AI/ML Engineer building explainable, evidence-grounded systems, from Bitcoin forensics to drug discovery.

I build AI systems that show their working. My projects combine machine learning, graph analytics, OCR and LLMs, but with one rule: the data is the source of truth, not the model. Every score, ranking and generated sentence traces back to real evidence, and when the evidence isn't strong enough, the system abstains instead of guessing.

I take ideas all the way from research to production: data pipelines, interpretable models, FastAPI backends, React + TypeScript frontends, Docker deployments and hundreds of automated tests.

focus:      [Explainable AI, Graph ML, Applied ML, MLOps, Backend Engineering]
building:   Offline forensics tools, biomedical discovery pipelines, compliance AI
principles: [evidence over generation, abstain rather than guess, test everything]
learning:   [System Design, Distributed ML pipelines, Computational Neuroscience]

Follow the coin back to the hand that sent it. Built for Smart India Hackathon 2026 (PS 26146, NTRO).

An air-gapped investigator console that clusters wallets into real-world owners, flags laundering patterns, traces funds to their cash-out points, and estimates which IP first broadcast a transaction from P2P relay timing.

• 🧠 Multi-engine detection: rules, anomaly detection and a Graph Neural Network, fused by a stacker with exact SHAP explanations
• 🛡️ Live red-team mode: inject a laundering pattern and watch whether the stack catches it, with nothing retrained
• 📜 Court-ready evidence: hash-chained chain-of-custody ledger (ISO/IEC 27037 style) and one-page PDF case reports

_{Benchmarks on simulated generator data, as documented in the repo.}

Python PyTorch (GNN) NetworkX SHAP React TypeScript Fully offline

Built for Smart India Hackathon 2026 (PS 26107).

Ask in English, Hindi or Telugu, or simply photograph a product label, and MetrIQ identifies the governing standard, explains why it matched, and links the official source behind every answer.

• 🔍 Retrieval-first design: deterministic code picks the answer; the LLM only explains what retrieval found
• 📷 Local OCR label inspection: every extracted value links back to the exact pixel box it came from
• 🧪 Battle-tested: adversarial and prompt-injection queries produce zero hallucinated standard numbers

FastAPI React 19 PostgreSQL ONNX OCR LLM grounding Multilingual

For any disease, which already-approved drugs are promising repurposing candidates, and why?

• 🧬 A transparent 10-stage pipeline linking disease genes → biological pathways → drug targets → side-effect profiles
• 📉 Narrows a ~1,430-drug universe to biologically connected candidates, scored by an interpretable ML model
• 🗂️ Built on real biomedical data: SIDER · ChEMBL · Open Targets · Reactome
• 🤖 LLM-written explanations grounded in pipeline outputs, with a deterministic fallback

FastAPI scikit-learn NetworkX React + TypeScript LLM explanations

A genetic algorithm learns to play Flappy Bird, written up as a full research paper: "Neuroevolution of a Flappy Bird Controller: An Empirical Study of Genetic Operators, Evaluation Noise and Generalisation."

• ⚡ Headless simulator compiled with numba for high-speed training
• 📊 606 logged training runs, statistical analysis and fully reproducible figures
• 🧪 Tests generalisation of evolved controllers on unseen levels

Languages

AI / ML

Data

Backend, Frontend & MLOps