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.

Interests

Artificial Intelligence
Probabilistic Reasoning
Hierarchical Planning with abstractions
Reinforcement Learning

Talks

Rashmeet Nayyar, Mansi Padave, Sanchayeeta Borthakur, Siddharth Srivastava

June 2019 In American Astronomical Society (AAS)

Physics-based Detection and Identification of Intergalactic clouds using Probabilistic Programming

In this talk, I present a poster on my collaborative interdisciplinary research on using first-order probabilistic logic for inferring properties of intergalactic space.

Poster

Press

American Astronomical Society awards ASU students Chambliss medals

Karin Valentine, ASU NOW, May 2020.

Rashmeet Kaur Nayyar receives Chambliss medal from American Astronomical Society

Erik Wirtanen, ASU Inner Circle, June 2020.

Awards

Chambliss Astronomy Achievement Student Award

American Astronomical Society Apr 2020

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

Publications

Content-based auto-tagging of audios using deep learning

In the recent years, deep learning and feature learning have drawn significant attention in the field of Music Information Retrieval …

Rashmeet Nayyar, Sushmita Nair, Omkar Patil, Rasika Pawar, Amruta Lolage

DOI

Content-based auto-tagging of audios using deep learning

Experience

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.

Projects

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.