Machine Learning, Deep Learning & AI in Oil and Gas - From the Energy Conference Network

This event ran April 19-20, 2016 and has now finished.

INTERESTED IN BEING A MEMBER OF THE ADVISORY BOARD?

If you are an industry expert and would like to share your expertise, we would like to speak with you.

Please contact:

 Melissa Zerber | Conference Producer
Energy Conference Network
+1 (855) 869-4260


Mark Allen
Managing Director, Sefran Energy

Mark Allen has a background of over 20 years’ technical management in petroleum engineering operations and reservoir studies gained at Tullow Oil, Shell and Schlumberger. He latterly served as Development Manager with Tullow Oil, a leading U.K. independent E&P company, where he led a team of engineers and geoscientists operating a portfolio of assets in Europe and Africa through the appraisal, development and production cycle. Since relocating to Silicon Valley in 2014 he now provides domain expertise to technology firms creating Big Data solutions targeting the oil & gas industry, leveraging the lessons learned from adjacent industry sectors. Mark holds a B.Eng in Mechanical Engineering, an M.Eng in Petroleum Engineering, and an MS in Management from Stanford Business School. He is a published technical author and an active member of the Society of Petroleum Engineers.

Gilad Cohen
CEO, Imubit

Gilad Cohen has over 15 years of experience in machine learning and hardware R&D, applied across various industries such as aerospace & defense, energy, oil & gas, transportation and manufacturing. Gilad is the CEO and co-founder of Imubit, a Silicon Valley startup that applies a new form of artificial intelligence to autonomous prediction of operational events. Prior to Imubit, Gilad founded and led Cigol, a specialized hardware and firmware security contractor in the aerospace and defense sector. Gilad holds a BSc in mathematics, a BSc and MSc in Electrical and Computer Engineering, all Cum Laude from Ben Gurion University in Israel, and is the author of several IEEE papers on multi-dimensional joint object registration and segmentation.

Amir Husain
Founder and CEO, SparkCognition, Inc

Amir Husain is a serial entrepreneur and inventor based in Austin, Texas. He is the Founder & CEO of SparkCognition, Inc., an award-winning Machine Learning/AI-driven Cognitive Analytics Company.

Amir is a prolific inventor with 13 awarded and over 40 pending US patent applications to his credit. In 2013, a low cost computing platform Amir invented was inducted into the collection of the Computer History Museum in Mountain View, the world’s largest such institution. His work has also been published in IEEE conferences and in leading tech journals including Network World, Computer World and others. His companies have won numerous awards such as Nokia’s 2015 IoT Open Innovation Challenge, the 2015 SXSW and Austin Chamber of Commerce A-List, the InnovateApp 2014 competition, the VMWORLD Gold award, Network World’s Hottest Products, CRN’s Innovation award, and PC World’s best product award.

Amir has been named the Top Technology Entrepreneur in Austin by the Austin Business Journal, and serves as an advisor and board member to IBM Watson, The University of Texas at Austin Computer Science Department, Makerarm, uStudio, Alif Laila Children’s Educational Society, and others.

Subrat Nanda
Senior Data Scientist & Analytics Leader, GE

Subrat Nanda holds a Master’s degree from University of Exeter, England (2003) in Autonomous Systems and Bachelors in Engineering (2001) from University of Nagpur, India. He is currently a Senior Data Scientist and Analytics Leader with GE Power Services, Houston, Texas. He also leads a team of data analytics professionals to design, develop, deploy and maintain data science solutions for industrial problems in the areas of Prognostics & Health Management, risk assessment for long term contractual service agreements and critical decision making. Prior to his current role, Subrat spent over nine years with GE Global Research. He has been working for over twelve years in applying machine learning, statistical modelling and actuarial methods to real world problems by fusing domain knowledge with data driven technologies.