Hi! I’m Amitesh (अमितेश, /əmɪte͡ɪʃ/), a senior at Caltech, studying computer science and engineering. My research interests lie at the intersection of causal inference and neural operators to better model our physical world. I hope to improve existing machine learning models of our natural and urban systems by informing them of underlying causality and vice-versa and to advance scientific machine learning more generally.

Keywords: ml/ai for science causal inference natural and urban systems

Currently, I’m working on using neural operators to learn constitutive relations for granular flow of diverse geometries in the Bhattacharya MechMat lab. Previously, I worked at the Reisman Chemical Synthesis Group where I explored supervised machine learning methods and graph neural networks to forecast regioselectivity in the C-H bond’s functionalization for improved industrial production of phenazines. I presented my work as a poster at the Caltech summer research symposium and was awarded the John Stauffer award to pursue this work.

I have also been involved with the MIT PatchCheck Foundation, working with Ramesh Raskar of the MIT Media Lab, where my work was centered around exploring methods to posit statistics and ml-aware COVID-19 vaccination policies. I have also spent a summer researching optimization and genetic algorithms at the Bulgarian Academy of Sciences.

In my free time, I like to read, lift weights, and run long-distance. One of my serious goals is to attain a 1000lbs squat + deadlift + bench total and run a sub 3 hour full marathon in the same week (see: 1003 club).