2nd Workshop on Knowledge Guided Machine Learning (KGML2021):
A Framework for Accelerating Scientific Discovery
Dates: August 9-11, 2021
Format: 100% virtual, open to public - available as zoom webinar and as YouTube live stream.
PLEASE VISIT OUR YOUTUBE CHANNEL FOR ALL RECORDED SESSIONS:
Supported by NSF Awards under the Harnessing the Data Revolution (HDR) Program:
RECORDED SESSIONS ARE NOW LIVE ON OUR YOUTUBE CHANNEL: https://www.youtube.com/channel/UCMYTOjm4uAI3xKWGY7_7xKA
KGML2021 Workshop Information:
The second Knowledge Guided Machine Learning Workshop (KGML2021) is a three day virtual workshop August 9-11, 2021.
This workshop builds on the recognition that current black-box machine learning (ML) models have met with limited success in many scientific domains. This is due to their large data requirements, inability to produce physically consistent results, and lack of generalizability to out-of-sample scenarios. Instead of a purely data-driven approach that ignores decades (sometimes centuries) of accumulated knowledge in the science domains, there is an increasing interest in Knowledge Guided Machine Learning (KGML) approaches to integrate such knowledge into the ML models. This workshop will discuss recent advances made in this emerging area as well as their applications in the context of four domains: freshwater science, hydrology, climate and weather, and translational biology.
The workshop will include invited talks and poster sessions 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.
Previous Inaugural Workshop: 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 on the KGML YouTube channel: https://z.umn.edu/kgmlworkshopyoutube. The audience consisted of a wide range of disciplines - data science, aquatic science, hydrology, weather/climate, and translational biology, seismology, ecology, agriculture, etc. Please visit the inaugural workshop webpage for the agenda along with slides and recorded video (for speakers who provided permission), workshop synopsis, and a program booklet with talk titles/abstracts/speaker-bio: https://sites.google.com/umn.edu/kgml/workshop
These workshops are 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, University of Virginia, University of Illinois Urbana-Champaign, Carnegie Mellon University, University of Chicago, University of Pittsburgh, and George Mason University.
Call for Posters:
Everyone is invited to share their research, including works in progress, that are related to the workshop themes (as described in the background above), by submitting short abstracts for review. Accepted abstracts will be invited to submit a poster that will be presented in one of the three poster sessions as outlined in the agenda below. Posters will be presented virtually via Gather.Town.
(All times are listed in Central Time, UTC -5)
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