Dr. Chen Liu is a Research Fellow at the School of Engineering, RMIT University. His research focuses on energy systems, smart grids, optimization algorithms, and the integration of renewable energy technologies. He is particularly interested in battery energy storage systems, electric vehicle (EV) infrastructure, and machine learning applications in power systems. Dr. Liu has contributed to advancing SCADA alarm management, real-time grid monitoring, and multi-objective optimization techniques. He is open to supervising Masters and PhD students in areas such as accelerated learning for neural networks and big data applications. His work emphasizes practical solutions for sustainable energy challenges, including optimal placement of EV charging stations, solar-PV integration, and frequency control in grids with high renewable penetration. Dr. Liu's research also explores data-driven approaches for energy market forecasting and grid reliability enhancement. He has published extensively in top journals and conferences, addressing topics ranging from quantum genetic algorithms to distributed training of neural networks.
Professor Tapabrata Ray is a distinguished faculty member at the School of Engineering and Technology, University of New South Wales (UNSW) Canberra. He serves as the founder and leader of the Multidisciplinary Design Optimization Research Group at UNSW, with his research profile available at www.mdolab.net. His office is located in Building 17 Room 202 at the University of New South Wales, ACT 2610, Australia, and he can be reached at +61 2 5114 5201 or t.ray@unsw.edu.au. Professor Ray's research interests span a wide range of optimization-related fields, with particular expertise in evolutionary algorithms, multi-objective optimization, and engineering design optimization. His work bridges computational intelligence with practical engineering applications across multiple domains including aerospace engineering, structural optimization, biomedical device design (particularly stents), and energy systems. His research approach often focuses on developing computationally efficient methods for solving complex optimization problems, with special attention to multi-fidelity approaches, surrogate modeling, and handling computationally expensive evaluations. Professor Ray's publication record demonstrates consistent high productivity across decades, with his most recent work (2023-2025) showing continued strong activity in evolutionary computation, multi-objective optimization, and biomedical applications. His research trends indicate growing interest in multi-concept optimization frameworks, stent design optimization, and applications in transportation and energy systems. His work often appears in top-tier journals including IEEE Transactions on Evolutionary Computation, Journal of Mechanical Design, and Swarm and Evolutionary Computation. Professor Ray has made significant contributions to the field of evolutionary algorithms and optimization, with applications spanning from aerospace engineering to biomedical device design. His work on multi-fidelity optimization, surrogate-assisted evolutionary algorithms, and multi-concept design frameworks represents cutting-edge research in computationally efficient optimization methods.
Dr. Gang Lei is a Senior Lecturer at the University of Technology Sydney (UTS), School of Electrical and Data Engineering. He holds a Ph.D. in Electrical Engineering (2009) from Huazhong University of Science and Technology, with postdoctoral research at UTS (2012–2016). His research focuses on AI-driven design optimization of electrical machines, electric vehicles, energy management systems, and control methodologies using digital twins and large language models. He has published over 300 papers (200+ journals, 140+ IEEE Transactions) with an h-index of 55. His awards include Best Paper Award (IEEE TEC 2024) and inclusion in Stanford’s Top 2% Scientists (2020–2024). He leads editorial roles for 6 Q1 journals including IEEE Transactions on Transportation Electrification and Industrial Electronics. His funded research includes advanced motor design for space robotics, battery management systems, and microgrid optimization. He collaborates internationally and serves as an assessor for national research projects in multiple countries. Teaching responsibilities include postgraduate courses on energy systems and optimization. Dr. Lei’s research group actively pursues multidisciplinary projects, emphasizing physics-informed AI and sustainable energy solutions. He currently supervises PhD candidates in electrical engineering and mathematics, focusing on emerging technologies like wireless power transfer and machine learning integration.
Associate Professor Yateendra Mishra specializes in renewable energy systems and smart grid technologies at Queensland University of Technology's School of Electrical Engineering & Robotics. He holds a PhD in Electrical Engineering (Power Systems) from the University of Queensland and has industry experience as a Transmission Planning Engineer at Midcontinent Independent System Operator (ISO). His research integrates renewable power systems modeling, distributed energy resources, and electricity markets. Recent projects include: Mitigating cyberattack risks in cyber-physical power systems Control systems for high-value distributed electrical storage Awards include the Advanced Queensland Fellowship (2016-2019) for enabling higher renewable penetration through smart inverter technologies. He mentors graduate students in power engineering and coordinates capstone projects. Current research explores grid stability under high renewable penetration and peer-to-peer energy trading frameworks.
Adam Berry is a Professor and Deputy Director of the Human Technology Institute at the University of Technology Sydney (UTS). He previously served as Deputy Director of the UTS Data Science Institute from 2021-2024. His work focuses on leading inclusive, responsible and innovative artificial intelligence for Australia, with emphasis on translating data into real-world impact through data curation, machine learning, and statistical approaches. Education: PhD in Computer Science, University of Tasmania (2004-2008) BSc (Hons) in Computer Science, University of Tasmania (1999-2003) Professor Berry's research spans ethical AI, energy systems, and accessibility. His work consistently bridges theoretical research with practical implementation, focusing on human-centered AI that delivers value while upholding rights and preventing harm. He has a strong track record in multi-disciplinary collaboration, bringing together expertise from computer science, social science, electrical engineering, and ethics. His recent publications reveal a strategic evolution from foundational work in energy systems and reinforcement learning toward increasing focus on ethical and responsible AI frameworks. While maintaining strong contributions to energy analytics (electricity price forecasting, carbon intensity prediction), his 2021 survey on ethical AI principles marks a significant pivot toward governance and implementation frameworks for trustworthy AI systems. Scientific Awards: CSIRO Collaboration medal (inaugural winner) Professor Berry has secured substantial funding from diverse sources including the Digital Health CRC, Australian Renewable Energy Agency, Department of Home Affairs, and industry partners. He has led multi-million dollar initiatives like the National Energy Analytics Research Program and currently oversees the Human Technology Institute's mission for responsible AI. His industry collaborations include partnerships with Reejig (ethical talent AI), Sydney Trains (delay prediction systems), and numerous energy sector organizations. He actively contributes to the Disability Research Network, applying data-driven approaches to improve outcomes in the disability sector. As part of the Trustworthy Digital Society research center, he advances human-centered AI through cross-disciplinary work that integrates technical expertise with social considerations. His leadership spans research design, capability development, and strategic partnership building across government, industry, and academic sectors.
Manuel Chica Serrano is a Senior Researcher at the University of Newcastle (Australia) and a Ramon y Cajal Senior Researcher at the University of Granada (Spain). He holds an Adjunct Lecturer position at the School of Information and Physical Sciences, University of Newcastle, where he conducted an Endeavour Research Fellowship (2016-2017). His interdisciplinary work bridges artificial intelligence , agent-based simulation , and marketing analytics . Education : BSc/MSc in Computer Science (University of Granada), PhD cum laude (University of Granada 2011) Research Areas : Metaheuristics, Machine Learning, Complex Systems, Agent-Based Modeling, Multiobjective Optimization With over 100 JCR publications (40+ Q1 journals) and 1.4k+ Google Scholar citations (h=20), his work focuses on evolutionary game theory applications in tourism sustainability, tax fraud detection, and maritime decarbonization. Recent studies include WPT retrofit modeling , startup user retention , and tax fraud dynamics . He supervises five PhD students and co-invented two international patents in AI applications. Scientific Contributions : CTO of ZIO Analytics , commercializing AI solutions Principal Investigator for €3M+ in R&D projects (including 2 EU-funded) 2017 Best Paper Award (IEEE CEC track) 2-year postdoctoral at four international institutions
Hemant Kumar Singh is an Associate Professor at the School of Engineering and Information Technology at the University of New South Wales (UNSW) in Canberra, Australia. He is based at the Australian Defence Force Academy campus and maintains an active research program in evolutionary computation methods for engineering design optimization. Institution: University of New South Wales (UNSW) School: School of Engineering and Information Technology Location: UNSW Canberra, Australian Defence Force Academy Email: h.singh@unsw.edu.au Education: PhD, University of New South Wales, Australia, 2011 B.Tech (Mechanical Engineering), Indian Institute of Technology, Kanpur, India, 2007 Research Interests: Dr. Singh's research focuses on developing efficient evolutionary computation methods for design optimization problems. His primary areas include multi-/many-objective optimization and decision-making, constraint handling in evolutionary algorithms, bilevel optimization, computationally expensive/surrogate-assisted optimization, and multi-concept optimization. His work bridges theoretical advances in optimization with practical engineering applications, particularly in design problems where computational resources are limited. Publication Trends: Dr. Singh's recent publications (2023-2025) demonstrate a strong focus on advancing multi-objective and bilevel optimization techniques, with particular attention to computationally expensive problems. His work integrates evolutionary algorithms with surrogate modeling, multifidelity approaches, and novel solution representation methods. A significant portion of his recent research addresses the challenge of multi-concept optimization, where design solutions involve fundamentally different conceptual approaches that need to be compared and optimized simultaneously. Awards and Recognition: Outstanding Reviewer Award, ACM Genetic and Evolutionary Computation Conference (GECCO) 2024 Best Paper Award, IEEE Congress on Evolutionary Computation (CEC) 2023 Best Paper Nomination, Parallel Problem Solving from Nature (PPSN), 2022 Winner, Competition on Online Data-driven Multi-objective Optimization, IEEE CEC 2019 Australia Bicentennial Fellowship 2016 Endeavour Australia Fellowship 2018 Research Supervision and Grants: Dr. Singh has successfully supervised numerous PhD and Masters students to completion, with many now holding prominent positions in academia and industry. He has secured significant research funding including two ARC Discovery Project Grants (2019-23, 2022-25), an Endeavour Australia Fellowship (2018), and an Australia Bicentennial Fellowship (2016). His research has been supported by collaborations with industry partners and international institutions across Australia, Germany, China, and the United States. Teaching: Dr. Singh teaches undergraduate courses including ZEIT 3500 Engineering Structures, ZEIT 3700 Mechanical Design 1, and ZEIT 4700 Mechanical Design 2 at UNSW Canberra.
Dr. Jonathan Clark is a Clinical Professor at the University of Sydney 's Central Clinical School. His research focuses on Head and Neck Surgery , with emphasis on Biomaterials , Surgical Oncology , and Virtual Surgical Planning . Recent work includes optimizing 3D-printed implants for mandibular repair and developing Clinical Quality Indicators for head and neck cancer care. Key Research Interests : Advanced surgical planning for jaw reconstruction Biomaterials for bone regeneration Clinical outcomes in head and neck cancer Recent Publications : 2025 study on surgical department research determinants in ANZ Journal of Surgery 2025 assessment of plasma-treated PEEK implants in Scientific Reports 2024 protocol for bioreactor-based periosteum preservation Advisees : Ali MESGARZADEH (Equity in Dental Rehabilitation) Yiqi WANG (Virtual Surgical Planning)
Dr. Hideaki Ogawa is an Adjunct Associate Professor in the School of Engineering at RMIT University, Australia. His research focuses on aerospace propulsion systems, hypersonic flows, and multi-objective design optimization. He specializes in scramjet combustor dynamics, plasma thrusters, and aeroelastic systems, with applications in space transport and small satellite propulsion. Research interests include aerospace engineering, fluid mechanics, mechanical engineering, and communications engineering. His work addresses challenges in hypersonic combustion, cavity enhanced mixing, and structural nonlinearities. Teaching interests span high-speed aerodynamics, computational fluid dynamics, and evolutionary algorithms. Recent research highlights include optimization of cusped field thrusters for microsatellites, numerical investigations of scramjet combustion, and nonlinear signal processing for aeroelastic systems. His publications emphasize advanced propulsion systems and their integration into space missions. Dr. Ogawa supervises projects on topics such as transonic buffet control, upstream cavity flows, and cracked fuel mixing in scramjets. His collaborations involve multidisciplinary teams addressing challenges in access-to-space systems and space traffic management. He holds a strong focus on practical applications of computational methods and systems engineering approaches.
Dr. Jinli Cao is a full-time Associate Professor in the Department of Computer Science and Information Technology at La Trobe University. She holds a BSc from Hebei University, China, and a PhD from the University of Southern Queensland, Australia (1997). Her research focuses on evolutionary computing, data privacy, deep learning for vulnerability assessment, and decision support systems. She has published over 150 papers in top venues such as VLDB and IEEE Transactions series. Dr. Cao leads an ARC-funded project on software vulnerability risk discovery and has secured three ARC grants. She has supervised 11 PhD, 2 Master’s, and 57 Honours students, many of whom work in academia and industries like Oracle and Commonwealth Bank. Teaching contributions include developing courses in databases, data warehouses, and artificial intelligence. Research Interests: Privacy-preserving data publishing and optimization Evolutionary algorithms for dynamic data partitioning Deep learning applications in cybersecurity and healthcare Graph-based machine learning for access control and anomaly detection Decision support systems and top-k query processing Her recent articles explore cutting-edge topics like privacy-preserving spatial crowdsourcing tasks, graph neural networks for traffic prediction, and cybersecurity frameworks for vulnerability prioritization. Awards include competitive ARC grants totaling $450,000 (2023-2025). She actively serves as an Associate Editor for Health Information Science and Systems and has examined over 100 PhD theses across Australian universities. Teaching highlights: Developed 20+ courses including Database Management Systems, Data Warehousing, and Artificial Intelligence. Coordinates units like Decision Support Systems and Intermediate Programming in Java.
Professor Troy Farrell is the Executive Dean of the Faculty of Science at Queensland University of Technology (QUT). He holds a PhD (QUT) and B.Sc (Hons) from the University of Newcastle. His expertise lies in applied mathematics and physical chemistry, focusing on industrial systems like batteries, solar cells, and biomass processing. He leads major research projects in electrochemical nano-diodes, metal-air batteries, and coal seam gas modeling. Professor Farrell has secured over $2.8M in external funding and led national initiatives such as the Mathematics in Industry Study Group (MISG) and the ATN Industry Doctoral Training Centre. He is a Fellow of the Queensland Academy of Arts and Sciences and has received prestigious awards for his research and teaching. His teaching focuses on mathematical modeling, partial differential equations, and calculus, emphasizing student-centered pedagogy and innovation. Key achievements include developing multiphase models for food drying, phase-field models for lithium-ion batteries, and population balance models for biomass pretreatment. Research Projects: Mathematical modeling of biofuel production from cellulosic materials Electrochemical nano-diodes using Poisson-Nernst-Planck models Optimization of lithium-air batteries for secondary power Multiscale modeling of porous materials with hybrid continuum/particle methods Agrochemical uptake in plant cuticles Awards: ANZIAM Mid-Career Research Award (2015) QUT Vice-Chancellor's Award for Partnerships (2013) Australian Government Citation for Teaching Excellence (2006) Teaching & Leadership: Professor Farrell pioneered innovative teaching methods recognized by the Carrick Institute, coordinating undergraduate and postgraduate programs in Mathematical Sciences. He has mentored over 13 doctoral students and actively bridges academia-industry collaboration through leadership roles in MISG and the ATN IDTC. His work emphasizes translating mathematical models into real-world solutions for energy, agriculture, and environmental sectors. Labs & Teams: He oversees interdisciplinary teams at QUT specializing in electrochemical systems, porous media modeling, and industrial mathematics. Current collaborations include projects with sugar cane industries, battery manufacturers, and coal seam gas operators to address challenges like biomass storage safety and energy storage optimization.
Professor Andrew Eberhard holds the position of Deputy Head of Department (Research and Innovation) in the Department of Mathematical and Geospatial Sciences (MGS) at RMIT University's School of Science. He has also served as Deputy Director of the Australian Mathematical Sciences Institute (AMSI) and currently sits on its board. His academic journey includes visiting professorships at institutions such as the University of Perpignan (France), Dalhousie University (Canada), and Blaise Pascal University (France). He has contributed significantly to optimization theory and applications, including roles as Co-chair of the AustMS special interest group (MoCaO) from 2018–2024 and Program Leader of the Platform Technologies Research Institute (2012–2015). Professor Eberhard's research focuses on nonsmooth analysis, convex functions approximation, optimization algorithms, and their applications in control theory, signal processing, and economic modeling. He has applied his theoretical work to real-world challenges like utility function estimation, air traffic flow management, and blood inventory optimization. His recent work includes advancements in stochastic optimization with integer variables and machine learning integration for enhancing algorithm performance. He has been an invited speaker at prestigious events such as the International Conference on Applied Mathematics (2024, Vietnam), Variational and Nonsmooth Analysis (2024, Hong Kong Polytechnic University), and ANZIAM's plenary session (2012, Australia). His teaching interests emphasize nonsmooth analysis and its interdisciplinary applications. Professor Eberhard has supervised numerous research projects, including those on hyperparameter optimization in machine learning, MIMO signal processing, and asset protection routing. His grants include leadership roles in developing algorithms for stochastic integer programming and multi-criteria decision-making under uncertainty. He collaborates actively through the Platform Technologies Research Institute and is affiliated with multiple academic journals as an editor or reviewer.
Prof Kai Qin is a Professor of AI and Data Science at Swinburne University, affiliated with the School of Science, Computing and Emerging Technologies. He holds roles including Director of the Intelligent Data Analytics Lab, Deputy Director of the Swinburne Space Technology and Industry Institute, and Vice President for Education at IEEE Computational Intelligence Society (CIS). Qin earned his B.Eng. from Southeast University (2001) and PhD from Nanyang Technological University (2007). His research focuses on Computational Intelligence (CI), encompassing Neural Networks, Evolutionary Computation, and Fuzzy Systems, with applications in Machine Learning, Remote Sensing, and Pervasive Computing. His work has garnered over 20k Google citations and recognition such as IEEE Fellow (2025). Key achievements include the 2012 IEEE Transactions on Evolutionary Computation Outstanding Paper Award and leadership in conferences like IJCNN 2022. He pioneered the Master of Data Science program at Swinburne (2018–2020) and leads initiatives in federated learning, onboard AI for satellite missions, and AI-driven medical diagnostics. Qin’s professional contributions span editorial roles in journals like Swarm and Evolutionary Computation and leadership in IEEE technical committees. His grants include SmartSat CRC projects for satellite AI and ARC-funded research on gravitational lensing and traffic analytics.
Associate Professor Andres Villegas Ramirez is affiliated with the University of New South Wales (UNSW) in the UNSW Business School , specifically the School of Risk and Actuarial Studies . He is also an Associate Investigator at the ARC Centre of Excellence in Population Ageing Research (CEPAR) , where he was previously a Research Fellow. Education: Doctoral studies at Bayes Business School (formerly Cass), London, focusing on mortality modelling and projection MSc in Industrial Engineering from Universidad de Los Andes, Colombia Research Interests: Andres specializes in mortality modelling , longevity risk management , and the application of analytics techniques in actuarial science and finance . His recent publications emphasize age-period-cohort models, actuarial valuation of insurance products, and demographic risk analysis. Key trends include interdisciplinary work combining actuarial science with public health and machine learning methodologies. Selected Publications (2025-2018): 2025: Return smoothing in pooled annuity products 2025: Age-Period-Cohort claims reserving models 2025: Socioeconomic mortality differentials in England 2024: Multi-state long-term care insurance valuation 2024: Variable annuity scenario selection via LASSO regression 2024: U.S. mortality improvement drivers 2022: Affine mortality models for age-cohort analysis 2022: Stacked regression ensembles for mortality forecasting 2022: Volatility management in pooled annuities 2022: Regularization approaches for age-period-cohort mortality projections 2021: Robustness in mortality improvement rate modelling 2020: Deprivation impacts on adult mortality inequalities 2020: NDC pension schemes and longevity heterogeneity 2018: Cause-specific mortality by socioeconomic factors 2018: Multiobjective reinsurance optimization with evolutionary algorithms Associated Institutions: ARC Centre of Excellence in Population Ageing Research (CEPAR) Bayes Business School (PhD alma mater) Universidad de Los Andes (MSc alma mater)
Roles and Affiliations: Distinguished Professor Saeid Nahavandi is the inaugural Associate Deputy Vice-Chancellor (Research) and Chief of Defence Innovation at Swinburne University of Technology. He leads defence innovation and research strategy, with a focus on autonomous systems, robotics, and AI. Previously, he served as Pro Vice-Chancellor (Defence Technologies) at Deakin University and founded its Institute for Intelligent Systems Research and Innovation. Research Focus: Specializes in robotics, haptics, autonomous systems, AI, and advanced modelling/simulation. His work bridges academia and industry, with collaborations spanning Airbus, Boeing, NASA, and NATO. He has secured over $150M in funding and established three tech startups. Key research areas include motion simulation, teleoperation systems, and defence technologies. Articles Overview: Over 1,300 publications span AI, robotics, and control engineering. Recent work emphasizes autonomous navigation reviews, uncertainty-aware AI, and motion cueing algorithms. His research addresses real-world applications like driver distraction detection and robotic ultrasound. Awards and Recognition: Recipient of the 2022 Clunies Ross Entrepreneur of the Year Award, 2021 Australian Space Awards Researcher of the Year, and multiple engineering excellence accolades. A Fellow of ATSE, IEEE, and other leading institutions. Grants & Industry Impact: Led ARC Training Centres for automated vehicles and energy storage. Notable grants include a $15M ARC Training Centre for Automated Vehicles in Rural/Remote Regions (2024–2029). Collaborates globally on defence, aerospace, and smart transportation projects. Labs & Teams: Heads Swinburne’s Defence Innovation Group and collaborates with Harvard University (as an Associate) and the University of Windsor (adjunct professor). Advises governments and industries on technology strategy and innovation.
