TY - GEN
T1 - EpiDAMIK 6.0
T2 - 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023
AU - Adhikari, Bijaya
AU - Rodríguez, Alexander
AU - Yadav, Amulya
AU - Pei, Sen
AU - Srivastava, Ajitesh
AU - Charpignon, Marie Laure
AU - Vullikanti, Anil
AU - Prakash, B. Aditya
N1 - Publisher Copyright:
© 2023 Owner/Author.
PY - 2023/8/6
Y1 - 2023/8/6
N2 - 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/
AB - 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/
UR - http://www.scopus.com/inward/record.url?scp=85171355526&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85171355526&partnerID=8YFLogxK
U2 - 10.1145/3580305.3599220
DO - 10.1145/3580305.3599220
M3 - Conference contribution
AN - SCOPUS:85171355526
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 5847
EP - 5848
BT - KDD 2023 - Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
Y2 - 6 August 2023 through 10 August 2023
ER -