Experience

Machine Learning + Particle Physics Researcher

September 2019 – Present San Diego, USA

Developing new graph- and attention-based generative models for sparse and irregular data like that prevalent in particle physics
Graph neural network (GNN) autoencoders for compression and anomaly detection, machine learning for particle flow reconstruction, Lorentz-group equivariant autoencoders, JetNet library for convenience and reproducibility in machine learning development in high energy physics
Developing and applying state-of-the-art GNN classifiers to set the most stringent constraints to date on double-Higgs production, allowing insight into the metastability of the universe

CERN Openlab Intern

June 2019 – August 2019 Geneva, Switzerland

Started our project on graph generative models for particle physics simulations, motivated primarily by the CMS experiment’s new High Granularity Calorimeter (HGCAL)

Experimental Quantum Information Science Researcher

June 2017 – June 2019 San Diego, USA

Designed and implemented a setup for a quantum gas microscope (QGM) to image with single-site resolution
Generated 2D dynamic, arbitrarily arranged, sub-micron optical tweezers, integrated with the QGM, via two methods, using: 1) a Digital Micromirror Device (i.e. holography), and 2) an acousto-optic deflector
Characterized a high (0.8) Numerical Aperture objective for the QGM using OSLO optical simulations and point-spread function image analysis in Python
Using an FPGA device, outputted RF waveforms that modulate laser beams with parabolic spatial intensity in order to produce a Bose-Einstein Condensate
Programmed FPGA and C electronic devices, and created and (3D) printed mechanical mounts and electronics circuits for experimental use

Neurophysics Researcher

September 2018 – June 2019 San Diego, USA

Software Intern

July 2016 – September 2016 Mumbai, India

Interned at a software startup which has since been bought by Moka
Developed and deployed a location prediction SparkJava server with Cassandra and Redis databases
Implemented ML k-means clustering and SVM linear classification algorithms on location data
Wrote NodeJS servers and pages for receiving users’ predicted locations and displaying the live data on maps
Designed Cassandra and MySQL databases storing user tracking data, and wrote server APIs for accessing/updating, along with web panels for easy viewing of the data (using said APIs)