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
Press
American Astronomical Society awards ASU students Chambliss medals
Rashmeet Kaur Nayyar receives Chambliss medal from American Astronomical Society
Awards
Chambliss Astronomy Achievement Student Award
Experience
AI Instructor
Clubes De Ciencia Arizona Summer Program
- 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
- 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
- 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
- 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.
- 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.