Data Science Research Associate, University of Houston (Aug 2023 – Aug 2024)
Developed a predictive model for polymer electrolytes' ionic conductivity, leveraging a suite of machine learning techniques including Random Forest, XGBoost, KNN, Linear Regression, and the Chemprop model, with a focus on correlating chemical composition to conductivity.
Curated a robust training dataset by meticulously extracting and synthesizing data from a range of experimental publications that reported on the ionic conductivity of various polymer electrolytes.
Demonstrated the superior predictive power of the XGBoost algorithm through comparative analysis, which consistently surpassed competing models in precision, highlighting my adeptness in both experimental data interpretation and the application of sophisticated machine learning methodologies.
Assistant Professor, Bennett University, India (Oct 2017- Dec 2020)
Crafted and delivered curriculum blending mathematical theories with data science practices.
Achieved outstanding student evaluations, reflecting effective teaching strategies.
Engaged in scholarly research and provided academic mentorship to students.