A comparative study of stochastic and deep generative models for multisite precipitation synthesis
- Bianca Zadrozny
- Dario Augusto Borges Oliveira
- et al.
- 2021
- ICML 2021
Jorge Guevara is a Research Scientist at IBM Research, where he has been working since 2016. He is currently a member of the Spatiotemporal Modeling group, focusing on IBM's research in the Climate and Sustainability domain. In this role, he co-leads the worldwide IBM research effort to provide climate adaptation strategies and impact estimates based on weather hazard and risk models. Additionally, he leads IBM Research's endeavor in developing AI-based weather generators for climate applications. Jorge holds a Ph.D. in Computer Science from the University of Sao Paulo in Brazil, during which he was a visiting researcher at the LITIS Laboratory at the University of Normandy in France. Prior to joining IBM Research, he worked as a computer science professor at the National University of Trujillo, Peru. Jorge has published numerous papers and holds several patents in the field of machine learning, with a focus on hybrid machine learning models based on kernel methods and real-valued logic, machine learning for natural resources, and stochastic weather generators. Jorge is interested in the theory, models, and algorithms of AI and their practical applications in solving real-world problems.