Yuxia Hu is a Professor at the University of Western Australia, affiliated with the School of Engineering (Civil, Environmental and Mining Engineering) and the School of Social Sciences, Planning and Transport Research Centre. Her research focuses on geotechnical engineering, particularly in large deformation FE analysis, offshore foundation systems, and soil-structure interaction. She has contributed to advancements in suction caissons, plate anchors, and computational mechanics, with applications in offshore wind energy and infrastructure stability. Research Interests: Large deformation FE analysis of soils, soil-structure interaction, offshore foundation systems, soil mechanics, and pavement engineering. Awards: Telford Premium, British Geotechnical Association Prize, and Significant Junior/Senior Paper Award. Grants: Leads projects on offshore anchors, carbon capture in pavements, and road maintenance optimization. Her work addresses challenges in geotechnical design and sustainable infrastructure, with a focus on numerical modeling and experimental validation. Collaborations span academia and industry, emphasizing practical solutions for complex soil-structure systems.
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.
Associate Professor John Pye leads research in high-temperature solar-thermal systems and industrial decarbonisation at the Australian National University's School of Engineering. He holds a BE/BSc (University of Melbourne) and a PhD (University of New South Wales) focused on solar thermal modelling. His work bridges engineering innovation and sustainability, with a focus on green steel production, CSP technologies, and hydrogen applications. As a Visiting Scholar at Sandia National Laboratories, he advanced solar thermal testing methodologies. Educations: Bachelor of Engineering (Mech.) and Bachelor of Science (University of Melbourne, 1997) PhD in System Modelling of Compact Linear Fresnel Reflectors (UNSW, 2008) His research interests include solar thermal energy systems, concentrated solar power (CSP), and hydrogen-based industrial processes. Notable contributions include system-level optimisation of CSP plants, techno-economic analysis of green steel production, and solar-thermal beneficiation of iron ore. His work often integrates AI for optimisation and free/open-source engineering software. Recent publications focus on solar thermal applications in steelmaking, particle-based CSP systems, and hydrogen plasma metallurgy. Projects include the Gen3 Liquids Pathway for CSP and solar-driven thermochemical processes. Collaborations span industry and academia, addressing decarbonisation challenges in steel production and energy storage. Supervises research in solar thermal engineering and low-carbon technologies, contributing to Australia's role in zero-emissions commodity production. Active in policy submissions related to green energy and manufacturing frameworks.
Dan Steinberg is a senior research scientist and team leader of the Decisions & Statistical Learning team at CSIRO Data61 in Canberra, Australia. His expertise lies in probabilistic machine learning, variational inference, Bayesian deep learning, causal inference, and their application to domains spanning synthetic biology, geospatial analytics, and algorithmic fairness. Education PhD in Computer Vision / Machine Learning (2013) – University of Sydney, Australian Centre for Field Robotics Bachelor of Engineering (Mechatronics, First-Class Honours) – University of Sydney (2008) Bachelor of Commerce (Finance) – University of Sydney (2008) Research Interests Steinberg’s core research agenda revolves around building scalable probabilistic models that can learn efficiently from limited or noisy data and provide principled uncertainty estimates. Key themes include: Variational Inference & Bayesian Deep Learning: developing lightweight yet powerful algorithms for approximate posterior inference in complex models (e.g., Aboleth, Revrand). Active Learning & Experimental Design: creating methods that decide which experiments or measurements will maximise information gain, with recent focus on in-silico protein engineering via Variational Search Distributions (VSD). Causal Inference: leveraging machine-learning tools to perform robust observational causal studies for evidence-based policy, including work on youth well-being and academic outcomes. Algorithmic Fairness: translating normative notions of equity into quantifiable objectives for regression-based decision systems. Large-scale Spatial Analytics: Landshark—an open-source TensorFlow toolkit for supervised learning on massive geospatial raster datasets. Notable Software & Tools Aboleth: A minimal-overhead TensorFlow framework for Bayesian deep learning. Landshark: Command-line tools for large-scale spatial inference. Revrand: Scalable Bayesian generalised linear models with non-conjugate likelihoods. libcluster: Extensible C++ library for hierarchical Bayesian clustering. Scientific Awards Oral Presentation Award – ICML 2025 Workshop on Scaling up Intervention Models (SIMS) Oral Presentation Award – NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty (BDU) Oral Presentation Award – NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning Spotlight Paper Award – NeurIPS 2014 (Extended and Unscented Gaussian Processes) Research Team & Collaborations As Team Leader – Decisions & Statistical Learning at CSIRO Data61, Steinberg directs a multi-disciplinary group that partners with government agencies (e.g., Jobs and Skills Australia, Australian Institute of Health and Welfare) and industry to deploy machine-learning solutions at scale. He has previously held roles as Principal Researcher at Gradient Institute (2019-2023), Senior Research Engineer at CSIRO Data61 (2016-2019), Researcher at NICTA (2013-2016), and Research Associate at the University of Sydney (2012-2013).
Dr. Huy Truong-Ba is a Lecturer at Queensland University of Technology (QUT), affiliated with the School of Mechanical, Medical and Process Engineering and the Centre for Data Science. He holds a Doctor of Philosophy from QUT. His research focuses on reliability and degradation modeling, maintenance optimization, and engineering asset management, with expertise in Markov decision processes and condition-based maintenance strategies. His academic journey includes roles as an Associate Supervisor for multiple PhD projects, such as 'Dispatch Optimisation for Concentrating Solar Tower Power Plant Under Uncertainty' and others in solar energy systems and mechanical engineering. He has collaborated with institutions like the Asset Institute and contributed to interdisciplinary projects in energy systems and transportation. Research interests span mechanical engineering, manufacturing engineering, and engineering practice, with a strong emphasis on applying data-driven methods to optimize maintenance and asset management. His work often integrates statistical modeling and stochastic optimization to address challenges in energy infrastructure, solar power, and industrial equipment degradation. Key collaborations involve Associate Professor Michael Cholette and Professor Tommy Chan, focusing on solar thermal systems, rail infrastructure maintenance, and material degradation under high-temperature conditions. Dr. Truong-Ba's contributions bridge theoretical models with practical applications, driving advancements in sustainable energy and infrastructure resilience.
Dr. Nameer Al Khafaf is a Lecturer in the School of Engineering at RMIT University, located at City Campus, Australia. His research focuses on renewable energy systems, smart grid technologies, and machine learning applications in energy management. He is actively involved in supervising research projects, including AI for Clean Energy and Sustainability and Multi-objective optimisation of battery storage systems . His research interests span photovoltaic generation forecasting, hydrogen hybrid energy systems, electric vehicle integration, and energy storage optimization. His work emphasizes leveraging deep learning and neural networks to address challenges in renewable energy integration and grid reliability. Dr. Al Khafaf’s recent articles highlight advancements in photovoltaic forecasting, hydrogen energy systems feasibility, and EV charge scheduling. These studies underscore his commitment to sustainable energy solutions and smart grid innovation. He is open to supervising Masters and PhD students in these areas. No scientific awards have been explicitly stated. His work involves collaborations with industry partners and international research teams, contributing to both academic and applied outcomes in energy systems.
Sobhan (Sean) Arisian is Associate Professor of Supply Chain and Logistics at La Trobe Business School. He leads the Sustainable Operations Management discipline and serves as Associate Investigator at the ARC Training Centre in Optimisation Technologies (OPTIMA). Research focuses on supply chain resilience, digitalization, and sustainability—particularly decarbonization of maritime transport and disaster relief logistics. Recent publications demonstrate strong emphasis on quantitative optimization models, with recurring themes in: Robust optimization for disruption management Sustainable logistics decarbonization Humanitarian supply chain coordination Quantum computing applications in operations Awarded the Inaugural Collaborative Research Award by BAM/ANZAM and Outstanding Reviewer recognition. Secured $382,742 from Australian Government for Quantum Enhanced Optimisation project and $234,400 DFAT National Focused Grant. Collaborates with University of Cambridge scholars and industry partners on digital transformation strategies. Editorial roles include Associate Editor for Transportation Research Part E and IEEE Transactions on Engineering Management.
Professor Marcus White is a distinguished academic and practitioner in architecture and urban design at Swinburne University of Technology, where he serves as Professor in the School of Design and Architecture. He co-directs the architectural firm Harrison and White Pty Ltd and leads the Spatio-Temporal Research Urban Design and Architecture Lab (STRUDAL) at Swinburne. His work bridges academia and professional practice, focusing on sustainable urban development, digital design, and healthcare environments. Education: PhD in Spatial Information Architecture, RMIT University (2009) Professor White’s research centers on innovative urban design solutions using computational methods such as parametric modeling, agent-based systems, virtual and augmented reality, and machine learning. His work addresses critical urban challenges including walkability, noise pollution, aging populations, and equitable access to public space. He also pioneers patient-centered design in stroke rehabilitation, integrating clinical insights with architectural innovation through Living Lab frameworks and VR-based co-design. His recent publications span topics such as pedestrian safety in metro stations, traffic noise annoyance mapping, AI-based SDG classification, and age-friendly urban design. These works reflect a consistent trend toward interdisciplinary, human-centered, and data-driven approaches to shaping healthier, more sustainable cities. Scientific Awards: National Citation for Outstanding Contributions to Student Learning Venice Biennale selection (2010, 2025) Victorian Architecture Medal (2018) Australian Institute of Architects Urban Design and Jackson Educational Awards (2018) European Healthcare Design Award (2022) RAIA Haddon Travelling Scholarship (2002) AIA National Emerging Architect Award Professor White actively supervises PhD students and has secured major grants from the Australian Research Council (ARC), The Florey Institute, and iMOVE Australia. His leadership in projects like NOVELL (Neuroscience Optimised Virtual Environments Living Lab) demonstrates a strong commitment to translating research into real-world impact. He collaborates extensively with clinicians, urban planners, and technologists to advance evidence-based design across sectors. Labs and Teams: Spatio-Temporal Research Urban Design and Architecture Lab (STRUDAL) NOVELL (Neuroscience Optimised Virtual Environments Living Lab) Collaborations with The Florey Institute of Neuroscience and Mental Health ARC Training Centre for Next-Gen Architectural Manufacturing
Micah Nehring is a Senior Lecturer at the School of Mechanical and Mining Engineering , The University of Queensland . He leads the High Performance Surface Mining Research Group , focusing on optimizing In-Pit-Crusher-Conveyor (IPCC) systems and advancing mine planning methodologies. His work bridges mathematical programming with practical mining challenges, particularly in ultimate pit limit determination and production scheduling . Research interests include: Mine Planning Production Scheduling Optimization IPCC Systems Integration Cut-off Grade Methodologies ESG Risk Incorporation Recent publications highlight trends in machine learning applications for coal mine roof characterization, electrification of surface mining fleets, and multi-objective optimization for sustainable underground operations. His work often combines mixed integer programming with discrete event simulation to address complex mining scenarios. Advising spans projects like: Forecasting Market Capitalisation via Neural Networks Battery Trolley Haulage Systems Hydro-Economic Modeling for Water Management Funding collaborations include Anglo American , NewCrest Mining , and Caterpillar Global Mining , with grants focused on carbon tax impacts , roof displacement prediction , and rock characteristics research.
Associate Professor Vivien Challis is a leading academic in Applied and Computational Mathematics at Queensland University of Technology (QUT), affiliated with the Faculty of Science and the School of Mathematical Sciences . She earned her PhD in Mathematics from The University of Queensland in 2009 and has since built an international reputation in structural optimisation, computational mechanics, and high-performance computing. Her research focuses on piezoelectric metamaterials , topology optimization , and GPU-accelerated computational methods , with recent work advancing robotics applications and biomedical prosthetics. She leads the 2022 Australian Research Council Discovery Project DP220102759 on optimising piezoelectric materials for robotics and contributed to ARC projects DP240102104 and DP170104307. 2025: GridapTopOpt.jl (Julia toolbox for level set optimization) 2025: Wave power absorption by floating plates (piezoelectric energy harvesting) 2025: FF-PINNTO framework (neural network-based optimization) In 2024, she received the JH Michell Medal from ANZIAM, recognizing her contributions to industrial and applied mathematics. Her teaching includes first-year calculus (MXB105) and second-year computational mathematics (MXB226), and she directed QUT's Mathematics Summer School (2022-2024).
Associate Professor Melih Ozlen serves as Deputy Head of Department (Academic Operations) in the Department of Mathematical and Geospatial Science within RMIT University's School of Science at the City Campus in Australia. With a strong academic background in operations research and optimization, he has established himself as a leading researcher in combinatorial optimization, mathematical programming, and multi-objective optimization. His research interests focus on practical applications of optimization techniques across various domains including healthcare logistics, wildfire management, network optimization, and power systems. Ozlen's work bridges theoretical optimization methods with real-world challenges, particularly in resource allocation problems under uncertainty. The publication record reveals a consistent research trajectory with emphasis on decomposition methods, evolutionary algorithms, and matheuristic approaches for solving complex optimization problems. His recent work shows increasing focus on healthcare applications while maintaining strong contributions to wildfire management and network optimization problems. As an active reviewer for prestigious journals including European Journal of Operational Research and Computers and Operations Research, as well as the Australian Research Council, Ozlen contributes significantly to the academic community. His professional memberships span key organizations including INFORMS, Australian Mathematical Society, and Australian Society for Operations Research. Ozlen actively supervises graduate students with current projects focusing on home care optimization, multi-objective mixed integer programming, and vehicle routing problems with applications to wildfire management. His teaching interests align with his research expertise in operations research, combinatorial optimization, mathematical programming, and multi-objective optimization.
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) .
Jack Collins is a Researcher and AI Research Scientist at Collaborative Robotics, focusing on robotics foundation models for collaborative robots. Previously, he served as a Postdoctoral Researcher at the University of Oxford's Applied Artificial Intelligence Lab and completed his PhD at Queensland University of Technology (QUT) in collaboration with CSIRO. His research emphasizes sim-to-real methods, robot simulation, and bridging the 'Reality Gap.' He holds a Bachelor of Engineering (Mechatronics) with First Class Honours from QUT. Education: PhD in Robotics (2018-2022), QUT/CSIRO BEng (Mechatronics) (Honours) (2014-2017), QUT Research Interests: Robot simulation and sim-to-real transfer Bimanual/mobile robotic manipulation Generative models and world modeling Task planning for long-horizon tasks Evolutionary robotics and component design Publications: Over 15 peer-reviewed articles focusing on simulation realism, robotic manipulation benchmarks, and generative modeling techniques. Key work includes the COMBO-Grasp framework and TWIST distillation method for sim-to-real transfer. Lab Affiliations: QUT Centre for Robotics (QCR), Oxford Robotics Institute's Applied AI Lab.
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.
Federica Sarro is a Professor of Software Engineering at University College London , where she serves as Head of the Software System Engineering group and leads the SOLAR group within the CREST centre . Her work bridges academic research and industrial applications in software engineering. Her research focuses on Search-Based Software Engineering , Empirical Software Engineering , and Software Analytics , addressing challenges in automated software management , optimisation , testing , and repair . She has pioneered advancements in Software Fairness , an emerging property of AI-enabled systems, and collaborates with global companies like Bloomberg , Google , Meta , and Microsoft . Professor Sarro has received the IEEE Rising Star Award (2021) for her scholarly and real-world impact. Her research has produced over 100 peer-reviewed publications and is highlighted in lectures on topics such as Automated Optimisation of Modern Software System Properties , Multi-objective Ensemble Generation , and Software Fairness . Awards: IEEE Rising Star Award (2021) She actively engages in research leadership, having established the SOLAR group and delivered invited talks worldwide. Her work emphasizes both theoretical innovation and practical deployment in modern software systems.
