Murali Kashaboina
  • Male
  • Naperville, IL
  • United States
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Profile Information

Short Bio:
Murali brings over 18 years of IT experience in real-time technologies and data science. Prior to founding Entrigna, Murali was the Managing Director of Enterprise Architecture at United Airlines and was the technical lead in the merger with Continental. Murali holds a Masters’ degree in Mechanical Engineering from University of Dayton, Ohio
Entrigna, Inc.
Job Title:
Chief Data Scientist
Job Function:
Data Science, Machine Learning, AI, Business Analytics, Deep Learning, Blockchain, BI, IoT
Prescriptive and Cognitive Services and Apps
LinkedIn Profile:
Contributing, Networking

Murali Kashaboina's Blog

Simple Correlational Analysis on Socioeconomic Factors Impacting Covid-19 Outbreak in US Counties

Posted on June 20, 2020 at 3:16pm 2 Comments


As part of my PhD work, I recently had to analyze any dataset(s) of my interest and present findings.  I ended up conducting a study on US County-wise Covid-19 data.  I wanted to share my key findings through this blog.

Study Question

The primary question I wanted to address through data analysis was “Do counties’ socioeconomic factors such as population size, poverty rate, unemployment rate, education percent and…


Key Graph Based Shortest Path Algorithms With Illustrations - Part 2: Floyd–Warshall's And A-Star Algorithms

Posted on January 25, 2020 at 2:29am 0 Comments

In part 1 of this article series, I provided a quick primer on graph data structure, acknowledged that there are several graph based algorithms with the notable ones being the shortest path/distance algorithms and finally illustrated Dijkstra’s and Bellman-Ford algorithms.  Continuing with the shortest path/distance algorithms, I have illustrated Floyd-Warshall and A* (A-Star) algorithms in this part 2 of the article.  As was stated in part 1, while the inner workings of these algorithms are…


Key Graph Based Shortest Path Algorithms With Illustrations - Part 1: Dijkstra's And Bellman-Ford Algorithms

Posted on January 14, 2020 at 3:00pm 0 Comments

While many of the programming libraries encapsulate the inner working details of graph and other algorithms, as a data scientist it helps a lot having a reasonably good familiarity of such details.  A solid understanding of the intuition behind such algorithms not only helps in appreciating the logic behind them but also helps in making conscious decisions about their applicability in real life cases.  There are several graph based algorithms and most notable are the shortest path…


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