جست‌وجوی استاد و پژوهشگر

• Construction Automation
• Workforce Management
• Digital Technologies in Construction
• +۳ مورد دیگر

Dr. Alireza Ahmadian Fard Fini is an Associate Professor at the University of Technology Sydney within the Faculty of Design and Society. With over 23 years of experience in the construction industry, his work bridges professional practice, teaching, and research, focusing on construction automation and workforce management . His research aims to enhance construction productivity through digital technologies and personalized workforce solutions. Education: PhD, University of New South Wales, Australia MEng, University of Calgary, Canada MSc, Iran University of Science and Technology, Iran BSc, Shiraz University, Iran Dr. Fini’s research spans construction automation (off-site processes, digital integration) and workforce management (skill development, safety). His recent publications highlight deep learning applications for progress monitoring, drone technology in material handling, and sustainable practices in timber construction. Collaborative projects with industry partners emphasize data analytics and cloud-based deployment for practical outcomes. Scientific grants include the CRC-P Industrialization of Nail-Laminated Timber , Beverly Homes’ Industrialization of Dowel Laminated Timber , and Edwards Scholarship for prefabricated timber systems . His teaching portfolio includes Design Team Management , Site Establishment , and Time Management at UTS. Future work will focus on standardizing timber panel stability , expanding UAV applications , and aligning mental health research with policy frameworks.

• Rock Mechanics
• Civil Engineering
• Mining Geotechnics
• +۴ مورد دیگر

Professor Anna Giacomini is a leading academic in Rock Mechanics and Civil Engineering at the University of Newcastle. She holds a PhD from the University of Parma, Italy, and has been at the University of Newcastle since 2005. Her roles include Director of the Priority Research Centre for Geotechnical Science and Engineering and Deputy President of the Academic Senate (Research). She specializes in rockfall hazard analysis, mine geotechnics, and numerical modeling of geomechanical systems. Her research focuses on improving safety in mining and civil environments, with over $7.5M in funding and 140+ publications. Key areas include rockfall trajectory analysis, energy absorption in safety barriers, and drapery systems. She has led 20 major projects through ACARP and pioneered low-cost photogrammetric monitoring systems for rock slopes. Professor Giacomini is also a co-founder of HunterWiSE, promoting women in STEM. She has received prestigious awards such as the 2022 NSW Premier’s Engineering Prize and the 2019 John Booker Medal. Her administrative roles include membership in the ARC College of Experts and leadership in gender equity initiatives. Her technical contributions span experimental and numerical rock mechanics, including advancements in discrete element modeling (DEM) and stochastic approaches for discontinuity shear strength prediction. She collaborates internationally with institutions like the Colorado School of Mines and the University of Bologna.

• Computational Mechanics
• Structural Crashworthiness
• Additive Manufacturing
• +۵ مورد دیگر

Professor Qing Li is a faculty member at the School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney. He obtained his PhD from Sydney in 2000, underwent postdoc training at Cornell University (2000-2001), and held academic roles at James Cook University (2004-2006). He joined Sydney in 2006 via a Sesqui senior lectureship, becoming Associate Professor in 2010 and Professor in 2014. He served as Director of Postgraduate Studies (2007-2012) and Director of Biomedical Engineering (2017-2019). Education: PhD (University of Sydney, 2000), ME (UTS), ME (Hunan) His research focuses on computational design, multidisciplinary optimization of nonlinear and time-dependent multifunctional materials, and biomedical applications. Key areas include additive manufacturing , biomechanics , and machine learning in structural reliability. Recent publications emphasize fracture modeling in biomaterials, reliability analysis, and tissue scaffold design. Recent publications highlight Bayesian learning for robotic systems, probabilistic transformation in reliability analysis, and phase field fracture models for additively manufactured composites. Collaborations with industry partners like Cochlear and Stryker span ARC , NHMRC , and MRFF projects. Awards: Clarivate Highly Cited Researcher (2020), Top 50 Australia Research Leader (2020), APACM Computational Mechanics Award (2016) Fellowships: ARC Future Fellow (2013-2017), ARC Australian Postdoctoral Fellow (2001) He supervises PhD students in projects like epidermal electrodes , Silver Diamine Fluoride remineralization , and virtual surgical planning . His leadership extends to the Centre for Advanced Materials Technology and editorial roles in computational methods journals.

• Water Engineering
• Hydrologic Modelling
• Hydraulic Modelling
• +۵ مورد دیگر

Dr. Michael Leonard is an Associate Professor in the School of Architecture and Civil Engineering at the University of Adelaide, affiliated with the Environmental Engineering department. His research focuses on applying numerical and statistical models to environmental engineering challenges, particularly flood risk assessment, hydrological modeling, and water security. He has expertise in stochastic rainfall modeling, multivariate extremes, and climate change impacts. Dr. Leonard’s work emphasizes understanding hazardous environmental systems and improving flood risk management strategies. He is eligible to supervise Masters and PhD students in these areas. His research interests include flood modeling, climate adaptation, and sustainable water resource management. He has contributed to projects such as coastal flood risk analysis, brackish groundwater desalination, and global hydrological change detection. His recent articles highlight trends in flood magnitude, rainfall extremes, and the integration of climate indices to predict hydrological extremes. He has also explored the implications of climate change on water distribution systems and infrastructure resilience. Dr. Leonard collaborates on initiatives like the Global Streamflow Indices and Metadata Archive (GSIM), advancing big data applications in hydrological analysis. His work bridges theoretical models with practical engineering solutions, addressing real-world challenges in water security and environmental sustainability.

• Smart Energy Systems
• System Optimization
• Multi-Objective Optimization
• +۹ مورد دیگر

Associate Professor Wei-Yu Chiu is an academic at Deakin University, affiliated with the Faculty of Science, Engineering and Built Environment/School of Information Technology. His research spans system optimization, multi-objective optimization, evolutionary computation, machine learning, and control theory, with applications in smart energy systems, control systems (bilinear matrix inequality), and robotics (warehouse automation). His work focuses on smart energy systems (demand response), control systems (bilinear matrix inequality), and robotics (warehouse automation). He actively supervises Masters and PhD students and seeks postdoctoral collaborators through the Deakin Fellowship. Recent publications (2023–2026) emphasize multiagent reinforcement learning for microgrid resilience, energy-efficient textile manufacturing with deep transfer learning, and bilinear matrix inequality optimization for control systems. Topics include blockchain-enabled energy trading, path planning in robotics, and risk-constrained battery utilization. Education: PhD in Mathematics, National Tsing Hua University, Hsinchu, Taiwan Collaborations are centered on multirobot systems, transactive energy, and synthetic data generation for energy networks.

Darryl Veitchمشاهده پروفایل

استاد

• Computer Networking
• Traffic Modelling
• Parameter Estimation
• +۴ مورد دیگر

Darryl Veitch is a Professor in the School of Electrical and Data Engineering at the University of Technology Sydney (UTS), affiliated with the Global Big Data Technologies Centre. He holds a PhD in Mathematics from the University of Cambridge (1990) and a BSc (Hons) from Monash University (1985). His research focuses on computer networking, including traffic modeling, parameter estimation, active measurement, and clock synchronization over networks. A Fellow of the IEEE, he has held academic positions globally, including as a Professorial Research Fellow at the University of Melbourne (2000–2014). Education: PhD in Mathematics, University of Cambridge, 1990 BSc (Hons) in Mathematics, Monash University, 1985 Key Roles: Professor at UTS since 2015 Professorial Research Fellow at University of Melbourne (2000–2014) His research interests span network timing, security, and measurement methodologies. Notable contributions include work on leap second implications for global timing systems and robust synchronization protocols. He has authored over 176 research outputs and contributes to academic leadership through conference organization and editorial roles. Awards: Fellow of the IEEE (recognized for contributions to network measurement and synchronization). Teaching & Supervision: Specializes in Computer Networking, Signal Processing, and Information Theory. Taught subjects like Transmission Systems (2016) and Web Systems (2018–present). Labs/Teams: Active contributor to UTS’s Global Big Data Technologies Centre, focusing on scalable network measurement and timing infrastructure.

Guy McCauleyمشاهده پروفایل

پژوهشگر ارشد

• Wave-Structure Interaction
• Hydrodynamics
• Offshore Renewable Energy
• +۳ مورد دیگر

Guy McCauley is a Research Fellow at the UWA Oceans Institute within the School of Earth and Oceans at The University of Western Australia. His research focuses on wave-structure interaction problems and the hydrodynamics of floating and shallowly submerged bodies, particularly in offshore renewable energy systems. Currently, he is engaged in field data analysis for the TetraSpar offshore floating wind turbine. Education: PhD in Hydrodynamics (2021), The University of Western Australia B.Eng. (Mechanical Engineering, Honours) (2016), The University of Western Australia B.Sc. in Physics (2016), The University of Western Australia Research Interests: His work addresses sustainable energy solutions and fluid dynamics challenges, including wave energy converters, computational fluid dynamics (CFD), and fluid-structure interaction. He contributes to UN SDGs related to affordable and clean energy (SDG 7), sustainable cities and communities (SDG 11), and climate action (SDG 13). Notable Projects: Leading the 'Advancing ocean renewable energy systems through physics and machine learning' grant (2025–2027), funded by The Matariki Network of Universities. Labs/Teams: Collaborates with interdisciplinary teams focused on offshore energy systems, including partnerships with researchers like Dr. Orszaghova and Dr. Wolgamot.

• Machine Learning
• Evolutionary Computation
• Multiobjective Optimization
• +۱۰ مورد دیگر

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.

Jeya Jeyakumarمشاهده پروفایل

استاد

• Mathematical Optimization
• Global Optimization
• Optimization under Uncertainty
• +۷ مورد دیگر

Professor Jeya Jeyakumar is a leading applied mathematician at the School of Mathematics and Statistics of the University of New South Wales (UNSW) , internationally recognized for pioneering contributions to mathematical optimization. His work bridges rigorous theoretical analysis with practical computational methods, advancing fields like global optimization, robust decision-making, and machine learning-inspired models. PhD in Optimization, University of Melbourne His research focuses on transforming complex mathematical concepts into robust optimization frameworks for uncertainty quantification, risk minimization, and multi-stage decision-making. Key applications include medical decision support tools (e.g., Alzheimer’s detection via handwriting analysis), radiation therapy planning, and Huntington’s disease characterization. Recent work spans distributionally robust optimization, polynomial optimization, and convexifiable systems. His 15 most recent publications address topics like data-driven optimization over measure spaces, adjustable robustness in medical contexts, and algebraic approaches to fuzzy sets. 2025: Marguerite Frank Award for EURO Journal on Computational Optimization 2019: Joint winner of Journal of Global Optimization Best Paper Prize 2017: Optimization Letters Best Paper Prize Professor Jeyakumar has secured multiple ARC Discovery Project grants (e.g., $471,300 in 2025 for risk-aware optimization) and industry collaborations. He supervises HDR students in areas like two-stage robust optimization and feature selection under uncertainty.

• Fuzzy systems
• Computational mathematics
• Numerical optimization
• +۵ مورد دیگر

Professor Gleb Beliakov is a faculty member at Deakin University's School of Information Technology within the Faculty of Science Engineering and Built Environment. He has held roles including Associate Head of School (Research) from 2009–2012 and currently leads the Computational Intelligence and Data Analytics Research cluster and the Data to Intelligence research centre. His expertise spans fuzzy systems, computational mathematics, numerical optimization, and high-performance computation. Education: PhD and MSc from the Russian Peoples Friendship University. Research Interests: Focuses on fuzzy systems, aggregation functions, parallel programming, and their applications in decision-making and data analysis. Grants: Includes projects on Trusted Autonomy (CRC), Satellite Communication Systems (Defence), and Explainable AI (ARC). His research emphasizes fuzzy measures, Choquet integrals, and their use in addressing complex problems like resource optimization and multimedia piracy. He has supervised numerous PhD and master's students, contributing to advancements in preference mining, image processing, and watermarking techniques.

Bing Wangمشاهده پروفایل

پژوهشگر

• Multi-objective Optimization
• Bilevel Optimization
• Evolutionary Computation
• +۴ مورد دیگر

Dr Bing Wang is a Research Associate at UNSW Canberra under the School of Engineering and Technology . His work focuses on developing advanced optimization algorithms for computationally expensive problems, particularly in multi-objective bilevel optimization and evolutionary computation . He has published extensively on hypervolume-based infill criteria, surrogate modeling, and solution transfer strategies. Current research emphasizes Creating resource-efficient algorithms for bilevel optimization problems Exploiting variable associations to enhance evolutionary algorithm performance Integrating kriging-based methods with classical optimization criteria His publication record spans high-impact venues like Soft Robotics , IEEE Access , and Complex and Intelligent Systems , with a strong focus on applying computational intelligence to real-world engineering and data science challenges. Collaboration networks include researchers like Himanshu Kumar Singh and Tapabrata Ray .

• Artificial Intelligence
• Machine Learning
• Optimisation
• +۱۳ مورد دیگر

Dr. Asef Nazari is a Senior Lecturer in Mathematics at Deakin University's School of Information Technology, Faculty of Science Engineering and Built Environment. With over two decades of experience, his work spans mathematical modeling, optimization, and AI applications in energy systems, supply chains, and cybersecurity. Education : PhD (University of Ballarat), MSc (Amirkabir University of Technology), BSc (University of Tabriz), Graduate Certificate in Higher Education Teaching (Deakin University) Research : Focuses on Electricity network planning with renewables Optimization techniques (non-smooth, integer programming) AI/ML for cryptocurrency markets and causal inference Community battery systems for EV charging Soft happy coloring in network analysis Supply chain and project scheduling optimization Grants : Leads industry collaborations like the REACH Scholarship (2025–2028) and has secured funding from Monash University/ClimateWorks and The Ian Potter Foundation. Teaching : Developed the 'Mathematics for AI' master's unit, emphasizing real-world problem-solving in optimization, operations research, and data analysis. Supervision : Currently guides 8 PhD students in topics like AI-driven authentication security and land-use modeling. Awards : Recognized as a Fellow of the Higher Education Academy (FHEA)

• Intelligent Transportation Systems
• Vehicular Cyber-Physical Systems
• Traffic Flow Modeling
• +۳ مورد دیگر

Dongyao Jia is a Senior Research Fellow at the School of Civil Engineering, The University of Queensland. His research focuses on intelligent transportation systems, vehicular cyber-physical systems, and traffic flow modeling, with applications in energy harvesting, car-following model calibration, and vehicle-to-everything communication. Research Themes: Traffic flow prediction, eco-driving optimization, vehicular networks, and cyber-physical system design Key Collaborations: Working with researchers in transportation engineering, computer science, and energy systems Recent publications highlight his work in integrating deep learning with traffic dynamics for acoustic energy harvesting and developing adaptive frameworks for car-following models. His studies on multi-agent reinforcement learning for C-V2X networks and heterogeneous platooning communication demonstrate technical depth across transportation and computer science disciplines. While no specific awards are mentioned in the text, his extensive publication record across high-impact journals like IEEE Transactions on Intelligent Transportation Systems and Transportation Research Part C indicates significant scholarly contributions. Technical Expertise: V2X communication protocols, traffic simulation platforms, machine learning integration for transportation Application Areas: Urban traffic management, sustainable mobility systems, vehicular safety networks

• Machine Learning
• Deep Learning
• Natural Language Processing
• +۲ مورد دیگر

Dr. Sripana Saha is an Associate Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Patna, India. With over 400 publications and an h5-index of 40, her research spans machine learning, deep learning, natural language processing, multiobjective optimization, and biomedical information extraction. She is recognized globally through prestigious fellowships and awards. Editorial Roles: Associate Editor for IEEE Transactions on Artificial Intelligence, IEEE/ACM Transactions on Computational Biology and Bioinformatics, and others. International Collaborations: Visited University of Heidelberg, University of Trento, University of Kyoto, and more as a visiting scientist. Her work has been cited 8,333 times (Google Scholar) and she was included in Stanford University's top 2% scientists list in Artificial Intelligence and Image Processing for four consecutive years. Key Scientific Awards: Lt Rashi Roy Memorial Gold Medal, Google-India-Women-in-Engineering-Award-2008, NASI-Platinum-Jubilee-Award-2016, SERB-Women-In-Excellence-Award-2018, Pattern-Recognition-Letters-Editor-Award-2023. Fellowships: Fulbright-Nehru-2024, Humboldt Research Fellowship, CNRS fellowship, and others. Actively engaged with ACM as an ACMI-W Council member (2023-2027) and organizer of the ACM India Winter School on AI/ML for Industry 4.0.

Max Wardمشاهده پروفایل

مدرس

• Algorithms
• Data Structures
• Computational Biology
• +۳ مورد دیگر

Max Ward is a Lecturer at The University of Western Australia (UWA), affiliated with the School of Physics, Maths and Computing and the Australian Centre for RNA Therapeutics in Cancer. His roles include teaching in Computer Science and Software Engineering while conducting interdisciplinary research in algorithms, computational biology, and AI/ML. He holds a PhD from UWA (2019) and has held postdoctoral positions at the University of Adelaide and Harvard University. Education: PhD in Computer Science, UWA (2019) Postdoctoral Researcher at Harvard University (until Dec 2022) Research interests focus on algorithms, graph theory, RNA structure prediction, and machine learning applications in healthcare and cybersecurity. Key projects include developing RNA folding algorithms (e.g., JAX-RNAfold) and cybersecurity frameworks for network hardening. Grants: "Computational Creativity to Cross Cultural Frontiers" funded by Australia's Department of Foreign Affairs and Trade (2022–2024) Labs/Teams: Collaborates with the Australian Centre for RNA Therapeutics in Cancer and contributes to UWA's interdisciplinary research initiatives.