I am an AI-focused Computer Science undergraduate at BITS Pilani with a strong interest in machine learning systems, reinforcement learning, and safety-aware AI. My work centers on building well-structured, reproducible, and dependable AI systems, with emphasis on algorithm–environment alignment, evaluation rigor, and real-world reliability rather than benchmark-driven optimization.
I have hands-on experience designing end-to-end AI workflows across discrete and continuous control, distributed and federated learning, multimodal perception, bio-acoustic analysis, and retrieval-augmented language systems, integrating model development with system-level considerations. Across these efforts, I prioritize clean abstractions, deterministic evaluation, transparent failure analysis, and deployment-aware design, aiming to develop AI systems that are explainable, robust, and defensible in practical engineering settings.
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Finite-Element-Model-Based-Neural-Twin-for-Structural-Dynamics-and-SHM
Finite-Element-Model-Based-Neural-Twin-for-Structural-Dynamics-and-SHM PublicStructural Health Monitoring system that unifies deep learning and Finite Element modeling for real-time fault detection and dynamic response prediction in smart structures.
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federated-fine-tuning-with-flower-distributed-computing
federated-fine-tuning-with-flower-distributed-computing PublicFederated learning setup using Flower and ALBERT for distributed text classification — with real-time metric visualization and multi-device coordination.
Python
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Multilingual-Child-Specific-Content-Safety-LLM
Multilingual-Child-Specific-Content-Safety-LLM PublicPython
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