Rashmeet Kaur Nayyar

Rashmeet Kaur Nayyar

Ph.D., Computer Science

Arizona State University

Hi there, I’m Rashmeet!

I’m a Computer Science Ph.D. student working with Dr. Siddharth Srivastava in the School of Computing and Augmented Intelligence at Arizona State University. I’m a member of the Autonomous Agents and Intelligent Robots (AAIR) research group. I primarily study Reinforcement Learning (RL) with the goal of building efficient autonomous sequential decision-making systems that solve long-horizon problems. Currently, I am focussed on automatically synthesizing temporal and state abstractions for transfer in RL and interleaving planning with RL.


  • Learning Abstractions for Transfer and Generalization in RL
  • Long‐horizon Planning under Uncertainty
  • Autonomous Sequential Decision‐making
  • Robotics


  • Ph.D. in Computer Science, 2020 - present

    Arizona State University

  • M.S. in Computer Science, 2018 - 2020

    Arizona State University

  • B.E. in Information Technology, 2013 - 2017

    Pune Institute of Computer Technology


Chambliss Astronomy Achievement Student Award

Awarded among 6 graduate medal winners chosen from hundreds of students nationwide for my interdisciplinary research.



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.