Vivek Maskara

Vivek Maskara

Grad Student at ASU | Researcher at The Luminosity Lab | Ex Senior Software Engineer, Zeta | Volunteer, Wikimedia Foundation

Arizona State University

Hello!

I am currently pursuing my Masters in Computer Science from Arizona State University. I completed my bachelor’s in Software Engineering from Delhi Technological University. I love writing code, developing apps and creating websites.

During my undergraduate years, I developed and published several apps and garnered over 1 million app downloads. Prior to joining ASU, I worked as a Senior Software Engineer at Zeta, Directi with the Express payments division for over 3 years.

I have been associated with the Wikimedia Foundation as a volunteer for the past 3 years, contributing to their Wikimedia Commons Android app.

Interests

  • Artificial Intelligence
  • Computer Vision
  • Android app development

Education

  • Master of Science, Computer Science, 2021

    Arizona State University

  • Bachelor of Technology, Software Engineering, 2016

    Delhi Technological University - New Delhi, India

Skills

Python

Java

PostgreSQL

PostgreSQL

Redshift

Tensorflow

Experience

 
 
 
 
 

Researcher

The Luminosity Lab, ASU

Mar 2020 – Present Tempe, Arizona

Responsibilities include:

  • Working on reducing the time people take to park their vehicles by providing step-by-step real-time guidance in multi-story indoor parking lots using computer vision and deep learning.
  • Using sentiment analysis of the restaurants’ Yelp reviews and user POI data to analyze what type of restaurants should be opened in a given location.
 
 
 
 
 

Senior Software Engineer

Zeta, Directi

Jun 2016 – Nov 2019 Bangalore, India
  • Built NFC based contactless payments & custom ordering solution for POS attributing to 1 million+ monthly transactions.
  • Contributed to over 20+ projects in Zeta spanning across Android, Raspberry Pi and backend microservices.
  • Responsible for optimizing query performance for PostgreSQL and building throughput and service health monitoring dashboards using Grafana and Kibana.

Accomplish­ments

TensorFlow in Practice Specialization

Introduction course on using Tensorflow
See certificate

Convolutional Neural Networks

Course on CNN by DeepLearningAI
See certificate

Recent Posts

Implementing YOLOV2 from using Tensorflow 2.0

In this notebook I am going to re-implement YOLOV2 as described in the paper YOLO9000: Better, Faster, Stronger. The goal is to replicate the model as described in the paper and train it on the VOC 2012 dataset.
Implementing YOLOV2 from using Tensorflow 2.0

Implementing YOLOV1 from scratch using Keras Tensorflow 2.0

In this notebook I am going to implement YOLOV1 as described in the paper You Only Look Once. The goal is to replicate the model as described in the paper and in the process, understand the nuances of using Keras on a complex problem.
Implementing YOLOV1 from scratch using Keras Tensorflow 2.0