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epiDAMIK 2026: Workshop on Data-driven Decision Making for Public and Population Health
Conference proceeding   Open access

epiDAMIK 2026: Workshop on Data-driven Decision Making for Public and Population Health

Alexander Rodríguez, Bijaya Adhikari, Ajitesh Srivastava, Kai Wang, Marie-Laure Charpignon, Serina Chang, Jiaming Cui, Wei Jin, Ananya Joshi, Anil Vullikanti, …
Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2, pp.13431-13432
ACM Conferences
KDD '26: The 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining
08/09/2026
DOI: 10.1145/3770855.3818263
url
https://doi.org/10.1145/3770855.3818263View
Published (Version of record) Open Access

Abstract

The epiDAMIK workshop serves as a platform for advancing the utilization of data-driven methods in the fields of epidemiology and public health research. These fields have seen relatively limited exploration of data-driven approaches compared to other disciplines. Therefore, our primary objective is to foster the growth and recognition of the emerging discipline of data-driven and computational epidemiology, providing a valuable avenue for sharing state-of-the-art research and ongoing projects. The workshop also seeks to showcase results that are not typically presented at major computing conferences, including valuable insights gained from practical experiences. Our target audience encompasses researchers in AI, machine learning, and data science from both academia and industry, who have a keen interest in applying their work to epidemiological and public health contexts. Additionally, we welcome practitioners from mathematical epidemiology and public health, as their expertise and contributions greatly enrich the discussions. Homepage: https://epidamik.github.io/.
 Applied computing Applied computing -- Health informatics Computing methodologies -- Machine learning Information systems -- Data mining

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