2024 LEAP Summer Momentum Fellowship


LEAP’s Summer Momentum Fellowship welcomes doctoral students in data science interested in having a summer research immersion in climate data science, with the opportunity to apply their data science/machine learning skills in climate modeling and develop research interests in climate data science. Each fellow receives a summer stipend, travel support, and access to LEAP resources, such as LEAP Pangeo and workspace at Columbia University’s Innovation Hub.

Momentum Fellowship project leads work closely with Fellows throughout the summer on a well-defined, yet open-ended, machine learning research problem in climate data science. Project leads will also guide their Fellows to present their summer research at future LEAP events and other workshops/conferences. 

Momentum Fellows are responsible for mentoring up to two (2) undergraduate students in the LEAP Research Experiences for Undergraduates (REU) program. Each project will host up to two (2) undergraduate students paired with one (1) Fellow.

2024 Summer Momentum Fellows

IMG_1627 - Kyle McEvoy

Project 3: Analysis of Climate Model Perturbed Physics Ensembles (Elsaesser, Medeiros)

Kyle is a third year PhD student in Statistics in the Department of Statistics & Data Science at UCLA, advised by Karen McKinnon. His research interests include multiple hypothesis testing with spatially correlated data, covariance matrix estimation, distributional shifts in precipitation under climate change, and incorporating distributional shifts into simulated precipitation. After his PhD, Kyle hopes to work as a research scientist, investigating the climate system and exploring the impacts of climate change. In his free time, Kyle loves hiking, camping, and exploring the outdoors, especially in the many beautiful places located across California (though the redwood forests and the Sierra Nevada mountains are his favorites).

profile - Prani Nalluri

Project 5: Understanding and Modeling the Impact of Air-Sea Heterogeneity on Surface Fluxes (Balwada)

Prani Nalluri (they/them)is a PhD student on the Geophysical Fluid Dynamics track in the Applied Physics and Applied Mathematics department at Columbia University. They are interested in improving parameterizations ofturbulent processes at submeso- and
meso-scales in the ocean. This summer, Prani will be working with Dhruv Balwada to develop ML-based parameterizations for subgrid-scale air-sea fluxes in coupled atmosphere-ocean models. In the future, Prani hopes to continue their research in a national lab or as a part of a climate-focused start-up. Outside of science, they spend their time distance running and creating art through sewing and sculpture.


Project 1: Quantifying Epistemic and Aleatoric Uncertainty in Climate Data (Mandt)

Jimeng is pursuing his Ph.D. in computer science at the Knight Foundation School of Computing and Information Sciences, Florida International University. His research
interests primarily focus on employing AI methods to model large-scale complex systems in real-world scenarios. He has proposed a groundbreaking ML framework named
FIDLAr, designed for flood prediction and mitigation. FIDLAr has demonstrated notable efficacy, outperforming traditional physics-based models in accuracy and efficiency. Expanding its utility, Jimeng is now customizing FIDLAr for more optimization and data generation tasks across diverse domains. Furthermore, he is actively engaged in building AI foundation models within the weather and climate science.

Sikorski_headshot - Antony Sikorski

Project 2: Understanding and Modeling Turbulent Flow in the Atmosphere (Shamekh)

Antony is a Statistics PhD candidate at the Colorado School of Mines. He also completed his MS in Data Science at Mines, and a BS in Applied Mathematics at the University of California, San Diego. His research interests primarily lie in merging machine learning and computational statistics to develop predictive methods for large spatial data volumes.
Following the completion of his PhD, Antony aims to perform research as a data scientistin industry. A proud personal achievement of his is recently being awarded the NSF GRFP. Outside of work, Antony enjoys skiing, live music, and traveling!

Obin Sturm Wrigley Headshot - Obin Sturm

Project 4: Understanding Ice Crystal Growth and Evolution in the Atmosphere (Lamb)

Obin Sturm is a PhD student in the Atmospheric Composition and Earth Data Science group at the University of Southern California. His research focuses on improving our understanding and predictive power of atmospheric chemistry and physics through computational models. These models encompass realistic process-based representations of fundamental phenomena, 3D Earth system models exploring the interactions of these processes at multiple scales, and modern AI and data-driven techniques bridging the first two approaches. Obin believes the use of all three can lead to new scientific discoveries,
and better inform environmental policies and decision making important for the well-being of both specific communities and our broader, global society. Obin’s recent contributions include data-driven, scientifically consistent aerosol superspecies to transport realistic volatility distributions in air quality forecasts, and creating an interface to extract detailed chemical states from NASA GEOS atmospheric composition forecasts for
comprehensive process-based analysis.


Click the image below to learn more about the Summer 2024 Momentum Fellowship Research Projects.

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