Rashmeet Kaur Nayyar

Rashmeet Kaur Nayyar

Ph.D., Computer Science

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.

Interests

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

Education

  • 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

Awards

Chambliss Astronomy Achievement Student Award

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

Experience

 
 
 
 
 

AI Instructor

Clubes De Ciencia Arizona Summer Program

Jun 2020 – Jun 2020 USA
  • Taught fundamentals of Artificial Intelligence to about 25 high-school students.
  • It was particularly challenging to design the course with advanced topics in a manner easily understandable by the students.
  • Conducted a week of lectures, practice sessions, and received an amazing feedback.
 
 
 
 
 

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 the learning experience for a class of about 150 students for the course “Introduction to Human-Computer Interaction” under Dr. Robert Atkinson.
  • Responsible for grading, clarifying doubts, and engaging in insightful discussions.
 
 
 
 
 

Application Developer

BNY Mellon Technology

Jun 2017 – May 2018 India
  • Developed an application from scratch called DORA for the Bank of New York Mellon using a technology stack of 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.

Projects

*

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.