Call for Papers: ACM SIGSPATIAL GeoHealth 2025

The 1st ACM SIGSPATIAL International Workshop on Geospatial Computing for Public Health (GeoHealth'25) focuses on research that addresses spatial aspects of health behaviors, outcomes, exposures, systems, and interventions, using techniques from spatial databases, geoinformatics, spatial data mining, machine learning, and modeling. Combined with emerging sources of public health data—from wearable devices and social media to climate sensors and administrative records—this expertise positions the community to make significant contributions to the evolving field of geospatial health informatics.

The workshop seeks high-quality full (8-10 pages) and short (up to 4 pages) papers that will be peer-reviewed. Once accepted, at least one author is required to register for the workshop and the ACM SIGSPATIAL conference, as well as attend the workshop to present the accepted work which will then appear in the ACM Digital Library.

GeoHealth 2025 solicits novel and previously unpublished research on all topics related to geospatial health including, but not limited to:

• Geospatial Analytics for Infectious Disease Surveillance
• Environmental Exposure Mapping (e.g., air quality, water, toxins)
• Modeling Health Impacts of Climate Change
• GIS for Health Equity and Access
• Spatial Decision Support Systems for Health Resource Allocation
• Urban Health and Built Environment Analysis
• Chronic Disease Mapping and Risk Factor Modeling
• Human Mobility and Public Health Interventions
• Integration of Remote Sensing and Health Data
• Real-time Outbreak Detection and Monitoring
• Geospatial Artificial Intelligence (GeoAI) in Health
• Privacy-aware Health Data Integration
• Social Determinants of Health and Spatial Inequality
• Data Fusion from Wearables, EHRs, and Census Data
• Health Communication via Spatial Dashboards and Visualizations
• Spatial Epidemiology and Spatiotemporal Modeling

Important Date

Submission deadline

August 22, 2025

Notification

September 26, 2025

Workshop date

November 3, 2025

Submission site

https://easychair.org/conferences/?conf=geohealth25

Workshop website

https://onspatial.github.io/geo-health-2025/

Paper Format

Manuscripts should be submitted in PDF format and formatted using the ACM camera-ready templates available at https://www.acm.org/publications/proceedings-template. For latex users, please make sure that you choose sigconf type (e.g., \documentclass[sigconf]{acmart}) in your latex file. Submissions to GeoHealth are single-blind – i.e., the names and affiliations of the authors should be listed in the submitted version.

Contact

Please contact me or geohealth25@easychair.org if you have questions about GeoHealth 2025.

Call for Papers: ACM SIGSPATIAL GeoSim 2025

The 8th ACM SIGSPATIAL International Workshop on Geospatial Simulation (GeoSim 2025) focuses on all aspects of simulation as a general paradigm to model and predict spatial systems and generate spatial data. New simulation methodologies and frameworks, not necessarily coming from the SIGSPATIAL community, are encouraged to participate. Also, this workshop is of interest to everyone who works with spatial data. The simulation methods that will be presented and discussed in the workshop should find a wide application across the community by producing benchmark datasets that can be parameterized and scaled. Simulated data sets will be made available to the community via the website.

The workshop seeks high-quality full (8-10 pages) and short (up to 4 pages) papers that will be peer-reviewed. Once accepted, at least one author is required to register for the workshop and the ACM SIGSPATIAL conference, as well as attend the workshop to present the accepted work which will then appear in the ACM Digital Library.

GeoSim 2025 solicits novel and previously unpublished research on all topics related to geospatial simulation including, but not limited to:

• Applications for Spatial Simulation
• Agent Based Models for Spatial Simulation
• Multi-Agent Based Spatial Simulation
• Big Spatial Data Simulation
• Disease Spread Simulation
• Spatial Data/Trajectory Generators
• Road Traffic Simulation
• Environmental Simulation
• GIS using Spatial Simulation
• Interactive Spatial Simulation
• Spatial Simulation Parallelization and Distribution
• Geo-Social Simulation and Data Generators
• Social Unrest and Riot Prediction using Simulation
• Spatial Analysis based on Simulation
• Behavioral Simulation
• Verifying and Validating Spatial Simulations
• Urban Simulation
• Digital Twin Simulation
• Synthetic Population Simulation

Important Date

Submission deadline

August 15, 2025

Notification

September 15, 2025

Workshop date

November 3, 2025

Submission site

https://easychair.org/conferences/?conf=geosim2025

Workshop website

https://geosim.org/

Paper Format

Manuscripts should be submitted in PDF format and formatted using the ACM camera-ready templates available at https://www.acm.org/publications/proceedings-template. For latex users, please make sure that you choose sigconf type (e.g., \documentclass[sigconf]{acmart}) in your latex file. Submissions to GeoSim are single-blind – i.e., the names and affiliations of the authors should be listed in the submitted version.

Contact

Please contact me or geosim2025@easychair.org if you have questions about GeoSim 2025.

Call For Papers: ACM TSAS Special Issue on Geosimulation (Deadline Extended)

Call For Papers

ACM Transactions on Spatial Algorithms and Systems (TSAS)

Special Issue on Geosimulation

Guest Editors

• Joon-Seok Kim, Emory University
• Andreas Züfle, Emory University
• Yao-Yi Chiang, University of Minnesota

Simulating past, present, and future events and phenomena in geospatial worlds empowers us to understand the fundamental composition and evolution of complex systems, forecast the course of complex dynamics, and prepare for numerous scenarios. Across the globe, researchers seek to model and simulate complex systems and phenomena such as cities, economies, civilizations, the spread of epidemics, transportation, urbanization, and migration to address global challenges, including the climate crisis, pandemics, international conflicts, deforestation, and sustainability.

Spatial algorithms and systems play a pivotal role in geosimulation, leveraging the community’s expertise in managing, modeling, querying, and mining spatial and spatiotemporal data. To date, the spatial algorithms and systems community has made a wide range of contributions to geosimulation, including acquiring and making available datasets to understand human mobility; improving our understanding of the spread of epidemics through geosimulation; enhancing land cover change prediction through simulation; and improving our understanding of human behavior through geosimulation. However, a wide range of questions and challenges remain unanswered, including gaining a deeper understanding of the relationship between human mobility and the spread of infectious diseases, improving spread prediction, and testing and implementing mitigative measures.

This special issue intends to bring together transdisciplinary researchers and practitioners from multiple areas, including Spatial Data Scientists (e.g., geographic information systems, databases, storage, big data, data mining, machine learning, security/privacy), Mathematicians, Epidemiologists, Computational Social Scientists, Psychologists, and Emergency Response and Public Safety experts, among others.

Topics of interest include (but are not limited to):

• Geosimulation Models and Applications
• Open Source General-Purpose Geosimulation Framework
• Simulated Geospatial Data Generation Framework
• Generative AI for Geosimulation
• Simulation for Geospatial Foundation Models
• AI and Geosimulation
• Spatial Data Science and Geosimulation
• Large-Scale Geosimulation
• Geosimulation and Intervention
• Geosimulation for Epidemiology
• Validation and Verification for Geosimulation
• Visual Analytics for Geosimulation
• Digital Twin Geosimulation

Manuscripts will be reviewed as they are received, but no later than the date shown below.

Important Dates

• Deadline for submissions of full-length papers: October 31, 2025
• First-round review decisions: December 15, 2025
• Deadline for revision submissions: February 1, 2026
• Notification of final decisions: March 15, 2026
• Tentative publication: May 2026

Submission Information

The journal welcomes articles on any of the above topics or closely related disciplines in the context of geosimulation. TSAS will encourage original submissions that have not been published or submitted in any form elsewhere, as well as submissions that may significantly contribute to opening up new and potentially important areas of research and development. TSAS will also publish outstanding papers that are "major value-added extensions" of papers previously published at conferences. Such extensions should contribute at least 30% new original work. In this case, authors will need to identify the list of extensions over their previously published paper in a separate document. For more information, please visit https://tsas.acm.org/authors.cfm or contact the special-issue guest-editors at tsas-geosimulation@acm.org.

Authors of papers accepted in this special issue by the end of September 2025 that are not an extension of a previous conference paper, will be offered to present their paper at ACM SIGSPATIAL 2025. This will be an oral presentation (not a poster).

PhD Student Recruitment: AI/ML, Data Science, and Simulation (Computer Science and Information PhD Program)

I am excited to invite highly motivated PhD students to join my research group, AI and Simulation Lab, at Emory University in the Computer Science and Information PhD program! If you are passionate about Artificial Intelligence (AI), Machine Learning (ML), Data Science, and Modeling & Simulation, and want to contribute to transformative research, this is the opportunity for you!

My interdisciplinary research group at Emory University is focused on advancing AI/ML techniques, developing data-driven solutions, and creating novel simulation models to tackle complex challenges in various fields, including healthcare, public health, urban planning, transportation, environmental science, and national security.

Research Areas Include:

• Artificial Intelligence & Machine Learning: Developing new algorithms, enhancing model performance, and applying AI to real-world applications.
• Data Science & Big Data Analytics: Innovating methods for large-scale data processing, analytics, and visualization to derive actionable insights.
• Modeling & Simulation: Creating advanced computational models to simulate and understand complex systems in diverse domains.
• Applied Computational Techniques: Bridging theory and application to solve practical problems using state-of-the-art computational methods.

What We’re Looking For:

• Academic Background: A strong foundation in Computer Science, Mathematics, Data Science, or a related field.
• Programming Proficiency: Experience with languages like Python, Java, C++, or R, and familiarity with machine learning frameworks such as PyTorch, or big data tools like Spark.
• Research Motivation: A keen interest in applying AI, ML, and simulation techniques to solve impactful real-world problems.
• Collaboration Skills: Ability to work effectively in an interdisciplinary team and engage in collaborative research efforts.
• Problem-Solving Mindset: Enthusiasm for exploring challenging research questions and developing novel solutions that have both theoretical and practical significance.

Why Join Us?

• Collaborative Research Environment: Work alongside experts and fellow researchers in a dynamic, interdisciplinary team.
• Cutting-Edge Resources: Access to state-of-the-art computational facilities and tools that enable high-impact research.
• Global Impact: Contribute to research with applications across healthcare, public health, autonomous systems, and more.
• Professional Development: Present your work at top-tier conferences, publish research papers, and receive mentorship to further your career in academia, national labs, or industry.

Program Details:

PhD students will enroll in the Computer Science and Information Program at Emory University, which provides a strong academic foundation in computer science with a focus on applied research in AI, ML, and Data Science. This program emphasizes interdisciplinary research and practical solutions to real-world problems.

How to Apply:

You can find information regarding PhD application at PhD Application Webpage. To apply for my research lab, please send the following documents to joonseok.kim at emory dot edu:

• Curriculum Vitae (CV): Provide a document listing relevant experiences and accomplishments, such as internships, awards and research experiences, that you believe will strengthen your application.
• Statement of Purpose: Write candidly about why you want to pursue a PhD in computer science and informatics, what kind of questions you have worked on in the past, what you intend to focus on in your studies and how you plan to use the research experience in your longer-term plan.
• Recommendation Letters: Three professionals with knowledge about your academic performance to write letters that evaluate your research potential and your teaching experience. Your application cannot be processed before we have received these letters.
• Transcripts: We will need copies of transcripts from each post-secondary institution you have attended, including your current one. They must be in English. and issued by the registrar's at your university. We do not require official transcripts (issued by the registrar's at your university) as part of the application process, only if and when you are offered and accept admission.
• TOEFL scores: For Internet-based TOEFL test, a minimum total score of 85 is required (90 recommended), and for the paper-based TOEFL test, a total score of 600 is required. For IELTS, 7 is the minimum score for admission. IETLS, Duolingo or Pearson Test of English (PTE) scores are acceptable. This requirement can be waived for applicants who have earned an undergraduate degree, or a 2+ year MS degree, in a country where English is the primary language.
• Any relevant publications (if applicable).

The application deadline for my lab is December 15, 2024.

At Emory University, we value diversity and inclusivity and encourage candidates from all backgrounds to apply.

📩 Apply Now to be part of groundbreaking research in AI, ML, Data Science, and Simulation at Emory University!

Book Chapter: GeoAI for Public Health

Our book, Handbook of Geospatial Artificial Intelligence edited by Song Gao, Yingjie Hu, and Wenwen Li, is published. It is my pleasure to contribute to the comprehensive handbook as a co-author of the chapter, GeoAI for Public Health. Special thanks to Andreas Züfle for his lead in writing the chapter.

Chapter

GeoAI for Public Health

By Andreas Züfle, Taylor Anderson, Hamdi Kavak, Dieter Pfoser, Joon-Seok Kim, Amira Roess.

Book	Handbook of Geospatial Artificial Intelligence
Edition	1st Edition
First Published	2023
Imprint	CRC Press
Pages	25
eBook ISBN	9781003308423

ABSTRACT

Infectious disease spread within the human population can be conceptualized as a complex system composed of individuals who interact and transmit viruses through spatio-temporal processes that manifest across and between scales. The complexity of this system ultimately means that the spread of infectious diseases is difficult to understand, predict, and respond to effectively. Research interest in GeoAI for public health has been fueled by the increased availability of rich data sources such as human mobility data, OpenStreetMap data, contact tracing data, symptomatic online surveys, retail and commerce data, genomics data, and more. This data availability has resulted in a wide variety of data-driven solutions for infectious disease spread prediction which show potential in enhancing our forecasting capabilities. This chapter (1) motivates the need for AI-based solutions in public health by showing the heterogeneity of human behavior related to health, (2) provides a brief survey of current state-of-the-art solutions using AI for infectious disease spread prediction, (3) describes a use-case of using large-scale human mobility data to inform AI models for the prediction of infectious disease spread in a city, and (4) provides future research directions and ideas.

20% Discount Available - enter the code AFL04 at checkout. For more information visit: www.routledge.com/9781032311661

Call for Papers: GeoSim 2023

6th ACM SIGSPATIAL International Workshop on Geospatial Simulation (GeoSim 2023)

November 13, 2023 @ Hamburg, Germany

CALL FOR PAPERS

The GeoSim 2023 workshop focuses on all aspects of geospatial simulation as a paradigm to understand, model, and predict spatial phenomena and aid decision making. New simulation methodologies and frameworks, not necessarily coming from the SIGSPATIAL community, are encouraged to participate. Also, this workshop is of interest to everyone who works with spatial data. The simulation methods that will be presented and discussed in the workshop should find a wide application across the community by producing benchmark datasets that can be parameterized and scaled.  Simulated data sets will be made available to the community via the website.

The workshop seeks high-quality full (8-10 pages) and short (up to 4 pages) papers that will be peer-reviewed. Once accepted, at least one author is required to register for the workshop and the ACM SIGSPATIAL conference, as well as attend the workshop to present the accepted work which will then appear in the ACM Digital Library.

TOPICS

We solicit novel and previously unpublished research on all topics related to geospatial simulation including, but not limited to:

• Disease Spread Simulation
• Urban Simulation
• Agent Based Models for Spatial Simulation
• Multi-Agent Based Spatial Simulation
• Big Spatial Data Simulation
• Spatial Data/Trajectory Generators
• Road Traffic Simulation
• Environmental Simulation
• GIS using Spatial Simulation
• Modeling and Simulation of COVID-19
• Interactive Spatial Simulation
• Spatial Simulation Parallelization and Distribution
• Geo-Social Simulation and Data Generators
• Social Unrest and Riot Prediction using Simulation
• Spatial Analysis based on Simulation
• Behavioral Simulation
• Verifying, and Validating Spatial Simulations
• Applications for Spatial Simulation

IMPORTANT DATES

Submission deadline:

September 15, 2023

Author notification:

October 9, 2023

Workshop date:

November 13, 2023

SUBMISSION GUIDELINE

Paper Format:

Manuscripts should be submitted in PDF format and formatted using the ACM camera-ready templates available at https://www.acm.org/publications/proceedings-template. Submissions to GeoSim 2023 are single-blind -- i.e., the names and affiliations of the authors should be listed in the submitted version.

Submission site:

https://easychair.org/conferences/?conf=geosim2023 

For more information, please visit the GeoSim website (https://geosim.org). If you have any question regarding GeoSim 2023, please contact me.

Call for Papers: ANNSIM 2023

2023 ANNUAL MODELING AND SIMULATION CONFERENCE

MAY 23 - 26, 2023
MOHAWK COLLEGE, ONTARIO, CANADA, IN-PERSON CONFERENCE

CALL FOR PAPERS

The 2023 Annual Modeling and Simulation Conference (ANNSIM’23) is the annual flagship conference sponsored by The Society for Modeling and Simulation International (SCS), which covers state-of-the-art developments in Modeling & Simulation (M&S). ANNSIM’23 invites original contributions to the theory, methodology, and practice of modeling and simulation in any scientific or engineering discipline. The following is the list of main tracks at ANNSIM’23.

• Annual Simulation Symposium (ANSS)
• Cyber Physical Systems (CPS)
• Emerging Topic –Digital Twins (ET-DT)
• Humans, Societies and Artificial Agents (HSAA)
• Machine Learning and Simulation (ML&S)
• M&S in Cybersecurity (MSC)
• M&S in Medicine (MSM)Annual Simulation Symposium (ANSS)
• Cyber Physical Systems (CPS)
• Emerging Topic –Digital Twins (ET-DT)
• Humans, Societies and Artificial Agents (HSAA)
• Machine Learning and Simulation (ML&S)
• M&S in Cybersecurity (MSC)
• M&S in Medicine (MSM)
• Theory and Foundations for Modeling and Simulation (TMS)
• Simulation of Architecture and Urban Design (SimAUD)
• Sustainability and Energy Systems (S&ES)
• Symposium on Performance Evaluation of Computer and Telecommunication Systems/Communications and Network Simulation (SPECTS/CNS)
• Simulation Education and Simulation-Based Learning (SimEd)

Authors interested in other M&S topics are encouraged to submit their papers, which will be assigned to an appropriate track by the program chair. Choose the “Let Program Chair Decide” option in the submission system.
The conference includes keynote speeches presented by technology and industry leaders, technical sessions, and tutorials for professional development. Scientists, engineers, managers, educators, and business professionals who develop or use M&S methodologies and tools are invited to participate and present original contributions.

Technical papers: Original, high-quality technical papers are solicited for peer review, presentation, and subsequent publication in the conference proceedingsif accepted. Papers are max 12 pages long with single column format. Visit the Author Kit page for more information: https://scs.org/authorskit

Important Dates

• Paper Submission: January 29th, 2023 (Extended), 2023
• Author Notification: March 7th, 2023
• Camera-Ready Paper Submission: March 20th, 2023
• Conference: May 23-26, 2023

Machine Learning and Simulation (MLS)

Chair: Joon-Seok Kim, Co-Chair: Andreas Züfle

CALL FOR PAPERS

Modeling and simulation (M&S) have advanced our understanding of complex systems, have improved our ability to predict future states, and have been leveraged to prescribe (near-)optimal interventions and policies in many different application areas. In the enterprise of exploring such complex systems, artificial intelligence and machine learning (AI/ML) have the potential to improve our ability to optimize simulation parameters, to train agent/actor behavior throughout simulations, and to evolve the simulation environment based on observations. The vision of this track is to push the boundaries of human understanding by marrying M&S with AI/ML.
This Machine Learning and Simulation (MLS) track is a dedicated forum to exchange our views, ideas, research methods, and applications to resolve the scientific questions of what and how cutting-edge AI/ML methods can synergize M&S and vice versa. This track explores M&S practices to which AI/ML methods are applied such as knowledge reasoning, computer vision, natural language processing, deep learning, and reinforcement learning.
In this track, we are seeking papers on original contributions across all modes of M&S leveraging AI/ML and interdisciplinary contributions that advance the state of the arts in AI/ML. This track will use the traditional format of having oral paper presentations. Topics of interest include, but not limited to:

• Advancing validation and verification (V&V) using AI and ML
• AI and ML methods for training and evolving agents
• Best practices of convergence of AI and Simulation
• Empirical comparisons of state-of-the-art AI and ML methods in M&S
• Facilitating experimentation using AI and ML
• M&S to improve AI and ML solutions
• Simulation modeling tools and methods based on AI and ML
• Simulation optimization using AI and ML
• Visionary methodology of AI and ML in simulation

Please visit the official web page (https://scs.org/annsim/) for full details on the conference and the submission procedure.
If you have any question about the Machine Learning and Simulation (MLS) track, please contact me.