Wednesday, August 25, 2021

Call for Papers: SpatialEpi 2021

2nd ACM SIGSPATIAL International Workshop on Spatial Computing for Epidemiology (SpatialEpi 2021)

Call For Papers (PDF version)

The workshop seeks high-quality full (8-10 pages) and short (4 pages) papers that have not been published in other academic outlets and are not concurrently under peer review. 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.

  • Contact Tracing 
  • COVID-19 Data Cleaning and Wrangling 
  • COVID-19 Data Mining 
  • COVID-19 Data Query Processing 
  • COVID-19 Effects on Human Mobility 
  • COVID-19 Hotspot Detection 
  • COVID-19 Simulation and Modeling 
  • COVID-19 and Social Media Spatially 
  • COVID-19 Tracking and Data Collection
  • Disease Spread Simulation
  • Managing Uncertainty in COVID-19 Data
  • Mapping and Visual Analytics of COVID-19
  • Prescriptive Analytics for COVID-19
  • Socioeconomic Impact of COVID-19
  • Spatial Analysis of COVID-19
  • Explicit COVID-19 Prediction Models

Important Dates

Submission deadline

September 15, 2021 (anywhere on earth)

Author notification

September 27, 2021 (anywhere on earth)

Camera-ready Due

To be announced

Workshop date

November 02, 2021 (anywhere on earth)

Submission site

For more information, please visit our workshop website:

Tuesday, August 3, 2021

Call for Papers: GeoSim 2021


The GeoSim 2021 workshop 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.

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

Special Topic:

The special topic for GeoSim 2021 brings focus to digital twins from geosimulation's perspective: a variety of challenges, applications and methodology in geospatial digital twins.

Paper Format:

Manuscripts should be submitted in PDF format and formatted using the ACM camera-ready templates available at

Submission Site:

For more information, please visit our workshop website: at

Monday, December 7, 2020

Annual Modeling and Simulation Conference (ANNSIM 2021) CFP

George Mason University, Fairfax, Virginia, USA, July 19-22, 2021

The Annual Modeling and Simulation Conference (ANNSIM) is a new annual conference of the Society for Modeling and Simulation International (SCS) after merging SpringSim and SummerSim into a single conference starting in 2021. Built on strengths of SpringSim and SummerSim, ANNSIM 2021 is the flagship conference of SCS to cover state-of-the-art developments in Modeling & Simulation (M&S). We invite original contributions to the theory and practice of modeling and simulation in all scientific and engineering disciplines. The conference is organized around a set of technical tracks that include:

  • AI and Simulation (AIS)
  • Annual Simulation Symposium (ANSS)
  • Communications and Network Simulation (CNS)
  • Cyber Physical Systems (CPS)
  • Emerging Topic – Aspects of Pandemic Modeling (ET-APM)
  • High Performance Computing and Simulation (HPC)
  • Humans, Societies and Artificial Agents (HSAA)
  • M&S based Systems Engineering (MSBSE)
  • M&S for Smart Energy Systems (MSSES)
  • M&S in Cyber Security (MSCS)
  • M&S in Medicine (MSM)
  • Theory and Foundations for Modeling and Simulation (TMS)

 ANNSIM 2021 will be a hybrid conference accommodating both physical and virtual participations. The physical venue of the conference will be in George Mason University (Fairfax Campus), Fairfax, VA, USA. The conference includes keynote speeches presented by technology and industry leaders, technical sessions, as well as professional development tutorials. Scientists, engineers, managers, educators, and business professionals who develop or use M&S methodologies and tools are invited to participate and present original contributions. All accepted research papers will be included in the conference proceedings and archived in the ACM Digital Library, IEEE Xplore (approval pending), and will be indexed in DBLP Computer Science Bibliography and SCOPUS. Best papers will also be considered for a special issue of SIMULATION: Transactions of the Society for Modeling and Simulation International.

Besides technical papers, submissions of the following types of contributions and/or proposals are also welcome:

  • Tutorials (max 2 hours). A tutorial proposal includes a tutorial description (max 2 pages) to be submitted to the tutorial track. In addition, a tutorial may submit a full paper to one of the technical tracks. The full paper will go through a review process and be included in the conference proceedings if accepted.
  • Extended abstracts (max 2 pages)
  • Live demonstrations to be given during the conference

Please visit for full details on conference submission procedure.

Important Dates:

  • Tutorial proposal submission: January 22, 2021
  • Paper submission: March 1, 2021
  • Author notification: April 30, 2021
  • Camera-ready paper submission: May 17, 2021
  • Conference dates: July 19-22, 2021

AI and Simulation (AIS) Track

Chair: Joon-Seok Kim, George Mason University, USA

Co-chair: Andreas Züfle, George Mason University, USA

Aims and Scope

Modeling and simulation (M&S) have advanced our understanding of complex systems and our controls over them in different areas. In the enterprise of exploring such complex systems, artificial intelligence (AI) and machine learning (ML) are a transformative technology shaping our knowledge and facilitating navigation in unconquered, unexplored, unknown frontiers. To push the boundaries of human understanding, marrying M&S with AI and ML is ineluctable and visionary. Leveraging the best of our knowledge of AI and ML allows us to accelerate, enhance, and delineate M&S.

This AI and Simulation (AIS) track is a dedicated forum to exchange our views, ideas, research methods, and applications to resolve the scientific question of what and how cutting-edge AI and ML methods can synergize M&S and vice versa. This track explorers M&S practices to which AI and ML methods are applied such as knowledge reasoning, computer vision, natural language processing, deep learning, and reinforcement learning.

In this track, we are seeking full papers (up to 12 pages) on original contributions across all modes of M&S leveraging AI and ML and interdisciplinary contributions that advance the state of the arts in AI and 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

If you have any questions regarding the AI and Simulation (AIS) track, please feel free to contact me.

Saturday, November 7, 2020

Data-Driven Mobility Models for COVID-19 Simulation

John Pesavento, Andy Chen, Rayan Yu, Joon-Seok Kim, Hamdi Kavak, Taylor Anderson, Andreas Züfle


Agent-based models (ABM) play a prominent role in guiding critical decision-making and supporting the development of effective policies for better urban resilience and response to the COVID-19 pandemic. However, many ABMs lack realistic representations of human mobility, a key process that leads to physical interaction and subsequent spread of disease. Therefore, we propose the application of Latent Dirichlet Allocation (LDA), a topic modeling technique, to foot-traffic data to develop a realistic model of human mobility in an ABM that simulates the spread of COVID-19. In our novel approach, LDA treats POIs as “words” and agent home census block groups (CBGs) as “documents” to extract “topics” of POIs that frequently appear together in CBG visits. These topics allow us to simulate agent mobility based on the LDA topic distribution of their home CBG. We compare the LDA based mobility model with competitor approaches including a naive mobility model that assumes visits to POIs are random. We find that the naive mobility model is unable to facilitate the spread of COVID-19 at all. Using the LDA informed mobility model, we simulate the spread of COVID-19 and test the effect of changes to the number of topics, various parameters, and public health interventions. By examining the simulated number of cases over time, we find that the number of topics does indeed impact disease spread dynamics, but only in terms of the outbreak's timing. Further analysis of simulation results is needed to better understand the impact of topics on simulated COVID-19 spread. This study contributes to strengthening human mobility representations in ABMs of disease spread.

Three promising high school students, John Pesavento, Andy Chen, and Rayan Yu, presented our recent research "Data-Driven Mobility Models for COVID-19 Simulation" at the 3rd ACM SIGSPATIAL Workshop on Advances in Resilient and Intelligent Cities (ARIC 2020)! I am a witness to their hard work during Aspiring Scientists Summer Internship Program (ASSIP). These dilligent students were mentored by Dr. Andreas Züfle and Dr. Hamdi Kavak and co-mentored by Dr. Anderson Taylor and myself. The presentation at ARIC 2020 was very clear. Well done!

You can check out interactive heatmaps and more information in John Peasavento's GitHub Page. This research is supported by National Science Foundation and by the Aspiring Scientists Summer Internship Program (ASSIP) at George Mason University.

J. Pesavento, A. Chen, R. Yu, J.-S. Kim, H. Kavak, T. Anderson, and A. Züfle, “Data Driven Mobility Models for COVID-19 Simulation,” In Proceedings of the 3rd ACM SIGSPATIAL Workshop on Advances in Resilient and Intelligent Cities (ARIC 2020), November 2020, pp. 29-38 

Tuesday, November 3, 2020

COVID-19 Ensemble Models Using Representative Clustering

Joon-Seok Kim, Hamdi Kavak, Andreas Züfle, Taylor Anderson


In response to the COVID-19 pandemic, there have been various attempts to develop realistic models to both predict the spread of the disease and evaluate policy measures aimed at mitigation. Different models that operate under different parameters and assumptions produce radically different predictions, creating confusion among policy-makers and the general population and limiting the usefulness of the models. This newsletter article proposes a novel ensemble modeling approach that uses representative clustering to identify where existing model predictions of COVID-19 spread agree and unify these predictions into a smaller set of predictions. The proposed ensemble prediction approach is composed of the following stages: (1) the selection of the ensemble components, (2) the imputation of missing predictions for each component, and (3) representative clustering in application to time-series data to determine the degree of agreement between simulation predictions. The results of the proposed approach will produce a set of ensemble model predictions that identify where simulation results converge so that policy-makers and the general public are informed with more comprehensive predictions and the uncertainty among them. 

Courtesy of NSFwe shared our vision at the 1st ACM SIGSPATIAL International Workshop on Modeling and Understanding the Spread of COVID-19 presented by Dr. Hamdi Kavak. 

J-S. Kim, H. Kavak, A. Züfle, T. Anderson, “COVID-19 Ensemble Models Using Representative Clustering,” SIGSPATIAL Special, July 2020, Volume 12, Issue 2, pp 33-41

Saturday, October 3, 2020

Vehicle Relocation for Ride-Hailing

Joon-Seok Kim, Dieter Pfoser, Andreas Züfle


Ever increasing traffic and consequential congestion wastes fuel and is a significant contributor to Green House Gas (GHG) emissions. Contributors here include ride-sharing services such as Uber, Lyft, and Didi, with their drivers not only transporting passengers, but also spending a considerable time in traffic searching for new ones. To mitigate their impact, this work proposes a novel algorithm to improve the efficiency the drivers' search for passengers. Our algorithm directs unassigned drivers to locations where new passengers are expected to emerge. We use a non-negative matrix factorization approach to model the time and location of passengers given historical training data. A probabilistic search strategy then guides drivers to nearby locations for which we predict new passengers. To ensure that drivers do not over subscribe to such areas, we randomize destinations and provide each driver with a home location destination when unassigned. An experimental evaluation using real-world data from Manhattan shows that our approach actually reduces the search time of drivers and the wait time of passengers compared to baseline solutions.

Please check out the video that I presented at the 7th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2020)!

Source code:
Additional materials:

J.-S. Kim, D. Pfoser, and A. Züfle, “Vehicle Relocation for Ride-Hailing,” In Proceedings of 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), 2020, doi: 10.1109/DSAA49011.2020.00074

Wednesday, September 2, 2020

Won the Challenge on Mobility Intervention for Epidemics

The challenge was designed for participants to compete each others with their own mobility intervention strategies to minimize scores defined by the organizers. Four mobility interventions are allowed as follows: 
  • To confine individuals in their neighborhood.
  • To quarantine individuals in their home.
  • To isolate individuals from others.
  • To hospitalize infected individuals.

It is important to understand that each intervention has different cost and efficiency. The score is the weighted sum of two exponential functions consisting of two dimensions: the number of infections and the number of interventions. It can be seen as an optimization problem with two opposite objectives. If the number of infections increases, the score increase exponentially. Also, if we intervene more and more, the score increases exponentially as well. That is, it is required to find a balanced strategy. The following video is my presentation introducing our solution at the workshop.

In the challenge, our solution is second ranked among all participants compliant with the challenge documents. The first two teams used the depreciated API that provides presymptomatic information and does not require contact tracing.

Due to complexity of social phenomena, it is a big challenge to predict the curves of epidemics that spread via social contacts and to control such epidemics. Misguided policies to mitigate epidemics may result in catastrophic consequences such as financial crisis, massive unemployment, and the surge of the number of critically ill patients exceeding the capacity of hospitals. In particular, under/overestimation of efficacy of interventions can mislead policymakers about perception of evolving situations. To avoid such pitfalls, we propose Expert-in-the-Loop (EITL) prescriptive analytics using mobility intervention for epidemics. Rather than employing a purely data-driven approach, the key advantage of our approach is to leverage experts' best knowledge in estimating disease spreading and the efficacy of interventions which allows us to efficiently narrow down factors and the scope of combinatorial possible worlds. We introduce our experience to develop Expert-in-the-Loop simulations during the Challenge on Mobility Intervention for Epidemics. We demonstrate that misconceptions about the causality can be corrected in the iterations of consulting with experts, developing simulations, and experimentation.

J.-S. Kim, H. Jin, and A. Züfle, “Expert-in-the-Loop Prescriptive Analytics using Mobility Intervention for Epidemics,” 1st ACM SIGKDD International Workshop on Prescriptive Analytics for the Physical World (PAPW 2020), August 2020