As the co-founder and CEO of Terra AI, John leads strategic planning and execution to drive the future of subsurface resource exploration. John has spent over six years developing and applying intelligent decision-making systems to critical natural resources in academia and industry. Previously, he led AI decision-making at KoBold Metals, founded a video compression startup, and designed autonomous systems at Boeing Phantom Works. John completed his PhD in Aeronautics and Astronautics at Stanford University as a member of the Stanford Intelligent Systems Lab (SISL). John is a Stanford Innovation Fellow and an AWS Startup CEO Fellow. In his spare time, he likes to go on hikes with his family and test out new cookbooks.

Anthony is the co-founder and CTO of Terra AI, where he leads the technical team from research and scientific validation through customer deployment. He brings together AI, subsurface modeling, and decision-making under uncertainty to help mining and energy companies understand what may lie underground and plan their next steps. Previously, Anthony served as Executive Director of the Stanford Center for AI Safety. His research focused on planning under uncertainty and evaluating the reliability of AI systems, with applications in autonomous driving, aviation, and sustainable resource development. He earned his PhD in Aeronautics and Astronautics at Stanford University as a member of the Stanford Intelligent Systems Lab. Outside of work, he enjoys climbing, making ceramics, and building community.

Markus leads Terra AI’s reservoirs division, driving product strategy and business development. Drawing on more than two decades of experience across academia and industry, he combines AI and reservoir modeling to help energy companies understand subsurface uncertainty and make better decisions throughout a project’s lifecycle. At OMV, Markus worked as a reservoir engineer and later advised senior leadership, with experience spanning oil and gas development, enhanced oil recovery, carbon storage, geothermal energy, and fractured reservoirs. As an Adjunct Professor at Stanford University, he develops AI-driven decision-support tools for large-scale geothermal and carbon-storage projects. He holds a PhD in Earth Sciences from Stanford and an MS in Petroleum Engineering from Montanuniversität Leoben and the Colorado School of Mines. Outside of work, Markus enjoys cooking and spending time outdoors with his family, especially hiking and exploring nature with his four kids.

Arec develops AI systems for subsurface modeling and decision-making, helping translate advances in machine learning into practical tools for sustainable energy and natural resource development. Previously, he worked on financial systems at BlackRock AI Labs, with earlier experience in autonomous driving and aerospace systems. Arec completed his PhD in Aeronautics and Astronautics at Stanford University, with a minor in Computer Science, as a member of the Stanford Intelligent Systems Lab (SISL), where his research combined probabilistic machine learning, optimization, and control to enable automated decision-making in high-stakes domains like autonomous vehicles and energy systems. Outside of work, he likes to camp, ski, and spend time outdoors.

Alex provides the geoscientific depth necessary to build and refine rigorous AI-driven geological models. He is an exploration geologist in Earth and Planetary Science focused on building a mechanistic understanding of the rock record. He previously served as a Campaign Science Lead on NASA’s Mars Science Laboratory (Curiosity Rover) mission and continues to serve on the science-operations team. His background includes a tenure as an exploration geologist in the energy sector, focused on subsurface structural and stratigraphic mapping, as well as experience helping guide the early development of Google’s Earth Engine cloud computing platform. Alex currently serves as a Visiting Scholar at UC Berkeley, where he completed his PhD in Earth and Planetary Sciences. In his spare time, Alex likes to SCUBA dive.

John develops machine learning tools to expand Terra AI’s reservoir modeling capabilities. He developed and deployed subsurface software at SLB for over 15 years, working on commercial projects on 5 continents and at their Silicon Valley research center. He has 5 patents pending on the use of machine learning in reservoir engineering. John received his MSc in Petroleum Engineering from Heriot-Watt University and BEng in Chemical Engineering from McGill University. In his spare time, John is drawn to adventure travel.

Danny builds relationships to accelerate exploration in the mining industry. He is a geologist with a background in finance, politics, and technology. Previously, he founded a SaaS startup and scaled it to clients spanning 30+ countries. Danny received his BA in Geoscience from Williams College. Outside of work, he enjoys surfing, golf, and playing rugby.

Kyle manages Terra AI’s technological infrastructure and product development. Previously, he managed the platform development at Nori Inc, a carbon removal marketplace, and did technology consulting for large scale data systems at Pariveda Solutions. He brings expertise in enterprise solution architecture and software development. Kyle received his BA in Electrical Engineering from University of Iowa.

Richard works on the deep learning toolbox and foundation modeling for fluid flow simulations, post-training agentic workflows, and model harness development. His research has focused on machine learning for subsurface imaging, carbon storage, and scientific computing. He also has industry experience working on machine learning problems for energy, finance, and medical imaging. Richard holds an MS in Computer Science from Georgia Tech, and likes working at the intersection of machine learning and scientific computing, where new modeling ideas can change how we solve large-scale physical problems. Outside of work, he enjoys playing volleyball and video games, and trying new food in the city.

Brandon leads program management for the delivery of our software products and for our successful collaborations with the world’s leading mining and energy companies. He previously led strategy, project and technical program development across the carbon removal and international healthcare industries. He also scaled a technology consulting firm as a co-owner. He brings industry experience bridging software, data science, and mission-driven impact, particularly in geospatial sensing and modeling. Brandon received his BS in Mathematics from University of Illinois at Urbana-Champaign. In his spare time, he likes to be outdoors in nature and he volunteers doing community organizing for climate solutions and decarbonizing transportation.

Jake builds the systems that turn Terra AI’s generative modeling research into fast, reliable production technology. His work sits at the boundary between applied ML research and systems engineering, with a focus on making models faster to train, faster to run, and robust enough to support real-world exploration. Before Terra, Jake built production perception systems across virtual reality, autonomous aviation, satellite imagery, and geospatial intelligence. At Meta and Google, he worked on the gaze-tracking systems that shipped with Quest Pro and Galaxy XR, with a particular focus on calibration and robustness. Jake holds an MS in Materials Science and a BS in Physics from Rochester Institute of Technology. Outside of work, Jake spends time with his wife and two young daughters and has more hobbies than time to pursue them.

Harrison develops Bayesian inference methods that combine generative models with sparse measurements to quantify geological uncertainty. He earned his PhD in Aeronautics and Astronautics at Stanford University with the Stanford Intelligent System Lab (SISL), where he researched Bayesian inference and generative modeling to evaluate the safety of complex autonomous systems and collaborated with NASA JPL, Ford, Motional, and Nissan. His industry experience includes risk quantification for physical AI, and autonomous decision making and safety validation in robotics and aerospace applications. In his spare time, he enjoys running, birding, and tinkering on side projects.

Tomer builds the systems supporting Terra AI’s GeoModeling and reservoir simulation workflows. Previously, he built multi-agent motion prediction systems for autonomous driving at Nissan. He also researched intelligent systems and decision-making for extreme environments at NASA. Tomer received his MS in Aeronautical and Astronautical Engineering from Stanford University with the Stanford Intelligent System Lab (SISL), where he worked on formal verification of neural networks. Outside of work, he likes to read novels, climb, and garden.

Selene supports internal operations and leads Terra AI’s marketing strategy. Previously, she researched the technology and demand stack underlying critical mineral supply chains and petrochemicals decarbonization at RMI and Currence AI (Sightline Climate). Selene received her BA in Geology and Environmental Analysis at Pomona College. In her free time, she enjoys baking and cooking with friends, as well as spending time outdoors in the mountains, oceans, and deserts.

Sal leads program management and execution across Terra AI's growing portfolio of enterprise deployments. He previously led a carbon capture pilot at Noya from R&D through full-scale deployment, and has a background in advanced and applied technology. Sal is passionate about the energy transition and decarbonization, and brings deep experience in cross-functional program delivery and a track record of taking complex technical work from idea to scale.