2nd annual workshop on Knowledge Guided Machine Learning

August 9-11, 2021

This virtual workshop will be held August 9-11, 2021, with presentations via Zoom and our YouTube channel. (Those links will be provided just prior to the workshop start date.)


The workshop will include invited talks by leading experts and contributed poster sessions. The workshop will bring together data scientists (researchers in data mining, machine learning, and statistics) and researchers from hydrology, atmospheric science, aquatic sciences, and translational biology to discuss challenges, opportunities, and early progress in designing a new generation of machine learning methods that are guided by scientific knowledge. Find out more information on KGML's website

Event Speaker

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


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