Teaching Program Short Course Package

SMART Infrastructure Facility

Table of Contents —SMART Teaching Mission ................................................................................................ 3

SMART Teaching ................................................................................................................ 4

SMART Research ............................................................................................................ 5-6

SMART Teaching Team ................................................................................................ 7-8

Short Courses ..................................................................................................................... 10

Short Course Overviews ............................................................................................. 11-17 FEIS801: Big Data Analytics with Application .................................................................... 11 FEIS802: Infrastructure System of Systems Engineering ............................................. 12 FEIS803: Introduction to Participatory Modelling ........................................................... 13 FEIS804: Introduction to Internet of Things ....................................................................... 14 FEIS805: Computational Methods in Supply Chain and Logistics ............................. 15 FEIS806: Urban Transport Planning for the Digital Age ............................................... 16 FEIS807: Introduction to Agent-Based Modelling of Urban Systems ........................ 17

SMART Teaching Mission —

VISION

To be internationally recognised as a leading provider of research and learning for smart infrastructure solutions.

OUR PRIORITY

• Sharing comprehensive project experiences embedded into course content• Market-driven professional development courses (PDCs)• School-focused discovery programs - Science, Technology, Engineering and Maths (STEM).

OUR STRATEGY

Over the next five years, SMART will progressively develop its comprehensive educational portfolio of academic teaching and PDCs. SMART will focus on PDCs aimed at three types of audiences:• Introductory courses Introducing concepts, methods or tools to relevant students or professionals• Advanced courses Practical tutorials on specific methods or tools for already qualified students or professionals• Master classes High level and strategic description of the benefits of a new approach or technology to executives and other interested parties.

OUR GOAL

To provide next generation training through practical project knowledge designed to increase understanding and skills through various course programs.

Established in 2011 by the University of Wollongong, the SMART Infrastructure Facility brings together experts from fields such as economics, modelling, data analytics and system engineering. This expertise is applied to various domains including energy, water, transport and cities. SMART provides a state-of-the-art facility to support this important research.

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The SMART Infrastructure Facility will be offering a range of short courses, which have been developed based on interdisciplinary collaboration in various research fields including data analytics, economics, system engineering, operation research, transport, water, energy and modelling & simulation.

The SMART teaching program is not just about teaching STEM subjects, or technical training, but about sharing comprehensive project experiences embedded into the courses, inspiring students to apply STEM tools to practical projects, and extending the capability and vision of professionals.

SMART aims to combine comprehensive and unique research and project experiences with the knowledge behind it to educate through case studies and live projects. SMART’s short courses aim to enhance understanding and application of the relevant theories and technologies.

INTERDICIPLINARY EXPERTS

SMART brings together experts from diverse fields, such as data analytics, economics, system engineering, operation research, transport, water, energy, modelling & simulation. SMART provides a state-of-the-art facility to support research and teaching. All projects and research are considered complex systems, which require experts from various domains to contribute. SMART’s short-term courses are developed by our interdisciplinary experts from different research fields.

BEYOND BOOKS

Rather than delivering book-based fundamental knowledge, SMART aims to combine comprehensive and unique research and project experiences with the techniques and knowledge of experts. Audiences will learn through case studies and real projects, which will enhance their understanding and application of the relevant theories and techniques.

DIVERSE TEACHING

A diverse range of teaching formats, including training courses, workshops and seminars will be integrated into SMART’s teaching program. SMART’s course materials are developed based on infrastructure projects with interdisciplinary expertise. They are suitable for audiences with different backgrounds, including but not limited to economics, data science, computer engineering, information technology, operation research, transport, urban planning, environmental engineering, water, energy, modeling and simulation.

COLLABORATION

SMART’s teaching program is developed through collaboration with SMART academic staff, Universtiy of Wollongong scholars and external experts from universities, research centers, industrial and governmental sectors worldwide.

SMART Teaching —

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SMART Research—The SMART Infrastructure Facility contributes to infrastructure planning in Australia through truly independent research coupled with deep academic rigour to ensure policy-makers and industry receive high quality and timely advice on major projects.

SMART’s research is spread across four key research themes each featuring two focused research groups with our Enabling Platforms supporting each of these themes. SMART’s research signifi cantly contributes to our academic and commercial impact.

The Data Analytics Lab develops novel approaches for decision-making problems, based on machine learning and optimisation methods. It also applies advanced analytics and optimisation to infrastructure systems, logistics and supply chains, healthcare, and emergency response management.

Enabling Platform Lead: Dr Rohan Wickramasuriya

The Advanced Simulation Lab aims to improve processes, methods and tools for decision making in complex and uncertain domains where stakeholders have diff ering perspectives, or no optimal solution is available.

Enabling Platform Lead: Dr Juan Castilla

The Digital Living Lab is a technology-agnostic innovation hub providing a testbed for a wide range of end-to-end Internet of Things projects.

Enabling Platform Lead: Dr Johan Barthelemy

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SMART Research—

SMART Water & Energy is dedicated to creating innovative and sustainable solutions to protect our environment from contamination, develop waste to energy pathways and secure aff ordable and reliable zero-carbon electricity supply.

The SMART Water & Energy research theme consists of two research groups;

WATER & BIORESOURCE TECHNOLOGIESResearch Group Lead - A/Prof Faisal Hai

ENERGY & RESOURCE EFFICIENCY Research Group Lead - Dr Ashish Agalgaonkar

ASSET MANAGEMENT & INFRASTRUCTURE SYSTEMS Research Group Lead - Dr Tieling Zhang

SUPPLY CHAINS & LOGISTICS Research Group Lead - Dr Tillman Boehme

SMART Systems & Logistics aims to provide in-depth analyses of intertwined supply chains and logistics in order to improve effi ciencies, as well as sophisticated modelling frameworks to represent and emulate complex asset management systems in various industries.

The SMART Systems & Logistics research theme consists of two research groups;

SMART CITIES & COMMUNITIES Research Group Lead - Dr Cole Hendrigan

FUTURE TRANSPORT & MOBILITY Research Group Lead - Dr Bo Du

SMART Cities & Transport uses a wide range of tools, like modelling, optimisation, simulation and data analytics to create the cities of tomorrow by addressing the challenges of today.

Th e SMART Cities & Transport research theme consists of two research groups;

DIGITAL HEALTH AND SMART AGED SERVICE Research Group Lead - A/Prof Ping Yu

SMARTER SCHOOLS & DIGITAL TECHNOLOGIES Research Group Lead - A/Prof Sarah Howard

SMART Health & Education draws on data, smart technologies, modelling and simulation techniques to support innovation and change in two of the fastest growing industry sectors in Australia today.

Th e SMART Health & Education research theme consists of two research groups;

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Snr Prof Pascal PerezDirector, SMART Infrastructure FacilityParticipatory Modelling of Complex Systems

Dr Hugh ForeheadResearch FellowAdaptive Communities, Environment & Web Mapping

Dr Shiva Pedram Associate Research FellowUser Experience Researcher & VR Technologies

Dr Cole HendriganResearch FellowTransport, Land Use & Urban Quality

Dr Juan CastillaAssociate Research FellowComplex Systems and Resilience & Sustainability Modelling

Dr Robert OgieAssociate Research FellowCritical Infrastructure & Disaster Modelling

Mr Joe BraniganSenior Research FellowInfrastructure Networks: Economics & Public Policy

Dr Ricardo PeculisSenior Research FellowAsset Management & Infrastructure Systems

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Dr Jun MaResearch FellowBig Data Analysis & Modelling with Applications

Dr Johan BarthelemyResearch FellowApplied Mathematics, Agent-Based Simulation & Internet of Things

Dr Bo (Bobby) DuLecturer in Transport & Course CoordinatorTransport & Urban Modelling

Dr Mehrdad AmirghasemiAssociate Research FellowComputational Optomisation and Operations Research

Grace KennedyAssociate Research FellowOrganisational & Model Based Systems Engineering

Dr Fariba RamezaniAssociate Research FellowEnergy, Transport & Regional Economics

SMART Academic Team —

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A/Prof Rodney ClarkeAssociate Professor,

University of Wollongong

Prof Peter CampbellHonorary Fellow

SUBJECT MATTER EXPERTS

Prof Graham HarrisHonorary Fellow

Prof Andrew McCuskerHonorary Fellow

Dr George Grozev Honorary Fellow

Prof Amal S. Kumarage Honorary Professorial

Fellow

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Dr Nicolas VerstaevelAssociate Research FellowArtificial Intelligence, Internet of Things & Smart Cities

Dr William ScottResearch EngineerModel Based Systems Engineering

Dr Farid ShirvaniResearch FellowModel Based Systems Engineering

Dr Jie (Jack) YangResearch FellowBig Data Mining, Machine Learning & Online Behaviour Analysis

Dr Rohan Wickramasuriya Senior Research FellowData Analytics & Spatial Modelling

Dr Fatemeh RezaeibaghaAssociate Research FellowCryptography & Cyber Security

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Short Courses —

Subject Course Conveners

FEIS801: Big Data Analytics with ApplicationDr Jun MaDr Jie (Jack) YangDr Bo (Bobby) Du

FEIS802: Infrastructure System of Systems Engineering

Dr Ricardo PeculisDr Farid ShirvaniDr William Scott

FEIS803: Introduction to Participatory ModellingDr Juan CastillaSnr Prof Pascal PerezDr Shiva Pedram

FEIS804: Introduction to Internet of ThingsDr Nicolas VerstaevelDr Johan BarthelemySnr Prof Pascal Perez

FEIS805: Computational Methods in Supply Chain and Logistics

Dr Mehrdad AmirghasemiDr Johan BarthelemyDr Tillmann Boehme

FEIS806: Urban Transport Planning for the Digital Age

Dr Bo (Bobby) DuDr Cole HendriganDr Nicolas Verstaevel

FEIS807: Introduction to Agent-Based Modelling of Urban Systems

Dr Johan BarthelemyDr Juan CastillaDr Nicolas Verstaevel

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FEIS801: Big Data Analytics with Application

Course Overview

This introductory course on data science covers the following topics: data manipulation, data analysis with statistic and machine learning, data visualisation and how to work with large data sets. These concepts will be illustrated using programming languages often used and freely available, namely R, Python and SQL. The course presents in a practical way multivariate statistical analysis methods such as Regression, Clustering, Principal Component Analysis, Factor Analysis and ANOVA.

Course Outline

The three day course will include:

Day 1 – Data Analysis Principles, Theory and Tools• Data analysis general principle and procedure - Requirement identification and data sourcing and gap analysis - Data profiling, cleansing and modelling, visualisation - Data model implementation and database/data warehouse design• Basic statistics, data mining and machine learning overview - Univariate and multivariate statistics - Supervised and unsupervised learning techniques - Big data analysis techniques• Data analysis tools - Element of Python and R, Jupyter notebook and R notebook - Data analysis packages: sklearn, pandas, numpy, R tastviews, matlibplot, ggplot2 - Hadoop ecosystems - Deep learning

Day 2 – Basic Statistics, Data Mining and Machine Learning• Descriptive statistics, ANOVA• Feature selection and feature engineering• Classification and regression methods - Linear regression, logistic regression, multivariate regression - Decision trees, classification and regression tree, random forest, support vector machine, K-nearest - Neural networks and deep learning• Clustering methods - K-means and hierarchical clustering, Gaussian Mixture Model, DBSCAN, Latent Dirichlet allocation (LDA)• Stochastic process and time series analysis briefing• Natural language processing techniques• Model evaluation techniques - ROC, AUC, AIC, BIC, Cross-Validation, etc

Day 3 – Case Studies• Smart card data analysis• Text mining• Audio and video analysis

Course Outcomes

By the end of this course you will be able to:• Understand the principle and theory of typical statistical, data mining and machine learning techniques• Collect, transform and manage data efficiently and effectively to real-world applications• Choose and apply appropriate methods and tools for data analysis• Create impressive and meaningful visualization from data

Course Pre-Requisite

Basic knowledge of linear algebra and programming experience is required.

FEIS802: Infrastructure System of Systems Engineering

Course Overview

Infrastructure systems are socio-technical systems within an organisational environment. The presence of social and organisational aspects increases complexity and influences these systems throughout their life cycle, from conception and planning, engineering, operation, upgrades and final disposal. Infrastructure systems will be examined as ‘System of Systems’ (SoS). Various approaches for System of Systems Engineering (SoSE) will be presented and discussed. ‘Systems Thinking’, considered the most adequate approach to deal with the complexity of sociotechnical SoS, will be presented and illustrated with practical examples. Designing for Adaptability and evolution in System of Systems Engineering (DANSE) methodology will be introduced. The course will address the fundamentals of modelling and simulation considered to be of great importance for Infrastructure SoSE.

Course Outline

The three day course will include:

Day 1 – Systems and System of Systems Concepts and Principles• System of Systems (SoS): why do they matter?• Understanding systems: core concepts, principles and characteristics• Types of Systems (natural, social and man-made systems)• Closed and Open Systems• Sociotechnical and information-driven systems• Complexity, Adaptation and Complex Adaptive Systems (CAS)• Systemic structure: what is it?• SoS core concepts, principles and characteristics• SoS-ness classification: how of much a system is SoS?• Hands-on examples: Airport, Multi-Mode Urban Transport, Emergency Services

Day 2 – Engineering System of Systems• System Engineering: revisiting SE approach and processes• SoS Engineering: what is the difference?• Systems complexity: how to deal with it?• Systems Thinking: how does it help? Methodologies and their practical use• SoS lifecycle according to SoSE• Architecting and managing SoS• SoS specification, design, verification & validation• Hands-on examples: Public Infrastructure Interdependency

Day 3 – Modelling for Engineering System of Systems• The Art & Science of Modelling• Modelling, the language to talk about systems: from rich picture to SysML• Introduction to SysML, Architecture Frameworks and UPDM• System Modelling Tools• SoS methodologies: an introduction to DANSE• Model-Based Systems Engineering for System of Systems• Urban Railway as SoS• Hands-on examples: Urban Railway Infrastructure, Resilience and System Integrity

Course Outcomes

By the end of this course you will:• Understand principles and concepts of systems and SoS, their commonalities and differences• Understand infrastructure as sociotechnical SoS and factors that drive success or failure• Apply SoS principles and concepts to analyse, specify and design Infrastructure SoS• Understand how modelling can help to deal with the complexity of Infrastructure SoS

Course Pre-Requisite

There are no pre-requisites for this course12

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FEIS803: Introduction to Participatory Modelling

Course Overview

If computer models are to be faithful representations of real-world systems, how can we possibly build them without input from the people who actually interact with and form part of systems in reality? This course introduces the use of participatory or collaborative model building to empower audiences to become architects of what would otherwise be a purely scientific modelling process occurring behind closed doors. Participatory modelling serves as the ‘glue’ for stakeholders to collectively explore the implications of their actions and decisions on social, economic and environmental outcomes of concern, particularly in those cases where responsibilities and burdens are unclear.

This introductory course will provide researchers, government and industry professionals with the basic knowledge and skills to facilitate collaborative modelling in interdisciplinary and cross-cultural settings. Attendees will gain access to a rich methodological toolbox that can help groups navigate through complex problems, engage in constructive dialogue towards common goals and identify leverage points for building sustainability and resilience in the systems they need to manage or are part of. Participants will learn how to capture the salient features of a complex system into a coherent and simple but elegant simulation model.

Course Outline

The four day course will include:

Day 1• Integrating science and democracy: An introduction• DrawToast exercise • Participatory modelling principles

Day 2• Systems Thinking 1: Systems Structure and behaviour • Mediated Modelling 1: Anatomy of the Mediated Modelling process • Mental models and cognitive biases in collaborative planning • MentalModeler: Fuzzy cognitive mapping • Formulating a conceptual model

Day 3• Systems Thinking 2: Systems and us• Mediated Modelling 2: Conducting a Mediated Modelling process • System Dynamics: Modelling with Stella • Specifying a quantitative model • Graphical user interfaces & model evaluation

Day 4• Systems Thinking 3: Creating change in systems • Nudges and choice arquitecture: Giving systems a gentle push in the right direction • The Analytic Hierarchy Process: Setting priorities in complex situations • Collaborative model exploration • Mediated Modelling 3: Strengths, weaknesses and lesson learnt Course Outcomes

By the end of this course you will be able to:• Know when and how a Participatory Modelling process should (or should not) be undertaken• Learn how Participatory Modelling has been previously applied in various parts of the world• Understand the strengths and limitations of Participatory Modelling• Design and facilitate a Participatory Modelling process• Apply systems thinking to conceptualise and develop a simulation model of a given problem• Know what to expect and how to evaluate the results of a mediated modelling activity

Course Pre-Requisite

Basic knowledge of environmental modelling and complex systems is preferred but not a requirement.

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FEIS804: Introduction to Internet of Things

Course Overview

With more than 30 billion connected devices expected by 2020, the Internet of Things (IoT) is radically changing the technological landscape. Application opportunities are endless: home automation, healthcare, predictive maintenance, agriculture, energy management, or transportation are some of those use cases. However, IoT is more than just sensors, it’s a process ranging from remote data collection to data analytics in order to grasp the full potential of your data.

This course offers not only an introduction to the IoT covering the theoretical background and current usages, but also provides practical knowledge through hands-on tutorials and workshops. Attendees will gain expertise on the whole IoT process.

Course Outline

The three day course will include:

Day 1 – What is the Internet of Things and why should we care?• Defining the Internet of Things: History, technologies, trends and business opportunities• Impact of IoT on society• IoT networks, protocols and interoperability• Introduction to embedded systems and autonomous systems• Python fundamentals• Introduction to IoT Hardware: Arduino, LoPy, Raspberry Pi • Tutorial/Workshop: Building your own sensor

Day 2 – LoRaWAN and The Things Network• Achieving Long Range and Low Power data transmission• Description of the LoRa radio protocol• LoRaWAN Architecture• The Things Network – A free to use and open LoRaWAN network• Tutorial/Workshop: Connecting your sensors to the Things Network• Managing payloads: Encoding and decoding messages• Tutorial: Building your own dashboard with Cayenne

Day 3 – Dashboards and building advanced applications• Publishing data: MQTT, HTTP Integrations• Leveraging your data: interoperability, discoverability and machine learning• Tutorial/Workshop: Graphically build your IoT Application with Node-Red• Hackathon session

Course Outcomes

By the end of this course you will:• Understand the Internet of Things and its applications• Extend your knowledge of Python• Discover hardware for the Internet of Things• Know the different network protocols for the Internet of Things• Have extended knowledge on LoRa and LoRaWAN • Deploy and connect sensors to the Things Network• Build IoT applications and routines

Course Pre-Requisite

Basic knowledge of Computer Science and Python is preferred but not required.

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FEIS805: Computational Methods in Supply Chain and Logistics

Course Overview

Today’s world is producing an ever increasing amount of data. Businesses then need data analysis to provide forward-looking guidance that yields better, more-informed decisions. This subject introduces quantitative methods to optimise the decisions to be made in the context of supply chain and logistic systems. Each method will be illustrated with real world case studies. As such, participants will learn to verify and enhance existing operating models.

The course starts with an introduction to supply chain and logistics and some representative problems with several real-life applications. Effective tools for tackling these problems, such as standard mathematical techniques or Linear Programming, are explained and their implementation in Microsoft Excel is emphasised. The course is concluded by introducing some advanced metaheuristics and their implementation in Excel VBA.

Course Outline

The three day course will include:

Day 1 – Introduction: Supply chain management • Introduction: Supply chain management – What and Why?• Example of a real-life case study - Travelling Salesman Problem (TSP) - Vehicle Routing Problem (VRP)• Introduction to basic VBA programming in Excel: Customise Excel macros (Basic data types, Arrays and matrices,• Conditional and iterative statements, Functions and subroutines)

Day 2 – Standard methods• Linear programming with applications to Manufacturing and Marketing• Network models - Shortest path - Maximum flow - Transhipment• Inventory control models• Selecting the right business decisions

Day 3 – Advanced methods: Evolutionary algorithms• A hard-to-solve problem: TSP or VRP• Introduction to Metaheuristics - Constructive methods - Local searches - Evolutionary algorithms - Hybrids algorithms• Tackling TSP or VRP using a metaheuristic.

Course Outcomes

By the end of this course you will be able to:• Comprehend a large number of relevant quantitative supply chain/ logistics methods• Select and apply appropriate quantitative methods to a given SCM/Logistics problem• Manipulate data to optimise supply chain/ logistics performances using IT solutions• Assess the relevance of methods, tools and techniques for the wider supply chain

Course Pre-Requisite

Basic knowledge of Supply Chain and Logistics, and familiarity with Excel is recommended.

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FEIS806: Urban Transport Planning for the Digital Age

Course Overview

Traditional methods for transport planning have been widely used in the past, however, more and more transport researchers and planners have realised the shortcomings of the classic methods in the digital age where historical and real-time data from various digital sources, such as GPS, smartphones, smart cards and Bluetooth sensors, are more readily available for better transport planning. Moreover, compared to traditional transport modes (e.g. bike, car, bus and train), more options (like autonomous vehicle, electric vehicles, connected vehicles, and scooters) are likely to emerge providing solutions for unsolved problems as well as posing new challenges in planning for their impacts on the demand for urban transport.

It is necessary to revisit the basics of urban transport planning to understand the effective use of digital data and new technologies and how they can be used to provide smarter mobility solutions.

This short course will provide transport researchers and planners with basic knowledge of the transport planning process, as well as major innovations and changes in the digital age. Real case studies will be shared as references for modern urban transport planning.

Course Outline

The three day course will include:

Day 1 – Traditional Concepts of Transport Planning• Trip generation • Trip distribution• Mode choice• Traffic assignment• Advantages and disadvantages of the classic 4-step method• Case Study 1 - A transport planning example using 4-step method

Day 2 – Recent Innovations and Changes to Urban Transport Planning• Trends in demand for urban mobility and choice preferences• Urban transport technology and supply options• Estimating cost of urban mobility and its impact on economic competiveness and environment.• Framework for generating sustainable solutions for short, medium and long-term implementation• Using digital data for urban transport innovations and improving urban liveability• Case Study 2 - Colombo, Sri Lanka

Day 3 – Smart Mobility and New Technologies• Internet of Things and digital technologies to improve urban mobility• Traffic simulation• Case Study 3 - Agent-based traffic simulation• Big data in transport planning and operation• Case Study 4 – Using opal card data to support public transport planning and operation• Smart mobility – now and future• Concerns with new technologies

Course Outcomes

By the end of this course you will understand:• Traditional methods are applied to transport planning• Modern technology based developments have led to change in how transport planning has been done over the last

several decades• Trends in society, environment and technology are likely to impact mobility, transport demand changes and supply in

future• Transport estimates need to reflect above changes, how transport models need to be adapted • Innovative approaches can improve urban mobility and reduce the overall cost

Course Pre-Requisite

Basic knowledge of Transport Planning and Data Analytics is preferred but not required.

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FEIS807: Introduction to Agent-Based Modelling of Urban Systems

Course Overview

Societies, modern cities and urban infrastructure systems are becoming more complex, interconnected, difficult to optimise, control and manage. Agent-based modelling (ABM) offers a new lens to understand and steer the functioning of these systems by conducting experiments on artificial societies of computer agents.

The course will begin by introducing fundamental principles of complexity and the dynamics of complex adaptive systems. A structured process to conceptualise, design, build, analyse and validate ABMs will then be explained and illustrated using real-world examples. The course will draw on applications in a wide variety of social, urban and infrastructure problems, to help illustrate the power of ABM as an effective and accessible tool to understand why systems don’t always behave as expected and what can be done to improve them.

Course Outline

The four-day course will include:

Day 1 — What are ABMs? • Motivation, concepts, and history - Growing artificial societies: Social science from the bottom-up - Fundamentals: Complex systems, interactions, adaptation, simple rules, randomness, and emergent behaviour - Why ABM is useful - ABM is and is nots - Comparison with other simulation methods• Examples: Exploring the NetLogo models library

Day 2 — How to build ABMs• Structuring an ABM project: ABM creation and design• Agents, Environments, Interactions, User Interface/Observer, and Schedule• Environmental topologies and agent interactions• Agent types and behavioural models• Putting everything together

Day 3 — How to analyse and use ABMs• Exploring and describing model results: Examining the data to find meaningful relationships• Sweeping the parameter space: The importance of multiple model runs• Verification• Validation• Replication

Day 4 — Tutorials: Building Agent-Based Models• Building a model from scratch: incremental model• A classic: the prey/predator model• Simulating the spread of the flu in a population• Traffic model

Course Outcomes

By the end of this course you will be able to:• Understand what ABMs are and how that can be used• Create an ABM from scratch and extend an ABM created by someone else• Analyse and critically evaluate the results of an ABM• Use ABMs to discover new ways to improve and optimise the behaviour of social, urban, and infrastructure systems• Implement agent-based models using NetLogo

Course Pre-Requisite

Basic knowledge of modelling and simulation is preferred but not a requirement.

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FOR MORE INFORMATION, PLEASE CONTACT:

DIRECTORSENIOR PROFESSOR PASCAL PEREZPASCAL_PEREZ@UOW.EDU.AU +61 2 4298 1241

CHIEF OPERATING OFFICERTANIA BROWNTANIA_BROWN@UOW.EDU.AU+61 2 4298 1431

COURSE COORDINATORDR BO (BOBBY) DUBO_DU@UOW.EDU.AU+61 2 4239 2270

SMART.UOW.EDU.AU/SHORT-COURSES

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SMART INFRASTRUCTURE FACILITY

SMART INFRASTRUCTURE FACILITY, UNIVERSITY OF WOLLONGONG

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SMART INFRASTRUCTURE FACILITY

SMART INFRASTRUCTURE FACILITY, UNIVERSITY OF WOLLONGONG