(in alphabetical order)
Norges Teknisk-Naturvitenskaplige Universitet
Data-driven Knowledge Engineering for Case-based Reasoning Systems
Abstract: This keynote will focus on how data-driven methods facilitate the creation of case-based reasoning (CBR) systems. Traditionally, CBR systems are created in collaboration with domain experts who transfer their experience on comparing similar situations into cases and case representations, similarity measures, or adaptation knowledge. Data-driven methods can help to utilize existing datasets for creating the foundations of a CBR system. In this keynote, we will present (1) how machine learning methods can define case representations, (2) how similarity measures can be derived from data, and (3) how domain experts can be involved in the development process. We will use healthcare and aquaculture projects examples to showcase how these methods can be implemented using the open-source tool myCBR.
Bio: Kerstin Bach is an associate professor at the Department of Computer Science at NTNU. My core competence field is Machine Learning and Artificial Intelligence. She is currently deputy head of the Data and Artificial Intelligence group and associated with the Norwegian Open AI Lab. She was awarded a PhD (summa cum laude) in Computer Science from the University of Hildesheim, Germany in 2012. Her main research interests are data-driven decision support systems as well as knowledge-intensive Case-Based Reasoning. She is the chair of the German Society for Computer Science’s Special Interest Group on Knowledge Management, co-chair of the AI4EU gender board and a board member of the Norwegian AI society.
Team: D. Sarah Stamps, Mike Dye, Emmanuel Njinju, James Gallager, Kodi Neumiller
Accessing Earth Science Data and the Need for Transdisciplinary Collaboration
Abstract: To tackle some of the world's most pressing problems, scientists and engineers need access to fundamental Earth science data. In this keynote, I will provide information about how to access a wide range of Earth observation datasets, such as real-time positioning data from Global Navigation Satellite Systems (GNSS), seismic data, and NASA satellite imagery. While having access to the data is one important step, being able to analyze the data for new and valuable results poses additional challenges. I will also present two case studies where I, as a domain scientist, collaborated with data scientists and engineers to solve outstanding questions in volcanic hazards assessment and upper mantle geodynamics. I will conclude with several lessons learned through these collaborations, such as the need for frequent communication, patience, and continued cross-domain education.
Bio: Dr. Sarah Stamps is a geophysicist that measures millimeter precision surface motions with GNSS/GPS and employs computational modeling to investigate the physics driving those motions. She is also an active member of the NSF EarthCube Science Committee and the NSF UNAVCO Education and Community Engagement Committee intent on helping to modernize geosciences for our cyber-connected society and advancing geoscience technical expertise in developing countries.