Data Science graduate student at Columbia University working across machine learning, causal inference, forecasting, and AI — building things that are technical enough for research and practical enough to ship.
I'm a Master's in Data Science student at Columbia University with a background in Computer Engineering (CGPA 9.66/10) and 5 peer-reviewed publications in IEEE and Springer. I work at the intersection of machine learning, causal inference, forecasting, and data-driven product decisions — taking complex problems and turning them into practical, scalable solutions.
I'm currently a Data Science Intern at Artie, and have previously interned at Deloitte and IIT Roorkee — alongside my role as a Graduate Student Assistant at the Northeast Big Data Innovation Hub at Columbia. Beyond the data, you'll find me wandering through Central Park or Riverside Park, hunting for new coffee shops and bookstores, and buying the most random things from Five Below. 🌸
Published research at the intersection of NLP, Quantum Computing, and Blockchain in IEEE Xplore and Springer.
A blend of statistical depth, engineering, and the tools I reach for to take ideas from notebook to production.
From exploratory analysis and recommender systems to healthcare AI and cloud-deployed apps.
An EDAV project on extreme heat's impact on U.S. public health — data wrangling and visual analytics (R, D3.js, ggplot2) surfacing heat-related hospitalizations and mortality across regions and demographics.
View project
A mobile app that reads food labels with OCR and classifies nutrition with a Random Forest, then recommends healthier alternatives via cosine similarity. Published in Springer LNNS.
Read paper
A healthcare tool that classifies ECG reports with a CNN (92% accuracy) and layers Generative AI to deliver simplified, profile-aware analysis and lifestyle guidance.
A recommendation engine for movies and books using Alternating Least Squares (ALS) and collaborative filtering to deliver the top-10 most relevant, personalized picks.
A playful, highly-stylized watch-party app — a Regency-era translator powered by Gen AI, gossip polls, character quizzes, and live chat, deployed on Netlify.
Visit site
An augmented-reality app that makes physics tangible — place 3D objects on detected surfaces to study gravity and friction, built in Unity with custom C# scripts.
A scalable task manager on AWS EC2 with auto-scaling and Django — multi-user real-time collaboration, full CRUD operations, and high availability across devices.
View code
A platform for NGOs to build customizable websites — donation integrations, event listings, and volunteer sign-ups to grow their online presence and community.
Five papers published in IEEE Xplore and Springer venues.
Literature Survey
A survey of current quantum-computing applications, highlighting emerging opportunities and key challenges for future growth.
View paper →Research Paper
Trend identification on Instagram using Latent Dirichlet Allocation and Non-Negative Matrix Factorization to surface evolving online themes.
View paper →Smart Contract Implementation & Research
A decentralized V2G system using Solidity smart contracts on Ethereum to manage grid demand and secure energy transactions between EVs and the grid.
View paper →Technical Paper
The development of FoodMO — a mobile app that uses OCR and ML to analyze food nutrients and provide healthier dietary recommendations.
View paper →Literature Review
A comprehensive survey of NLP techniques covering the latest approaches, real-world applications, and promising future research directions.
View paper →A selection of what I've been building — from healthcare digital twins to deep-learning and forecasting models.
A healthcare digital-twin prototype that fuses multi-source physiological data to simulate patient-level treatment responses.
An agentic-AI workflow over an online-retail dataset — LLM-driven agents that query, analyze, and surface business insights from transactional data.
A deep-learning regression model that predicts real-estate prices from property and location features, with feature engineering and evaluation.
A sequence-to-sequence English-to-French translator built with LSTM encoder–decoder networks for neural machine translation.
A K-Nearest-Neighbors classifier that predicts heart-disease risk from clinical features, with exploratory analysis and model tuning.
A Support Vector Regression model that forecasts avocado prices from historical sales and seasonality trends.
Notes on machine learning, causal inference, and the projects I'm building — in plain language, for anyone curious. New pieces land on Medium.
Read on MediumWhether you're exploring a collaboration, looking for a data-driven problem-solver, or just want to chat about ML, causal inference, or forecasting — my inbox is open.
