Wednesday, December 13, 2017

Come Work with Us: 2 Postdocs in Urban Simulation

The George Mason University Department of Geography and Geoinformation Science within the College of Science, has an immediate opening for two postdoctoral fellows (up to 2-years), subject to budgetary approval. These positions will be part of the “Urban simulation” project team conducting research as part of the DARPA’s “Ground Truth” program, a network of DARPA-funded teams across the USA. The GMU team is directed by Andreas Z├╝fle, Dieter Pfoser, and Andrew Crooks and supported by Carola Wenk at Tulane University. George Mason University has a strong institutional commitment to the achievement of excellence and diversity among its faculty and staff, and strongly encourages candidates to apply who will enrich Mason’s academic and culturally inclusive environment.

Postdoc 1

Responsibilities:
The primary job responsibilities of this position consist of the design, development and refinement of an agent-based simulation framework for urban areas. Using the existing Multiagent Simulation Toolkit (MASON) platform (written in Java), new agent logic will have to be implemented, thus creating agents that use socially plausible rules for mobility and interaction with other agents. A main goal is to create computationally efficient agent logic, thus allowing millions of agents to make decisions, find shortest paths between locations, and interact with their simulated world at the same time. For this purpose, implemented algorithms will need to be highly parallelizable, thus allowing to scale simulation via distribution among computing clusters located at GMU and Tulane. The successful candidate will also supervise graduate-level research assistants, collaborate with fellow scholars, and promote the department’s accomplishments through publications, presentations, and other public events.

Required Qualifications:
  • Ph.D. in computer science, data science, or closely related field;
  • Strong programming skills in Java;
  • Excellent written communication skills demonstrated by prior publications;
  • A track record that demonstrates the ability to work well with interdisciplinary research teams.

Preferred Qualifications:
  • Solid knowledge of graph algorithms;
  • Experience with Agent-Based Modeling and social science simulation;
  • Experience in design and implementation of software systems.

Postdoc 2

Responsibilities:
The primary job responsibilities of this position will be the design of an agent-based model based on the first principles underlying human needs, social interactions, and mobility to define socially plausible causalities. This model will contribute towards the design, development and refinement of an agent-based simulation framework for urban areas. Using the existing Multiagent Simulation Toolkit (MASON) platform (written in Java), new agent logic will have to be implemented, thus creating agents that use socially plausible rules for mobility and interaction with other agents. The successful candidate will also supervise graduate-level research assistants, collaborate with fellow scholars, and promote the department’s accomplishments through publications, presentations, and other public events.

Required Qualifications:
  • Ph.D. in computer science, data science, or closely related field; 
  • Experience with Agent-Based Modeling and social science simulation; 
  • Excellent written communication skills demonstrated by prior publications; 
  • A track record that demonstrates the ability to work well with interdisciplinary research teams.

Preferred Qualifications:
  • Strong programming skills in Java;
More Information: https://jobs.gmu.edu/postings/42109

Monday, December 11, 2017

Come work with us

The  Department of Computational and Data Sciences is currently looking for 2 Assistant Professors starting in the Fall of 2018.

Assistant Professor, Statistics and Visualization (Tenure-Track)

Required Qualifications:
Applicants must hold a Ph.D. in a closely related field from an accredited institution, and should have research interests congruent with the associated research centers listed below. Applicants should have a promising research record with a deep knowledge of and interest in computation as applied to statistical analysis and visualization, with a strong emphasis in one or more of the following fields: machine learning, Bayesian inference, and knowledge mining. Candidates should have a demonstrated ability or strong potential to attract funding and provide strong contributions to the continued growth of the academic programs.

More Information: https://jobs.gmu.edu/postings/41997


Assistant Professor, Data Science (Tenure-Track )

Required Qualifications:
Applicants must hold a Ph.D. in data science from an accredited institution or an aptly related field such as computer science, computational science, a physical science, etc., and should have research interests congruent with the associated research centers listed below. Candidates should have a demonstrated ability or strong potential to attract funding and provide strong contributions to the continued growth of the academic programs. Applicants should have a promising research record and a demonstrated deep knowledge in the expanding field of data science, with a strong emphasis in one or more of the following fields: data mining, knowledge mining, big data analytics, data engineering, and image analysis.

More Information: https://jobs.gmu.edu/postings/42005

About the Department: CDS is a rapidly growing department offering an undergraduate degree in Computational and Data Sciences and graduate programs in CSI (Computational Sciences and Informatics) and CSS (Computational Social Science). Interdisciplinary research directions are in modeling, simulation, data science, and computational social science. The successful candidate will benefit from a highly collaborative research environment that supports a wide array of shared facilities and dynamic research centers, such as the DataLab, the university-wide Center for Social Complexity, and the Department’s Center for Simulation and Modeling.


Friday, November 17, 2017

New Paper: Social Media and Cancer Campaigns

Continuing our work on geosocial analysis we recently had a paper entitled "Social Media Engagement with Cancer Awareness Campaigns Declined During the 2016 U.S. Presidential Election" published in  World Medical and Health Policy. In the paper we show through the analysis of Twitter and Google Trends, how public engagement with breast cancer and prostate cancer awareness months between 2015 and 2016 changed. Specifically we found that attention to breast cancer and prostate cancer declined in 2016 (during the U.S. presidential election), when compared to 2015. Based on our finding we suggest that future cancer education campaigns—and campaigns for other health issues and policies—would benefit from monitoring the broader issues producing social media engagement, and adjusting their timing or communication strategies to ensure that public engagement with their key messages remains strong even in a crowded social media marketplace. Below you can read the abstract to our paper, see some of our key findings along with the full citation to the paper.

Abstract:
Cancer awareness campaigns compete with other health and social issues for public attention. We examined whether public engagement with breast cancer and prostate cancer declined in 2016 during the U.S. presidential election compared to 2015 on Twitter and Google Trends. We found that attention to breast cancer and prostate cancer declined in 2016 before the election as compared to 2015 in Twitter posts and Google searches. The findings suggest that cancer information seeking behavior, passive exposure to health communication, and active participation in social media about cancer all decreased during the peak weeks of the 2016 election season. Future health promotion initiatives and information dissemination efforts will benefit from monitoring the major issues garnering social media attention and then adjusting their timing or communication strategies to ensure that public engagement with their key policy messages remains strong when emerging news stories capture public interest.

Keywords: Twitter; breast cancer; prostate cancer

Twitter Traffic about Breast Cancer (a) and Prostate Cancer (b) in the Period October 2015–November 2016, and Comparison of October and November Traffic in 2015 and 2016 for Breast Cancer (c) and Prostate Cancer (d). All 2015 Data are Shown in Red and All 2016 Data are Shown in Blue.


Full Reference:
Vraga, E. K., Radzikowski, J., Stefanidis, A., Croitoru, A., Crooks, A.T., Delamater, P., Pfoser, D. and Jacobsen, K. H. (2017). Social Media Engagement with Cancer Awareness Campaigns Declined During the 2016 U.S. Presidential Election. World Medical and Health Policy, 9(4): 456–465. (pdf)

Saturday, November 04, 2017

Call For Papers: Spatial ABMs: Current Practices & Future Trends

Special Issue theme: Spatial Agent-Based Models: Current Practices and Future Trends

Editors: Alison Heppenstall (Leeds) and Andrew Crooks (GMU)

Journal: GeoInformatica

Impact rating: 2.3

Overview

Over the last decade the agent-based modeling (ABM) paradigm has provided a new lens for understanding the effects of interactions of individuals and how through such interactions macro structures emerge, both in the social and physical environment of spatial systems. However, such a paradigm has been hindered due to computational power and a lack of large fine scale datasets. Within the last few years we have witnessed a massive increase in computational processing power and storage, combined with the onset of Big Data. Today geographers find themselves in a data rich era. We now have access to a variety of data sources (e.g., social media, mobile phone data, etc.) that tells us how, and when, individuals are using spaces. These data raise several questions: can we effectively use them to understand and model spatial systems as complex entities? How well have ABM approaches lent themselves to simulating the dynamics of spatial processes? What has been, or will be, the influence of Big Data on increasing our ability to understand and simulate spatial systems? What is the appropriate level of spatial analysis and time frame to model spatial phenomena? This special issue will concentrate on the best of current practice and future trends. We are interested in papers that will introduce the reader to:
  • Applications: Well-developed and transparent applications; 
  • Methodological innovations: use of ‘big data’; machine learning methods; calibration and validation methods within agent-based models; 
  • Thought pieces: What is the future of ABM? What do ABMs need to achieve to become as accepted similar to methods from climate change? How have and can agent-based models be used for policy making? 

Indicative deadlines 
  • Abstract (250 words): December 8, 2017 
  • Full Paper: April 30, 2018 
PLEAS E NOTE: 

Abstracts must first be submitted directly to the guest editors via email:

Further submission will then be invited based on the content assessed in the abstract. 

Full papers need to be between 5000 – 7000 words in length. Details of the journal submission requirements can be found at : http://www-users.cs.umn.edu/~shekhar/service/geoinformatica/guidelines2.doc

Tuesday, October 31, 2017

Happy Halloween....

As today is Halloween, I thought I would write a brief post on zombies and how they can be used to demonstrate disease models (even the Centers for Disease Control and Prevention (CDC) has a post about Zombies preparedness). There are several good examples of using zombie outbreaks as demonstrations for the utility of modeling (or just showing how modeling concepts can be applied to the spread of zombies). 

These range from exploring  the spatial and temporal dynamics of a zombie epidemic (e.g. Sander and Topaz, 2014). To that of the work of Alemi et al. (2015), who produced a "danger map" of what would happen if the continental United States  was overrun with zombies (an interactive version is available here and shown below). In their paper, they demonstrate how epidemiological processes akin to a Susceptible-Infected-Recovered (SIR) model (which we have wrote about before) can be used to model the spread of zombies. As its a zombie model, the SIR changes to a SZR model (Munz et al., 2009), where:
 "S represents the susceptible population, the uninfected humans, Z represents the infected state, zombies, and R represents our removed state, in  this  case  zombies  that  have  been  terminated  by  humans (canonically  by  destroying  their  brain  so  as  to  render  them inoperable)." (Alemi et al., 2015)
Zombie Town USA

Below you can see an attempt of modeling a zombie outbreak (only the SI parts) in one of the buildings on the George Mason Fairfax campus utilizing NetLogo (you can download the model code from here).



More complex individual based models have also been created like the one shown below by Horio and Arrowsmith (2015) which was used to showcase how zombies can be used to describe complex adaptive systems and agent-based modeling.

https://www.informs.org/ORMS-Today/Public-Articles/October-Volume-42-Number-5/The-Pedagogy-of-Zombies


If readers know of any over good Halloween (horror) like models, please let us know.

References:
Alemi, A.A., Bierbaum, M., Myers, C.R. and Sethna, J.P. (2015), 'You Can Run, You Can Hide: The Epidemiology and Statistical Mechanics of Zombies', Physical Review E, 92(5): 052801.
Horio, B. and Arrowsmith, N. (2015), 'The Pedagogy of Zombies', OR/MS Today, 42(5).
Munz, P., Hudea, I., Imad, J. and Smith, R.J. (2009), 'When Zombies Attack!: Mathematical Modelling of an Outbreak of Zombie Infection', in Tchuenche, J.M. and Chiyaka, C. (eds.), Infectious Disease Modelling Research Progress, Nova Science Publishers, Hauppauge, NY, pp. 133-150.
Sander, E. and Topaz, C.M. (2014), 'The Zombie Swarm: Epidemics In The Presence of Social Attraction And Repulsion', in Smith, R. (ed.) Mathematical Modelling of Zombies, University of Ottawa Press, Ottawa, Canada, pp. 265-300.