My research spans privacy-preserving federated learning, edge-aware scheduling, cognitive AI studies, and domain-specific applications in healthcare, finance, and embedded systems. Here's a curated list of my major projects and publications:
Master’s Thesis, 2025
Investigated performance, scalability, and privacy metrics across multiple Federated Learning paradigms in constrained environments.
Submitted – WinTechCon 2025
Proposed a memory-aware scheduling mechanism to optimize federated training throughput on automotive edge systems.
Under Review – Cognitive Science Journal, 2025
Conducted a cognitive experiment comparing human perception and AI predictions across visual classes.
Under Review – Conference Submission, 2025
Designed a cross-institutional federated learning framework for clinical prediction tasks using EHR datasets.
Under Review – Conference Submission, 2025
Developed a federated system for collaborative stock market prediction across financial institutions.
Research Internship, HAL Bangalore
Analyzed procurement cycles and proposed vendor selection strategies, improving supply chain turnaround time by 20%.
Research Internship, BSNL Mysore, 2020
Evaluated 5G and IoT technologies and contributed to BSNL’s strategy for modernizing its network infrastructure.
Published in IARJSET, 2021
Created a secure eye-tracking authentication system using Morse code patterns, enabling access for users with physical limitations.
Presented at ICSEM, 2020
Designed a VHDL-based FIFO architecture for high-throughput video buffering in embedded systems.