Jay Shah

Jay Shah

Graduate Teaching Assistant
Arizona State University

  • jgshah1[at]asu.edu
  • +1(480)-494-7171

About Me

↳ I am a Computer Science Graduate student at Arizona State University (ASU) specializing in Artificial Intelligence. My main research interests are in the domains of Deep Learning and Machine Learning.
↳ I have a strong background in Systems Programming and currently I am a Teaching Assistant at Arizona State University tutoring students on topics related to Neural Networks, Machine Learning, Optimization techniques and Transfer Learning.

Work Experience

Graduate Teaching Assistant

Oct 2019 - Present

▸ Teaching Assistant for CSE 598: Introduction to Deep Learning in Visual Computing (Coursera-Arizona State University)
▸ Tutoring students on the topics: Fundamentals of Machine Learning, Neural networks & backpropagation, Optimization techniques for neural networks, Modern convolutional neural networks, Unsupervised learning & generative models and Transfer learning.

  • Deep Learning
  • Neural Networks
  • Transfer Learning

Research Intern - Computer Vision

Jun 2019 - Aug 2019 • 3 months

▸ Developed a closed-loop, cross-platform application for camera-based vitals monitoring of groups of people using proprietary Philips' contactless monitoring technology.
▸ Improved it's camera-based person detection & identification solutions, using state-of-the-art science and technologies to be used in Philips' clinical products in ICU and NICU.
▸ With the aim of achieving an industry-leading balance between performance and complexity ready for clinical use.

  • Deep Learning
  • Contactless Vitals monitoring
  • Computer Vision
  • Systems Programming

Graduate Research Assistant

Nov 2018 - Jun 2019 • 8 months

▸ Collaborated with Frank Wilczek, Professor of Physics at MIT, ASU & Nobel Laureate (2004), & Nathan Newman, Professor and Lamonte H. Lawrence Chair in Solid State Science at ASU to study human color perception and how we can use Machine Learning to expand our senses.
▸ Built tools for Automatic Art Authentication system using image analysis, classification, detection, unmixing & estimation of paint pigments.

  • Machine Learning
  • Image Processing
  • Python
  • Pattern recognition

Machine Learning Engineer (Intern)

Jan 2018 - Jul 2018 • 7 months

▸ Created sets of Machine Learning coding challenges that were used in HackerRank tests for technical recruiting and screening processes with Heraldo Memelli.
Creating and reviewing HackerRank test challenges for online contests.
▸ Researching the best practices around Software Development, Data Science, Machine Learning, coding and debugging.

Related Links: List of my Editorials to Artificial Intelligence challenges on the HackerRank website

  • Machine Learning
  • Data Science
  • Python
  • Software development
Summer 2017 • 4 months

▸ Worked on Significance-based Large-Scale 3D Point Cloud Compression and Management with Prof. Lin Weisi on a Research Grant of S$537,696 (AcRF-Tier 2).
▸ Improved coding performance in terms of 3D feature selection, point-cloud simplification & significance evaluation according to human perception.

  • Video Comporession
  • Machine Learning
  • Image Processing

▸ Worked on Authorship Obfuscation, rephrasing the document of author so that the software cannot recognize the original author of the document at IR-LAB, DAII-CT.
▸ Major research work in the domains of Natural Language Processing and Information Retrieval.

  • Natural Language Processing
  • Information Retrieval
  • Keras
  • Theano

Projects Completed

Real-Time Video Story Telling As A Service

Spring 2019

▸ Built an end-to-end Cloud-based Full-Stack service for the visually impaired people that provides audio descriptions of videos streamed via a mobile device.
Technologies Used: Python, Android, REST APIs, SpringBoot, Google App Engine, Google Firebase, Google Functions and Video Intelligence API.

  • Google Cloud Platform
  • Full Stack Application
  • NLP
  • Cloud Computing

Video Surveillance As A Service

Spring 2019

▸ Full-Stack Video surveillance application, automatically scales-out & scales-in on user demand & cost-effectively using AWS cloud resources.
Technologies Used: AWS (EC2, S3, EBS, Snapshot, SQS, IAM) Java, SpringBoot, Shell Scripting, REST APIs.

  • Amazon Web Services
  • Full Stack Application
  • Real time Video Processing
  • Cloud Computing
Spring 2019

▸ An intelligent and interactive data visualization tool for analyzing the WebMD dataset to provide engaging insights to any general user.
Technologies Used: d3, Python, HTML, CSS, JavaScript, JSON, Plotly, NLTK.

  • d3
  • Data Visualization
  • HTML
  • CSS

Document Clustering and 3D Visualization

Spring 2019

▸ Visualizing trending news topics as clusters in 3D space for better analysis and interactions.
▸ A framework for cluster identification by topic similarity, classifying individual documents/articles and enabling the reader to explore the document corpus in an easy and efficient manner.
Technologies Used: Python, LDA, t-SNE, NMF, Plotly

  • Document Clustering
  • 3D Visualization
  • Clustering
  • LDA
  • t-SNE

Binary Neural Networks

Fall 2018

▸ Implemented from scratch in Python the Deterministic and Stochastic versions of BNNs & compared its performance to traditional neural networks in terms of memory usage and computation complexity on Fashion-MNIST dataset. Achieved a significant decrease in memory usage and time taken for training.
▸ Implementation of the paper: Courbariaux, M., Bengio, Y.: Binarynet: training deep neural networks with weights and activations constrained to + 1 or − 1. CoRR (2016)

  • Binary Neural Networds
  • Python

Aesthetic Features of an Image

Summer 2017

▸ Worked on extraction of aesthetic features of an image and understanding human-image correlation.
▸ Researched about the influential factors for an aesthetic judgement.

  • Human-Image Correlation
  • research
  • human perception

Parallel Random Forest

Dhirubhai Ambani Inst. of Info. & Comm. Technology (DA-IICT)
Fall 2017

▸ Implemented random forests using both GPUs and CPUs in order to improve performance using parallelism, reducing unnecessary data accesses and removing data redundancy.

  • GPU
  • CUDA
  • decision-trees
  • random-forest

Relevant course work and skills

Technical Courses

Data-Structures & Algorithms, Intro to Programming, Object Oriented Programming
Data Mining, Social Media Mining, Data Visualization, Semantic Web Mining, Database Management Systems, Distributed and Parallel Database Systems, Software Engineering,
Human-Aware Artificial Intelligence, Intro to Machine Learning, Fundamentals of Statistical Learning, Neural Networks,
Distributed Operating Systems, Systems Software, Cloud Computing, Computer Networks, Operating Systems, Compiler Dessign, GPU Programming, Cryptography
Stochastic Simulation, Algebraic Structures, Probability and Statistics, Discrete Mathematics, Calculus & Complex Variables, Theoretical Computer Science,
Signals and Systems, Digital Logic Design, Analog Circuits, Embedded Hardware Design

Technical Skills

Programming Languages: Python, C/C++, Java, Matlab, SQL and Shell scripting
Machine Learning & Deep Learning Frameworks: Tensorflow, PyTorch, scikit-learn, R-studio, Tableau, Gephi, OpenCV, NLTK, matplotlib, NumPy, SciPy, Pandas
Web Technologies: HTML, CSS, JavaScript, d3, Android, Amazon Web Services, Google Compute Engine, Google App Engine, SpringBoot, Git, MySQL


  • M.S. in Computer Science
    Arizona State University (2020)
  • B.Tech. in Information & Communication Technology
    Dhirubhai Ambani Institute of Information & Communication Technology (2018)

Professional Activities

Additional Links