Academic

Research Assistant, University of Maryland, Baltimore County (January 2018 to Current)

Collected, analyzed, and interpreted multimodal data captured by sensors, intelligent systems, and user inputs.
Developed explainable machine learning models with wide-scale applications in both education and healthcare domains. Devised an explainable method using locally interpretable model explanations (LIME) to interpret both white and black box model predictions.
Developed predictive models to solve both classification and regression problems.
Authored 10 publications in the areas of artificial intelligence and data science in education, cognitive psychology, and health care.
Taught Database applications course to a class of 60 students. Trained students on Pl/SQL.
Classes taught totaled over 100 students and comprised a Decision Support Systems course and Health Care Informatics course. Conducted RapidMiner data science boot camp

Industry

Data Scientist Co-op (Ph.D. level), Bayer Crop Science R&D (January to May 2021)

Developed Genotype Accuracy Model framework in Python to evaluate Bayesian-based genomic imputation algorithm using observational masking. This framework supports cost-cutting in the breeding pipeline by providing insights to breeders about high-yield seed selection.
Deployed an R Shiny Dashboard to identify recombination breakpoints across the genome and explored optimization methods to improve genotype imputation engine performance near and away from recombination breakpoints.
Developed a scalable framework in Domino Data Lab to impute missing genotypes across the genetic map with high accuracy for various crops like corn and soy.
Developed a framework in R to execute pedigree-based breeding program simulations using AlphaSimR.
Collaborated across teams to identify needs and gaps in data science pipelines and provide solutions.

Data Science Intern (Ph.D. level), Edison Software Inc (June to August 2020)

Perform statistical modeling on consumer behavioral data captured by the Edison platform (e-mail). This includes mathematical optimization, probability and sampling, and causal inference.
Extract data from multiple sources and code models that process large data sets using Impala SQL and python.
Develop interpretable and accurate regression models to predict monthly e-commerce sales in US based on Edison data by implementing user cohorts, regularization, and heuristic methods.
Performed feature analysis to understand the impact of different variables on model predictions

Predictive Analytics - Data Science Intern, Highmark Health (May to June 2020, reduced due to pandemic)

Analyzed the technological changes in the organization during the pandemic.
Performed a case study to understand the challenges, benefits, and timing of changes made by Highmark health during the pandemic.

Software Engineer, Accenture Services Pvt Ltd (August 2014 to December 2015)

Responsible for gathering SAP OTC business requirements and communicate client business requirements by constructing easy to understand data and process models.
Developed and implemented 5+ levels of testing, unit testing, integration testing, system testing, user acceptance testing and Regression testing for entire SAP OTC process.
Identified and documented As-Is business processes in Aviation oil domain related to the SAP system.
Created a concordance for plant configuration in SAP SD. This was a first of its kind and used as a template for other configurations.

Technical Intern, Efftronics Systems Pvt Ltd (October 2013 to May 2014)

Development and monitoring of Air quality control system using multi-sensor fusion techniques.
Researched various methods to improve air quality in the workplace.

Programming: Python, Matlab, R-Programming, PySpark

Data science & Visualization Tools: RapidMiner, Weka, QlikView, Qlik Sense, Tableau

Database: MySQL, SQL server, Cassandra

Development Tools: Anaconda, PyCharm, Git, Domino Data Lab

Operating System: Windows, Linux, Mac