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Dr Saeed Akhlaghpour is an Associate Professor of Information Systems at UQ Business School. Prior to joining the University of Queensland in 2015, he held academic positions at Middlesex University London (UK), and McGill University (Canada) - where he also obtained his PhD in Management.

Saeed is a Fellow of the Higher Education Academy. He teaches Master of Commerce, MBA, and executive education courses. Previously, he has taught courses such as IT for Business Value, e-Marketing Strategy and Social Media, International Business Environments, and Managing Data & Databases to MBA and undergraduate business students in Brisbane, London, Montreal, and Tehran.

Saeed’s research has been published in top-tier academic outlets including the American Journal of Sociology, Journal of the Association for Information Systems, Journal of Information Technology, Information and Organization, Journal of the American Medical Informatics Association, International Journal of Medical Informatics, International Journal of Information Management, Journal of Business Research, and the Best Paper Proceedings of Academy of Management. His articles are cited in the EU policy documents and have received research and impact awards from the Administrative Sciences Association of Canada, and the Australasian chapter of the Association for Information Systems (AIS).

His research interests include:

• Data protection, privacy and cybersecurity (particularly, organisational response to data breaches, and managing personal health information (PHI))
• Digital health and transformation of healthcare services
• Diffusion and adoption of digital innovations (particularly, management and IT fashions)

Saeed is a Chief Investigator in an Australian Research Council (ARC) funded Linkage Project studying Digital Hospital implementation in Queensland hospitals and health services. He received the UQ Business School Cross Cross-Disciplinary Research Award in 2021.

He is currently a Section Editor of the Australasian Journal of Information Systems, Associate Editor of the Asia Pacific Journal of Information Systems (APJIS), and Editorial Board member of Information and Organization (I&O), International Journal of Medical Informatics, and Journal of Electronic Commerce in Organizations. He co-chairs the Digital Health Care track at the Australasian Conference on Information Systems (ACIS), and the AI in Business & Society track at the International Conference on Information Systems (ICIS) (https://icis2023.aisconferences.org/track-descriptions/#toggle-id-10)

Mikael Bodén has a PhD in Computer Science and statistical machine learning from the University of Exeter (UK) but has spent the last decade and a half in biological research environments, including the Institute for Molecular Bioscience/ARC Centre of Excellence in Bioinformatics and the School of Chemistry and Molecular Biosciences, where he is currently located. He is the director of UQ’s postgraduate program in bioinformatics. Mikael Bodén has supervised 7 postdocs from funding he received from both ARC and NHMRC; he has been the primary advisor for 11 PhD and 3 MPhil graduates; he is currently supervising another 6 PhD students in bioinformatics and computational biology. Mikael Bodén collaborates with researchers in neuroscience, developmental biology, protein engineering and bioeconomy to mention but a few, and contributes expertise in the processing, analysis and integration of biological data; this is exemplified by recent publications in Science, Nature Catalysis, Nature Communications, Cell Systems, Nucleic Acids Research and Bioinformatics.

Dr Steffen Bollmann joined UQ’s School of Electrical Imaging and Computer Science in 2020 where he leads the Computational Imaging Group. The Group is developing computational methods to extract clinical and biological insights from magnetic resonance imaging (MRI) data. The aim is to make cutting-edge algorithms and tools available to a wide range of clinicians and researchers. This will enable better images, faster reconstruction times and the efficient extraction of clinical information to ensure a better understanding of a range of diseases. Dr Bollmann was appointed Artificial Intelligence (AI) lead for imaging at UQ’s Queensland Digital Health Centre (QDHeC) in 2023.

His research expertise is in quantitative susceptibility mapping, image segmentation and software applications to help researchers and clinicians access data and algorithms.

Dr Bollmann completed his PhD on multimodal imaging at the University Children’s Hospital and Swiss Federal Institute of Technology (ETH) Zurich, Switzerland.

In 2014 he joined the Centre for Advanced Imaging at UQ as a National Imaging Facility Fellow, where he pioneered the application of deep learning methods for quantitative imaging techniques, in particular Quantitative Susceptibility Mapping.

In 2019 he joined the Siemens Healthineers collaborations team at the MGH Martinos Center in Boston on a one-year industry exchange where he worked on the translation of fast imaging techniques into clinical applications.

Dr Archie Chapman is an Associate Professor in Computer Science in the School of IT and Electrical Engineering.

Archie develops and applies principled artificial intelligence, game theory, optimisation and machine learning methods to solve large-scale and dynamic allocation, scheduling and queuing problems. His recent research has focused on applications of these techniques to problems in future power systems, such as integrating large amounts of renewable power generation and using batteries and flexible loads to provide power network and system services, while making best use of legacy network and generation infrastructure.

Prior to joining UQ, Archie was Research Fellow in Smart Grids at the University of Sydney (2011-2019), and a postdoc fellow at the University of Southampton (2009-2010), where he completed his PhD.

Summary of Research:

• My current research at UQ is as Professor in this School (teaching AGRC3040 Crop Physiology) and as an Affiliate Professor of QAAFI. Since 2020, with full-time appointment at UQ, my research portfolio has included multiple projects in applications of machine learning and artificial intelligence into the ag domain. This area is developing rapidly and across UQ, I am engaging with faculty in multiple schools (ITEE, Maths and Physics, Mining and Mech Engineering) as well as in the Research Computing Centre to develop new projects and training opportunities at the interface of field agriculture and these new digital analytics.
• My career research has been around genetic and environment effects on physiology of field crops, particularly where drought dominates. Application of quantitative approaches (crop simulation and statistical methods) and phenotyping (aerial imaging, canopy monitoring) to integrate the understanding of interactions of genetics, growth and development and the bio-physical environment on crop yield. In recent years, this work has expanded more generally into various applications in digital agriculture from work on canopy temperature sensing for irrigation decisions (CSIRO Entrepreneurship Award 2022) through to applications of deep-learning to imagery to assist breeding programs.
• Much of this research was undertaken with CSIRO since 1996. Building on an almost continuous collaboration with UQ over that time, including as an Adjunct Professor to QAAFI, Prof Chapman was jointly appointed (50%) as a Professor in Crop Physiology in the UQ School of Agriculture and Food Sciences from 2017 to 2020, and at 100% with UQ from Sep 2020. He has led numerous research projects that impact local and global public and private breeding programs in wheat, sorghum, sunflower and sugarcane; led a national research program on research in ‘Climate-Ready Cereals’ in the early 2010s; and was one of the first researchers to deploy UAV technologies to monitor plant breeding programs. Current projects include a US DoE project with Purdue University, and multiple projects with CSIRO, U Adelaide, La Trobe, INRA (France) and U Tokyo. With > 8500 citations, Prof Chapman is currently in the top 1% of authors cited in the ESI fields of Plant and Animal Sciences and in Agricultural Sciences.

Gordon holds a PhD in Chemical Engineering from the University of Cape Town, South Africa, which focused on the application of machine vision, image processing and machine learning algorithms for modelling grade in froth flotation systems.

Gordon spent ten years working for the Victorian Government developing technical computing and modelling solutions. These included the development of the Environmental Systems Modelling Platform, a tool that aims to bring multiple environmental models and datasets into a single easy to use software package, and the development of the Native Vegetation Regulations Tool, to calculate the interactions between proposed clearings and models of rare and threatened species, and thereby determine the required offset credits. More recently, Gordon worked as a data scientist at the Victorian Centre for Data Insights, where he worked with a team focused on delivering innovative data driven solutions across the government sector.

Gordon now applies his data analytics, modelling and technical computing skills at the JKMRC where he works with the Advanced Process Prediction and Control group developing tools for improved time series analysis and visualisation of industrial data and comminution process models.

I undertake multi-disciplinary collaborative research developing mathematical, computational and visualisation approaches and techniques that facilitate the research and education in animal and plant systems.

My major research theme is development of mathematical, computer graphics and simulation approaches and techniques that facilitate the study of genetics, physiology, morphogenesis and ecology at the scale of cells, individual plants and insects and their components. These developments in computational biology are being used to increase our understanding of the dynamics of morphogenesis, and as a tool in applied research and education.

MBChB FRCA FANZCA FFICM PhD

Prospective Students

Please visit the link (https://sites.google.com/view/dsteam/pros-students).

Short Biography

Dr. Dan Dongseong Kim is Deputy Director of UQ Cybersecurity and an Associate Professor (in the commonwealth system, is broadly equivalent to a North American full professor) (continuing appointment) in Cyber Security at The University of Queensland (UQ), Brisbane, Australia. Before UQ, he was a faculty member (permanent academic staff; Senior Lecturer 2015-2018, Lecturer 2011-2014) in Cyber Security in the Department of Computer Science and Software Engineering at The University of Canterbury (UC), Christchurch, New Zealand from 2011 to 2018. From 2008 to 2011, he was a postdoc at Duke University, Durham, North Carolina in the US. He was a visiting scholar at the University of Maryland, College Park, Maryland in the US in 2007. His research interests are in Cyber Security and Dependability for various systems and networks. Please visit his research team webpage: https://sites.google.com/view/dsteam/

Publications

• Google Scholars (6000+ citations, h-index: 40, i10-index: 111): https://scholar.google.com/citations?user=dIIYVQkAAAAJ&hl=en
• DBLP (170+ publications): https://dblp.org/pid/k/DongSeongKim.html

Research Focus: Cyber G.A.M.E

• Graphical Models for Cyber Security: Model-based Cyber Security Risk Analysis
• AI for Cybersecurity & Cyber Security for AI: Securing AI systems and Cybersecurity using AI techniques
• Moving Target Defense (MTD): Resilient and Proactive Defence
• Evolving Attacks and Defense Automation: Red team and Blue team Automation and evaluation using AI

Professional Activities (selected)

• Associate Editor, IEEE Communications Surveys and Tutorials (impact factor: 33.84 (2022-2023), #1 impact factor among all the IEEE journals), 2021 - present.
• Editorial Board Member, Elsevier Computers and Security (impact factor: 5.6 (2023)), 2019 - present.
• Editorial Board Member, Elsevier Computer Networks (impact factor: 5.6 (2023)), April 2022 - present.
• An Elected Member of the IFIP WG 10.4 on Dependable Computing and Fault Tolerance (2021 onwards).
  • The IFIP WG 10.4 consists of over 60 experts in the field of fault-tolerance, dependable and secure computing.
• Steering committee member of IFIP/IEEE DSN, 2021-2025.
• Steering committee chair of IEEE PRDC, 2022-present.
• Steering committee member of IEEE PRDC, 2019-present.
• TPC member of international conferences including IFIP/IEEE DSN, SRDS, ISSRE, ICDCS, etc.

Selected publications

• Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey, IEEE Communications Surveys & Tutorials (Impact factor in 2021: 33.84)
• Toward Proactive, Adaptive Defense: A Survey on Moving Target Defense, IEEE Communications Surveys & Tutorials 2020 [Impact Factor 2018-19: 22.973]
• A Survey on Threat Situation Awareness Systems: Framework, Techniques, and Insights, ACM Computing Surveys, 2022 [Impact Factor 2021-22: 10.282, ranked 4/137 in Computer Science Theory & Methods]
• Evaluating the effectiveness of shuffle and redundancy MTD techniques in the cloud. Computers & Security (2021) [Impact factor: 4.438]
• Threat-Specific Security Risk Evaluation in the Cloud. IEEE Trans. Cloud Comput. 9(2): 793-806 (2021) [Impact factor: 4.714]
• Dynamic Security Metrics for Measuring the Effectiveness of Moving Target Defense Techniques, Computers & Security, Elsevier, 2018 [Impact factor: 4.438]
• Assessing the Effectiveness of Moving Target Defenses using Security Models. IEEE Transactions on Dependable and Secure Computing (IEEE TDSC), 2016. [CORE Rank A, Impact factor: 6.404]
• Recovery from software failures caused by Mandelbugs. IEEE Transactions on Reliability, 2016. [CORE Rank A, Impact factor: 2.79]
• Sensitivity Analysis of Server Virtualized System Availability. IEEE Transactions on Reliability, 61(4): 994-1006, 2012. [CORE Rank A, Impact factor: 2.293]
• Scalable Optimal Countermeasure Selection using Implicit Enumeration on Attack Countermeasure Trees, IEEE/IFIP DSN 2012 [CORE Rank A*]

Research Sponsors (past and current)

• NSF (US), IBM T.J. Watson (US), US Army Research Lab. (US), NEC (Japan), Tait Comm. (NZ), MBIE (NZ), NPRP (Qatar), ADD (Republic of Korea), NRF (Republic of Korea), etc.

Recent Ph.D. graduates (selected)

1. Kok Onn Chee (Ph.D., University of Queensland, 2024: Principal Advisor): Security Modelling and Analysis of Internet of Things against Evolving Attacks.

2. Minjune Kim (Ph.D., , University of Queensland, 2023: Principal Advisor): Security and performance evaluation of software defined networking adopting moving target defenses (Research Engineer at CSRIO's Data61, Australia).

3. Dilli P. Sharma (Ph.D., University of Canterbury, 2020; senior supervisor at UC, -> co-supervisor at UQ): Software-defined networking based moving target defenses. (Postdoc at U. of New Brunswick, Canada -> Postdoc at the University of Toronto, Canada).

4. Taehoon Eom (Ph.D., 2020, KAU, Korea, co-supervisor): Security modeling and analysis for performance enhancement in software defined network (Researcher at KAU-> Research Professor at KAU -> Artificial Intelligence Industry Cluster Agency (AICA), South Korea).

5. Hooman Alavizadeh (Ph.D., Massey University, NZ, 2019, co-supervisor): Effective Security Analysis for Combinations of MTD Techniques on Cloud Computing (a Postdoc, Massey University -> Postdoc at UNSW Canberra->Lecturer at U of Sydney-> Lecturer (continuing academic staff), La Trobe University, Australia).

6. Kieran Morris (Ph.D., ECE, University of Canterbury, NZ, 2019, co-supervisor): Reliability and resilience evaluation of distribution automation (first employment: Tait communications, NZ-> Noted Ltd ).

7. Simon (Enochson) Yusuf (Ph.D., Computer Science, NZ, University of Canterbury, Dec 2018, senior supervisor): Dynamic Cyber Security Modeling and Analysis (Postdoc at UQ-> Lecturer (continuing academic staff) at Federal University Kashere (FUK), Gombe, Nigeria -> Lecturer at Whitecliffe College, New Zealand. )

8. Mengmeng Ge (Ph.D., Computer Science, University of Canterbury, 2018; senior supervisor): Graphical security modeling and assessment for the Internet of things (Lecturer (continuing academic) in Cybersecurity at Deakin University -> RMIT University, Australia -> Deloitte New Zealand-> Senior Lecturer, University of Canterbury, New Zealand).

9. Iman Elmir (Ph.D., Hassan 1st Univ. Morocco, 2017, co-supervisor): Security Modeling and Analysis of Intrusion Tolerant Data Centers

10. Jin B. Hong (Ph.D., Computer Science, University of Canterbury, April 2015, senior supervisor): Scalable and Adaptable Security Modeling and Analysis. (First employment: Postdoc, UC, NZ -> Lecturer, then Senior Lecturer in Cybersecurity at U of Western Australia)

11. Nguyen Tuan Anh (Ph.D., August 2015, KAU, co-supervisor): Availability Modeling and Analysis of Data Center Systems using Stochastic Reward Nets. (Postdoc, Kunkuk University, South Korea -> an Academic Research Professor, Kunkuk University, South Korea)

Cyber Security Research Experience

Dan Dongseong Kim has been working on various topics in computer and network security since 2001. Dan started his research with crypto algorithms design, implementation, and testing for hardware devices such as FPGA/ASICs. Then, he worked on machine learning/data mining approaches for (host-based, network-based) intrusion detection from 2001 onward. His master's thesis was a machine learning (ML)-based network intrusion detection. He worked on various computer and network security topics such as an intelligent SIEM (it was called enterprise security management at that time), authentication protocols for RFID systems, security and privacy for Wireless Sensor Networks (WSNs), etc. His Ph.D. topics were security and privacy for WSNs. He spent one year as a visiting scholar at The University of Maryland (UMD), College Park, Maryland in the US in 2007 under the supervision of Prof. Virgil D. Gligor.

He started working on dependability more seriously in addition to Cyber Security in 2008 when he started his postdoc research at Duke University under the supervision of the Hudson Chaired Professor Kishor S. Trivedi. He worked on research projects funded by the US NSF, NEC Japan, and IBM T.J. Watson in the area of dependability (availability/performance) of data centers/cloud computing and cybersecurity modeling & analysis.

Since he became a faculty member at The University of Canterbury, New Zealand in August 2011, he explored deeply the area of graphical models for cybersecurity, metrics, measurement, and efficient evaluation methods for automated cybersecurity modeling and analysis and applied those key ideas to Cloud computing, Internet of Things (IoT), Moving Target Defenses (MTD), cyber deception, and automated cyber-attacks generation. He worked with diverse groups of people from various countries including Australia, Bangladesh, Brazil, China, Germany, India, Iran, Italy, Japan, Saudi Arabia, South Korea, Mongolia, Morocco, Malaysia, Nepal, New Zealand, Nigeria, Pakistan, Qatar, USA, UK, and Vietnam.

Academic Genealogy

As for his academic genealogy, his Ph.D. thesis advisor was

• Jong-Sou Park (Pennsylvania State University, Ph.D., 1994); his one was
• Paul Thomas Hulina (Pennsylvania State University, Ph.D., 1969); then it runs back through
• Jon Gustav Bredeson (Northwestern University, Ph.D., 1967),
• Seifollah Louis Hakimi (The University of Illinois at Urbana-Champaign (UIUC), Ph.D., 1959),
• Mac Van Valkenburg (Stanford University, Ph.D., 1952),
• Oswald Garrison Villard, Jr. (Stanford University, Ph.D., 1949),
• Frederick Emmons Terman (widely credited as being the father of Silicon Valley) (Massachusetts Institute of Technology (MIT), 1924, Sc.D.) and
• Vannevar Bush (Jointly Harvard/MIT, D. Eng., 1916) to
• Arthur Edwin Kennelly (Professor at Harvard/MIT) (who was working in Thomas Edison's West Orange Laboratory from December 1887 to March 1894) and Dugald C. Jackson.

His postdoc advisor is Professor Kishor S. Trivedi (UIUC, Ph.D., 1974) who is a Life Fellow of IEEE and the Hudson Distinguished Professor of Electrical and Computer Engineering at Duke University, USA. Please visit the academic tree from Duglad C. Jackson up to the ancestors at the academic tree (link).

Associate Professor Mark Utting's research interests include software verification, model-based testing, theorem proving and automated reasoning, programming language design and implementation. He received his PhD from UNSW on the semantics of object-oriented languages, and since then has worked as an academic at several Queensland universities, as well as Waikato University in NZ and the University of Franche-Comte in France. He is passionate about designing and engineering good software that solves real-world problems, has extensive experience with managing software development projects and teams both in academia and industry, and has worked in industry, developing next generation genomics software and manufacturing software. He is author of the book ‘Practical Model-Based Testing: A Tools Approach’, as well as more than 80 publications on model-based testing, software verification, and language design and implementation. His current research focus is on using software verification to give strong guarantees about the correctness of compilers, correctness of blockchain smart contracts, freedom from information leaks of ARM64 binary programs, and the correctness of AI-generated code.