The 4th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery (epiDAMIK 4.0 @ KDD2021)

Bijaya Adhikari; Ajitesh Srivastava; Sen Pei; Sarah Kefayati; Rose Yu; Amulya Yadav; Alexander Rodríguez; Arvind Ramanathan; Anil Vullikanti; B. Aditya Prakash

doi:10.1145/3447548.3469475

The 4th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery (epiDAMIK 4.0 @ KDD2021)

Bijaya Adhikari
, Ajitesh Srivastava
, Sen Pei
, Sarah Kefayati
, Rose Yu
, Amulya Yadav
, Alexander Rodríguez
, Arvind Ramanathan
, Anil Vullikanti
, B. Aditya Prakash

College of Information Sciences and Technology

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

5 Scopus citations

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.

Original language	English (US)
Title of host publication	KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Publisher	Association for Computing Machinery
Pages	4104-4105
Number of pages	2
ISBN (Electronic)	9781450383325
DOIs	https://doi.org/10.1145/3447548.3469475
State	Published - Aug 14 2021
Event	27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021 - Virtual, Online, Singapore Duration: Aug 14 2021 → Aug 18 2021

Publication series

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

Conference

Conference	27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021
Country/Territory	Singapore
City	Virtual, Online
Period	8/14/21 → 8/18/21

UN SDGs

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

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Software
Information Systems

Access to Document

10.1145/3447548.3469475

Cite this

Adhikari, B., Srivastava, A., Pei, S., Kefayati, S., Yu, R., Yadav, A., Rodríguez, A., Ramanathan, A., Vullikanti, A., & Prakash, B. A. (2021). The 4th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery (epiDAMIK 4.0 @ KDD2021). In KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 4104-4105). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). Association for Computing Machinery. https://doi.org/10.1145/3447548.3469475

Adhikari, Bijaya ; Srivastava, Ajitesh ; Pei, Sen et al. / The 4th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery (epiDAMIK 4.0 @ KDD2021). KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2021. pp. 4104-4105 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

@inproceedings{254cf5138f9243909f88d63ccddd452a,

title = "The 4th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery (epiDAMIK 4.0 @ KDD2021)",

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.",

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

note = "Publisher Copyright: {\textcopyright} 2021 Owner/Author.; 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021 ; Conference date: 14-08-2021 Through 18-08-2021",

year = "2021",

month = aug,

day = "14",

doi = "10.1145/3447548.3469475",

language = "English (US)",

series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",

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Adhikari, B, Srivastava, A, Pei, S, Kefayati, S, Yu, R, Yadav, A, Rodríguez, A, Ramanathan, A, Vullikanti, A & Prakash, BA 2021, The 4th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery (epiDAMIK 4.0 @ KDD2021). in KDD 2021 - Proceedings of the 27th 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. 4104-4105, 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021, Virtual, Online, Singapore, 8/14/21. https://doi.org/10.1145/3447548.3469475

The 4th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery (epiDAMIK 4.0 @ KDD2021). / Adhikari, Bijaya; Srivastava, Ajitesh; Pei, Sen et al.
KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2021. p. 4104-4105 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

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

TY - GEN

T1 - The 4th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery (epiDAMIK 4.0 @ KDD2021)

AU - Adhikari, Bijaya

AU - Srivastava, Ajitesh

AU - Pei, Sen

AU - Kefayati, Sarah

AU - Yu, Rose

AU - Yadav, Amulya

AU - Rodríguez, Alexander

AU - Ramanathan, Arvind

AU - Vullikanti, Anil

AU - Prakash, B. Aditya

PY - 2021/8/14

Y1 - 2021/8/14

N2 - 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.

AB - 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.

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DO - 10.1145/3447548.3469475

M3 - Conference contribution

AN - SCOPUS:85114945340

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

SP - 4104

EP - 4105

BT - KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

PB - Association for Computing Machinery

T2 - 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021

Y2 - 14 August 2021 through 18 August 2021

ER -

Adhikari B, Srivastava A, Pei S, Kefayati S, Yu R, Yadav A et al. The 4th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery (epiDAMIK 4.0 @ KDD2021). In KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. 2021. p. 4104-4105. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). doi: 10.1145/3447548.3469475

The 4th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery (epiDAMIK 4.0 @ KDD2021)

Abstract

Publication series

Conference

UN SDGs

All Science Journal Classification (ASJC) codes

Access to Document

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Cite this