Graduate Projects
Differential Privacy in Large Language Models
In this project, I explore the intersection of Differential Privacy (DP) and Federated Learning (FL) in Large Language Models (LLMs) to address key privacy concerns. DP provides strong theoretical guarantees that prevent adversaries from inferring the presence or absence of individual data points in a dataset. This project investigates how DP can mitigate privacy risks such as membership inference, model inversion, and data extraction attacks in FL-based LLMs. By implementing Differentially Private Stochastic Gradient Descent (DP-SGD) and evaluating privacy-utility trade-offs, the research aims to enhance privacy protection in decentralized LLM training. The findings contribute to developing more secure and privacy-preserving AI models in distributed learning environments.
Unifamily – University Community App
Leading a team of five in the ongoing development and deployment of a University Community Application on AWS with full infrastructure automation. The platform enables students to create and share posts across categories like Roommate Search, Sell, Carpooling, and Other. Key functionalities include university email-based (or SSO-based) authentication, Google Chat integration for buyer-seller communication, and content moderation to prevent sensitive images or text. Additionally, we are implementing data deletion capabilities, post status management, and an AI-powered item description generator for selling posts. As a bonus, we aim to integrate semantic similarity search for improved post discovery. The system is being built with a focus on scalability, security, and automation, ensuring a robust and maintainable cloud infrastructure.
Professional Projects
Large Scale Object Storage Migration
Conceived and executed a customized large-scale object storage migration solution from AWS S3 to Google Cloud Storage, meeting unique project specifications. Successfully live migrated 700 TB (3 billion objects) of data, resulting in a 30% cost reduction and an impressive 60% reduction in migration time, compared to conventional storage transfer service.
Cloud Wellness Assessment Automation
Enhanced and implemented a Cloud Wellness Assessment template aligned with the well-architected framework. Designed and deployed an agile, cloud-native automation solution on Google Cloud for conducting Cloud Wellness Assessments across Google Cloud projects, reducing 60-70% of manual efforts.
ITSM – Monitoring Tools Integration
Architected and implemented a bespoke Python-based integration solution to seamlessly connect diverse monitoring alert sources, including Google Cloud monitoring, AWS CloudWatch, and Grafana, with the organization’s ITSM tool. This automation initiative streamlined incident generation and enhanced the efficient handling of recurring incidents.