1st Workshop on Knowledge Guided Machine Learning (KGML):
A Framework for Accelerating Scientific Discovery
Dates: August 18-20, 2020. (Daily times: 9:30am-12:30 and 1:30-4:30pm CDT)
Format: 100% virtual, open to public - available as zoom webinar and as YouTube live stream.
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Inaugural Workshop Information:
The inaugural workshop took place August 18-20, 2020 virtually over zoom. Over 1000 people registered from over 30 countries, and a variety of topics and vibrant discussions were recorded and are available to watch here: https://z.umn.edu/kgmlworkshopyoutube.
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This workshop is part of a 2-year conceptualization project funded by the NSF's Harnessing the Data Revolution (HDR) program involving researchers from the University of Minnesota, University of Wisconsin, Penn State, Colorado State University, and the University of Virginia. This project aims to develop a framework that uses the unique capability of data science models to automatically learn patterns and models from data, without ignoring the treasure of accumulated scientific knowledge. Specifically, the project builds the foundations of knowledge-guided machine learning (KGML) by exploring several ways of bringing scientific knowledge and machine learning models together using pilot applications from four domains: aquatic ecodynamics, climate and weather, hydrology, and translational biology. For more information, please see the project website.
The workshop will include invited talks and panel discussions by data scientists (researchers in data mining, machine learning, and statistics) and researchers from four application areas (aquatic sciences, hydrology, atmospheric science, and translational biology) to discuss challenges, opportunities, and early progress in bringing scientific knowledge to machine learning. In doing so, it aims to foster interdisciplinary collaborations and interactions among these communities. The expected outcomes are the identification and greater understanding of challenges and opportunities in knowledge guided machine learning.
Please see below for the general structure of the workshop; follow links (session title) under each session for specific details.
Tuesday, August 18:
Session Chairs: Vipin Kumar and Michael Steinbach
10:05- 10:30 Anuj Karpatne, Virginia Polytechnic Institute and State University: "Science-guided Machine Learning: Advances in An Emerging Paradigm Combining Scientific Knowledge with Machine Learning" (slides) (link to recorded presentation)
PM session: Aquatic Sciences, 1:30-4:30 (CDT)
Session chairs: Paul Hanson and Hilary Dugan
1:30-1:40 Paul Hanson University of Wisconsin, Madison: Introduction and opening of "Ecological knowledge guides machine learning: (i) process-guided phosphorus modeling, (ii) state-space modeling of lake oxygen dynamics” (slides) (link to recorded presentation)
1:40-2:00 Robert Ladwig, University of Wisconsin, Madison: Continuation and closing of "Ecological knowledge guides machine learning: (i) process-guided phosphorus modeling, (ii) state-space modeling of lake oxygen dynamics” (slides) (link to recorded presentation)
2:00-2:20 Alison Appling, United States Geological Survey: "Applications of knowledge-guided machine learning for water resources management" Note: This presentation is not publicly available. If you would like access to this recorded presentation, please contact firstname.lastname@example.org.
3:10-3:30 Sylvia Lee, Environmental Protection Agency: "Harnessing the literature, datasets, and models to understand continental-scale lake phosphorus recovery times" (slides) (link to recorded presentation)
3:30-3:50 Charuleka Varadharajan, Lawrence Berkeley National Laboratory: "Data-driven approaches to building efficient machine learning models for aquatic science and hydrology"(slides) (link to recorded presentation)
Wednesday, August 19:
AM session: Hydrology, 9:30-12:30 (CDT)
Session chairs: John Nieber and Chris Duffy
10:15-10:40 Chaopeng Shen, Pennsylvania State University: "From parameter calibration to parameter learning: Revolutionizing large-scale geoscientific modeling with big data" (slides) (link to recorded presentation)
10:40-11:05 Julian Koch, Geological Survey of Denmark and Greenland - Denmark: "Groundwater table modelling at high spatial resolution using machine learning and process-based models" (slides)(link to recorded presentation)
12:00-12:20 Michael J Friedel, Pacific Northwest National Laboratory: "Multiphysics-informed learning algorithm for vadose zone transport modeling – preliminary results" (link to recorded presentation)
12:20-12:30 Overall Questions and Session Summary
Session chairs: Imme Ebert-Uphoff and Elizabeth Barnes
4:00-4:30 Panel Questions and Discussion (link to recorded panel)
Thursday, August 20:
AM session: Translational Biology, 9:30-12:30 (CDT)
Session Chairs: Aidong Zhang and Kevin Janes
9:30-9:40: Introduction, Kevin Janes and Aidong Zhang (link to presentation)
PM session: Closing Session, 1:30-3:30 (CDT)
YOUTUBE LINKS: https://www.youtube.com/watch?v=Z7H1AxLb1Ck
Moderators: Imme Ebert-Uphoff, Colorado State University & Paul Hanson, University of Wisconsin, Madison
Topic: Where do we go from here?
Elizabeth Barnes - Department of Atmospheric Science, Colorado State University
Chris Duffy - Department of Civil and Environmental Engineering, Pennsylvania State University
Hilary Dugan - Department of Integrative Biology, University of Wisconsin, Madison
Imme Ebert-Uphoff - Department of Electrical and Computer Engineering, Colorado State University
Paul Hanson - Center for Limnology, University of Wisconsin, Madison
Kevin Janes - School of Engineering & Applied Science, University of Virginia
Vipin Kumar - Department of Computer Science and Engineering, University of Minnesota
John Nieber - Department of Bioproducts and Biosystems Engineering, University of Minnesota
Michael Steinbach - Department of Computer Science and Engineering, University of Minnesota
Aidong Zhang - School of Engineering & Applied Science, University of Virginia