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Tuesday, Mar 10th, 2020

11:30 am – 1:30 pm CT (webinar starts at 12:30 pm)

Norris Centers – Houston

816 Town & Country Blvd, Suite 210, Houston, TX 77024


Big Machine Learning with Physics & Data Driven Modeling:
Why Understanding Analytics and Machine Learning is Important for Engineers

Digital data collected by many industries support many, if not, most of its activities. Data integrity, security, mining, analysis, and transfer are critical to its particular goal of providing insight. Related topics such as the use and management of massive data sets (“big data”), data value and ownership, cybersecurity, cloud computing, machine learning, and virtual twin modeling and simulation represent only a small subset of the derivative uses of digital data being contemplated by all industries. Particular questions to be answered by data analysts, data scientists, and/or subject material experts

  • Once data has been collected, what we do with it?
  • How do we extract knowledge and value from collected data to benefit operations, gain efficiencies, and improve safety and security?
  • What are the business propositions that sensed data collection, storage, and analysis bring to the table?
  • How effectively is data analyzed?
  • What data analysis methods and tools should the industry be adopting that aren’t now being used?

This talk attempts to highlight the importance of analytics and machine learning as a means to provide valuable answers, thus providing valuable insights. The future of Big Data and Data Analytics is not only for the Energy Industry, but all industries in general.

Meet Our Speakers


Ed Marotta has held faculty positions as an Assistant Professor at Clemson University (1997) and Associate Teaching & Research Professor at Texas A&M (2003), all within the Mechanical Engineer Department. He has held the position as Technical Manager for the Multi-Physics Simulation Group within the North America Technology Center, FMC Technologies Inc. In this capacity, he was responsible for developing a Center of Excellence for modeling and simulation of multi-physics phenomena for Surface and Subsea applications. Ed led the Systems Analytics, Modeling & Analysis group within GE HQ where data-driven and physics models were developed for System of Systems analysis. Most recently, Ed has developed Workshop courses that teach the end-to-end fundamentals of Data Science for engineers in Oil & Gas, and other industries. His workshop course is being considered by ASME HQ as a key educational tool to implement across all industries.

In addition, he has published over 100 Journal, Conference, and white-paper papers within the area of Thermo-Fluid Sciences and Digital Twins, and holds numerous patents. He currently held the position of Adjunct Professor with the ME (Subsea Engineering Program) and MET departments at the University of Houston teaching courses in Data Science and Computational Methods.

Ed received a B.S. in Chemistry from the University of Albany (SUNY) and a M.S. and Ph.D. in Mechanical Engineering with specialization in Thermo-Fluid Sciences from Texas A&M University. He holds the grade of Fellow in the American Society of Mechanical Engineers (ASME) and Associate Fellow in the American Institute of Aeronautics and Astronautics (AIAA). Also, Ed formally held the position of Associate Editor for a major ASME journal. He is actively involved in local ASME Chapters as well as the ASME & SPE OTC technical subcommittees.