June 2023 – December 2023
Coordinated with 5-member team in applying GNNs for hospital monitoring, resulting in a 27.4% enhancement in patient movement prediction accuracy and optimizing resource allocation Adopted NLP techniques to process text-based comments, achieving patient outcome prediction accuracy of 89%
January 2020 - July 2022
Performed exploratory data analysis using Tableau, Jupyter Notebook, and Python (Pandas, NumPy, and Bokeh) to craft 10+ insightful, interactive visualizations, facilitating better decision-making and client understanding Deployed a standalone pipeline through CI/CD to build and integrate test microservices with Docker, AWS, and CDK Managed and optimized database operations for over 8 million records in PostgreSQL and Oracle
August 2022 - Feb 2024
MS in Data Science
August 2017 - June 2020
BE in Computer Engineering
This project introduces an innovative approach to image dehazing using a deep learning model that leverages convolutional layers, residual connections, and concatenation techniques. Implemented with TensorFlow 2.0, this model demonstrates significant improvements over traditional state-of-the-art methods.
This project introduces an innovative approach to image dehazing using a deep learning model that leverages convolutional layers, residual connections, and concatenation techniques. Implemented with TensorFlow 2.0, this model demonstrates significant improvements over traditional state-of-the-art methods.
The project is centered around developing an AI-driven robotic assistant with 360-degree object detection and voice interaction, offering visually impaired individuals accurate navigation and object detection experience, leveraging 360°cameras and ResNet18 for comprehensive object detection.
This project explores the application of Q-learning and SARSA in a controlled grid environment, where an autonomous agent navigates a 4x4 grid to maximize rewards by collecting batteries and avoiding obstacles. Used Python, Gynamsium, Google Colab and Reinforcement Learning Concepts
This project develops an AlexNet model from scratch to classify images using Pytorch, Kaggle Notebook and Python. It explores various optimization techniques and data augmentation methods to enhance model accuracy and generalization, achieving significant improvements in image categorization
This R project conducts a detailed survival analysis of ovarian carcinoma patients, focusing on evaluating the efficacy of two different treatment protocols. Utilizing the Kaplan-Meier estimator and log-rank tests, it compares survival outcomes among patients