Welcome! We focus on Predictability and Applied Research for the Earth-system with Training and Optimization (PARETO). As such, we use machine learning (e.g., neural networks) and numerical modeling systems (e.g., CESM) to answer pressing questions and address challenges in modeling the Earth system. Examples of problems that we are tackling include:
Our research also strives to incorporate open-source software and data, accessible communication, and multi-discipline collaboration (particularly with computer science).
Our group name, PARETO, is inspired by the Pareto frontier, a fundamental concept in optimization theory. The concept was named after the Italian economist Vilfredo Pareto (1848-1923) and delineates trade-offs between competing objectives.
Fall 2025 group photo. Pictured from left to right: Assistant Professor Maria J. Molina, Sunny Sharma, Jon Starfeldt, Dean Calhoun, Kyle Hall, Jhayron S. Perez-Carrasquilla, Yamin Guo, and Emily Wisinski. Visiting Postdoctoral Fellow Manuel Titos not pictured. Learn more about our group [here].
If you are interested in joining our group as a graduate researcher, please note that all interested applicants must apply online to be considered.
Recent News
☁ [October] Sunny Sharma will be continuing his graduate studies and pursuing a PhD degree in AOSC. We are so excited to keep learning with you!
☁ [September] Congratulations to Emily F. Wisinski for passing her PhD comprehensive exam! Well done, Emily!
☁ [September] Congratulations to Kyle J. C. Hall for passing his PhD comprehensive exam! Well done, Kyle!
More news available [here].
Recent Publications
☁ Molina, M. J., A. McGovern, *J. S. Pérez-Carrasquilla, X. Li, and R. Tanamachi (In Press). Using Generative Artificial Intelligence Creatively in the Classroom and Research: Examples and Lessons Learned. Bulletin of the American Meteorological Society. [Link]
☁ Mayer, K. J., K. Dagon, and M. J. Molina (In Press). Can Transfer Learning be used to Identify Tropical State-Dependent Bias Relevant to Midlatitude Subseasonal Predictability? Artificial Intelligence for the Earth Systems. [Link]
☁ Wang, Z., R. Rios-Berrios, D. P. Stern, A. J. Baker, T. Beucler, S. J. Camargo, J.-P. Duvel, X. Feng, C.-Y. Lee, M.-D. Leroux, H. Li, J. Macholl, M. J. Molina, K. M. Núñez Ocasio, H. A. Ramsay, E. A. Ritchie, B. A. Schenkel, A. M. Stansfield, P. Vaittinada Ayar, and *E. Wisinski (2025). On the Definition and Tracking of Tropical Cyclone Seeds from a Climate Perspective. Bulletin of the American Meteorological Society. [Link]
☁ *Pérez-Carrasquilla, J. S., and M. J. Molina (2025). An Earth-System-Oriented View of the S2S Predictability of North American Weather Regimes. Artificial Intelligence for the Earth Systems. [Link]
More publications available [here].
Any opinions, findings and conclusions or recommendations expressed herein do not necessarily reflect the views of the University of Maryland.
