epiDAMIK 5.0: The 5th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery

Bijaya Adhikari; Amulya Yadav; Sen Pei; Ajitesh Srivastava; Sarah Kefayati; Alexander Rodríguez; Marie Laure Charpignon; Anil Vullikanti; B. Aditya Prakash

doi:10.1145/3534678.3542917

epiDAMIK 5.0: The 5th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery

Bijaya Adhikari, Amulya Yadav, Sen Pei, Ajitesh Srivastava, Sarah Kefayati, Alexander Rodríguez, Marie Laure Charpignon, Anil Vullikanti, B. Aditya Prakash

College of Information Sciences and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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/.

Original language	English (US)
Title of host publication	KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Publisher	Association for Computing Machinery
Pages	4850-4851
Number of pages	2
ISBN (Electronic)	9781450393850
DOIs	https://doi.org/10.1145/3534678.3542917
State	Published - Aug 14 2022
Event	28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022 - Washington, United States Duration: Aug 14 2022 → Aug 18 2022

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference	28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022
Country/Territory	United States
City	Washington
Period	8/14/22 → 8/18/22

All Science Journal Classification (ASJC) codes

Software
Information Systems

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1145/3534678.3542917

Cite this

Adhikari, B., Yadav, A., Pei, S., Srivastava, A., Kefayati, S., Rodríguez, A., Charpignon, M. L., Vullikanti, A., & Prakash, B. A. (2022). epiDAMIK 5.0: The 5th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery. In KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 4850-4851). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). Association for Computing Machinery. https://doi.org/10.1145/3534678.3542917

Adhikari, Bijaya ; Yadav, Amulya ; Pei, Sen et al. / epiDAMIK 5.0 : The 5th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery. KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2022. pp. 4850-4851 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

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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/.",

author = "Bijaya Adhikari and Amulya Yadav and Sen Pei and Ajitesh Srivastava and Sarah Kefayati and Alexander Rodr{\'i}guez and Charpignon, {Marie Laure} and Anil Vullikanti and Prakash, {B. Aditya}",

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doi = "10.1145/3534678.3542917",

language = "English (US)",

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Adhikari, B, Yadav, A, Pei, S, Srivastava, A, Kefayati, S, Rodríguez, A, Charpignon, ML, Vullikanti, A & Prakash, BA 2022, epiDAMIK 5.0: The 5th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery. in KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, pp. 4850-4851, 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022, Washington, United States, 8/14/22. https://doi.org/10.1145/3534678.3542917

epiDAMIK 5.0 : The 5th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery. / Adhikari, Bijaya; Yadav, Amulya; Pei, Sen et al.

KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2022. p. 4850-4851 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Adhikari, Bijaya

AU - Yadav, Amulya

AU - Pei, Sen

AU - Srivastava, Ajitesh

AU - Kefayati, Sarah

AU - Rodríguez, Alexander

AU - Charpignon, Marie Laure

AU - Vullikanti, Anil

AU - Prakash, B. Aditya

PY - 2022/8/14

Y1 - 2022/8/14

N2 - 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/.

AB - 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/.

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M3 - Conference contribution

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T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

SP - 4850

EP - 4851

BT - KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

PB - Association for Computing Machinery

Y2 - 14 August 2022 through 18 August 2022

ER -

Adhikari B, Yadav A, Pei S, Srivastava A, Kefayati S, Rodríguez A et al. epiDAMIK 5.0: The 5th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery. In KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. 2022. p. 4850-4851. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). doi: 10.1145/3534678.3542917

epiDAMIK 5.0: The 5th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

UN SDGs

Access to Document

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