MS Computer Science @ NYU | Data Science & Big Data Enthusiast
I'm a graduate student at New York University pursuing my Master's in Computer Science with a focus on Big Data and Machine Learning. I have a strong foundation in data science, machine learning, and software development. My experience spans from building ML models for smart city optimization to developing real-time object detection systems. I've published research in IEEE and Springer conferences and won Runner-up at the IBM WatsonX Hackathon.
Full-stack analytics dashboard with MongoDB integration for real-time data visualization and user insights. Built with React, Node.js, and Express.
Built machine learning and time-series forecasting models to predict traffic flow and optimize smart-city infrastructure planning using Python, TensorFlow, and SQL.
Implemented YOLO and Faster R-CNN for autonomous driving applications. Optimized detection accuracy and inference speed. Published in IEEE conference.
Smart assistant platform using IBM Watsonx for vision-care professionals. Won Runner-up at IBM WatsonX Hackathon. Built with TypeScript and REST APIs.
Implemented MapReduce jobs on Hadoop cluster for processing large-scale datasets with optimized performance using Python and HDFS.
Deep learning pipeline to enhance image clarity using DCP and MAP-Net. Integrated with YOLO for object detection. Published in Springer.
Implemented and evaluated YOLOv8, EfficientDet-D4, Faster R-CNN, and SSD-MobileNetV2. Found YOLOv8 to deliver best balance of speed and accuracy.
Developed deep learning pipeline combining DCP and MAP-Net for video dehazing. Achieved superior results with YOLOv8.
Currently open to opportunities in Data Science and ML Engineering
Brooklyn, NY