Exploring the roots of life through code and curiosity 🌱💻🔍
Working with a team of researchers from various domains at the COOL lab,
our goal is to explore the origins of life on Earth and investigate the potential
presence of life on other planets.
As a graduate researcher, my role involves applying Machine Learning and Generative AI
to the problem domains of protein folding, the Ribosome, and molecular evolution.
I am pursuing a Master's degree at Georgia Tech, specializing in Machine Learning, while actively conducting research at the COOL lab.
I have experience creating Computer Vision solutions to address challenges in the dairy and farming sectors. This involved developing lightweight models to ensure fast inference times and optimizing them for efficiency. Additionally, I successfully adapted these models for deployment on both mobile and IoT devices.
I devoted a significant amount of time to crafting and refining the data processing pipeline,
leveraging tools like Airflow and Spark.
Within this framework, I developed machine learning solutions aimed at addressing pertinent
issues in chip design.
Notable projects included predicting slack, classifying path groups, conducting root cause analysis,
and predicting clock skew.
Over a span of 4 years, I immersed myself in the study of diverse computer algorithms
and actively participated in numerous projects.
Some noteworthy endeavors include the "Open Chat" Facebook Scholarship Winning Project in 2019 and my final year undergraduate project on "Image Segmentation of Geographical Data".