👋 Hey, I'm Akash Nath.
I write research, ship products, and try to make deep learning work in places where people say it won't — on a $10 chip, in ancient scripts, inside an MRI scanner.
About
I grew up in Assam, Northeast India — a region where good internet arrives late and cutting-edge research arrives even later. That probably explains why I'm obsessed with building AI that works under constraints: on cheap hardware, in ancient languages, with limited data.
I started coding seriously at 17, I graduated from Assam University in year 2025, I have presented a research paper as first-author and deployed an AI model on an ESP-32 microcontroller costing less than a textbook. That project — an AI that classifies fire severity in real-time — was presented as an oral and e-poster at the World Summit on Disaster Management, 2025. It was my proof that serious research doesn't need a lab in a rich city.
Right now I'm building a brain-tumor MRI classifier that tells doctors when it's unsure. Most AI systems optimise for looking confident. I think a system that says "I don't know, please double-check this one" is far more valuable in medicine — so that's what I built. It's under review at IEEE CVMI 2026.
I'm also training a GPT-2 for Sanskrit — because one of the world's oldest written languages deserves a language model, and nobody else in my corner of the world was doing it. When I'm not doing research, I'm shipping products, competing on CodeChef (4-star), and applying for M.Tech AI/ML programs.
Now
Most AI just gives you an answer. MEDIAX gives you an answer plus a confidence score — and when it's not sure about a glioma, it says so. That's the feature. Submitted to IEEE CVMI 2026.
Sanskrit is one of humanity's oldest knowledge systems and it has almost no AI tooling. I trained a 97.7M-parameter language model on the AI4Bharat Sangraha corpus with a custom Devanagari BPE tokeniser. Evaluation ongoing.
Co-authored work on using attention-enhanced bottleneck convolution features fed into a stacked ensemble of classical ML classifiers. Accepted in the Journal of Theoretical and Applied Information Technology, April 2026.
Exploring advanced computer vision systems focused on real-world reliability, medical imaging, and edge AI deployment. Currently expanding research in deep learning, uncertainty-aware vision models, and efficient AI architectures for next-generation applications in healthcare and disaster response.
Published work
EfficientNet-B3 with multi-scale feature fusion and Monte Carlo Dropout. Hits 93.75% accuracy and AUC 0.983. The real contribution: a clinical triage layer that drops glioma miss-rate from 22% to 2.8% by trading some recall for safety. Built for clinicians, not benchmarks.
Combines an attention-augmented bottleneck CNN as a feature extractor with a stacked ensemble of classical classifiers. More robust on constrained feature regimes than a single deep model. Paper ID 63530-JATIT.
A hybrid EfficientNetB3 + BiLSTM that classifies six fire-severity levels from a live camera stream in under 500ms — deployed on ESP-32 hardware. 94.39% accuracy on the MIVIA dataset. Presented as first-author oral and e-poster at the World Summit on Disaster Management, Graphic Era University, 2025.
97.7M parameters, trained on the AI4Bharat Sangraha corpus. Custom 16K Devanagari BPE tokeniser. Evaluation across perplexity, type-token ratio, and script purity. Composite eval score 7.55/10 at checkpoint 83,000. Draft paper targeting ACL/EMNLP/LREC-COLING.
Selected builds
EfficientNetB3 + BiLSTM hybrid that classifies six fire severity levels from an ESP-32 CAM stream in under 500ms. Uses a Gemini-assisted + human-expert labelling pipeline on the MIVIA dataset. First-author at WSDM 2025 in Dehradun. This is the one that got me into research.
97.7M-parameter transformer trained on the AI4Bharat Sangraha corpus with a custom 16,000-token Devanagari BPE tokeniser. Trained iteratively across RTX 2060, T4, and A100 environments with a tuned bf16 + AdamW + NVMe-cached pipeline. One of the few Sanskrit LLMs built from first principles.
Built from scratch in PyTorch — multi-scale EfficientNet-B3 fusion, MC-Dropout, temperature scaling, two-phase training. The clinical triage layer is the real innovation: it cuts glioma miss-rate from 22% to 2.8% by refusing to make a confident call when the model is genuinely unsure. Submitted to IEEE CVMI 2026.
Image restoration, recolouring, and semantic search in one platform. Clerk for auth, Stripe for subscriptions, MongoDB for persistence, and a credit-based metering system that makes inference cost predictable. This is what "shipping" actually looks like — not just a Colab notebook.
Where I've been
Tools
Highlights
FAQs
Get in touch
Whether it's research, an M.Tech opening, a consulting chat, or just wanting to nerd out about AI — my inbox is open.
I'm based in Assam, India. I reply to emails. I'm also active on LinkedIn and GitHub — the links are all right here. If you're a researcher, recruiter, or founder building something in AI, I'd genuinely love to hear about it.
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