Biography:
I am currently working as a post-doctoral associate at Idaho National Laboratory, where I lead research efforts in biomass characterization for biofuel production by integrating hyperspectral imaging and NIR sensing technologies. This work focuses on improving the identification and conversion of biomass components, contributing to more efficient biofuel production. Additionally, I work on the detection of Rare Earth Elements (REEs) in e-waste and leverage AI to address challenges related to food security, food safety, and sustainable agriculture.
Throughout my PhD journey, spanning 10 semesters, I served as a Graduate Assistant Lecturer at Texas A&M University's Department of Electrical and Computer Engineering. In this role, I guided and supported senior capstone design endeavors encompassing microcontroller programming, database management, web development, and PCB design. My doctoral research primarily revolved around formulating error estimation methods tailored for small and sparse datasets. Beyond this, my academic pursuits extend to areas such as deep learning, reinforcement learning, computer vision, natural language processing, and big data analytics.
Previously, I worked as a Statistical Researcher at Bayer, where I applied various machine learning techniques to analyze soil environmental clusters and their impact on crop yield. I used clustering, dimensionality reduction, feature selection, and regression methods to identify the important soil parameters and the interactions between soil clusters and treatments. I also gave statistical recommendations based on the results. Additionally, I have multiple certifications and publications in the fields of electronics, computer, and data science. I have strong skills in Python, R, C, C++, MATLAB, AWS, Keras, TensorFlow, Pytorch, Scikit-learn, and other programming and ML packages.