Work Experience

• Data Scientist
  CEA Paris-Saclay, France (Sep 2021-Present)

• Built Natural Gas consumption analysis of US at county level to find correlation between temperature and consumption, and their dependence on socio-economic variables like income, employment, housing etc

• Developed interactive visualization tools and deployed them as a cloud application (Using Plotly, Streamlit, Voila, and Heroku)

• Designing methods to quantify regional budgets of anthropogenic CO2 emissions using ground-based near real-time activity data on energy and mobility. Carbon Monitor: https://carbonmonitor.org

• Machine Learning Intern
  Orange Labs, Cesson-Sévigné, France (Feb 2021-Aug 2021)

• Validated and enhanced data within a knowledge graph in the Internet of Things domain using state of the art algorithms for deep graph learning and graph embeddings for knowledge graphs

• Achieved excellent performance for embeddings with 98% ROC-AUC and > 80% average precision scores in prediction of missing links in the graph by using an end-to-end machine learning pipeline

• Machine Learning Intern
  Laboratoire Hubert Curien, Saint-Étienne, France (Apr 2020-Jul 2020)

• Generated dynamic graph embeddings (numerical representation of proteins) using deep Autoencoders and predicted missing protein interactions in the network with an accuracy score of 80% using SVM

• Identified important protein clusters (ranging from 1 to 24 clusters depending on the cell type) from dynamic graph embeddings using K-NN and clustering algorithms