AI for Earth Summer School

AI for Earth Summer School is sponsored by University of Minnesota. An 8-week program introducing students to AI/ML research for environmental challenges, combining virtual and in-person experiences.

AI for Earth Summer School | Department of Computer Science & Engineering | College of Science and Engineering AI for Earth Summer School Loss of bio-diversity, and food, water and energy security for the growing population of the world are some of the greatest environmental challenges facing humanity.

These challenges have been traditionally studied by science and engineering communities via process-guided models that are grounded in scientific theories. Motivated by the phenomenal success of machine learning (ML) in advancing areas such as computer vision and language modeling, there is a growing excitement in the scientific communities to harness the power of machine learning to address these societal challenges.

In particular, massive amounts of data about Earth and its environment are now continuously being generated by a large number of Earth-observing satellites, in-situ sensors, as well as physics-based models. These information-rich datasets, in conjunction with recent ML advances, offer huge potential for understanding how the Earth's ecosystem have been changing, and for devising policies to manage them in a sustainable fashion.

The summer school is part of an NSF-funded research project led by Prof. Vipin Kumar in the Department of Computer Science and Engineering, with day-to-day program direction and logistics coordinated by the program team. Participants will receive a stipend of $2,000 paid in installments throughout the summer.

For the in-person weeks: students will be provided with housing in university dorms, meal plans, and reimbursement for travel expenses. You can reach us by email at [email protected] . You can review the material from last year’s summer school.

Application deadline: Sunday, March 15, 2026 An information session was held on February 16. Please refer to the FAQ, slides, and recording for more information.

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