEED (PI), “Causal machine learning for predictive materials design”, 2023-2024
Rising Stars in Computational and Data Sciences, Sandia National Laboratories, 2022
Best Student Poster Presentation Award, Electronic Materials and Applications, ACerS Meeting, 2020
Doctoral Dissertation Fellowship, Doctoral Student Travel Fellowship, University of Connecticut, 2020
Phi Kappa Phi Honors Society John Tanaka Graduate Student Award, University of Connecticut, 2019
Best Poster Award, Annual Graduate Poster Competition, MSE, University of Connecticut, 2019
Brian D. Proffer Student Excellence Award, University of Michigan-Flint, 2015
Student Involvement and Leadership (SIL) Emerging Award, University of Michigan-Flint, 2015
Education
PhD, Materials Science and Engineering, University of Connecticut, 2020
BS, Physics and Abstract Mathematics, University of Michigan-Flint, 2015