Specializing in Machine Learning, Embedded Systems, and Intelligent Automation
- IEEE DataPort Publication:Dataset, Graphs, and Feature Descriptors for: Comparative assessment of graph-convolutional neural network architectures for Δ-learning of QM/MM energy corrections on the QM9 dataset. (Highly utilized resource with over 342,000+ downloads)
"Passionate about transforming data into intelligent and impactful solutions. Interests lie in machine learning, data analytics, and intelligent automation."
Enjoy exploring the intersection of AI, software development, and real-world innovation — from building predictive models and visualizations to developing data-driven applications.
Currently focused on scalable and ethical AI systems.
- EEG Dataset Research Intern at Amrita Vishwa Vidyapeetham — Working on EEG signal processing and ML approaches for brain-computer interface applications (Apr 2026 – Present)
- Core Member — R&D Department, INIT Club — Organized GitHub workshops (Anokha PR Game), conducted git training sessions, and contributed to open-source learning initiatives (2025 – Present)
| Project | Description | Tech |
|---|---|---|
| VitalCache | ESP32-based biomedical sensing with real-time cache memory simulation | C++, ESP32, MAX30102 |
| SmartFS | ML-driven embedded filesystem for Teensy 4.1 with intelligent block allocation | C++, Teensy 4.1, ML |
| Biomaterial Crystal Gen | Diffusion-based generative models for biomaterial structural patterns | Python, Diffusion Models |
| QM/MM Δ-Learning | SchNet & GNN models bridging quantum mechanics and molecular mechanics | PyTorch, SchNet, GNN |
| CollapseTracker | Empirical dataset documenting progressive model collapse across recursive generations | ML Research, Dataset |
| AutoPulse SheetSync | Full-stack automation dashboard with Google Sheets sync & live metrics | Python, Flask, Google API |
📍 Amrita Vishwa Vidyapeetham, Coimbatore
