Work

Novel research paper analyzing & quantifying LLM feature universality using SVCCA & RSA metrics and a sparse autoencoder dictionary-learning pipeline trained on Google Gemma 2-2B and Gemma 2-9B residual-stream activations. Unveiled striking results of mid-layer similarity and implemented feedback from Stanford and Berkeley Ph.D. reviewers/industry professionals. Presented findings at ACL 2025 in Vienna, Austria: the #1 ranked conference in natural language processing.

Building an AI-powered productivity platform using React, Node, Express, & AWS (Lambda, S3, DynamoDB), that enforces task completion via financial stakes; implementing Stripe payments, AI vision-based evidence validation using OpenAI Vision, and serverless task enforcement workflows on AWS Lambda with full CI/CD and monitoring.

AI-powered Spotify playlist generator that creates and saves playlists based on user-provided natural language descriptions. Leveraging Python, Flask, the Spotify Web API, and the OpenAI API, the application searches for existing Spotify playlists that align with the user's preferences and generates a playlist of the Top-K most frequent tracks from such playlists using a heap and hashing techniques for optimized track ordering. Incorporates extensive error and edge-case handling for faulty inputs, API failures, and whitelist errors. Deployed on a simple web application developed with HTML, CSS, and JavaScript; hosted on Heroku.

Lung cancer risk classifier that utilizes a logistic regression model (Scikit-learn) trained on a custom dataset generated through a rule-based algorithm created using weighted features, biases, and mathematical relationships grounded in extensive research on the key contributors to lung cancer risk. Fine tuned the model using techniques like cross-validation, grid search, and gradient-based optimization to achieve 96.5% model classification accuracy. Deployed on a simple web application developed with HTML, CSS, and JavaScript; hosted on Heroku.

Java-based desktop keyboard macro application with a modern, intuitive GUI built using AWT and Swing. Vortex enables users to automate text input by specifying custom messages, repeat counts, and optional delays, offering a simple yet powerful tool for keyboard automation and productivity.

Developed computer vision models to assist autonomous vehicles using convolutional neural networks and transfer learning. Implemented a VGG16-based architecture trained on the CIFAR-10 dataset with TensorFlow and Keras, achieving 95% classification accuracy in object detection tasks.