About me

My name is Sanjit Shashi. I am a postdoctoral scholar primarily housed in the Physics department in New York University, working primarily with Stefano Martiniani. My research is primarily focused on machine learning and artificial intelligence.

I graduated from the University of Chicago in 2016 with a bachelor's in math and completed my PhD in theoretical physics in 2023 at the University of Texas at Austin. Before joining NYU, I was a postdoc at the University of California, Santa Cruz, working in quantum gravity then machine learning.

Research

For a complete list of my papers, please refer to my page on the inSPIRE database or to my CV.

I am broadly interested in scientific applications of machine learning. At NYU, I am currently working on using machine learning to predict de novo inorganic crystal structures for use in material science. I have also worked on ML for biochemical molecular dynamics, with an emphasis of simulating protein structures. I am also interested in theoretical facets of ML, particularly how tools from geometry and physics can be used to describe learned representations of data.

Most of my physics work concerns holographic duality or "holography," a mathematical equivalence between theories of quantum gravity and theories describing quantum phenomena with no gravity. Duality is nothing new to physics, with perhaps the most well-known example being the equivalent treatments of light as either a particle or a wave. While my quantum-gravity work is in the my past, the profundity of duality remains an important lynchpin in my approach to theoretical frameworks.

Contact info

Email: s.shashi@nyu.edu

GitHub: SanjShashi

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