I am an assistant professor in the Department of Applied Mathematics at the University of California Santa Cruz. Before I moved back to academia, I was a staff scientist in Silicon Valley at the Palo Alto Research Center (formerly Xerox PARC and currently a part of SRI). 

If you've come across any of my papers that are behind a paywall and you want access to it, drop me an email. 

The key questions I'm interested in are:

• Physical consistencies and interpretability in neural architectures-- How well do neural architectures satisfy physics (by construction) ?

• Limit of predictability for neural architectures (on physical problems) 

• Synergistic integration of neural models with physical models (for problems in turbulence, climate dynamics, and generally chaos). See my talk on this for an overview. 

• Data assimilation through the eyes of learning algorithms. See my talk on this for an overview.

• Theory of deep learning from a dynamical systems perspective (and also the other way around) See Talk 1 and Talk 2 for an overview.

In another life, I used to do:

• Reduced-order modeling (data-driven and otherwise) of non-linear systems (usually chaotic and turbulent)

• Computer architecture and algorithms (mostly for scientific computing) for exascale-capable hybrid-parallel (MPI+OpenMP/CUDA) physics simulators

• Computational geometry (algorithms for optimization) for fluid interface modeling 

Google Scholar:link          Github: link         ResearchGate:link         LinkedIn:link       Twitter:link

Email:  aschatto@ucsc.edu       ashesh6810@gmail.com

CV

Outside academia, I am on the advisory board for Vayuh where we are  building cool things in the climate tech space.