CFP: epiDAMIK @ KDD 2023 Workshop
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Alexander Rodriguez
May 19, 2023, 11:38:02 AM5/19/23
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Dear colleague,
The epiDAMIK @ SIGKDD 2023 workshop is a forum to discuss new insights into how data science 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’.
Our target audience consists of data science and machine learning researchers from both academia and industry who are interested in epidemiological and public-health applications of their work. Additionally, we are aiming to attract researchers and practitioners from the areas of mathematical epidemiology and public health, who are increasingly dealing with more complex models and novel data sources––these problems bring up novel challenges from a data science and machine learning perspective.
The past iterations of the workshop were co-located with SIGKDD since 2018. These were a great success with insightful contributed works as well as high-quality keynotes.
To reflect the broad scope of work, we encourage submissions that span the spectrum from theoretical analysis, algorithms and implementation, to applications and empirical studies, from both data mining and public health viewpoints.
Topics of interest include, but are not limited to:
Planning for public health policy
Role of open source data and community in epidemiology
Data-driven advances in control and optimization (like immunization)
Forecasting disease outcomes including COVID-19 projections
Graph mining and network science approaches to epidemiology
Crowdsourced methods for detection and forecasting
Use of novel datasets for prediction and analysis (including EHR records)
Data mining data for hospital-acquired infections like C.diff, MRSA etc.
Identifying health behaviors
Handling missing and noisy data
Disease forecasting challenge (like the CDC FluSight) experiences
Interpretable and expert-driven AI for public health
Experiences of real-time forecasting
Infodemic, misinformation, and disinformation
Fairness in resource allocation and surveillance
We invite the submission of full regular research papers (6-8 pages) as well as short, work-in-progress, demo or position papers (2-4 pages). Short summary versions of recently published major papers (2-4 pages) are also welcome.
We recommend papers to be formatted according to the standard double-column ACM Proceedings Style. All papers will be peer reviewed and single-blinded, thus, they should contain the name of authors and their affiliations. Authors whose papers are accepted to the workshop will have the opportunity to participate in a poster session, and some may also be chosen for oral presentation. There are no restrictions on already submitted work or authors simultaneously posting their manuscripts to any pre-print server. The accepted papers will be made available online but will not be considered archival. Therefore, authors are free to resubmit the paper to pre-print servers and future conferences and journals.
For paper submission, please proceed to the submission website. Please send any questions to mcha...@mit.edu and arodr...@gatech.edu.
The epiDAMIK @ SIGKDD 2023 workshop is a forum to discuss new insights into how data science 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’.
Our target audience consists of data science and machine learning researchers from both academia and industry who are interested in epidemiological and public-health applications of their work. Additionally, we are aiming to attract researchers and practitioners from the areas of mathematical epidemiology and public health, who are increasingly dealing with more complex models and novel data sources––these problems bring up novel challenges from a data science and machine learning perspective.
The past iterations of the workshop were co-located with SIGKDD since 2018. These were a great success with insightful contributed works as well as high-quality keynotes.
To reflect the broad scope of work, we encourage submissions that span the spectrum from theoretical analysis, algorithms and implementation, to applications and empirical studies, from both data mining and public health viewpoints.
Topics of interest include, but are not limited to:
Planning for public health policy
Role of open source data and community in epidemiology
Data-driven advances in control and optimization (like immunization)
Forecasting disease outcomes including COVID-19 projections
Graph mining and network science approaches to epidemiology
Crowdsourced methods for detection and forecasting
Use of novel datasets for prediction and analysis (including EHR records)
Data mining data for hospital-acquired infections like C.diff, MRSA etc.
Identifying health behaviors
Handling missing and noisy data
Disease forecasting challenge (like the CDC FluSight) experiences
Interpretable and expert-driven AI for public health
Experiences of real-time forecasting
Infodemic, misinformation, and disinformation
Fairness in resource allocation and surveillance
We invite the submission of full regular research papers (6-8 pages) as well as short, work-in-progress, demo or position papers (2-4 pages). Short summary versions of recently published major papers (2-4 pages) are also welcome.
We recommend papers to be formatted according to the standard double-column ACM Proceedings Style. All papers will be peer reviewed and single-blinded, thus, they should contain the name of authors and their affiliations. Authors whose papers are accepted to the workshop will have the opportunity to participate in a poster session, and some may also be chosen for oral presentation. There are no restrictions on already submitted work or authors simultaneously posting their manuscripts to any pre-print server. The accepted papers will be made available online but will not be considered archival. Therefore, authors are free to resubmit the paper to pre-print servers and future conferences and journals.
For paper submission, please proceed to the submission website. Please send any questions to mcha...@mit.edu and arodr...@gatech.edu.
Workshop website: https://epidamik.github.io/
Submission website: https://openreview.net/group?id=KDD.org/2023/Workshop/epiDAMIK
Important Dates
All deadlines are set at 11:59 PM Pacific Daylight Time.
Submission site open: April 10, 2023
Workshop paper submissions: May 30, 2023
Workshop paper notifications: June 23, 2023
Camera-ready papers due: July 20, 2023
Workshop date: August 7, 2023
Sincerely,
The PC chairs:
Bijaya Adhikari (University of Iowa), Alexander Rodriguez (Georgia Tech), Amulya Yadav (Penn State), Sen Pei (Columbia), Ajitesh Srivastava (USC), Marie-Laure Charpignon (MIT).
The PC chairs:
Bijaya Adhikari (University of Iowa), Alexander Rodriguez (Georgia Tech), Amulya Yadav (Penn State), Sen Pei (Columbia), Ajitesh Srivastava (USC), Marie-Laure Charpignon (MIT).
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