I am a graduate student majoring in the Quantitative Biomedical Sciences program at Dartmouth College, with a focus on Data Science, I have a strong background in both biomedical engineering and data analysis. My interest in data science was sparked during my junior year of college and has since been further developed through online courses and projects. My experience as a technical writer at Analytics Vidhya, Medium, and my work as a Computer Vision Engineer at Visionify, where I focused on developing object detection models for edge devices and deploying them to the cloud platform, have provided me with practical skills in machine learning and deep learning.
I have around 4 years of programming experience with Python and through my coursework, I have an implementation understanding of programming languages like R, and SQL. I am interested in Statistical analysis and interpretation of data, and this interest led me to participate in the Case Competition organized by the University of South Carolina. I secured 2nd position amongst 34 teams all over the United States for the Identification of Breast Cancer using Unsupervised learning on the Genetic Expression dataset.
Data Science, MS at Dartmouth College
Data Scientist at Schaeffler (June 2023 -- September 2023)
- Formulated a scalable precision and recall metric using Python for deep learning, enabling robust handling of diverse large-scale class labels and generating valuable business insights.
- Engineered state-of-the-art deep learning models using Tensorflow for bearing defect detection, including end-to-end data handling, preprocessing, and YOLO to Pascal VOC conversion in Azure.
- Spearheaded innovative semi-supervised learning for deep learning models, significantly improving defect detection accuracy by 30% with cost-effective synthetic annotations.
- Utilized SQL queries to boost overall system performance by an impressive 40%, ensuring efficient data retrieval and processing for deep learning tasks.
Computer Vision Engineer at Visionify.ai (August 2021 -- August 2022)
- Implemented data engineering pipelines on large-scale data using SQL and Azure that improved the performance of object detection deep learning models by 19% for the company’s biggest vendor - Walgreens.
- Monitored and tracked application lifecycle on Azure DevOps and Jira Software by creating user stories and tasks; this enhanced the efficiency of model pipelines by 75%.
- Developed and deployed an NVIDIA TensorRT model for object recognition and classification on Azure using OpenCV, Python, PyTorch, and CUDA; accuracy increased by 5% from 85% to 90%.