The workshop schedule is still being finalized. Below is the list of confirmed speakers (and titles) thus far, by session.

Machine Learning Session 1 and Session 2:

• Animashree Anandkumar, California Institute of Technology: Enabling Zero-Shot Generalization in AI4Science

• Arindam Banerjee, University of Illinois Urbana-​Champaign: Learning for Long Range Temporal Prediction

• Auroop Ganguly, Pacific Northwest National Laboratory, Northeastern University: Advancing the science of hydroclimatology and preparedness to flooding with integrated natural-build-human process models and data-driven sciences

• Elizabeth Barnes, Colorado State University: "IDK": Neural networks that say I Don't Know to learn better

• George Karniadakis, Brown University: Approximating functions, functionals, and operators using deep neural networks for diverse applications

• Henry Adams, Colorado State University: Topology in Machine Learning

• J Nathan Kutz, Washington University: Data-driven model discovery and physics-informed learning

• Jordan Read, United States Geological Survey: Advancing Water Prediction With Knowledge-Guided Machine Learning Partnerships: Perspectives from the U.S. Geological Survey

• Vipin Kumar, University of Minnesota: Workshop Introduction and Knowledge Guided Machine Learning: Challenges and Opportunities

• Xiaowei Jia, University of Pittsburgh: Physics-Guided Machine Learning for Model Initialization Using Physical Simulations

• Yan Liu, University of Southern California: Graph Convolutional Networks for Physics-informed Machine Learning

• Zhenong Jin, University of Minnesota: Knowledge guided machine learning for agroecosystem sustainability: applications to modeling N2O emission and ecohydrology

Climate/Weather Session:

• Antonios Mamalakis, Colorado State University: Assessing methods of explainable artificial intelligence (XAI) by using attribution benchmark datasets

• Katie Dagon, National Center for Atmospheric Research: Applying Machine Learning to Associate Precipitation Extremes with Synoptic-Scale Weather Events

• Laurie Trenary, George Mason University: Using CMIP6 data to improve empirical prediction of observed surface temperatures.

• Pedram Hassanzadeh, Rice University: Building physical consistencies into neural networks for weather/climate modeling

• Peter Dueben, European Centre for Medium-Range Weather Forecasts: Challenges when preparing machine learning tools for use in operational weather predictions

• Pierre Gentine, Columbia University: Hybrid modeling (physics plus machine learning) to improve prediction of the hydrological cycle

Aquatic Sciences Session:

• Anuj Karpatne, Virginia Polytechnic Institute and State University: Biology-guided Neural Networks: How Can We Integrate Biological Knowledge with Neural Networks for Discovering Phenotypic Traits from Fish Images?

• Charuleka Varadharajan, Lawrence Berkeley National Laboratory: Using Machine Learning to Develop a Predictive Understanding of the Impacts of Extreme Hydrologic Perturbations on River Water Quality

• Janet Barclay, United States Geological Survey: Where groundwater seeps: incorporating groundwater discharge knowledge into stream temperature predictions

• Lake Expeditions 2020 (Speakers TBA): Lake Expedition 2020: a virtual collaboration of early career researchers integrating machine learning and aquatic sciences

• Moritz Feigl, University of Natural Resources and Life Sciences, Vienna, Austria: Learning from mistakes - Assessing the performance and uncertainty in process-based models

• Sam Oliver, United States Geological Survey: Process guided deep learning for decision-ready predictions

Hydrology Session:

• Andrew Bennett, University of Washington: Embedding neural networks to simulate turbulent heat fluxes in a process-based hydrologic modeling framework

• Ankush Khandelwal and Xiang Li, University of Minnesota: Investigating the role of source characteristics in meta-learning: Applications to Hydrology

• Daniel Althoff, Stockholm University: Explainable machine learning: a peek into black-box models

• Katherine Ransom, United States Geological Survey: Process-Informed Machine Learning Predictions of Nitrate in Groundwater Used for Drinking Supply in the Conterminous United States

• Li Zheng, Southern University of Science and Technology, China: Let AI learn and learn from AI: deep learning in hydrological research

• Jonghyun Harry Lee, University of Hawaii: ML-based scalable data assimilation with hydrological applications

Translational Biology Session:

• Aidong Zhang, University of Virginia: Transfer Learning and Meta-learning for Knowledge Transfer in Biomedical Applications

• Brian Hie, Stanford University: Predicting evolution with neural language models

• Jisoo Park, Novartis: Decoding cancer cell maps to guide precision medicine

• Peter Kasson, University of Virginia: Better learning through chemistry: knowledge-guided inference of biomolecular kinetics

• Russell Schwartz, Carnegie Mellon University: Learning how somatic mutability shapes cancer progression risk

• Tamer Kahveci, University of Florida: Counting motifs on evolving network topologies

Agenda:

The agenda is still being finalized, but our conference will be organized as follows (listed in Central Time):

8/9, 9:30-12:25 Session one: workshop introduction + ML

8/9, 12:30- 1:25 Poster Session day one: ML + Climate/Weather

8/9, 1:30-4:25 Session two: Climate/Weather

8/10, 9:30-12:25 Session three: Hydrology

8/10, 12:30- 1:25 Poster Session day two: Hydrology + Aquatic Sciences

8/10, 1:30-4:25 Session four: Aquatic Sciences

8/11, 9:30-12:25 Session five: Translational Biology

8/11, 12:30- 1:25 Poster Session day three: Translational Biology + ML

8/11, 1:30-4:25 Session six: ML + workshop close

Workshop Organizers:

University of Minnesota: Vipin Kumar, John Nieber, Michael Steinbach, Ju Sun

University of Wisconsin-Madison: Hilary Dugan, Paul Hanson, Robert D. Nowak, Stephen Wright

Colorado State University: Elizabeth Barnes, Imme Ebert-Uphoff

University of Virginia: Kevin Janes, Aidong Zhang

George Mason University: Benjamin Cash, Timothy DelSole

United States Geological Survey (USGS): Alison Appling

University of Illinois, Urbana-Champaign: Arindam Banerjee

Pennsylvania State University: Chris Duffy

University of Chicago: Rebecca Willett

University of Pittsburgh: Xiaowei Jia

Carnegie Mellon University: Pradeep Ravikumar