Staff Directory

Yu joined the lab as a PhD student since September 2017. She received a B.Sc. degree in Atmospheric Sciences from Nanjing University. She is now studying land-atmospheric interactions and she is especially interested in how water cycle and carbon cycle are coupled through land-atmosphere processes.

Yongquan received his B.S. degree in Mathematics and Applied Mathematics in 2019 at Lanzhou University, Cuiying Honors college; and his M.Phil. degree in Atmospheric and Environmental Science in 2021 at Hong Kong University of Science and Technology, Interdisciplinary Program Office. He is interested in studying deep learning methods and their integration with numerical methods for the simulation of physical processes in atmospheric and climate dynamics.

Xu received B.S. in Geographical Information System at Wuhan University in 2016, and PhD in Physical Geography at Peking University in 2021. Before joining the Gentine Lab, his research focused on land-atmosphere interactions, vegetation dynamics and eco-hydrology.

Weiwei is investigating the terrestrial carbon cycle using remote sensing and flux tower data

Sophie Abramian joined the lab in 2024 after completing her PhD at the Laboratoire de Météorologie Dynamique in Paris. Her research investigates atmospheric convection, focusing on the physical mechanisms driving deep convective systems and their role in extreme precipitation. To address these questions, she utilizes various numerical simulations, ranging from idealized models to global high-resolution cloud-resolving simulations of the tropical atmosphere. Currently, Sophie is examining how the diversity of mesoscale convective systems in the tropics is influenced by the dynamics of cold pool populations. By integrating machine learning with isentropic analysis, she aims to enhance our understanding of these complex interactions.

Dr. Shuolin (Shawn) Li is a Postdoctoral Research Scientist in the Data Science Institute collaborating with Professor Pierre Gentine, Professor Upmanu Lall, and Professor Tian Zheng.

In collaboration with the Learning the Earth with Artificial Intelligence and Physics (LEAP), Dr. Li’s research at the Data Science Institute focuses on developing new algorithms for the use of sparse and indirect Earth observations that are organized across multiple space and time scales, in order to inform climate model parameterization. These data will refine initial parameterization based on machine learning approaches and informed by high-resolution high-fidelity simulations. Dr. Li will be employing various tools such as Bayesian inference, neural networks, and physical parameterizations with an initial focus on land, atmosphere or ocean. Furthermore, he will collaborate with scientists at the National Center for Atmospheric Research (NCAR) to refine and optimize parameterization processes.

Before joining Columbia University in July 2023, Dr. Li earned his Ph.D. in Fluid Dynamics and Hydrology and an M.S. in Computer Science from Duke University, where he was mentored by Professor Gabriel Katul.

I joined the lab as a Ph.D. student in 2021 Fall. I received B.S. and M.S. in Atmospheric Sciences at National Taiwan University. My previous study worked on the unique hydro-climatological cycle in Taiwan’s montane cloud forest. I am interested in understanding land-atmosphere interactions in forests, and will specifically focus on the impacts of fog on forest hydroclimate and productivity during my Ph.D. life.

Pierre Gentine is a Professor in the department of Earth and Environmental Engineering and in the department of Earth and Environmental Sciences. He is director of the National Science Foundation Science and Technology Center "Learning the Earth with Artificial intelligence and Physics" and a director of the Graduate Program in Earth and Environmental Engineering. Dr. Gentine and his group investigate the multiscale nature of the continental hydrologic and carbon cycle, with observations (remote sensing and in situ), models and machine learning.

Mitra joined Gentine lab in 2023 upon graduating as a Ph.D. student from EPFL (the lab of Ecohydrology) and receiving a postdoctoral grant from the Swiss National Foundation. Her area of focus is fluid flow in porous media and how that interacts with life. Transforming such complex interactions into quantifiable processes using extractable data interests her the most. Her current focus is studying the role of vegetation in regulating the water and carbon cycle in semi-arid regions.

Michelle Krakora graduated from UCLA with a B.S. in Environmental Science and Conservation Biology. During undergrad, Michelle worked on several research projects in areas of hydrology, marine biology, and conservation biology at UCLA, Brookhaven National Laboratory (BNL), and Lawrence Berkeley National Laboratory (LBNL). While at LBNL, Michelle researched climate and hydrological drivers for harmful algal blooms and the hydrological effects of wildfires. 

Before starting as a Bridge to PhD Scholar, Michelle worked for state and federal governments on Department of Defense Cleanup sites dealing with hazardous waste and water quality issues. As an Environmental Engineering Bridge Scholar, Michelle is working to understand decadal variability of soil moisture in different regions to better advise water resource management.

Margaret (Maggie) is interested in improving the parametrization of marine boundary layer clouds using machine learning and high-fidelity numerical simulations. As a DOE Computational Science Graduate Fellow, she is excited to apply high-performance computing methods within atmospheric science.

Maggie has previously worked as a data scientist at a climate-tech startup and as a researcher at an environmental consulting firm. She received her A.B. in Earth & Planetary Sciences from Harvard University, where she researched Arctic methane emissions. 

Kara is an associate research scientist interested in aerosols and microphysics as well as machine learning.

Research Interest: Ocean Warming, Air-Sea Fluxes, Machine Learning, Model Parameterization, Physical Oceanography, Data Science, Chemical Oceanography. Julia received a Bachelor of Science in Chemical Engineering from Washington University in St. Louis. She worked as an engineering consultant in process, energy, and environmental engineering for two years before starting a PhD at Columbia University in 2022. Her research is conducted through the National Science Foundation (NSF)-funded Science and Technology Center, Learning the Earth with Artificial Intelligence and Physics (LEAP). She focuses on exchanges between the ocean and atmosphere, specifically examining ocean heat uptake. She uses machine learning to leverage in situ observations, remote sensing, and climate models to improve parameterizations of air-sea fluxes of heat and momentum. She is co-advised by Dr. Pierre Gentine (from Columbia University’s Department of Earth and Environmental Engineering) and Dr. Laure Zanna (a Professor of Mathematics and Atmosphere/Ocean Science at New York University).

Juan received his B.S. in Environmental Science from the National University of Singapore. He is interested in hybrid machine learning approaches to efficiently learn, distill, and characterize invariant physical processes of complex climate dynamics from sparse and incomplete observations.

Dr. Joseph Lockwood is a Postdoctoral Research Scientist in the Data Science Institute, where he is part of the 7th cohort of the DSI Postdoctoral Fellows Program, advised by Professor Pierre Gentine and Professor Tian Zheng in collaboration with Learning the Earth with Artificial Intelligence and Physics (LEAP), an NSF Science and Technology Center.

Before joining Columbia University in July 2024, Dr. Lockwood graduated with a PhD in Geoscience (Applied Physics) with a minor in Computational Science and Engineering from Princeton University in May 2024. He received a graduate certificate in Science and Technology from the Princeton School of Public and International Affairs.

As an applied scientist, Dr. Lockwood’s research centers on applying deep learning and machine learning to weather and climate modeling. He has published scientific articles on machine learning, climate risk, energy and decarbonization and economics.

Jisu is investigating boundary layer turbulence using high-resolution simulations

Jianing Fang joined the lab as a PhD student in September 2021. He received a B.Sc. degree from Johns Hopkins University with majors in Earth & Planetary Sciences, Computer Science, and Applied Mathematics & Statistics. His first research project focuses on capturing the diurnal cycle of photosynthesis using solar-induced fluorescence. He is also interested in applying hybrid-physics machine learning to the carbon cycle.

Jiangong Liu obtained his PhD in physical geography at The Chinese University of Hong Kong in 2020. Jiangong is interested in land-atmosphere interactions, plant photosynthetic physiology, and wetland biogeochemistry. He currently uses explainable machine learning and meta-learning for better understanding and modeling terrestrial carbon and water fluxes.

Jatan is primarily interested in applying principled statistical techniques and machine learning (ML) to address complex real-world problems, in particular ones that involve quantifying the ecological and socioeconomic impact of climate change. He is currently developing physics-informed ML models to study wildfires in the western United States using multiscale climate, vegetation, and population datasets.

Carla joined the lab in 2024 after finishing her PhD at the University of Edinburgh. Her research focuses on the impacts of aerosols in the climate system which she investigates through the application of traditional statistical and machine learning methods, such as causal inference. Currently, she is working towards building an aerosol emulator using (physics-informed) generative models.

Aya received her dual B.S. degree in Data Science from Duke University and Duke Kunshan University in 2022.  She is interested in using deep learning methods and GIS data to uncover underlying patterns of climate change. Aya is currently studying the effects of climate change on leaf-out phenology and leaf senescence patterns.

Alex studies atmospheric boundary layer processes and turbulence primarily using computational methods such as machine learning and large-eddy simulation. He is currently focusing on applications of machine learning to the stably stratified atmospheric boundary layer. Other problems that Alex has recently investigated include topographic effects on wind flow and generation of turbulence at the inflow boundary of fluid dynamics simulations.

Adam is interested in research at the intersection of machine learning and climate adaptation. With specific interests in socio-environmental models and climate risk, Adam aims to investigate sustainable and equitable solutions for hydroclimate resilience. Adam received their BS and MS from Stanford University in Civil and Environmental Engineering and Management Science and Engineering respectively.