Karthik Kashinath's research spans the areas of fluid dynamics, nonlinear dynamics and climate dynamics. Karthik uses and develops methods from machine learning, artificial intelligence, statistics, applied topology, dynamical systems theory and complexity theory to discover patterns in datasets from a range of physical systems. He also investigates ways to incorporate prior knowledge about the physics and dynamics of such systems into machine learning models and statistical models. A current research focus area is using novel big data analytics and pattern discovery methods for large complex systems such as Earth’s climate, especially to improve our understanding of extreme weather and climate events and how they are changing under global warming.
Karthik Kashinath received his Bachelors from the Indian Institute of Technology, Madras in 2007, Masters from Stanford University in 2009, and PhD from the University of Cambridge, U. K. in 2013. His educational background is in mechanical and aerospace engineering and applied physics. He has worked on various projects spanning a wide range of disciplines from supersonic air-breathing engines to battery technologies to acoustics to complex chaotic systems and turbulence. Since 2013 he has been a part of Lawrence Berkeley National Laboratory working as a climate and atmospheric scientist.