Conference proceeding
The 4th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery (epiDAMIK 4.0 @ KDD2021)
KDD '21: PROCEEDINGS OF THE 27TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, pp.4104-4105
01/01/2021
DOI: 10.1145/3447548.3469475
Abstract
The 4th epiDAMIK@SIGKDD workshop is a forum to discuss new insights into how data mining can play a bigger role in epidemiology and public health research. While the integration of data science methods into epidemiology has significant potential, it remains under studied. We aim to raise the profile of this 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.
Details
- Title: Subtitle
- The 4th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery (epiDAMIK 4.0 @ KDD2021)
- Creators
- Bijaya Adhikari - University of IowaAjitesh Srivastava - University of Southern CaliforniaSen Pei - Columbia UniversitySarah Kefayati - IBMRose Yu - University of California, San DiegoAmulya Yadav - Pennsylvania State UniversityAlexander Rodriguez - Georgia Institute of TechnologyArvind Ramanathan - Argonne National LaboratoryAnil Vullikanti - University of VirginiaB. Aditya Prakash - Georgia Institute of Technology
- Resource Type
- Conference proceeding
- Publication Details
- KDD '21: PROCEEDINGS OF THE 27TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, pp.4104-4105
- Publisher
- Assoc Computing Machinery
- DOI
- 10.1145/3447548.3469475
- Number of pages
- 2
- Language
- English
- Date published
- 01/01/2021
- Academic Unit
- Computer Science
- Record Identifier
- 9984411087402771
Metrics
4 Record Views