Nisha Chandramoorthy

Nisha Chandramoorthy is an Edenfield Early-Career Assistant Professor in the School of Computational Science and Engineering at Georgia Tech. Her research focuses on mathematical analyses and rigorous computational methods for understanding and engineering nonlinear dynamical systems, particularly those exhibiting chaotic behavior. Key research themes include statistical response to perturbations, probability measure transport, high-dimensional Bayesian inference, and machine learning algorithm generalization.

• Education:
• Ph.D. and M.S. from MIT (2021, 2016)
• Bachelor's degree from Indian Institute of Technology, Roorkee (2014)

Her work bridges fundamental nonlinear systems theory with computational challenges in systems biology, climate studies, and machine learning. Prior to Georgia Tech, she was a postdoctoral researcher at MIT's Institute for Data, Systems and Society (IDSS).

No scientific awards or active grants are explicitly mentioned in the provided text. She maintains an academic website at https://ni-sha-c.github.io/.