Joshua Eckroth, Ph.D., is Chief Architect at i2k Connect, Inc. He concurrently serves as an Associate Professor at Stetson University in Central Florida. Dr. Eckroth holds a doctorate from The Ohio State University in the area of Artificial Intelligence and presently focuses on developing and deploying solutions involving natural language processing and other uses of deep learning. Dr. Eckroth has spoken at numerous academic and professional conferences such as the Association for the Advancement of Artificial Intelligence (AAAI) and Innovative Applications of Artificial Intelligence (IAAI) conferences. In 2018, he published AI Blueprints (Packt), a book that focuses on building practical AI solutions and deploying them in realistic environments.

Dr. Andy Wang is a technologist and serial entrepreneur with 20+ years of experience. He is the founder and CEO of Prescient Devices, Inc., which develops advanced data solutions for the oil & gas and manufacturing industries. Previously he was the founder and CTO of GTI IoT Technology, which developed low-power wireless sensing solutions for the public infrastructure sector. Andy has 15+ patents and has spoken at conferences and events around the globe. Andy received his Ph.D. from Massachusetts Institute of Technology.

Nathan Zenero is Vice President of Engineering for Well Drive. He is a petroleum engineer with experience in many aspects of upstream oil and gas including offshore planning and design, drilling, completions, production, and reservoir simulation. His primary focus is using data to solve real-world problems in the context of successful management principles.

With over 25 years of experience in the oil and gas industry, Peter Kowalchuk has a wealth of knowledge and expertise in the field. He began his career working in various locations around the world, including South America, the USA, and Asia, with companies such as Schlumberger and Halliburton. He started in field operations, running wireline and logging while drilling jobs, before transitioning to data management, processing, and real-time centers. After several years in upstream operations, he joined Halliburton's consulting team where he implemented digitalization workflows for operators of all sizes. More recently, Peter joined Microsoft where he works on projects implementing AI in various industries. Peter holds a Bachelor's degree in Electronic Engineering from the Universidad Simon Bolivar, an MBA from Texas A&M, and a MSc in Data Science from the City University of New York.

Bernard has over 42 years of Oil & Gas Geoscience experience; he is an expert in integration of multi discipline geoscience approaches with emerging high-end geophysics. He has worked with ExxonMobil for 41 years in exploration, development, and production projects around the world. Recently worked high profile development in Guyana and pre-salt Brazil. Since retirement, he is involved in the SEG Evolve program as mentor and is participating with several companies in their development programs as senior geoscience consultant. Bernard continues to be at the front end of developing interpretation and management decision workflows integrating the latest machine learning application to high end geophysics. Currently expending his oil and gas experience to geologic CO2 storage and geothermic projects. Bernard holds an PhD degree from Ecole National de Geologie and geophysics (France), a master in geology from the Ecole des Mines (France) and a master in quantic physics from the University of Paris (France). Bernard presented several times at EAGE, AAPG SEG and Image annual conventions.

Uchenna Odi is currently employed by Aramco Americas as a Petroleum Engineering Specialist and Team Lead for the Upstream Digital Transformation group. At Aramco Americas, Uchenna focus on applied analytics and digital transformation for upstream workflows. Previously, Uchenna was a data scientist for Devon Energy where he focused on advanced analytics for upstream subsurface workflows. Before Devon Energy, Uchenna was a research scientist for ENI Petroleum which included a visiting scientist post at the Massachusetts Institute of Technology Energy Initiative (MITEI). At MITEI, he was responsible for being an ENI presence on upstream related ENI funded research at MIT. MITEI projects included studying nanoemulsion EOR mechanisms and modeling hydraulic fracturing. In addition, he was responsible for scouting MIT related startups at the MIT Liaison Program for potential ENI investment. Before Aramco Americas, Devon, ENI, and MIT; Uchenna finished his PhD and MS in petroleum engineering at Texas A&M University. He also has a BS in chemical engineering and Executive MBA (energy focused) from the University of Oklahoma. Uchenna's research areas involve carbon dioxide, enhanced oil recovery, reservoir fluids, and applied data science.

Kumar is a technology leader and cloud architect with twenty years of experience delivering innovative enterprise solutions. He was a key player in growing two energy startups from the ground up and is currently Principal Solutions Architect and energy ML specialist at AWS. Kumar led the first two releases of OSDU on AWS, a standard based data platform for the energy industry, and currently supports ML initiatives at several global energy companies. Past initiatives have included well optimization, production-forecasting, power theft detection, predictive maintenance, computer vision applications, intelligent stores, etc. Kumar is a proponent of low-code/no-code machine learning and sees it as the mechanism for leveraging domain expertise to quickly build AI solutions.

Rachel Shaffer is a Data Science Manager in Chevron’s Technical Center. Her team delivers data science solutions that bring business value to Chevron’s entire integrated value chain.

Rachel has 13 years of experience in data science, analytics, and software product management at Chevron within Information Technology, Human Resources, and Downstream & Chemicals. Prior to her current role, she led Chevron’s workforce analytics center of excellence, where she delivered workforce analysis and technology solutions that influenced HR decisions and people strategy. Rachel has also worked as a data scientist in Chevron’s Information Technology Company and as a business analyst managing software for Chevron’s Downstream retail and lubricants businesses. Rachel holds a Master of Engineering in Industrial Engineering Operations Research from University of California at Berkeley and a Bachelor of Science in Management Science and Engineering from Stanford University.

Weichang Li leads the AI Technology Group at Aramco Americas’ Houston Research Center where he joined in 2015. His current research focuses on developing machine learning and signal processing algorithms/models for geophysics, geoscience and petroleum engineering applications. Prior to Aramco, he had been with ExxonMobil’s Corporate Strategic Research lab since 2008 where he led the machine learning team from 2011-2014. Weichang has co-organized the SEG machine learning post-convention workshop from 2018 to 2022, the SIAM Data Mining workshop in Geoscience Applications in 2018, and is the associate editor for Geophysics special section on Machine Learning, and recently IEEE Transaction on Neural Network and Learning Systems special issue on Deep Learning for Earth Sciences and Planetary Geosciences. He has published over 100 papers and 15 patents. Weichang obtained his M.S. (dual) in Electrical Engineering and Computer Sciences, and Ocean Engineering (2002), and Ph.D. in Electrical and Oceanographic Engineering (2006), all from MIT.  

Reza Rastegar is a Principal and Sr. Director of Advanced Analytics, AI, and ML at Occidental Petroleum. With more than ten years of experience in Oil and Gas, Reza has been responsible for leading teams and products and services through all aspects of the product/service life cycle. Working with many internal/external customers and partners in the past few years, Reza has identified and pursued business/product opportunities in applied sciences, engineering, AI, and ML. Reza has a Ph.D. and an M.S. in Applied Mathematics, M.S. in Artificial Intelligence, and B.E. in Electrical and Computer Engineering.  

Andy Flowers started his oil & gas career as a logging engineer working in international operations in North Sea, West Africa, Middle East and South America. He moved into research and development specializing in system design for field data acquisition, real-time systems, data integration, engineering modelling and remote communications. He became Manager of Global Data Analytics at ConocoPhillips in August 2019 having previously been Director of Advanced Analytics at Marathon Oil. Andy graduated in Mechanical Engineering from the University of Sussex, and gained a MS in Computer Systems Engineering from the University of Edinburgh. 

Dr. Yuxing Ben is a reservoir engineer at Occidental, where she develops hybrid physics and data-driven solutions in the subsurface engineering technology group. She was the principal developer of machine learning technology for Anadarko's real-time drilling and hydraulic fracturing platforms. She won the best paper award from URTeC 2019 and was selected as a SPE distinguished lecturer for 2021. She has over 17 years of experiences in oil and gas industry. Her work has resulted in the publication of over 30 papers and the granting of three US patents. She earned a BS in theoretical mechanics at Peking University, and a PhD in chemical engineering from the University of Notre Dame.

Tao Lin is a data scientist at Aramco Houston Research Center, focusing on machine learning research and applications in oil and gas. He holds a PhD degree in Physics from University of California at Riverside. Prior to current role, he worked in CGG for High Performance Computing (HPC) and machine learning (ML) applications in seismic imaging, Advanced Micro Devices (AMD) for ML algorithm optimization, and Amazon Web Services (AWS) for cloud computing and ML consultation in oil and gas industry. Currently in Aramco Houston Research Center, Tao leads the research projects on integrating novel machine learning and digitalization technology into geoscience and O&G production, such as AI-based petrophysical properties characterization, production optimization and seismic imaging, as well as sustainability and emission monitoring.