I build things with data, code, and statistical rigour. My work spans from LSTM forecasting apps to contaminated chemical detectors.
I'm an MSc Data Science student at the University of Nottingham, with a First-Class Honours degree in Mathematics from the University of Delhi. I'm interested in the space where statistical theory meets real-world prediction problems.
My recent work spans from building a privacy-first file conversion tool (DeltaConvert) to training LSTM models for currency forecasting (Stochastix), to winning a statistical ML competition by proving that a two-parameter linear model can beat a Random Forest when the data structure is right.
I care about understanding why a model works.
Privacy-first file conversion tool for images, documents, and OCR, all processed server-side. Built with Flask, deployed on Hetzner with Docker Compose, Nginx, and SSL via Certbot.
LSTM-based 30-day currency exchange rate forecaster. 29 trained models, React frontend with interactive Plotly charts, daily ECB audit cron job.
Chemicals: Per-type linear regression beating Random Forest (score 98, 1st of all groups). Trains: XGBoost on Sheffield–Nottingham increment (MSE 28,078). MATH4069 coursework.
PHP/MySQL hospital management system with role-based access, patient search, admissions, prescriptions, parking permits, and a custom audit trail. Graded Distinction at UoN.
Web scraping 2,000+ reviews with BeautifulSoup, NLP sentiment analysis, and Random Forest booking prediction model.
AI-powered financial chatbot parsing 10-K and 10-Q reports with rule-based logic to provide user-friendly corporate insights.
10-year exploratory analysis of 6 major banks' stock data (2014–2024) using Pandas, Matplotlib, and Seaborn.
Machine Learning, Statistical ML, Big Data, Databases, Time Series & Forecasting
SOUTH ASIA POSTGRADUATE EXCELLENCE AWARDFirst-Class Honours in Probability, Statistics, Mathematical Finance, and Linear Algebra
INSPIRE SCHOLARSHIP FOR TOP 1% NATIONALLYQA on AI-generated mathematical models. Evaluated calculus and algebra outputs for hallucination and factual accuracy. Developed complex maths questions to improve model comprehension.
Built supervised and unsupervised ML models (linear regression, K-means clustering). Created detailed visualisations. Conducted 15 peer reviews and supported fellow interns.
Tutoring primary and secondary school students in mathematics. 100% pass rate in final exams. Focus on building problem-solving intuition through interactive, positive learning environments.
