I recently wrapped up my Master's in Computer Science from Virginia Tech, where I dived into cybersecurity, machine learning, and computer vision. Even got to publish some papers in top journals and conferences—super exciting stuff!
I’m passionate about building scalable systems and exploring the future of AI—especially autonomous, agentic AI systems that can reason, act, and adapt in dynamic environments. I love blending cloud-native architecture with intelligent software to create meaningful real-world impact.
When I'm not coding, you'll often find me hiking, discovering new spots, or zooming around in Go-Karts. And hey, I've been told I bring a lot of energy to the table!
GPA: 3.77
Worked with Dr. Jin-Hee Cho on an Interdependent Mission Impact Assessment (iMIA) framework using Bayesian reasoning to infer the performance and game-theoretic enemy behavior modeling to simulate the attack-defense interactions with the mission system.
Received the Best MS Research Award, Department of Computer Science, Virginia Tech, 2024.
CGPA: 8.93 (B.Tech: 8.67 & M.Tech: 9.70).
Worked with Prof. O. P. Vyas on my master's thesis titled "A Multi-Agent Framework to Detect In-Progress False Data Injection Attacks for Smart Grid".
Percentage - 98.7
Ellucian IQ GenAI: Architected and deployed a scalable, production-grade Generative Agentic AI platform using AWS Serverless (Lambda, Step Functions, DynamoDB) and AWS Bedrock, laying the foundation for company-wide AI integration.
Implemented advanced Retrieval-Augmented Generation (RAG) using AWS OpenSearch vector search, improving answer relevance by 60% through contextual grounding and semantic enrichments.
Reduced perceived latency from 10s to under 1s through asynchronous processing with SNS streaming and WebSockets, significantly enhancing user experience.
Delivered an AI-powered rich text editor in React.js, using the GenAI platform for dynamic text enhancements, context-aware content generation, and personalized AI interactions with support for rich text formats like quill delta.
Employed Test-Driven Development (TDD) and authored automation tests using Playwright, ensuring reliable high-performance backend services and UI, which reduced bugs and improved software quality.
Built a robust course selection shopping cart service using AWS Lambda and DynamoDB with a React.js frontend, scaling to handle over 10,000 peak concurrent users (PCU) during registration periods. Developed WebSocket APIs and SQS queues for real-time pricing and availability updates via DynamoDB streams, eliminating registration conflicts and improving user experience.
Migrated a monolithic patient monitoring application to a microservices architecture using Java SpringBoot, Spring Cloud, and Hibernate, improving system scalability and maintainability.
Implemented a real-time analytics dashboard using Kafka streams and Elasticsearch, enabling clinicians to monitor critical patient metrics across multiple units, resulting in 35% faster response times to clinical deterioration events.
Developed comprehensive JUnit and Mockito test suites integrated with the Jenkins CI/CD pipeline with Snyk and SonarQube scans, reducing regression testing time by 50% while expanding test coverage by 90%.
Developed a novel approach using GANs for NURD correction in the intravascular ultrasound images using PyTorch and received the Philips spot Award for my contribution.
Developed a comprehensive framework that scans the devices and detects malicious activity in an intranet in real-time for SCADA/ICS networks using Snort, Scapy, Flask, D3.js for data visualization.
