Perfect Observability is a Myth - Restraining Bolts in the Real World

Developed a framework for imposing constraints on an AI agent in a world with nosiy observations. `poster attached`

Card Shuffling using Markov chains

Evaluated overhand, top-to-random, Knuth, transposition, thorp, and riffle card shuffling techniques. `presentation attached`

Vision-based Manipulator movement with Fetch

Implemented a visual-feedback based method to guide the Fetch mobile manipulator’s end-effector to reach the target object without using AR-markers. `video attached`

Higgs Boson Particle discovery

Performed exploratory data analysis, and compared classification of ATLAS experiment events using advanced machine learning techniques such as XGBoost and neural networks.

AI Pacman Agent

Comprehensive implementation of AI methods such as DFS, BFS, UCS, A* search, minimax, expectimax, and alpha-beta pruning to create Pacman in a multi-agent environment using Python.

Denoising and Stacked Autoencoders

Built & evaluated denoising capabilities of a denoising autoencoder with different levels of noise. Trained a stacked autoencoder layer-by-layer in an unsupervised fashion, & fine-tuned the network with the classifier.