09.01.2019 - Jay M. Patel - Reading time ~4 Minutes
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Data scientist with over five year’s experience in data analytics, machine learning, statistics and text mining.
I have coauthored 1 book, 10 papers, 26 conference presentations and am passionate about explaining data science to non-technical business audiences.
Frequent speaker at data science events hosted by Federal community of practice (CoP) as part of DigitalGov initiative.
Machine Learning: classification and regression (linear, logistic, support vector machine, random forest, convolutional neural network (CNN/ConvNet), recurrent neural networks), cluster analysis, feature engineering. Analyzing unstructured data using Natural-language processing (content and knowledge based recommender systems).
Statistical Methods: hypothesis testing (ANOVA, t-test) and confidence intervals, correlation (bivariate, partial, distances), time series, principal component analysis and dimensionality reduction.
Software and Programming Languages: Python (scikit-learn, keras, pandas, matplotlib, numpy, scipy, NLTK, spaCy), R (shiny, knitr, ggplot2, tidyverse, caret), SQL (MySQL, SQLite, MongoDB), Apache Spark (MLlib), Apache Hadoop, Weka, Eclipse RCP/Java, KNIME, SPSS, Stata, Microsoft Excel.
INDEPENDENT CONSULTANT (SPECROM.COM), MUMBAI, INDIA (05/2018 - Present)
Principal Data Scientist
Implementing text analytics and NLP algorithms for social listening, sentiment analysis and business process improvement (learn more at specrom.com).
Provide continued support, bug fixes and feature addition to the data analytics python modules and R packages (HTdescR and HTqsarR) developed by me as part of the ongoing projects at US Environmental Protection Agency.
Work with stakeholders in development on best standard practices on regression and statistical modeling by being an official voting member on ASTM Committee E11 on Quality and Statistics.
US ENVIRONMENTAL PROTECTION AGENCY, ATHENS, GA, USA (12/2015 - 05/2018 )
Office of Research and Development (ORD)
Data Scientist (ORISE Fellow)
Fulfilled all the data science duties for US EPA’s and US FDA’s joint Tox21 program and chemistry safety for sustainability (CSS) program.
Compiled and curated data from various sources and used that to develop machine learning based classification and regression models.
US ENVIRONMENTAL PROTECTION AGENCY, ATHENS, GA, USA (05/2013 - 11/2015)
Data Scientist (Contract)
Directed development of a predictive machine learning based models (more info) as part of a contract valued at over $150,000 with US federal government (Order no. EP13W000134 and EP14W000201, DBA Patel, Jay).
Led a team for development of content based recommender system as part of decision analytics dashboard to generate regulatory intelligence insights by using web scraping plus Natural-language processing based model on unstructured data in HTML and pdf format.
THE UNIVERSITY OF GEORGIA (08/2010 - 05/2013)
Franklin College of Arts and Sciences
Designed and applied a virtual screening workflow based on machine learning classification model to identify high activity enzyme mutations and validated it experimentally using site saturation mutagenesis.
In a separate project, developed a partial least square model for predicting solvation energies for a enzyme mutation and experimentally validated it.
Project resulted in four peer reviewed papers in top international journals (Impact factor ~10).
THE UNIVERSITY OF GEORGIA, ATHENS, GA, USA
M.S., Chemistry (05/2013)
INSTITUTE OF CHEMICAL TECHNOLOGY (FORMERLY UICT/UDCT), MUMBAI, INDIA
B.Tech, Chemical Engineering (06/2010)
Stevens, C.T., Patel, J. M., Koopmans, M., Olmstead, J., Hilal, S.M., Pope, N., Weber, E. J. & Wolfe, K. (2018) Demonstration of a consensus approach for the calculation of physicochemical properties required for environmental fate assessments. Chemosphere.194, 94-106.
Stevens, C.T., Patel, J. M., Jones, W. J. & Weber, E. J. (2017) Prediction of hydrolysis products of organic chemicals under environmental pH conditions. Environ. Sci. Tech., 51(9), 5008-5016.
Patel J.M., Phillips R.S. (2014) Effects of hydrostatic pressure on stereospecificity of secondary alcohol dehydrogenase from Thermoanaerobacter ethanolicus support the role of solvation in enantiospecificity. ACS Catalysis. 4, 692-694.
Patel J.M. (2009) Biocatalytic synthesis of atorvastatin intermediates. J. Mol. Catal. B: Enzym. 61, 123-128.
Patel, J. M., Stevens, C.T., Weber, E. J. Estimation of hydrolysis rate constants for carbamates. American Chemical Society (ACS) Annual Spring Meeting 2017, San Francisco, CA, April 02 - 06, 2017.
Weber, E. J., Card, M. Patel, J. M., Stevens, C.T. Cheminformatics applications and physicochemical property calculators: a powerful combination for the encoding of process science. Gordon Research Conference: Water, Holderness, NH, June 26 - July 01, 2016.