جست‌وجوی استاد و پژوهشگر

• Hydrogen for Iron/Steelmaking
• Electric Vehicle Capacity Expansion Modelling
• Industry Decarbonisation
• +۳ مورد دیگر

Changlong Wang is an Academic Visitor at the University of Oxford's School of Engineering Science. He leads projects on hydrogen energy, green steel supply chains, and electric vehicle infrastructure. He co-leads the IEA's proposed 'Hydrogen for Iron/Steelmaking' task and represents Australia in multiple IEA Hydrogen TCP initiatives. His work integrates machine learning optimization for decarbonization strategies. Education: BEng (1st Hons) from Australian National University PhD from University of Melbourne Research Interests: Focuses on hydrogen applications in steelmaking, EV charging grid impacts, and industrial decarbonization through advanced modeling. Active in global energy policy collaborations like the RACE for 2030 CRC and Australian Clean Energy Council. Awards: Includes the 2023 Australian Eureka Prize for his team's hydrogen strategy work, alongside multiple postgraduate awards recognizing academic and industry contributions. Advising & Grants: Collaborates with governments (South Australia), international bodies (IEA), and industry leaders (Port of Rotterdam). Leads the South Australian green iron supply chain study and contributes to the Electric Vehicles theme within RACE CRC. Labs/Teams: Member of the Systems Engineering Research Group, driving interdisciplinary solutions for energy transition challenges.

• Breast Cancer
• Radiology
• Radiation Dose
• +۳ مورد دیگر

Sahand Hooshmand is a Lecturer in the Discipline of Medical Imaging Sciences at the University of Sydney and a Radiographer at Lumus Imaging. He holds a BMedSci (2015), MDR (2018), and PhD (2023). His research focuses on breast cancer imaging, radiation dose optimization, and associated risks. He coordinates the Medical Image Optimisation and Perception Group (MIOPeG) and edits the Journal of Medical Radiation Science (JMRS). Education: Bachelor of Medical Science (2015) Master of Diagnostic Radiography (2018) PhD in Medical Imaging (2023) Research Interests: Screening mammography efficacy, radiation dose minimization, and risk-benefit analysis in diagnostic imaging. His work emphasizes translating research into clinical practice to improve patient safety. Awards: 2021 Teaching Award for fostering an inclusive and effective learning environment in undergraduate radiography education. Advising & Grants: Supervises Putu Irma Wulandari on AI performance assessment in breast imaging. Engages in collaborative grants focused on medical imaging technology and radiation safety. Labs/Teams: Lead of MIOPeG, focusing on optimizing imaging protocols; editorial board member of JMRS, advancing radiography science through peer-reviewed publishing.

Giorgos Petrouمشاهده پروفایل

پژوهشگر ارشد

• Building Physics
• Urban Modelling
• Indoor Environmental Quality
• +۶ مورد دیگر

Giorgos Petrou is a Research Fellow in Building Physics and Urban Modelling at the UCL Institute for Environmental Design and Engineering, and a Doctoral Researcher (MPhil/PhD) at the UCL Energy Institute. His work focuses on integrating building simulation, environmental data, and health equity to address climate change impacts on indoor environments. He contributes to the NIHR-funded Health Protection Research Unit (HPRU) on Environmental Change and Health, examining indoor air quality and ventilation in residential settings. Giorgos holds a BSc in Physics from the University of Warwick, an MRes in Energy Demand Studies from UCL, and is nearing completion of a PhD on Bayesian calibration of archetype-based indoor temperature models. His research spans thermal simulation, data-driven statistical methods, and causal inference in built environment studies. He has presented at conferences including the AIVC Conference and Building Simulation and Optimisation Conference. His research interests emphasize the intersection of climate adaptation, housing policy, and health equity, with a focus on vulnerable populations such as care home residents. He explores trade-offs in natural ventilation strategies, model validation methodologies, and participatory approaches to policy design.

• Operations Research
• Multi-Objective Optimization
• Transportation Networks
• +۳ مورد دیگر

Matthias Ehrgott is a Professor in the Department of Management Science at the Lancaster University Management School (LUMS). His research focuses on multi-objective optimization, operations research, transportation systems, and healthcare logistics. He leads the OptiWaSP project (2022–2025), which designs efficient walking school bus routes integrating optimization and instance generation. He is affiliated with the Centre for Transport & Logistics (CENTRAL), STOR-i Centre for Doctoral Training, and Health Systems Optimisation groups. His editorial roles include serving on the editorial boards of EURO Journal on Decision Processes , Optimization , 4OR , and others. Recent work emphasizes applications in railway timetabling, radiation therapy planning, and sustainable urban mobility. He advises PhD students like Malek Almousa, an Associate Lecturer. Key research themes include bi-objective optimization, algorithm design for multi-criteria problems, and real-world implementations in healthcare and transportation. His projects bridge theoretical advancements with practical solutions for complex decision-making environments.

• Material discovery and design
• Density functional theory
• Artificial intelligence
• +۱۲ مورد دیگر

Dr Sherif Abbas is a Research Fellow at the Applied Artificial Intelligence Institute (A2I2) within Deakin University, Australia. Since 2021 he has held this role after being awarded the competitive Alfred Deakin Postdoctoral Research Fellowship at the Institute for Frontier Materials. His work is positioned at the intersection of material science, physics, chemistry and artificial intelligence, leveraging cutting-edge AI methodologies to solve complex challenges in energy storage, sensing and computational materials discovery. Education PhD in Physics, University of Sydney (2017) Research Interests Abbas’s research focuses on the accelerated discovery and rational design of advanced functional materials through the synergistic use of density-functional theory (DFT) and state-of-the-art machine-learning techniques. Specific thrusts include: Rechargeable battery chemistries (Li-ion, solid-state, Al-rich cathodes) Solar-energy-harvesting and photovoltaic materials Supercapacitor and superionic conductor design Gas-sensing surfaces and CO₂-capture frameworks Superconducting, ferroelectric and multiferroic compounds 2D van der Waals heterostructures and their optoelectronic applications His methodological toolkit spans Bayesian optimisation, generative models, graph neural networks and physics-informed machine-learning potentials that enable multiscale simulation from the atomic level to device performance. Publication Trends Across 103 outputs (2019–2025), Abbas demonstrates a clear trajectory toward physics-informed AI for materials. There is a marked concentration on energy-storage interfaces (solid-state electrolytes, dendrite suppression) and on low-dimensional systems where quantum confinement and van der Waals interactions govern functionality. Recent work increasingly couples rigorous first-principles data with scalable ML surrogates, underscoring a shift from static property prediction to dynamic, device-relevant simulations. Scientific Awards & Fellowships Alfred Deakin Postdoctoral Research Fellowship (Deakin University, 2021–present) Doctoral Supervision & Funding Abbas currently co-supervises two doctoral candidates: Thuy Linh La: "Enhancing Scalability of Machine Learning Models for Material Simulation" Hajer Abdulhafid Mohamed Derbi: "Applied Artificial Intelligence in Dental Field" Both projects are embedded within Deakin’s Applied Artificial Intelligence Initiative and benefit from internal fellowship funds and external ARC linkage grants coordinated by A2I2. Laboratory & Entrepreneurial Activities He is an integral member of the cross-disciplinary teams at A2I2, collaborating closely with the Institute for Frontier Materials and external partners across Australia. In parallel, he founded mathpractice.xyz (2023–present), an educational technology venture aimed at democratising advanced mathematics and AI training resources for students and early-career researchers.

• Control Theory
• Artificial Intelligence
• Robotics
• +۵ مورد دیگر

Dr. Hamid Abdi is a Senior Lecturer in Engineering Management at the School of Engineering, Deakin University. He holds a PhD from Deakin University (2011) and has over 20 years of combined academic and industrial experience in control systems, robotics, and engineering education. His research focuses on control theory (time-delay systems, fault tolerance), robotics (dynamics, teleoperation), and industrial automation (SCADA, PLC systems). He has supervised 12+ doctoral/master students and led projects in energy systems, medical robotics, and IoT applications. Education: BEng/MEng (Control Engineering, Sharif University, 1998-2000), PhD (Deakin University, 2011). Professional affiliations include Senior Membership in IEEE and collaboration with Colorado State University. Research interests span AI on hardware systems, dynamical systems modelling, entrepreneurship in engineering, and optimisation. Recent projects include robotic ultrasound systems (HaptiScan), energy storage control software, and haptic teleoperation for medical diagnostics. His work has led to multiple awards, including the Alfred Deakin Best Thesis Medal (2013) and VC Staff Award (2015). Teaching areas: Process control, robotics, entrepreneurship, signal processing Grants: $542,594 in industry-funded research (e.g., Telstra, Energys Australia) Labs/Teams: Institute for Intelligent Systems Research and Innovation, Centre for Intelligent Systems Research

• Artificial Intelligence
• Machine Learning
• Operations Research
• +۵ مورد دیگر

Dr. Yuan Sun is a Lecturer in Business Analytics and Artificial Intelligence at La Trobe University's La Trobe Business School. His research focuses on leveraging machine learning for combinatorial optimization, including problem reduction methods and hybrid algorithms. He has contributed to top-tier journals like IEEE Transactions on Pattern Analysis and Machine Intelligence and conferences such as ICML and NeurIPS. Sun collaborates with researchers from institutions like Monash University and Singapore Management University, and is affiliated with the ARC Training Centre in Optimisation Technologies (OPTIMA). He co-organized the ACM GECCO 2024 conference and serves on program committees for major AI conferences. Academic Position: Lecturer at La Trobe University (2022–present) Education: PhD in Artificial Intelligence (University of Melbourne), BSc in Applied Mathematics (Peking University) Research interests span machine learning, operations research, and optimization, with notable work on integrating AI with digital twins for sustainable power grids and federated learning frameworks like F3KM. Sun's methods enhance constraint programming and ant colony optimization through supervised learning, addressing complex real-world problems. Grants and collaborations include the 'Quantum Enhanced Optimisation for Energy Efficient Data Centres' project and consulting for the Australian mining workforce analysis. Teaching roles include Algorithms & Analysis and Evolutionary Computing, with joint supervision of PhD projects on combinatorial optimization and mixed-integer programming.

• Numerical Geophysics
• Groundwater Modelling
• Mantle Convection
• +۳ مورد دیگر

Dr. Sia Ghelichkhan is a Tenure-Track Lecturer at the Research School of Earth Sciences (RSES) and member of the Institute for Water Futures at ANU. She holds a PhD in Geophysics from Ludwig Maximilian University of Munich (2019). Her expertise lies in numerical geophysics, particularly in developing large-scale models for groundwater systems, mantle convection, and glacio-isostatic adjustment. As co-lead of the Geoscientific Adjoint Optimisation Platform (G-ADOPT), she integrates advanced numerical methods to address Earth system dynamics. Her research combines adjoint data assimilation techniques with observational data to constrain mantle flow and geodynamic processes. Research interests focus on continental-scale groundwater modelling, mantle rheology, and dynamic topography. She leads projects on mantle convection retrodictions (e.g., early Cenozoic Atlantic region) and collaborates on global geodynamic models. Supervised student: Ruby Turner. Affiliations include the Environmental Geodesy Research Group and Climate & Ocean Geoscience Department. Active in high-resolution mantle flow simulations and exascale computing initiatives (e.g., TerraNeo). Awards: Not explicitly listed in provided texts. Grants include ANU Futures Scheme 2.0 and projects on sea-level modelling, geodynamic inversion, and mantle flow reconstructions. Laboratory involvement through G-ADOPT platform development.

Xiaojun Zengمشاهده پروفایل

استاد

• Machine Learning
• Computational Intelligence
• Decision Support Systems
• +۵ مورد دیگر

Prof. Xiaojun Zeng is a Professor of Machine Learning and Optimisation at the Department of Computer Science, University of Manchester. He holds a B.Sc. in Mathematics from Xiamen University and a Ph.D. in Computation from the University of Manchester. His research focuses on computational intelligence, machine learning, decision support systems, computational finance, health informatics, and data mining. He has published over 200 papers and contributed to projects like the EU-funded IoTRAIN initiative, aiming to modernize IoT education in Iran and Iraq. He has received prestigious awards, including the Microsoft European Retail Application Developer Award (2003) and the European IST Award (1999). His work spans academic leadership roles, including serving as an Associate Editor for IEEE Transactions on Fuzzy Systems and involvement in international conferences. Prof. Zeng’s research also addresses energy demand management and sustainable development goals, reflecting a commitment to interdisciplinary innovation. Education: B.Sc. in Mathematics, Xiamen University M.Sc. in Control Theory and Operations Research, Xiamen University Ph.D. in Computation, University of Manchester Research Interests: Computational intelligence, machine learning applications in healthcare and finance, decision support systems, and IoT integration in engineering education. His work bridges theoretical advancements with practical solutions in retail pricing, energy systems, and smart grids. Key Projects: IoTRAIN (2020–2023): EU project enhancing IoT education in Iran/Iraq. EPSRC and EU Framework projects on pricing, QoS, and cloud computing. Awards: Microsoft European Retail Application Developer Award (2003) European IST Award (1999) Grants & Advising: Supervised 40+ students and contributed to funded projects like the UK EPSRC pricing decision support initiative and EU H2020 Big Data in Finance. His research also impacts industrial collaborations in petroleum pricing and retail revenue management. Labs & Affiliations: Member of the Data Science Institute, Machine Learning and Optimisation group, and Christabel Pankhurst Institute at the University of Manchester.

• Machine Learning
• Ensemble Learning
• Feature Selection
• +۴ مورد دیگر

Gavin Brown is a Professor of Computer Science at the University of Manchester, affiliated with the Data Science Institute and the Machine Learning and Optimisation group. His research focuses on machine learning theory, ensemble methods, feature selection, and hardware-accelerated learning. He holds roles in interdisciplinary initiatives like the Centre for Digital Trust and Society, EnnCore project (privacy-preserving neural architectures), and the Robotics and Artificial Intelligence Centre. Education: PhD in Computer Science from the University of Birmingham (2003), with a thesis on 'Diversity in Neural Network Ensembles'. Research interests span theoretical foundations of machine learning, including bias-variance decomposition, ensemble diversity, and algorithmic stability. Applications include healthcare (e.g., outlier detection in vital signs), robotics (low-cost prediction hardware), and ethical AI (conceptual guarding of neural networks). He has pioneered work on feature selection for resource-constrained systems and neuromorphic computing. Recent articles emphasize unifying ensemble theory, hardware-efficient learning (FPGA-based feature selection), and clinical applications of machine learning. Projects include EnnCore (privacy-preserving AI), RAI Centre (robot-AI ethics), and Data Visualisation for clinical trials. He leads or co-leads 8 major projects, including £multi-million initiatives in digital trust, robotics, and AI. Supervised 24 doctoral students, with a focus on interdisciplinary work combining theory and applied machine learning.

Maximilien Gadouleauمشاهده پروفایل

دانشیار

• Coding theory
• Network coding
• Information theory
• +۱۳ مورد دیگر

Dr. Maximilien Gadouleau is an Associate Professor in the Department of Computer Science at Durham University, where he has been working since January 2012. He is a member of the Algorithms and Complexity research group, with secondary affiliations in Networks Engineering Science and Theory and Scientific Computing. Dr. Gadouleau received his MSc and PhD in Computer Engineering from Lehigh University in December 2005 and May 2009, respectively. After graduation, he held postdoctoral research positions at Université de Reims Champagne-Ardenne (until May 2010) and Queen Mary, University of London (until December 2011). Dr. Gadouleau's research spans multiple areas of theoretical computer science and discrete mathematics. His work focuses on the intersection of coding theory, network coding, and information theory with various branches of mathematics including combinatorics, graph theory, discrete optimization, matroid theory, logic, and group theory. He also investigates applications to network design, data storage, cryptography, data compression, and computation models. His recent work has particularly emphasized Boolean networks, finite dynamical systems, and semigroup theory. Dr. Gadouleau's extensive publication record demonstrates consistent contributions to theoretical computer science, with recent work showing a growing emphasis on the connections between discrete mathematics, dynamical systems, and coding theory. His research often bridges theoretical foundations with practical applications in network communication and data processing. Carnegie Trust for the Universities of Scotland: Research Assessor (2018) EPSRC Peer Review College member (2017) Dr. Gadouleau has successfully supervised PhD students including Izzy Friedlander (graduated July 2024) and Karl Southern (graduated July 2023). He was awarded an EPSRC first grant titled "Memoryless computation and network coding" which started on October 1, 2013. This grant supported research on computational models without memory and their applications to network coding problems. Dr. Gadouleau is an active member of the academic community, serving on editorial boards for journals including "Design, Codes, and Cryptography," "AIMS Mathematics," and "Mathematics and Systems Science." He regularly participates in international conferences and workshops, particularly in the areas of cellular automata, Boolean networks, and coding theory.

• Civil Engineering
• Environmental Engineering
• Physical Geography and Environmental Geoscience
• +۷ مورد دیگر

Barnaby Dobson is a Research Fellow at Imperial College London's Department of Civil and Environmental Engineering, part of the Faculty of Engineering. His research focuses on integrated water infrastructure modeling, emphasizing holistic evaluation of water systems' impacts on river quality. He developed the WSIMOD software to simulate interactions between water systems and management practices, with recent work exploring sewage spills' effects on river biodiversity through machine learning. Education and Background: 2023–current: Imperial College London, Research Fellow (Fluids Section) 2019–2023: Imperial College London, Research Associate (Environmental and Water Resources Engineering Section, CAMELLIA Project) 2018–2019: University of Oxford, Research Associate (Geography Department, Marius Project) 2014–2018: University of Bristol, PhD (Water Resource Systems Modelling, supervised by Dr. Francesca Pianosi and Prof. Thorsten Wagener) Research Themes: Integrated water system modeling Uncertainty quantification in socio-physical systems Optimisation for water resource control Linking wastewater systems to ecological impacts Key Projects: WSIMOD: Novel software for simulating water system interactions CAMELLIA Project: Integrated water systems modeling Marius Project: National water supply modeling for England and Wales

Wolfram Wiesemannمشاهده پروفایل

استاد

• Decision-making under uncertainty
• Supply chain management
• Healthcare optimization
• +۱ مورد دیگر

Wolfram Wiesemann is Professor of Analytics & Operations and Head of the Analytics, Marketing & Operations Department at Imperial College Business School. His research focuses on decision-making under uncertainty with applications in supply chain management, healthcare, and energy. Research Focus: Development of tractable approximation schemes for decision-making under uncertainty with rigorous error bounds. Applications span operations management, energy systems, and financial engineering. Teaching: BA1803 (Optimisation and Decision Models) and BA1806 (Introduction to Machine Learning) for MSc programs. Received Imperial College Business School Dean's Teaching Awards and nominations for Student Academic Choice Awards. Editorial Roles: Editor-in-Chief of Operations Research Letters and department co-editor for Management Science. Previously served on editorial boards of multiple leading journals. Research Impact: COVID-19 research on hospital care prioritization featured in Financial Times, Daily Mail, and France24.

• Spacecraft Systems Engineering
• Orbital Aerodynamics
• Space Debris Mitigation
• +۳ مورد دیگر

Dr. Peter Roberts is a Reader in Spacecraft Engineering at The University of Manchester's Mechanical and Aerospace Engineering department. His work focuses on orbital aerodynamics, spacecraft systems, and sustainable space technologies. He leads projects like the EU-funded DISCOVERER initiative, advancing Very Low Earth Orbit (VLEO) missions. Key achievements include the Icarus drag sail on TechDemoSat and the SOAR mission demonstrating drag-reducing materials. Roberts has secured grants from ESA, UKSA, and DARPA, coordinating large consortia. His research addresses space debris mitigation, aerodynamic materials, and atmospheric interactions. He advises on sustainable space practices, contributing to UN SDGs related to environmental protection. Notable awards include the 2022 Engineer's C2I Aerospace Award for DISCOVERER. Roberts collaborates internationally, including with Stuttgart University, and mentors over 15 postgraduate researchers. His labs and teams focus on experimental gas physics, spacecraft design, and VLEO mission systems.

• Computational Fluid Dynamics
• Multiobjective Optimisation
• Active Noise Control
• +۶ مورد دیگر

Sergey Utyuzhnikov is a Professor of Computational Mathematics and Reader in Mechanical and Aerospace Engineering at The University of Manchester since 2016. He holds a DSc (Habilitation) in Computational Mathematics from Moscow Institute of Physics & Technology (1997) and was certified as a Full Professor in Computational Mathematics by the Ministry of Education of Russia (1999). He has held academic roles at Moscow Institute of Physics & Technology (1986–2004) and Cranfield University (2004–2005), before joining the University of Manchester in 2005 as a School Senior Research Fellow. Educations: BSc, MSc, Moscow Institute of Physics & Technology (1977–1983) PhD (Candidate of Science) in Computational Mathematics (1986) DSc (Habilitation) in Computational Mathematics (1997) Research Interests: Focus on computational fluid dynamics, multiobjective optimization, active noise control, turbulence modeling, hypersonic flows, and geophysics. His work addresses challenges in boundary equations, nonlocal boundary conditions, and combustion modeling. He actively contributes to UN Sustainable Development Goals through aerospace engineering research. Key Projects: Leading the Aerospace Engineering research theme at the University of Manchester Co-investigator on projects like 'Development of advanced techniques for aerodynamic assessment of blunt bodies in hypersonic flow' Awards and Memberships: Fellow of the Institute of Mathematics & Applications (FIMA) since 2007 Membership in the Russian Academy of Sciences Academic expert for the Engineering & Physical Sciences Research Council (EPSRC) Labs & Collaborations: Part of the Aerospace Research Institute and collaborates internationally on topics like domain decomposition methods and turbulence modeling.