Knowledge Guided Machine Learning: Accelerating Discovery using Scientific Knowledge and Data
À propos de cet e-book
Knowledge Guided Machine Learning: Accelerating Discovery using Scientific Knowledge and Data provides an introduction to this rapidly growing field by discussing some of the common themes of research in KGML using illustrative examples, case studies, and reviews from diverse application domains and research communities as book chapters by leading researchers.
KEY FEATURES
- First-of-its-kind book in an emerging area of research that is gaining widespread attention in the scientific and data science fields
- Accessible to a broad audience in data science and scientific and engineering fields
- Provides a coherent organizational structure to the problem formulations and research methods in the emerging field of KGML using illustrative examples from diverse application domains
- Contains chapters by leading researchers, which illustrate the cutting-edge research trends, opportunities, and challenges in KGML research from multiple perspectives
- Enables cross-pollination of KGML problem formulations and research methods across disciplines
- Highlights critical gaps that require further investigation by the broader community of researchers and practitioners to realize the full potential of KGML
À propos de l'auteur
Anuj Karpatne is an Assistant Professor in the Department of Computer Science at Virginia Tech. His research focuses on pushing on the frontiers of knowledge-guided machine learning by combining scientific knowledge and data in the design and learning of machine learning methods to solve scientific and societally relevant problems.
Ramakrishnan Kannan is the group leader for Discrete Algorithms at Oak Ridge National Laboratory. His research expertise is in distributed machine learning and graph algorithms on HPC platforms and their application to scientific data with a specific interest for accelerating scientific discovery.
Vipin Kumar is a Regents Professor at the University of Minnesota’s Computer Science and Engineering Department. His current major research focus is on knowledge-guided machine learning and its applications to understanding the impact of human induced changes on the Earth and its environment.
