Rashmeet Kaur Nayyar

Rashmeet Kaur Nayyar

Research Assistant

Arizona State University

Hi, there!

I am a Ph.D. student in Computer Science in the School of Computing, Informatics, and Decision Systems Engineering at Arizona State University. I am a member of the Autonomous Agents and Intelligent Robots (AAIR) research group, advised by Dr. Siddharth Srivastava. I research key Artificial Intelligence principles to help build efficient systems that can reason about, plan, and act under uncertainty.


  • Artificial Intelligence
  • Probabilistic Reasoning
  • Hierarchical Planning with abstractions
  • Reinforcement Learning


Chambliss Astronomy Achievement Student Award

Awarded among 6 graduate medal winners chosen from hundreds of students nationwide.



Graduate Research Assistant

Arizona State University

Sep 2018 – Present USA
  • Developing an automated physics-based AI system to detect and identify intergalactic clouds.
  • Exploring a Graphical model-based Open-Universe Probabilistic Programming approach developed in Bayesian Logic (BLOG) with inference using Markov Chain Monte Caro methods in Java.
  • Analyzing UV Spectra obtained from the Cosmic Origins Spectrograph aboard the Hubble Space Telescope.
  • Collaborating with Dr. Sanchayeeta Borthakur at the School of Earth and Space Exploration, ASU.

Graduate Student Assistant

Arizona State University

Aug 2018 – Dec 2018 USA
  • Enriched student experience for the course “Intro to Human-Computer Interaction” under Dr. Robert Atkinson.

Application Developer

BNY Mellon Technology

Jun 2017 – May 2018 India
  • Developed the DORA Application from scratch for the Bank of New York Mellon using Java, AngularJS, Jasmine, Karma, Maven, Grunt, Jenkins, and Kanban agile methodology on NEXEN cloud-based platform.

Research Project Intern

Innobytes Technologies Pvt. Ltd.

May 2017 – Dec 2016 India
  • Tackled the problem of inaccurate prediction of tags for audios in MagnaTagATune dataset (Keras, Tensorflow)
  • Achieved an AUC-ROC score of 0.886 through CNN & CRNN deep neural network implementations.


Card Shuffling using Markov chains

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

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.

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.