Webinar | From Models to Agents: Applied AI and Data Science in Energy
Over the past decade, artificial intelligence (AI) and data science have evolved from standalone analytical tools into foundational components of modern energy intelligence platforms. Since 2015, Enverus has applied data science to solve complex challenges across oil and gas, energy transition, power and renewables.
In this session, Jiarao Huang and Ben Motil will share practical, production-scale use cases, including satellite-based computer vision for operational and emissions monitoring, machine learning for cost benchmarking, and advanced forecasting and economic models for power markets and renewables.
The webinar will connect these foundational models to the broader AI and machine learning ecosystem, highlighting how they evolve into scalable systems through cloud-native platforms, streamlined data architectures, and standardized model serving. It will conclude with a forward-looking perspective on agentic AI through Enverus One, demonstrating how proprietary data and models can be orchestrated into customizable, decision-centric workflows.
Attendees will gain a clear, practical understanding of how AI is applied today—and how it is rapidly evolving toward agent-driven systems in the energy industry.
Join the Calgary Branch for this event.
For any questions about this event, please contact [email protected].
About the presenters
Jiarao Huang, PhD., serves as director of data science and engineering at Enverus, where she leads global teams across Canada, the U.S., and Europe to deliver AI-driven data solutions for the energy industry. Her work spans oil and gas, energy transition, power, and renewables, supporting all major Enverus business units. Under her leadership, her teams have built and scaled data science products that generate over $200M in annual recurring revenue, enabling energy professionals to optimize operations and improve capital efficiency.
Jiarao holds a PhD in electrical and computer engineering from the University of Alberta. She began her career in academia before transitioning to industry, where she focuses on translating advanced AI into real-world applications. She is recognized for building high-performing data science organizations and for advancing AI from standalone models to integrated, decision-centric systems in the energy sector.
Ben Motil is a lead data scientist and professional engineer with a background in chemical engineering, economics, and energy markets. His experience spans process engineering, design, and operational support roles, as well as advanced data science and analytics positions across the energy sector.
Ben has extensive experience developing and applying data-driven models and algorithms to complex energy systems, including forecasting and time series analysis, geospatial and satellite-based machine learning, transmission and network modelling, emissions analytics, and market analysis. He holds a master’s degree in economics, and his career reflects an evolution from traditional engineering and operational roles, to quantitative financial and market modelling, and ultimately to large-scale data science and algorithm development. He is an active APEGA member.
This event applies to the following Work Readiness Program skill areas:
