Conference proceeding
epiDAMIK 5.0: The 5th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
08/14/2022
DOI: 10.1145/3534678.3542917
Abstract
Similar to previous iterations, the epiDAMIK @ KDD workshop is a forum to promote data driven approaches in epidemiology and public health research. Even after the devastating impact of COVID-19 pandemic, data driven approaches are not as widely studied in epidemiology, as they are in other spaces. We aim to promote and raise the profile of the emerging research area of data-driven and computational epidemiology, and create a venue for presenting state-of-the-art and in-progress results-in particular, results that would otherwise be difficult to present at a major data mining conference, including lessons learnt in the 'trenches'. The current COVID-19 pandemic has only showcased the urgency and importance of this area. Our target audience consists of data mining and machine learning researchers from both academia and industry who are interested in epidemiological and public-health applications of their work, and practitioners from the areas of mathematical epidemiology and public health. Homepage: https://epidamik.github.io/.
Details
- Title: Subtitle
- epiDAMIK 5.0: The 5th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery
- Creators
- Bijaya Adhikari - University of IowaAmulya Yadav - Pennsylvania State UniversitySen Pei - Columbia UniversityAjitesh Srivastava - University of Southern CaliforniaSarah Kefayati - IBMAlexander Rodríguez - Georgia Institute of TechnologyMarie-Laure Charpignon - Massachusetts Institute of TechnologyAnil Vullikanti - University of VirginiaB. Aditya Prakash - Georgia Institute of Technology
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
- Publisher
- ACM
- DOI
- 10.1145/3534678.3542917
- Language
- English
- Date published
- 08/14/2022
- Academic Unit
- Computer Science
- Record Identifier
- 9984530379602771
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