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I'm a 2nd-year B.Tech student in Computer Science & Engineering at Amrita Vishwa Vidyapeetham, Coimbatore. My interests lie at the intersection of Machine Learning, Data Analytics, Embedded Systems, and Intelligent Automation.
I enjoy exploring AI, software development, and real-world innovation — from building predictive models and visualizations to developing data-driven applications. I'm also a Core Member of the Research & Development department at INIT Club, my university's premier tech club.
Currently focused on scalable and ethical AI systems, and working on EEG dataset research under the guidance of Dr. Abhishek.
A versatile toolkit spanning AI/ML, embedded systems, web development, and cybersecurity.
Research, internships, and leadership roles that shape my career.
Working in a team of 4 on EEG (Electroencephalogram) datasets, exploring signal processing and machine learning approaches for brain-computer interface applications. Currently in month 2 of the internship.
Research & Development core member at INIT, the premier tech club. Organized GitHub workshops (Anokha PR Game), conducted git training sessions, and contributed to open-source learning initiatives for the student community.
Completed a 60-day intensive data science internship, working on real-world datasets, building predictive models, and developing end-to-end data analytics pipelines.
Completed multiple software development tasks including automation tools, API integrations, and Python-based applications across 3 project cycles.
Built practical software projects, strengthened Python development skills, and contributed to real-world problem-solving through guided internship programs.
From embedded firmware to generative AI — here's what I've been building.
ESP32-based embedded system integrating real-time biomedical sensing (PPG, LM35, MAX30102) with cache memory simulation — Direct, Fully Associative, and Set-Associative mapping.
Custom-built filesystem for Teensy 4.1 microcontrollers with machine learning-driven block allocation. Unlike regular filesystems, SmartFS watches access patterns to optimize storage.
Diffusion-based generative models that learn and create structural patterns from biomaterial datasets, combining generative AI with structural interpretation for material science.
Full-stack automation dashboard connecting data pipelines, automating processing workflows, syncing with Google Sheets, and visualizing live business metrics through an interactive interface.
Telegram productivity assistant for calculations, weather updates, news, task reminders, and natural chat — all without paid AI APIs. Built with python-telegram-bot.
Predictive modeling using SchNet, GNNs, and MLPs to bridge the gap between quantum mechanics and molecular mechanics simulations on the QM9 dataset.
Empirical dataset documenting progressive model collapse across recursive generations of synthetic training data. An important study on AI model degradation.
Continuous learning through industry-recognized certifications and competitive events.
Open for collaborations, research opportunities, and interesting projects.
Whether you have an exciting project, research opportunity, or just want to say hi — I'd love to hear from you.