B.Tech in Robotics & AI at UEM Kolkata. I build autonomous systems, train deep learning models, and ship things that move in the real world.
I'm a Robotics & AI Engineering student at the University of Engineering and Management, Kolkata (2024–2028), ranked in the top 15% of my cohort. I sit at the intersection of autonomous systems and machine intelligence.
My work spans the full stack — from writing CUDA kernels and SLAM pipelines to building multi-agent LLM systems and RAG applications. I care deeply about systems that work reliably in the real world, not just in notebooks.
I completed an AI/ML internship with IBM SkillsBuild (via Edunet Foundation), contributed a merged PR to scikit-learn, won 1st place at UEM's robotics challenge, and publish technical writing on Medium reaching 500+ readers.
A fully articulated hexapod robot with 3 DOF per leg (18 servos total), controlled via ESP32 with gait sequencing, tripod walk patterns, and wireless control. Built from scratch — chassis, electronics, and firmware.
Full autonomous navigation stack — SLAM mapping, A* global planning, DWB local obstacle avoidance, and YOLOv8 vision integration. 92% goal success across 50+ sim trials.
An automated Paper Summarizer and research assistant. Utilises a multi-agent architecture and RAG system powered by the Google Gemini API to retrieve, analyze, and synthesize academic papers efficiently.
A scalable framework utilizing PyTorch's DistributedDataParallel to train deep learning models across multiple GPU nodes, significantly reducing training time for large datasets.
An AI-augmented security tool that parses abstract syntax trees (AST) of codebases and leverages LLMs to identify potential vulnerabilities, suggesting secure refactors directly to developers.
A privacy-preserving machine learning framework simulating distributed edge devices. Models are trained locally on device partitions, and only aggregated weights are shared securely with the central server.
Contributed an optimization to the feature scaling preprocessing pipeline in the official scikit-learn repository. The code was reviewed by maintainers and merged into the main branch, improving execution performance by ~15%.
Open to internships, research collaborations, and interesting problems in robotics, autonomous systems, and applied AI. Based in Kolkata — available remotely.
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