Post-Docs
Facheng Gong
Facheng earned his Ph.D. in Petroleum Engineering from the University of Alberta and holds a B.Eng. and M.Sc. from the China University of Petroleum (East China). His research focuses on oil/gas exploitation and subsurface storage solutions, with expertise in Computational Fluid Dynamics–Discrete Element Method (CFD-DEM) simulations, geothermal energy development, and CO₂ storage.
Master’s Students
Matthew Kumar
Matthew earned his B.S. in Earth, Environmental, and Planetary Sciences from Rice University in 2025, and he is currently pursuing a Master’s degree in Energy Geoscience at Rice. His academic and research interests center on subsurface modeling, reservoir dynamics, and geologic carbon storage. His research focuses on reservoir-scale multiphase flow simulations to evaluate near-wellbore halite precipitation risks linked to CO₂ injection.
Undergraduate Students
Kemi Tytler
Kemi a rising sophomore at Hanszen College pursuing a Bachelor of Science degree in Chemical Engineering. She is interested in CO₂ conversion and renewable energy sources. In addition to her academic work, she serves as an ECO representative with the Office of Sustainability at Rice University. Through her research in the SEEM Lab, Kemi has expanded her understanding of carbon capture and storage through modeling and has deepened her passion for advancing environmental sustainability in the energy sector.
Visiting Scholars
Jose Ubillus
Jose is a sustainability and engineering professional with a background in CCS and Data Science. Currently, he works at Vaulted Deep, as Data & Systems Lead. He holds a Master in Petroleum Engineering from UT-Austin and has experience in reservoir simulation, multiphase flow laboratory and data analytics. Passionate about understanding fluid dynamics for energy storage.
Eric Liu
Eric is a rising freshman at the University of Texas at Austin pursing a Bachelor of science in general geology with a plan to double major in mechanical engineering. He is interested in physics informed neural network (PINN) and its applications within computational fluid dynamics (CFD).