2023 LEAP Summer REU Program


LEAP’s 2023 REU program offers summer undergraduate research experiences (SUREs) on synergistic innovations in data science and climate science. This program is in partnership with the Summer at SEAS program, the DSI Scholars program and the Significant Opportunities in Atmospheric Research and Science (SOARS) program at UCAR. The program hosts undergraduate researchers and offers a wide array of enrichment learning and networking opportunities.

The Center is committed to building a diverse research community at the intersection of geosciences and data sciences with the objective to build a LEAP community on par with the U.S. population in terms of gender and race diversity.


The 3-week Momentum Bootcamp (online, June 5–23, 2023) will be led by: 

  • Tian Zheng (Chief Convergence Officer & Education Director, LEAP; Professor & Chair of the Department of Statistics, Columbia University; Affiliate Member of the Data Science Institute)
  • Candace Agonafir (Postdoc, Depts. of Data Science + Civil Engineering, Columbia University)
  • Yu Huang (PhD Student, Dept. of Earth + Environmental Engineering, Columbia University)

The Summer 2023 Research Experience (in-person, June 25 – July 29, 2023) will be led by: 

  • Pierre Gentine (Director, LEAP; Maurice Ewing and J. Lamar Worzel Professor of Geophysics, Departments of Earth and Environmental Engineering and Earth and Environmental Sciences, Columbia University)
  • Mike Pritchard (Institutional Integration Director, LEAP; Associate Professor of Earth Systems Science, University of California at Irvine)
  • Stephan Mandt (Associate Professor of Computer Science and Statistics, University of California at Irvine).

2023 Participants

Sammy Agrawal

Sammy Agrawal

(Massachusetts) Computer Science, Columbia University

Thomas Chen

(New Jersey) Computer Science, Columbia University

Mark Irby-Gill

Mark Irby-Gill

(New York) Geological Engineering, Red Rocks Community College

Rebecca Porter

Rebecca Porter

(Kansas) Interdisciplinary Studies, University of Saint Mary

Amanda Sun

Amanda Sun

(California) Computer Science + Environmental Studies, Dartmouth College

Screen Shot 2023-05-23 at 2.53.37 PM

Subashree Venkatasubramanian

(Washington) Computer Science, Columbia University

Project Scope

Topic: Machine learning (ML) to break deadlock in achieving climate simulations with high-resolution physics.

Short Description: Climate model predictions are full of uncertainty. Some of it is because processes like cloud formation are crudely represented simply because they are too computationally intensive to model explicitly. Multi-scale climate models that embed small realizations of these explicit physics provide an opportunity to sidestep Moore’s law, by learning these physics with machine learning models. Once trained, the machine learning models can be coupled to the climate predictions, with fast inference allowing high-resolution physics in climate simulations ahead of schedule. An open research challenge is finding reproducible, reliable ways to achieve performant ML workflows in situations of real-world operational complexity. In this context, LEAP has innovated a ML training data set harvested from a state-of-the-art climate simulator, which exposes REU students to the relevant pipeline issues in a real-world research setting at the frontier of climate science and data science.

Learning Outcomes: Familiarity with datasets used in modern climate simulations, baseline “ML Operations” such as quality controlling and data engineering, building training pipelines to perform machine learning, assessing goodness of fit and exploring new algorithms. For those who already come with this basic foundation, advanced learning outcomes could include experimenting with stochastic and generative deep learning algorithms that attempt to fit the non-deterministic component of chaotic cloud dynamics.

Potential Continuation of Research After REU: Successful or standout fits could be considered for follow-on work that goes to the next step of coupling well-performing architectures to global climate simulators. The resulting “hybrid” (ML + physics) climate simulators may exhibit interesting pathologies or physically desirable behaviors, either of which provide  opportunities for collaborative analysis of climate prediction output.

Important Dates

  • March 10, 2023: REU Application deadline
  • March 31, 2023: REU Application decision notification
  • June 5 – June 23, 2023: Momentum Bootcamp (online)
  • June 25, 2023: Move in to Columbia University campus
  • June 26 – July 28, 2023: In-Person Research Experience
  • July 29, 2023: Move out


NSF logo

Contact Us