Datascience & Machine Learning
Bitbootcamp, New York, NY (2016)
Random Forests, GBM, Naive Bayes, k-means clustering, collaborative filtering
About Me
Creative, motivated and Insightful
Executive Summary
Data scientist with research experience in theoretical physics.
Experience in machine learning with python and R. Predictive models from gigabytes of computational data. Experience in quantitative modeling, simulations, and data visualization. Excellent research and analytical skills with strong computational background. Well organized, with attention to detail and accuracy.
• Machine learning with classification, regression, clustering, anomaly detection, analytics and deep learning • Topic modeling with natural language processing • First principles electronic structure, molecular dynamics and Monte Carlo methods • Editorial board member for Journal of Postdoctoral Research • Programming in Python, C++, Octave • 27 published articles in scientific journals such as Physical Review Letters, Physical Review B, Scientific Reports • 2 invention disclosures for US Patents
Skills
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Data Modeling 85%
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Machine Learning 90%
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Condensed Matter Theory 95%
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Predictive Modeling 90%
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Probability and Statistics 80%
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Python 95%
Education
PhD in Physics
HBNI, India (2011)
Thesis: Classical and quantum simulations of novel functional materials
M. Sc. in Physics
Indian Institute of Technology (2002)
Thesis: Artificial neural networks
Experience
Postdoctoral Fellow
Oak Ridge National Lab (2014-2016)
Developed materials for clean energy technologies
Postdoctoral Fellow
University of Missouri (2011 - 2014)
Developed analytical models of microscopic interactions
Staff Scientist
BARC, Mumbai, India (2002 - 2011)
Studied phase transition processes using statistical models
