Complex systems, such as social networks, the brain, financial systems, and infrastructure networks are composed of a large number of entities in interaction and exhibiting emerging phenomena. The development of sensing devices collecting data about those entities and microsimulations using agent-based modelling techniques are then often crucial to model, analyze and simulate such complex systems.
After his PhD in Applied Mathematics at the University of Namur (Belgium), Dr. Johan Barthélemy joined the SMART Infrastructure Facility of the University of Wollongong (Australia) where he is a Research Fellow. He is leading the SMART IoT Hub and the Digital Living Lab developing sensors and edge computing devices for IoT applications using LPWAN networks including connected beer kegs, smart cameras and water level monitoring and low cost gas sensing.
In addition he also works on the development of new tools and frameworks to simulate large scale agent-based microsimulation. This include i) the design of synthetic population generators to build a disagregated database of the system’s entities; and ii) the development of microsimulations to simulate and observe the agents’ behaviour. Applications of this work include transportation modelling and disease spreading which can involve millions of agents. The research is interdisciplinary and requires methods from applied mathematics, statistics and high performance scientific programming.
Its collaboration with NIASRA also led to the development of mipfp, a widely-used statistical package for the R programming language.
Finally, as a member of the Namur Center For Complex Systems (University of Namur) he collaborates on projects related to algorithmic complexity, data analytics and agent-based modelling.
Our work on 3D printed resulted in a lot of media exposure for research at the University of Wollongong
Beneficiary
Quantification
Description
Evidence
Description
Smarter Schools for a Smarter Planet
The<strong> Smarter Schools for a Smarter Planet </strong>project aims at enabling regional high school students studying science, technology, engineering and mathematics gain skills in the construction and use of smart technologies and the Internet-of-Things to monitor water issues relevant to their school environment. The project is working with ten high schools throughout the Illawarra.<br /><br />It is a component of the Smart Waterways project which is a collaboration between Wollongong City Council, Shellharbour City Council, Kiama Municipal Council, Shoalhaven City Council, Lendlease Calderwood Valley Communities and UOW's SMART Infrastructure Facility.
<strong>Let’s build a network of sensors monitoring environmental conditions</strong><p>Over the past 50 years the Illawarra Shoalhaven had more than 30 serious floods and three extreme floods, resulting in a fatality.</p><p>Floods have devastating consequences and affect the economy, environment and local people. To help combat the issue the Federal Government awarded $478,449 to the region for the <a href="https://www.infrastructure.gov.au/cities/smart-cities/collaboration-platform/Smart-Water-Management-Project-Illawarra-Shoalhaven.aspx" target="_blank" rel="noopener">Smart Waterways project</a>.</p><p>The project use smart technology via the installation of sensors to gather data to monitor and provide information about the region’s waterways.</p><p>There are several project components including <a href="http://digitallivinglab.uow.edu.au/" target="_blank" rel="noopener">Smarter Schools for a Smarter Planet</a>, which is aimed at enabling regional high school students studying science, technology, engineering and mathematics gain skills in the construction and use of smart technologies to monitor water issues relevant to their school environment.<br /><br /><strong>Working with the high schools students and teachers</strong><br /><br />UOW researchers engaged with ten high schools in the Illawarra and conducted workshops to explain to the student what is the Internet-of-Things (IoT), how they can start building sensors and their use in real-world applications. The workshop also introduced the concepts of open data and open software to the students. Each student were given the IoT development kit used during the workshop.<br /><br />The project also aimed at helping the high school teachers to develop their own IoT sessions through dedicated workshops. The project team then provide both the necessary equipment and technical support to the teachers and their schools.<br /><strong><br />Open data</strong><br /><br />The environmental data collected by the sensors built by the students will be shared with the community and will be also used by the other components of the Smart Waterways project.<br /><br /><strong>A collaborative project</strong><br /><br />The Smart Waterways project is a collaboration between Wollongong City Council, Shellharbour City Council, Kiama Municipal Council, Shoalhaven City Council, Lendlease Calderwood Valley Communities and SMART Infrastructure Facility, University of Wollongong.<br /><br /></p>
A novel high-resolution model of moss-bed microclimate in maritime Antarctica: importance of understanding microclimate for understanding species distributions
Randall, Krystal
Doctor of Philosophy
Computer Vision and Machine Learning in Disaster Management and Monitoring Process
Iqbal, Umair
Doctor of Philosophy
Inferring Traffic Dynamics Using Smart Sensing and Artificial Intelligence
Ph.D.
in Applied Mathematics,
University of Namur,
Department of Mathematics,
Ph.D. Thesis: A parallelized micro-simulation platform for population and mobility behaviour - Application to Belgium 2007 - 2014
M.Sc.
in Mathematics,
University of Namur,
Department of Mathematics,
Master thesis: Contribution of a new dissimilarity measure to a graph-based segmentation algorithm 2005 - 2007
B.S.
in Applied Mathematics,
University of Namur,
Department of Mathematics 2003 - 2005
Altough my position is research only, I had the opportunity to teach 2 separates courses at the University of Namur (Belgium) as an invited lecturer in 2016 and 2017. Those courses are briefly described here under.
1. Multivariate Data Analysis and Statistical softwares [SMATM102]: The course is an introduction to data analysis. The course firstly presents the statistical methods adapted to the univariate data. Then, the various methods of multivariate statistical analysis are presented, in a theoretical and practical way: ANOVA, Contingency Table, Linear Models, Regression, Clustering, Principal Component Analysis and Factor Analysis.
2. Special Questions of Mathematics - Introduction to Data Science with Python and SQL [SMATM130]: This course aims to give tools and methods to conduct data analytics using programming languages widely used and freely available, namely Python 3 and SQL. This introduction to data science details briefly the following topics: data manipulation, data analysis with statistic and machine learning, data visualization and how to work with large data sets.
The 2 courses are part of the curriculum for the Master's Degree in Applied Mathematics. For both courses, I designed and prepared the material, delivered the courses content to the student and evaluated the learning activities. This included lectures, lab classes, project supervision, designing assessments, assesments and markings.
Keywords
Mathematical modelling and simulation; Agent-based simulation; High performance, GPU and scientific computing; Statistics and probability; Synthetic population generation; Internet-of-Things; Artificial Intelligence for edge-computing
Our work on 3D printed resulted in a lot of media exposure for research at the University of Wollongong
Beneficiary
Quantification
Description
Evidence
Description
Smarter Schools for a Smarter Planet
The<strong> Smarter Schools for a Smarter Planet </strong>project aims at enabling regional high school students studying science, technology, engineering and mathematics gain skills in the construction and use of smart technologies and the Internet-of-Things to monitor water issues relevant to their school environment. The project is working with ten high schools throughout the Illawarra.<br /><br />It is a component of the Smart Waterways project which is a collaboration between Wollongong City Council, Shellharbour City Council, Kiama Municipal Council, Shoalhaven City Council, Lendlease Calderwood Valley Communities and UOW's SMART Infrastructure Facility.
<strong>Let’s build a network of sensors monitoring environmental conditions</strong><p>Over the past 50 years the Illawarra Shoalhaven had more than 30 serious floods and three extreme floods, resulting in a fatality.</p><p>Floods have devastating consequences and affect the economy, environment and local people. To help combat the issue the Federal Government awarded $478,449 to the region for the <a href="https://www.infrastructure.gov.au/cities/smart-cities/collaboration-platform/Smart-Water-Management-Project-Illawarra-Shoalhaven.aspx" target="_blank" rel="noopener">Smart Waterways project</a>.</p><p>The project use smart technology via the installation of sensors to gather data to monitor and provide information about the region’s waterways.</p><p>There are several project components including <a href="http://digitallivinglab.uow.edu.au/" target="_blank" rel="noopener">Smarter Schools for a Smarter Planet</a>, which is aimed at enabling regional high school students studying science, technology, engineering and mathematics gain skills in the construction and use of smart technologies to monitor water issues relevant to their school environment.<br /><br /><strong>Working with the high schools students and teachers</strong><br /><br />UOW researchers engaged with ten high schools in the Illawarra and conducted workshops to explain to the student what is the Internet-of-Things (IoT), how they can start building sensors and their use in real-world applications. The workshop also introduced the concepts of open data and open software to the students. Each student were given the IoT development kit used during the workshop.<br /><br />The project also aimed at helping the high school teachers to develop their own IoT sessions through dedicated workshops. The project team then provide both the necessary equipment and technical support to the teachers and their schools.<br /><strong><br />Open data</strong><br /><br />The environmental data collected by the sensors built by the students will be shared with the community and will be also used by the other components of the Smart Waterways project.<br /><br /><strong>A collaborative project</strong><br /><br />The Smart Waterways project is a collaboration between Wollongong City Council, Shellharbour City Council, Kiama Municipal Council, Shoalhaven City Council, Lendlease Calderwood Valley Communities and SMART Infrastructure Facility, University of Wollongong.<br /><br /></p>
A novel high-resolution model of moss-bed microclimate in maritime Antarctica: importance of understanding microclimate for understanding species distributions
Randall, Krystal
Doctor of Philosophy
Computer Vision and Machine Learning in Disaster Management and Monitoring Process
Iqbal, Umair
Doctor of Philosophy
Inferring Traffic Dynamics Using Smart Sensing and Artificial Intelligence
Ph.D.
in Applied Mathematics,
University of Namur,
Department of Mathematics,
Ph.D. Thesis: A parallelized micro-simulation platform for population and mobility behaviour - Application to Belgium 2007 - 2014
M.Sc.
in Mathematics,
University of Namur,
Department of Mathematics,
Master thesis: Contribution of a new dissimilarity measure to a graph-based segmentation algorithm 2005 - 2007
B.S.
in Applied Mathematics,
University of Namur,
Department of Mathematics 2003 - 2005
Altough my position is research only, I had the opportunity to teach 2 separates courses at the University of Namur (Belgium) as an invited lecturer in 2016 and 2017. Those courses are briefly described here under.
1. Multivariate Data Analysis and Statistical softwares [SMATM102]: The course is an introduction to data analysis. The course firstly presents the statistical methods adapted to the univariate data. Then, the various methods of multivariate statistical analysis are presented, in a theoretical and practical way: ANOVA, Contingency Table, Linear Models, Regression, Clustering, Principal Component Analysis and Factor Analysis.
2. Special Questions of Mathematics - Introduction to Data Science with Python and SQL [SMATM130]: This course aims to give tools and methods to conduct data analytics using programming languages widely used and freely available, namely Python 3 and SQL. This introduction to data science details briefly the following topics: data manipulation, data analysis with statistic and machine learning, data visualization and how to work with large data sets.
The 2 courses are part of the curriculum for the Master's Degree in Applied Mathematics. For both courses, I designed and prepared the material, delivered the courses content to the student and evaluated the learning activities. This included lectures, lab classes, project supervision, designing assessments, assesments and markings.
Keywords
Mathematical modelling and simulation; Agent-based simulation; High performance, GPU and scientific computing; Statistics and probability; Synthetic population generation; Internet-of-Things; Artificial Intelligence for edge-computing
