I build machine learning systems that can explain themselves, and the interfaces that make those explanations useful to someone who isn't an ML engineer.

Computer Science undergrad at VIT Chennai (AI & ML, class of 2027). I spent a summer at the Indira Gandhi Centre for Atomic Research replacing a manual industrial weld-inspection workflow with an end-to-end YOLOv8 pipeline, and presented a paper on automated AWS cost and security cleanup at ICANDIT 2026.

The through-line, whether it's a research notebook or a game engine: a system nobody can inspect is a system nobody should trust. So everything below ships with the model and the dashboard, the metric and the interface.

An adversarial study of post-hoc explainability: can an attacker leave a security model's prediction untouched while corrupting the explanation the analyst reads? Across 235,795 phishing URLs, an intrusion-detection set and a fraud corpus, four attacks on SHAP, LIME and Integrated Gradients say yes. Of the four defenses that follow, the hybrid one cuts mean explanation drift by 91.1% while holding classification performance.

PyTorch SHAP LIME Captum XGBoost scikit-learn pytest

Most ATS tools hand you a score. This one hands you the reasoning: Shapley values, LIME, counterfactuals and attention heatmaps that name the exact skills which earned the score, then price out what adding the missing ones is worth. Built on SBERT embeddings over 1M+ job postings and 2,484 real resumes, with the Shapley and LIME implementations written from first principles and 6 GB of model artifacts streamed from memory-mapped arrays.

FastAPI Streamlit sentence-transformers scikit-learn Plotly Docker Kubernetes

Reads a damaged SD card sector by sector, rebuilds the files its index lost, and says which ones will actually open, with the evidence behind every verdict. It ships with a card it damages on purpose and a manifest of exactly what it did, so every run is graded against ground truth in the browser: 100% recall, zero false positives across 25 planted files.

Python FastAPI LangGraph ChromaDB Next.js TypeScript Docker

Residents raise maintenance complaints with photos and follow them to resolution, while the committee triages through a strict OPEN → IN PROGRESS → RESOLVED workflow with priorities and configurable overdue detection. The audit trail is append-only and enforced at the database level, not merely in application code.

Next.js 16 TypeScript PostgreSQL Drizzle ORM Tailwind CSS Radix UI Vitest

Seven hand-built levels, nine movement abilities and fourteen trap behaviours, deliberately written as a clean reference codebase rather than a jam prototype. 47 C# files across a four-layer architecture, where every playable character is a ScriptableObject, so adding one touches zero lines of code; Unity CI runs edit-mode and play-mode tests and ships Windows and WebGL builds on every push.

Unity 2022 LTS C# Cinemachine Unity Input System GitHub Actions Git LFS

Packages, not scripts: modules with clear boundaries you can swap or test in isolation. Configuration in YAML with typed dataclasses, not constants buried three files deep. Test suites that run without cloud credentials, a GPU or a 6 GB download, because a test nobody can run is documentation at best.

And sensible failure modes throughout: the RAG layer falls back to TF-IDF, the recommender falls back to rules, the explanation layer falls back to a template. And the tool that deletes cloud infrastructure won't do it unless you ask twice.

Winner, Hack The Gap, VIT Chennai (2025) · full-stack platform connecting underserved students to mentors, recognised for solution design and practical impact
First Runner-Up, Figma × Apple Vision Pro Design Challenge, GDSC VIT Chennai (2023) · spatial-computing UI prototype
Paper presented, ICANDIT 2026, INTI International University, Malaysia · Automated AWS Resource Cleanup for Optimization of Cost and Security
Outreach Head, TechnoVIT & Vibrance (2024–25) · led a 25-member team across two flagship university festivals
Certifications · Google UX Design Professional Certificate · Introduction to Generative AI, Google Cloud (100%) · Python Data Structures, University of Michigan (97.6%)

Away from the keyboard I shoot photography and play badminton. The photography is where most of my design instinct comes from: framing, hierarchy and knowing what to leave out of the frame turn out to be the same problem as designing an interface.

Open to SDE and AI/ML roles. The fastest way to reach me is email or LinkedIn.