Keynote Speakers
Rosie Lickorish is a Research Software Engineer at IBM, specializing in the development of next-generation tools and technologies designed to drastically accelerate solutions for today’s most urgent global challenges. Her technical focus involves leveraging geospatial data, AI models, and scalable cloud architectures, with a critical emphasis on applications for climate change mitigation and adaptation. A recognized innovator within the organization, Rosie is designated as a Senior Inventor at IBM and holds two high-value patents.
Pavel Perezhogin received his PhD in 2021 from the Institute of Numerical Mathematics in Moscow under the supervision of Dr. Andrey Glazunov. He subsequently worked as a Postdoctoral Associate and later as a Research Scientist at the Courant Institute, New York University, in the group of Prof. Laure Zanna, as part of the M2LinES project.
Broadly, Pavel's research focuses on improving the representation of unresolved turbulent phenomena in ocean models and evaluating their impact on climate projections. Throughout his career, he has investigated various approaches to parameterizing the effects of mesoscale eddies in eddy-permitting ocean models using physics-based and machine-learning methods, with parameterizations evaluated in GFDL, NCAR, and European ocean models. His PhD research connects the mesoscale parameterization problem to the broader three-dimensional turbulence-closure problem, revealing common physical principles across engineering and geophysical turbulent flows. His Postdoc research addresses problems on how to utilize machine-learning methods while respecting physical principles, such as, for example, dimensional consistency.
Broadly, Pavel's research focuses on improving the representation of unresolved turbulent phenomena in ocean models and evaluating their impact on climate projections. Throughout his career, he has investigated various approaches to parameterizing the effects of mesoscale eddies in eddy-permitting ocean models using physics-based and machine-learning methods, with parameterizations evaluated in GFDL, NCAR, and European ocean models. His PhD research connects the mesoscale parameterization problem to the broader three-dimensional turbulence-closure problem, revealing common physical principles across engineering and geophysical turbulent flows. His Postdoc research addresses problems on how to utilize machine-learning methods while respecting physical principles, such as, for example, dimensional consistency.