About Me

I am a PhD candidate in Earth and Environmental Engineering at Columbia University, advised by Prof. Pierre Gentine. My research integrates terrestrial ecosystem science, machine learning, and Earth system modeling to improve understanding and prediction of how the land biosphere responds to climate variability and long-term change. I develop hybrid land modeling frameworks that combine mechanistic representations of the carbon–water–energy cycle with the flexibility of modern AI, enabling end-to-end learning of ecosystem processes from globally distributed observations.

I completed my undergraduate studies at Johns Hopkins University in Computer Science, Earth and Planetary Sciences, and Applied Mathematics. At Columbia, I have built fully and automatically differentiable land biosphere systems that integrate satellite constraints, atmospheric inversions, and eddy-covariance observations into a coherent modeling and discovery platform. These models reveal how environmental gradients regulate plant traits and functional diversity worldwide, diagnose coupled ecosystem responses to heat and drought, and improve multiyear predictions across heterogeneous landscapes.

My work also leverages AI to construct observational benchmarks that harmonize multi-sensor satellite records into consistent photosynthesis and canopy-structure proxies from diurnal to multi-decadal scales. These datasets support ecological attribution, drought and heat-stress monitoring, and evaluation of land and weather models.

In addition to my academic research, I work with the NeuralGCM team at Google Research, contributing to efforts at the interface of machine learning and Earth system modeling.

My long-term vision is to build an integrative computational ecology approach that unifies ecological theory, remote sensing, hybrid modeling, and scalable scientific computing to strengthen detection, attribution, and forecasting of terrestrial ecosystem change.

Research Pedigree

My research is shaped by collaborations with leading groups at NASA JPL, the Max-Planck Institute for Biogeochemistry, Seoul National University, and teams across the United States, Europe, and Asia. These collaborations have applied the modeling frameworks I developed to study water stress, nutrient limitations, boreal forest turnover, Amazonian carbon variability, and other emergent ecological questions.

Research Interests

• Hybrid modeling of terrestrial carbon, water, and energy dynamics
• Global spatial organization of plant traits and ecosystem function
• AI-based fusion of satellite records into long-term ecological benchmarks
• Ecological detection, attribution, and forecasting of climate impacts

On the Job Market

I am currently on the academic job market and open to faculty and postdoc opportunities beginning in 2026. I welcome inquiries regarding opportunities, collaborations, and invited talks.