Dr. Andy Nguyen is a Senior Lecturer in the School of Engineering at the University of Southern Queensland. He holds a PhD from Queensland University of Technology (QUT), an MEng from the National University of Civil Engineering (NUCE), and a BEng from NUCE. His research focuses on structural health monitoring, integrating machine learning and deep learning techniques to assess infrastructure integrity. Key areas include damage detection in bridges, pavements, and buildings, as well as sustainable construction materials like bamboo. Nguyen leads projects such as the 'Next Generation Living Laboratory for Engineering Education and Engagement,' emphasizing real-world applications of technology in civil infrastructure. His work spans crack detection algorithms, finite element model updating, and vibration-based structural analysis. He collaborates on AI-driven solutions for autonomous vehicle object detection and smart maintenance planning. Nguyen’s contributions include over 50 peer-reviewed publications and active supervision of postgraduate research in composite materials and transport infrastructure. His research outputs highlight advancements in computational mechanics, sensor technologies, and data-driven methods for infrastructure resilience. Nguyen’s expertise bridges civil engineering challenges with cutting-edge machine learning, advancing both theoretical and applied solutions for sustainable and safe structures.
Dr. Andy Nguyen is a Senior Lecturer in Structural Engineering at the University of Southern Queensland, within the School of Engineering. He is an active researcher and educator, specializing in the Structural Health Monitoring (SHM) of critical civil infrastructure such as bridges, buildings, and transport tunnels. Bachelor of Engineering (BEng), NUCE, 1999 Master of Engineering (MEng), NUCE, 2003 Doctor of Philosophy (PhD), Queensland University of Technology (QUT), 2014 Dr. Nguyen's research is at the forefront of integrating advanced technologies into civil engineering. His primary focus is on developing and deploying sophisticated SHM systems that utilize sensors, data analytics, and machine learning to provide real-time insights into the structural integrity of ageing infrastructure. His work aims to enable proactive maintenance, extend the lifespan of structures, and enhance public safety. He has successfully implemented monitoring systems on major bridges and high-rise buildings in Queensland and New South Wales, with systems capable of even detecting distant earthquake events. His research interests span Structural Health Monitoring, Machine Learning for Engineering, Damage Detection, Finite Element Model Updating, Sustainable Building Materials like bamboo, and the application of AI for automated condition assessment of transport infrastructure. The analysis of his recent publications reveals a strong and consistent research trajectory centered on the application of data-driven and AI methods to solve practical problems in civil infrastructure. His work frequently combines signal processing techniques (like Stockwell Transform) with deep learning models for tasks such as crack detection in concrete and pavement. He also conducts significant research on model updating for complex structures like cable-stayed and arch bridges, using vibration data and optimization algorithms. The integration of machine learning for overload classification and the development of cost-effective, automated monitoring systems are key trends in his recent output. Advanced Queensland Fellow (2024-2027) Dr. Nguyen is actively involved in research supervision and collaboration. He is currently supervising several postgraduate students on projects related to AI-powered condition assessment, bamboo as a sustainable building material, and railway track design. He receives research funding from the Queensland Government through his Advanced Queensland Fellowship. His research has direct practical applications, as evidenced by his public engagement, such as writing for The Conversation on safeguarding ageing bridges, and his work with the Australian Network of Structural Health Monitoring. Dr. Nguyen's work embodies the development of a next-generation 'Living' Laboratory for engineering education, where research, teaching, and real-world infrastructure monitoring are integrated. His current projects involve creating smart, automated fault detection systems and advancing 'digital twin'-based monitoring platforms for infrastructure.
Dr Zeke Ahern is a Research Fellow in Transport Engineering and Planning at Queensland University of Technology (QUT), affiliated with the School of Civil & Environmental Engineering. He holds a PhD from QUT. His research focuses on transportation safety, crash frequency modeling, and optimization of public transit systems. Dr Ahern’s work emphasizes multi-objective frameworks for analyzing crash data, improving safety infrastructure, and enhancing urban mobility through advanced statistical and computational methods. He has collaborated on projects involving raised safety platforms, parking payment behavior, and integrated bus route design. His publications reflect contributions to transportation engineering, traffic safety, and data-driven decision-making in civil infrastructure. Research Interests: Dr Ahern’s primary areas include crash frequency modeling, transportation safety infrastructure evaluation, and the application of optimization algorithms (e.g., simulated annealing, metaheuristics) to transportation problems. He develops tools like the Metacountregressor Python package to assist in analyzing count data models, bridging software engineering and transportation research. Publications highlight trends in data-driven safety analysis and infrastructure optimization. His work spans both theoretical advancements (e.g., hypothesis testing for crash models) and practical applications (e.g., raised platform effectiveness reviews). Recent efforts emphasize integrating multiple objectives into transportation planning, such as balancing safety, efficiency, and cost-effectiveness.
Hanyu Gu is a Senior Lecturer in the School of Mathematical and Physical Sciences at the University of Technology Sydney (UTS), part of the Faculty of Science. He holds a PhD in Power Engineering and Automation from Shanghai Jiao Tong University (1999) and has extensive industry experience in telecommunications, airline optimization, and mining. His research focuses on combinatorial optimization, decomposition methods, stochastic programming, and machine learning applications. Notable awards include second place in the 2020 ROADEF competition. He collaborates with institutions like the UTS Transportation Research Centre and has contributed to projects such as optimisation engines for airline management and underground mining algorithms. Current research explores hybrid algorithms, Bayesian optimisation, and scheduling under uncertainty. Education: Bachelor in Industrial Automation, Shanghai Jiao Tong University (1994) Master in Control Theory and Application, Shanghai Jiao Tong University (1997) PhD in Power Engineering and Automation, Shanghai Jiao Tong University (1999) Industry Experience: ZTE (1999–2001): Senior Wireless Communication Engineer CTI, Melbourne (2007–2011): Airline Management Optimisation Researcher NICTA (2011–2013): Underground Mining Optimisation Researcher Grants: ARC Linkage Project LP0883855 (2008–2012): Developed optimisation tools for transportation crewing, valued at $840,000. Research interests span decomposition methods for large-scale problems (e.g., airline scheduling), stochastic programming for resource sharing, and hybridisation of mathematical programming with constraint programming. Recent work includes Bayesian optimisation for knapsack problems and relax-and-solve algorithms for project scheduling. His articles frequently address optimisation in logistics, healthcare, and transportation, emphasizing practical industry applications and algorithmic innovation. Awards: Second place in the ROADEF 2020 competition for maintenance planning solutions. Advising & Grants: Supervises Masters and PhD students in operations research and optimisation. Collaborates with Ausgrid, UGL, and ANC on optimisation projects (e.g., employee training timetabling, logistics). Active in the Optimisation Group of UTS Transportation Research Centre, he bridges academic research with real-world challenges in scheduling, logistics, and resource management. Ongoing efforts include advancing metaheuristics and integrating machine learning with traditional optimisation techniques.
Dr. Saima Ahmad is a Senior Lecturer at RMIT University's School of Management, focusing on cultivating sustainable work environments and investigating leadership's impact on individual well-being. With a PhD in Management from Monash University, her research spans organizational behavior, workplace dynamics, and leadership ethics, addressing critical issues such as bullying, resilience, and digital disruption. She coordinates courses in the RMIT MBA program and serves on editorial boards for the European Management Journal and PLoS One . Education: PhD in Management from Monash University Her research emphasizes positive leadership styles and their influence on employee engagement and organizational sustainability. Recent work explores servant leadership in the construction industry and the role of green human resource management in fostering environmental citizenship. She has pioneered studies on workplace bullying and its mitigation through ethical leadership frameworks. Her scientific awards include the 2022 RMIT GSBL Dean’s Merit Award for HDR Leadership Excellence for her contributions as HDR Coordinator (2022-2024), where she enhanced PhD completion rates and student support systems. She actively supervises Masters and PhD research candidates, focusing on leadership and organizational behavior.
Sanjoy Paul is an Associate Professor at the University of Technology Sydney (UTS) Business School, specializing in supply chain management and operations research. He holds roles as Associate Editor of Business Strategy and the Environment and Global Journal of Flexible Systems Management . His research focuses on supply chain resilience, risk modeling, and sustainable practices, with applications to global disruptions like pandemics and IT outages. Paul has published in top-tier journals such as the European Journal of Operational Research and secured grants from government bodies including the Department of Defence. Education and Career: Prior to UTS, he worked at RMIT University and Bangladesh University of Engineering and Technology. He holds a PhD from UNSW, recognized with the Stephen Fester Prize for outstanding thesis. His career spans academic roles from Lecturer (2017) to Senior Lecturer (2019) before his current position since 2023. Research Contributions: Paul’s work bridges theoretical models and real-world applications, including recovery frameworks for supply chains during crises and strategies for sustainable practices in post-pandemic contexts. He frequently advises media on supermarket pricing, supply chain disruptions, and business strategies, appearing in outlets like The Guardian and ABC News . Awards and Recognition: His honors include the ASOR Rising Star Award, Research with Relevance Award, and inclusion in the top 2% global scientists (2020–2023). He has contributed to policy debates on supermarket competition, EV market dynamics, and Australia’s industrial strategies.
Dr. Hamid Alinejad-Rokny is a Scientia Senior Lecturer at UNSW Sydney and Adjunct Associate Professor at Concordia University. He leads the UNSW BioMedical Machine Learning (BML) Lab within the Graduate School of Biomedical Engineering. His research focuses on applying machine learning, bioinformatics, and statistical methods to understand genomic mechanisms underlying diseases like cancer and neurodevelopmental disorders. Dr. Rokny holds a PhD in Biostatistical Machine Learning from UNSW and has secured over $13M in grants as a principal or co-investigator. He has published 80+ papers, including 10 as first author and 45 as senior author. Education: Bachelor’s in Software Engineering (2004-2009), Master’s in Artificial Intelligence (2009-2012), PhD in BioMedical Machine Learning (UNSW, 2014-2018), Postdoc at Harry Perkins Institute (2017-2019). Research interests include medical AI, deep learning, genomic data analysis, and systems biology. He actively supervises 20+ PhD/Master’s students and collaborates with industry partners like CSIRO and 23Strands. Awards include the DECRA 2023, NHMRC MERIT, and International Autism Fellowships. He also serves as a keynote speaker at conferences like HUGO and an Honorary Lecturer at Macquarie University. Grants total $2.75M as lead investigator and $10.6M as co-investigator. Industry partnerships include PORSPA Advance ($4.7M) and Australian Digital Domains ($3.6M). His lab develops tools like MaxHiC and DeepGenePrior for genomic analysis. Labs/Teams: Director of UNSW BML Lab, Health Data Theme Leader at UNSW Data Science Hub. Active in mentoring 13 researchers globally and co-supervising international teams.
Professor Alan Wee-Chung Liew serves as Head of School at Griffith University's School of Information and Communication Technology, Australia. He joined Griffith University in 2007 after holding positions as Assistant Professor at Chinese University of Hong Kong and Senior Research Fellow at City University of Hong Kong. Professor Liew's research spans Artificial Intelligence, Machine Learning, Medical Imaging, Computer Vision, and Bioinformatics . His work focuses on developing AI solutions for healthcare applications, image analysis, and pattern recognition problems. He leads methodological innovations in machine learning algorithms while maintaining strong connections to real-world applications. His recent publications demonstrate a clear trend toward interdisciplinary AI applications, particularly in medical imaging, healthcare analytics, and trustworthy AI systems. The research shows increasing focus on explainability, privacy preservation, and practical deployment of AI solutions in critical domains. Professor Liew has received significant recognition including: Fellow of the Queensland Academy of Arts and Sciences Fellow of the Australia Computer Society Senior member of IEEE (USA) Stanford University's World's Top 2% Scientists (Computer Science: AI & Image Processing) He actively supervises numerous PhD students across diverse AI topics including medical imaging, graph neural networks, and trustworthy AI. His research is supported by substantial funding from government agencies including ARC, NHMRC, and international collaborations. Professor Liew co-leads the AI4Health lab and the TrustAGI lab , which focus on developing ethical, reliable, and safe AI technologies with strong industry and hospital partnerships.
Professor Xiaodong Li is a faculty member at RMIT University's School of Computing Technologies, serving as Assistant Associate Dean for Data Science & Artificial Intelligence. He holds a Ph.D. in Artificial Intelligence from the University of Otago, New Zealand. His research focuses on machine learning, evolutionary computation, swarm intelligence, and optimization techniques with applications in blockchain security, renewable energy, and logistics. He has received prestigious awards including the 2013 ACM SIGEVO Impact Award and the 2017 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, and is an IEEE Fellow. His academic contributions include editorial roles at IEEE Transactions on Evolutionary Computation and leadership in IEEE Task Forces on Swarm Intelligence and Multi-modal Optimization. Current research interests span automated code generation, quantum AI-driven logistics, and anomaly detection. Supervision projects highlight interdisciplinary applications in AI ethics, solar energy monitoring, and fraud detection. Education: Ph.D. in Artificial Intelligence, University of Otago, New Zealand Key Roles: IEEE Fellow, ARC College of Experts (2023–2025) Publications: Over 280 peer-reviewed articles, including works on niching methods and evolutionary algorithms. Research trends show strong emphasis on hybrid optimization techniques, blockchain security, and AI-driven solutions for sustainability challenges. Recent articles explore dynamic environments, quantum rerouting strategies, and explainable machine learning systems. Awards: ACM SIGEVO Impact Award, IEEE Fellow, ARC College Membership Grants/Projects: Multiple industry-collaborative grants in smart logistics and energy systems. He leads the Data Science & AI team at RMIT, fostering innovation in large-scale optimization and metaheuristics. Active in international conferences like GECCO and IEEE CEC, he promotes open-source benchmark datasets for algorithm testing.
Dr Ripon Kumar Chakrabortty is a Decision Scientist at the School of Systems & Computing, UNSW Canberra . His research focuses on applying Artificial Intelligence and Optimization Models to complex systems, particularly in Global Supply Chains, Logistics, and Medical Informatics . He coordinates the Master of Decision Analytics program and leads the Artificial Intelligence Community of Practice at the Australian Institute of Project Management. PhD in Computer Science, UNSW (2017) M.Sc. & B.Sc. in Industrial Engineering, BUET Diploma in Logistics, Monarch Institute Certified by Chartered Institute of Logistics and Transport Australia His research portfolio includes over 5 million AUD in Category 1 grants from Australian Research Council , Department of Defence , and other government agencies. He has supervised 24 PhD/Master's students to completion or ongoing supervision, with expertise in Explainable AI , Healthcare Analytics , and Smart Manufacturing . Recent scientific recognitions include a Highly Cited Paper , Best Paper at IEEE IEEM , and Champion at IEEE CEC Competition . His editorial roles span prestigious journals like IEEE Transactions on Industrial Informatics and Mathematics (MDPI) .
Dr. Shahab Abdulla serves as an Associate Professor at the University of Southern Queensland within the College of English Language, specializing in pathway programs for international students transitioning to tertiary education. His academic role bridges mathematics, engineering, and language preparation through foundational courses including Linear Systems and Control, Mathematical Methods, and General Science. His academic qualifications include: BSc from University of Technology, Baghdad MSc from University of Technology, Baghdad PhD from University of Southern Queensland Dr. Abdulla's research spans Biomedical Engineering , Complex Medical Systems , and Networked Control , with pioneering work in Deep Learning for environmental forecasting and medical diagnostics. His AI research encompasses Neural Networks, Reinforcement Learning, and graph-based models applied to climate impact assessment and resource management, reflecting a commitment to solving interdisciplinary global challenges through computational innovation. Analysis of his 15 most recent publications reveals consistent application of decomposition techniques (wavelet, Fourier, curvelet) integrated with deep learning architectures across domains including ocean energy forecasting, medical imaging, and social network analysis. This demonstrates a methodological signature of combining signal processing with AI to extract patterns from complex temporal and spatial data. Though specific awards aren't detailed, his leadership is evidenced by chairing IEEE conferences and international committee service. He has supervised over 14 higher-degree research students while leading the Advanced Data Analytics Modelling Research Simulation Group, generating 55+ Q1 journal publications with 1000+ recent citations and 30+ global seminars. The Advanced Data Analytics Modelling Research Simulation Group under his direction develops AI-driven solutions for environmental monitoring, medical diagnostics, and engineering systems, maintaining active collaborations across the Middle East, US, Japan, Europe, China, and Canada.
Professor Amir H Gandomi is a leading academic in data science and artificial intelligence at the University of Technology Sydney, where he serves as Professor of Data Science at the Data Science Institute within the Faculty of Engineering and Information Technology. An ARC DECRA Fellow with over 450 journal papers and 14 books, his research has garnered more than 70,000 citations with an H-index exceeding 110. Ranked 18th among over 17,000 researchers in Genetic Programming bibliography and 24th in Artificial Intelligence & Image Processing by Stanford University, Prof. Gandomi is recognized as one of the world's most influential scientific minds. His research interests span machine learning, evolutionary computation, global optimization, and big data analytics, with applications across healthcare, structural engineering, environmental science, and cybersecurity. He has developed innovative frameworks like Adaptive Strategy Management for large-scale optimization and Boundary Update methods for constrained optimization problems. His work bridges theoretical advancements with practical implementations in diverse fields including medical diagnostics, renewable energy site selection, and smart infrastructure. Prof. Gandomi's publication portfolio demonstrates consistent high-impact contributions across multiple disciplines, with recent work focusing on AI-driven healthcare solutions, optimization algorithms, and climate modeling. His research shows strong interdisciplinary connections between computer science, engineering, and medical applications, with particular emphasis on practical implementations of theoretical frameworks. The breadth of his work reflects both deep technical expertise and the ability to apply computational methods to solve real-world problems across various domains. 2024 IEEE TCSC Award for Excellence in Scalable Computing (MCR) 2023 Achenbach Medal from Stanford University 2022 Walter L. Huber Prize (highest-level mid-career civil engineering research award) 2025 Sigma Xi Young Investigator Award 6 consecutive years as Clarivate Analytics Highly Cited Researcher AmCham Alliance Award in AI As a dedicated educator and mentor, Prof. Gandomi has supervised numerous research students in evolutionary machine learning, structural health monitoring, and uncertainty-aware AI systems. His funded research projects include Amazon Research Awards for medical report generation, Climate Change AI grants for drought prediction, and Digital Finance CRC projects for cyber threat detection. He leads the Data Science Institute's efforts in developing practical AI solutions while maintaining strong industry partnerships and international collaborations across multiple continents.
Dr. Mehrdad Amirghasemi is a Senior Lecturer in Business Analytics at the Faculty of Business and Law, University of Wollongong (UOW). He holds a PhD in Operations Research and specializes in metaheuristic analytics, supply chain logistics, network design, and social media applications. His work has been published in high-impact journals like Annals of Operations Research and Expert Systems with Applications . Education: PhD in Operations Research His research focuses on developing optimization algorithms for real-world challenges, such as urban planning (e.g., ArkiCity mobile app) and disaster recovery through social media analysis. He has secured over A$2M in grants, including projects funded by the Australian Research Council (ARC). Awards include the Global Challenges Travel Prize (2020) and the Shape Your Future Award from Amazon Web Services. He actively contributes to projects like SMART Rail Collaboration and 3D printed surfboard fin innovations. Grants: Over 20 projects, including ARC Linkage Projects and community resilience initiatives Supervision: Current PhD topics on AI in healthcare resource allocation and emotional intelligence in project management Dr. Amirghasemi also teaches advanced analytics courses, such as MBAS913: Prescriptive and Predictive Analytics for Medical Imaging.
Dr. Daryl Essam serves as Deputy Head of School at UNSW Canberra within the School of Systems & Computing at the University of New South Wales. With an extensive publication record spanning over two decades, his academic career demonstrates significant contributions to operations research, optimization, and artificial intelligence fields. His leadership role as Deputy Head of School for Research indicates his senior academic standing within the institution. Dr. Essam's research interests span a broad spectrum of computational intelligence and optimization techniques. His primary focus areas include evolutionary algorithms, particularly genetic programming, and their application to complex optimization problems. He has made significant contributions to constraint handling techniques in evolutionary computation, dynamic optimization, and large-scale problem solving. His work bridges theoretical advances in computational intelligence with practical applications in supply chain management, project scheduling, and resource allocation problems. The interdisciplinary nature of his research connects computer science, operations research, and industrial engineering, with particular emphasis on developing robust algorithms that can handle uncertainty and dynamic changes in real-world scenarios. Analysis of Dr. Essam's recent publication trends reveals a clear progression toward increasingly complex and large-scale optimization problems. His work has evolved from foundational research in evolutionary algorithms to addressing practical challenges in supply chain resilience, sustainable operations, and integrated decision-making systems. A notable trend is the increasing interdisciplinary nature of his research, with growing collaborations across engineering, business, and environmental science domains. His most recent work focuses on robust optimization approaches for inventory management, carbon-aware supply chains, and hybrid transportation systems involving electric vehicles and drones, reflecting contemporary challenges in sustainable operations. Dr. Essam's research has been consistently supported through academic collaborations and institutional research frameworks. His extensive publication record, including numerous journal articles in top-tier venues and book chapters, demonstrates sustained research productivity. His work shows strong patterns of collaboration with researchers across Australia and internationally, particularly with colleagues at UNSW and other Australian institutions. The research themes have evolved from fundamental algorithm development to increasingly application-focused work addressing real-world industrial challenges. Dr. Essam's teaching experience includes Computer Languages and Algorithms and Introduction to Programming (Java), indicating his contribution to foundational computer science education. His administrative role as Deputy Head of School for Research suggests leadership responsibilities in shaping the research direction of his school and supporting the research activities of colleagues.
Professor Regina Berretta is an Honorary Professor at the University of Newcastle's School of Information and Physical Sciences, specializing in Computing and Information Technology. With expertise spanning computer science, mathematics, and applied optimization, she has established herself as a leading researcher in metaheuristic methods for solving complex combinatorial problems. Currently serving as a Chief Investigator at the ARC Training Centre for Food and Beverage Supply Chain Optimisation, she applies her mathematical modeling skills to address critical challenges in food industry supply chains, with the Centre receiving over $2 million in funding to train next-generation researchers. Professor Berretta earned her PhD, Master of Engineering (Electrical), Bachelor of Mathematics, and Teachers Certificate from Universidade Estadual de Campinas in Brazil. Her educational background in computational and applied mathematics has provided the foundation for her extensive research career focused on integer programming and metaheuristic approaches to optimization problems. Her academic journey includes progression from Lecturer at the University of Newcastle (2003-2007) to various leadership positions including Head of Discipline of Computer Science and Software Engineering (2011-2014) and Assistant Dean of Equity, Diversity and Inclusion (2019-2022). Her research expertise centers on the design of mathematical models and development of efficient computational techniques to tackle large and complex combinatorial optimization problems across diverse application areas. Professor Berretta has made significant contributions to bioinformatics through her work on genetic signature identification from gene expression datasets, and to supply chain optimization through her research on perishable food inventory management, lot sizing, and scheduling problems. Her methodological specialties include memetic algorithms, evolutionary computation, and integer programming approaches that have proven effective for problems that are otherwise computationally intractable. Analysis of her recent publications reveals a strong interdisciplinary focus, with optimization techniques increasingly applied to food supply chain challenges while maintaining connections to bioinformatics applications. Her work demonstrates a consistent pattern of translating theoretical optimization methods into practical solutions for industry problems, particularly in the agricultural sector. Additionally, her more recent publications show growing engagement with gender equity issues in STEM fields, reflecting her leadership in relevant initiatives. Co-founder of HunterWISE, promoting girls and women in STEM Leader of Google CS4HS project for five consecutive years Recipient of over $4 million in research funding through 35 grants Author of more than 80 papers and book chapters Chief Investigator at ARC Training Centre for Food and Beverage Supply Chain Optimisation Professor Berretta has demonstrated exceptional leadership through her administrative roles and community initiatives. As co-founder of HunterWISE, she has developed a comprehensive approach to increasing female participation in STEM through school programs and professional networking events. Her leadership of the Google CS4HS project has directly impacted high school computer science education in the Hunter region. Her research collaborations span multiple disciplines and institutions, reflecting her ability to bridge theoretical computer science with practical industry applications, particularly in the food supply chain sector where her optimization models have demonstrated significant cost and waste reduction potential.
