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QEarth on Alveo Cards: Accelerating Machine Learning

Thursday, January 28, 2021

11:30 am - 1:30 pm

About the Event:

The goal of this presentation is to highlight the integration of Xilinx hardware and software acceleration with innovative machine learning driven products and services to bring material value to a broad population of oil and gas operators. The entire QEarth workflow brings contextually relevant AI-driven earth-modeling capabilities for asset operators while Xilinx acceleration creates strategic and operational opportunities via the space of real time intelligence.


Please Note:

A 30 minute question and answer session will follow the event for students to engage with the speaker and discuss career paths.


Speaker Bios:

Orlando De Jesus
Senior Technical Advisor, Quantico

Orlando De Jesus is a Senior Technical Advisor for Quantico Energy Solutions with more than 30 years of professional experience in developing, implementing, designing and redesigning engineering solutions. De Jesus holds a Ph.D. in Electrical and Computer Engineering from Oklahoma State University and Project Management Specialist from Universidad Simon Bolivar, Caracas Venezuela.


Nathan Chang
Director of Operations, Quantico

Nathan Chang is the Director of Operations at Quantico Energy Solutions, an energy services company dedicated to applying machine learning to subsurface G&G and D&C challenges. Chang is a graduate of the University of Houston and has over 15 years of professional experience. Seven of those years he spent with Fugro NV in various strategic roles with the majority of his time leading Global Innovation and R&D.


Lisa Liu
Senior Engineer, Xilinx

Lisa Liu is a Senior Engineer in Xilinx. Liu has been working with FPGA platforms for more than 10 years. Her recent work is focused on library development including but not limited to BLAS, SPARSE and HPC for Xilinx FPGAs and Alveo cards.



Online streaming
FREE for Members and UH Student Members.

$15 for Non-Members
Martin Huarte Espinosa
Associate Director
HPE Data Science Institute