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Speaker "Ram Narasimhan" Details Back

 

Topic

An Overview of problems in Industrial Data Science

Abstract

In this presentation, we will explore the gamut of the problems that make up the landscape of Industrial Data Science. 
These are analytical and quantitative use cases from Industries such as Automotive, Mining, Transportation, O&G, Healthcare, Aviation, and Power. These are the problems that data scientists in the "Big Iron" companies -- like GE, Siemens, Honeywell, ABB, Komatsu, Caterpillar etc. -- are grappling with. 
 
We will look into how these problems are different from the use cases of traditional data science . (A/B testing, Recommendations.) We will also examine what makes these class of industrial problems difficult. 
 
Industrial data science focuses on assets -- on its Maintenance, Utilization, and in making fleet level tradeoffs. Often, we have to combine business and financial impact before tackling the data science problems. (Or else, there is no real payoff.) The use cases discussed are technical, but the goal of this presentation is to provide an overview of Industrial Data Science.

Profile

RAM NARASIMHAN is a Data Scientist with GE Digital. He has worked on data analysis efforts with a number of GE business verticals including Aviation, Transportation and Healthcare and Power.  Ram’s interests are in increased utilization of resources of all types for any business. Prior to this role, he was Managing Director at United Airlines in Chicago, where his analytics team supported Scheduling, Operations, and Maintenance for the airline. He has a master’s in Industrial Engineering and a doctorate in Operations Research.