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
  • Used two-photon microscopy to measure pO2 in the mouse somatosensory cortex
  • Imaged the cortex to measure vasomotion relative to pO2
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)