Bio: Ryan Lagerquist has been doing research at the intersection of machine learning (ML) and atmospheric science for over 8 years. He earned his B.Sc. Honours in Atmospheric Science from the University of Alberta in 2014, M.Sc. in Meteorology from the University of Oklahoma in 2016, and Ph.D. in Meteorology from the University of Oklahoma in 2020. Ryan is currently a postdoc at CIRA/NOAA, where he is applying ML to radiative-transfer parameterization in NWP models and short-term forecasting of convective initiation/decay. Ryan's research interests span all scales of meteorology (from nowcasting to climate), deep learning, interpretable ML, and physics-guided ML. Ryan is passionate about interdisciplinary communication and teaching and has taught several workshops on ML at atmospheric-science conferences and workshops across the country.

Bio: Grey Nearing is an Assistant Professor at the University of Alabama and Research Director at Upstream Test, Public Benefit Corporation (https://upstream.tech/). His research focuses on the intersection of data-driven and hypothesis-driven environmental science for water related issues.

Bio: Prabhat leads the Data and Analytics Services team at NERSC; his group is responsible for supporting over 7000 scientific users on NERSC’s HPC systems. His current research interests include Deep Learning, Machine Learning, Applied Statistics and High Performance Computing. In the past, Prabhat has worked on topics in scientific data management; he co-edited a book on ‘High Performance Parallel I/O’.

Prabhat is the Director of the Big Data Center collaboration between NERSC, Intel, Cray, UC Berkeley, UC Davis, NYU, UBC, Oxford and Liverpool. The BDC project aims at enabling capability, data-intensive science applications on the NERSC Cori system.

Prabhat received a B.Tech in Computer Science and Engineering from IIT-Delhi (1999); ScM in Computer Science from Brown University (2001) and a PhD in Earth and Planetary Sciences from U.C. Berkeley (2020).

Prabhat has co-authored over 150 papers spanning several domain sciences and topics in computer science. He has won 5 Best Paper Awards, 3 Industry Innovation Awards, and he was a part of the team that won the 2018 Gordon Bell Prize for their work on ‘Exascale Deep Learning’.

Bio: Jordan S Read is chief of the U.S. Geological Survey’s Data Science Branch in the Water Resources Mission Area. Jordan’s data science projects include data visualizations, reproducible workflows, and integrating process knowledge into machine learning methods to improve environmental predictions.

Jordan received his PhD from the University of Wisconsin-Madison in 2012 in environmental fluid mechanics. His dissertation “Physical processes in small temperate lakes” included the study of temperature dynamics and gas fluxes in 40 lakes distributed around the world, as well as the engineering of experimental lake manipulations. Jordan is currently a principal investigator for the Long Term Ecological Research – North Temperate Lakes, is a Lead-PI on a project to modeling climate change impacts on lake temperatures and has served as a steering committee member of the Global Lake Ecological Observatory Network. Jordan is an associate editor of the journal "Limnology and Oceanography Letters".

Bio: Kathleen Weathers studies ecosystem processes within and among aquatic, airborne, and terrestrial systems.

She was co-Chair of the Global Lake Ecological Observatory Network (GLEON) for 10 years, guiding GLEON from its infancy to adulthood. GLEON is a world-wide grassroots collaboration of 800 research partners studying 150 lakes in 53 countries. Their aim: understand, predict, and communicate lakes’ response to environmental change using data from lake-based sensors. This work encompasses impacts from human activities such as road salting, agriculture, and climate change.

Weathers and her colleagues have created a new model for collaborative research that explicitly empowers early career scientists.

Weathers is an expert on fog, which carries nutrients, pollutants, and sometimes disease-causing pathogens. She studies links between ocean, air, and fog-dominated forests and recently, how fog may affect transfer of pathogens from water to land.

Ponette-Gonzalez, Weathers, students, and colleagues are studying the effects of mineral dust and black carbon – both of which impact ecosystems and human health. Mineral dust can deliver toxic pollutants to ecosystems and is a growing concern as climate change exacerbates drought.

Black carbon, created by burning fossil fuels, is known to cause lung and heart disease; this collaborative team is studying the role of vegetation in managing black carbon in urban areas.

Bio: Dr. Cathy Wu is the Unidel Edward G. Jefferson Chair in Engineering and Computer Science at the University of Delaware. She has conducted bioinformatics research for 25 years and has led/co-led major bioinformatics resources including the international UniProt Consortium. Recognized as a “Highly Cited Researcher” (top 1%) for six consecutive years, she has published more than 270 peer-reviewed papers with an h-index of 65. Dr. Wu serves as the founding director of the Center for Bioinformatics and Computational Biology and the Data Science Institute at UD, a nucleating effort to catalyze interdisciplinary research and address big data problems across fields impacting our society.