جستجوی استاد راهنمای دانشگاهی

Hongyu Zhangمشاهده پروفایل

مدرس

• Stochastic programming
• Energy systems optimisation
• Machine learning and AI
• +۳ مورد دیگر

Dr Hongyu Zhang is a Lecturer in Optimization for Machine Learning and AI at the School of Mathematical Sciences, University of Southampton. His expertise spans operational research and energy systems optimization, with a focus on stochastic programming and quantum computing applications. PhD Operational Research, NTNU MSc Operational Research with Data Science, University of Edinburgh BSc Mathematics and Applied Mathematics, Huaqiao University His research develops optimization models and algorithms for large-scale energy system planning, integrating machine learning, artificial intelligence, and quantum computing tools to address complexities in energy transition and policy-making. Key areas include multi-timescale uncertainty analysis, decomposition methods, and offshore energy hub modelling. Recent publications show strong trends in energy systems optimization (7 papers), algorithm development for stochastic programming (4 papers), and quantum computing applications (2 papers). His work addresses European energy security, hydrogen infrastructure development, and carbon capture technologies. Currently accepting PhD applications, Dr Zhang contributes to academic supervision and teaches optimization methods at the university. He is affiliated with the Operational Research group and CORMSIS center.

Dirk Sudholtمشاهده پروفایل

استاد

• Randomised Algorithms
• Algorithmic Analysis
• Combinatorial Optimisation
• +۵ مورد دیگر

Dr. Dirk Sudholt is a Full Professor at the University of Passau and a Visiting Professor at the University of Sheffield. He holds a Ph.D. from Technische Universität Dortmund and has held postdoctoral positions at the International Computer Science Institute (ICSI) in Berkeley and the University of Birmingham. His research focuses on randomized algorithms, algorithmic analysis, and combinatorial optimization, with expertise in the theoretical analysis of bio-inspired search heuristics like evolutionary algorithms and ant colony optimization. His work emphasizes rigorous runtime analysis to understand algorithmic performance and design principles. Education: PhD in Computer Science, Technische Universität Dortmund (2008) Diploma in Computer Science, Technische Universität Dortmund (2004) Research Interests: Runtime analysis of evolutionary algorithms Algorithmic design for multimodal optimization Noise robustness in metaheuristics Parallel and distributed evolutionary computation Grants: SAGE: Speed of Adaptation in Population Genetics and Evolutionary Computation (EU FP7, 2014–2016) Teaching: University of Passau: Courses on algorithms, evolutionary computation, and randomized algorithms

• Multi-Objective Optimisation
• Nature Inspired Optimisation Algorithms
• Multidisciplinary Design Optimisation Under Uncertainties
• +۷ مورد دیگر

Dr. Edmondo Minisci is a Senior Lecturer in Mechanical and Aerospace Engineering at the University of Strathclyde, part of the Faculty of Engineering. He leads the Intelligent Computational Engineering Laboratory (ICE-Lab) within the Aerospace Centre of Excellence. With over 25 years of experience, his expertise spans model-based analysis and design optimization of complex mechanical/aerospace systems, including aeronautical vehicles, wind turbines, and sustainable energy systems. He actively supervises PhD students in areas like multi-fidelity methods, intelligent control, and sustainable agriculture engineering. Dr. Minisci has received prestigious awards, including the NATO STO “Panel Excellence” Award (2024) and the Sir Arthur C Clarke Award (2015). His research focuses on multidisciplinary design optimization under uncertainties, machine learning applications, and aerospace technologies for sustainable development. Key projects include optimizing high-lift devices, wind turbine performance, and precision agriculture systems. He has contributed to numerous conferences, including organizing the International Conference on Bioinspired Optimisation Methods and serving as an examiner for PhD theses. His work bridges computational intelligence with practical engineering challenges, emphasizing resilience and innovation in aerospace and energy sectors. Research Interests: Multi-Objective Optimization, Nature-Inspired Algorithms, Uncertainty Treatment, Machine Learning, Intelligent Control. Recent Projects: Symbolic Computation for Differential Equation Systems, EPSRC-funded Uncertainty Quantification in Aeroelastic Systems, and Advanced Natural Language Models for Satellite Communications.

Huy Truong-Baمشاهده پروفایل

مدرس

• Reliability and Degradation Modelling
• Maintenance Optimization
• Engineering Asset Management
• +۵ مورد دیگر

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.

• Renewable Energy Systems
• Smart Grid Technologies
• Machine Learning in Energy
• +۳ مورد دیگر

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.

Djamila Ouelhadjمشاهده پروفایل

استاد

• Operational Research
• Mathematical Optimisation
• Meta-heuristics and Hyper-heuristics
• +۶ مورد دیگر

Professor Djamila Ouelhadj of the University of Portsmouth is a leading academic in Operational Research, affiliated with the School of Mathematics and Physics and Centre for Operational Research and Logistics . She serves as Course Leader for MSc Logistics and Supply Chain Management and contributes to EU-funded projects like SEABILLA and LEANWIND . Her work spans mathematical optimization, meta-heuristics, and decision support systems applied to logistics, transportation, healthcare, and renewable energy. Research Themes : Operational Research, Logistics Analytics, AI for Decision Making, Sustainable Transportation Her recent publications focus on Mobility-as-a-Service (MaaS) systems, drone scheduling for wind farm inspection, and multi-criteria frameworks for inclusive transport. Articles highlight integration of machine learning , stochastic modeling , and robotics in solving real-world optimization problems. She employs multi-objective methods , simheuristics , and clustering techniques to address challenges in renewable energy, urban mobility, and security. Scientific Contributions include: Co-investigator on EU grants for maritime security and wind farm optimization Principal investigator for HEIF-funded Operational Research Centre Chair of scheduling streams at OR53-OR55 conferences Her advising extends to 15 theses, covering topics from nurse rostering to carbon footprint reduction. Collaborative grants include TSB and EU Horizon 2020 projects. She actively contributes to European Journal of Operational Research and leads the Logistics and Operational Research Group (LORG) in developing intelligent decision support systems for complex scheduling problems.

John Pyeمشاهده پروفایل

دانشیار

• High-Temperature Solar Thermal Energy
• Concentrating Solar Power (CSP)
• Industrial Decarbonisation
• +۴ مورد دیگر

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.

Dobrila Petrovicمشاهده پروفایل

دانشیار

• Operational Research
• Business Analytics
• Artificial Intelligence
• +۳ مورد دیگر

Dr Dobrila Petrovic is an Associate Professor of Operational Research and Business Analytics in the Department of Strategy, Analytics and Operations at Nottingham Business School, Nottingham Trent University. With over 20 years of academic experience in the UK, she serves as Research Coordinator for the department and teaches Management in Organisations, Research Methods, and Data Driven Decision Making on postgraduate programs. Operational Research Business Analytics Fuzzy Logic Applications Supply Chain Optimization Multi-Objective Optimisation Her recent research focuses on fuzzy scenario-based optimization for supply networks, type-2 fuzzy cognitive maps, and multi-attribute decision-making for autonomous vehicle safety. She has supervised 17 PhD students and 12 postdoctoral researchers throughout her career. EPSRC Reviewer ESRC Reviewer Leverhulme Trust Reviewer European Science Foundation Reviewer International Journal of Systems Science Associate Editor

Belen Martin-Barraganمشاهده پروفایل

پژوهشگر

• Machine Learning
• Mathematical Programming
• Operational Research
• +۶ مورد دیگر

Belen Martin-Barragan is a Reader in Management Science at the University of Edinburgh Business School , with a focus on the Department of Management Science and Business Economics . Her research bridges Machine Learning and Mathematical Programming , emphasizing Explainable Artificial Intelligence (XAI) and applications in Operational Research , including classification , clustering , inventory management , and routing optimization . Her work includes developing interpretable machine learning models for credit scoring, healthcare data, and sustainable logistics. She has led EPSRC-funded projects on Optimisation Models for Interpretable Analytics and contributed to journals like European Journal of Operational Research , Risk Analysis , and Computers and Operations Research . Key methodologies involve Mixed-Integer Linear Programming , Support Vector Machines , and stochastic dynamic programming . Her research fingerprint spans Machine Learning (97%), Mathematical Programming (75%), and Optimization Algorithms (20%). She has been a Research Champion and Deputy Director of Research (Ethics and Integrity) at the Business School, with affiliations to the Credit Research Centre and Edinburgh Strategic Resilience Initiative .

Lei Xingمشاهده پروفایل

مدرس

• Electrochemical Energy Conversion
• Carbon Capture and Utilisation
• Artificial Intelligence
• +۵ مورد دیگر

Dr. Lei Xing is a Lecturer in Digital Chemical Engineering at the University of Surrey, where he also serves as a Fellow of the Institute for Sustainability and the Institute for People-Centred AI. He joined the Department of Chemical and Process Engineering in September 2022, following postdoctoral research positions at Oxford University, Purdue University, and the University of Birmingham. Dr. Xing earned his PhD in Chemical and Process Engineering from Newcastle University. His academic journey has equipped him with expertise spanning chemical engineering, energy systems, and artificial intelligence, enabling his interdisciplinary research approach. His research focuses on sustainable industrial-agri-food systems through industrial decarbonization and AI-based digitalization within circular economy frameworks. Key areas include electrochemical energy conversion (fuel cells, electrolysers), carbon capture and utilisation, AI-enabled multi-criteria assessment, and model predictive control. He specializes in multi-physics and multi-phase flow modeling, multi-objective optimisation, and techno-economic analysis of renewable energy systems, with particular emphasis on practical applications for achieving Net Zero targets. Dr. Xing's recent publications demonstrate a strong integration of artificial intelligence with traditional chemical engineering approaches. Many studies develop digital twin models for electrochemical systems, optimize carbon capture processes, and advance sustainable hydrogen production technologies. His work consistently bridges theoretical modeling with practical engineering applications, incorporating life cycle assessment and techno-economic analysis to evaluate environmental and economic viability of energy solutions. Chartered Member of IChemE (MIChemE) Fellow of Higher Education (FHEA) Associate Editor for Fuel Cells and Frontiers in Energy Research Editorial Board Member for Energy and AI, CCST, and Energies Dr. Xing actively supervises multiple PhD students working on cutting-edge projects at the intersection of AI and sustainable energy systems. His research is supported by significant grants totaling over £1.2 million from EPSRC, Royal Society, Horizon Europe, and other funding bodies. These projects address critical challenges in decarbonizing hard-to-abate sectors, developing circular economy approaches for fertilizer production, and advancing CO2 electrolysis technologies. He leads research initiatives within the Sustainable Energy and Materials group at Surrey, collaborating with colleagues across chemistry, mechanical engineering, electrical engineering, and computer science disciplines. His work on the OLINWASTE project represents a major EU-funded effort to transform olive mill waste into valuable bioproducts, while his C-Cir project focuses on accelerating the commercial translation of CO2 electrolysers.

• Supply Chain Resilience
• Digital Transformation
• Sustainable Operations
• +۳ مورد دیگر

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.

Neil W. Bressloffمشاهده پروفایل

استاد

• Biomedical Engineering
• Cardiovascular Engineering
• Computational Fluid Dynamics
• +۱۱ مورد دیگر

Professor Neil W. Bressloff is a leading academic in the School of Mechanical Engineering at the University of Leeds, where he holds the position of Professor of Biomedical Engineering & Design. He serves as Head of School and has a distinguished research profile spanning aerospace and biomedical engineering. His work bridges computational design, haemodynamics, and clinical innovation. Education: PhD in Mechanical Engineering, University of Cranfield MSc in Advanced Mechanical Engineering, Imperial College London PGCE in Mathematics and PE, University of Leeds BA Hons in Engineering Sciences and Economics, St John’s College, University of Oxford Prof Bressloff's research focuses on applying advanced computational and optimization methods to solve clinical problems in cardiovascular engineering. His work includes haemodynamic modeling , design of coronary stents and bioresorbable scaffolds , and innovative heart valve development . He pioneered the design of the ArterioSorb™ coronary scaffold via Arterius Ltd, a UK-based medical device company. His methodology, originally developed for aerospace design, enables systematic exploration of device performance under anatomical variation. The recent publications reflect a strong trend in computational biomechanics , medical device optimization , and surrogate-based design . These works integrate CFD , finite element analysis , and multi-objective optimization to improve clinical outcomes. Keywords across articles include Biomedical Engineering, Haemodynamics, and Computational Design, with subfields ranging from stent deployment mechanics to transcatheter valve performance. Scientific Awards: Leading Opinion Paper, Biomaterials (2011) Prof Bressloff has secured over £1.5 million in research funding from UKRI and industry partners over the past five years. He actively supervises postgraduate researchers, including Taskin Md Siham Sayeed, and fosters strong collaborations with clinicians and industrial partners. His leadership in the Rolls-Royce and BAE Systems University Technology Partnership advanced aerodynamic design methodologies before transitioning to impactful biomedical applications. He leads a research group focused on computational engineering and design , with emphasis on translating aerospace-inspired optimization into clinical solutions. The team works on next-generation cardiovascular devices, leveraging digital design and patient-specific modeling to address unmet needs in coronary and valvular disease treatment.

Thomas Jansenمشاهده پروفایل

دانشیار

• Evolutionary Algorithms
• Optimization
• Computational Intelligence
• +۳ مورد دیگر

Thomas Jansen is an Associate Professor and Head of the Department of Computer Science at Aberystwyth University. His research focuses on evolutionary algorithms, stochastic search heuristics, and theoretical computer science, with particular emphasis on optimization, dynamic environments, and algorithm performance analysis. He has led and contributed to several research projects, including the Wales Randomised Optimisation Algorithms Network (focused on advancing AI and optimization techniques) and the ImAppNIO project (bridging theory and practice in nature-inspired optimization). His academic contributions include foundational work on fixed budget analysis, multi-objective optimization, and the role of populations in dynamic optimization problems. Jansen has edited conference proceedings such as the 2015 ACM Conference on Foundations of Genetic Algorithms, further cementing his role in advancing the theoretical underpinnings of evolutionary computation. Key collaborations include work with researchers like Christine Zarges and Per Kristian Lehre, exploring topics like lexicase selection and immunological algorithms. His research has been presented at major conferences like PPSN and EvoCOP, and he actively participates in organizing workshops on randomized optimization algorithms.

• Urban Design
• Architecture
• Sustainable Urban Development
• +۱۲ مورد دیگر

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

• Artificial Intelligence
• Constraint Programming
• Optimisation
• +۱۰ مورد دیگر

Professor Kenneth Brown is affiliated with the Department of Computer Science at University College Cork (UCC) , Ireland. He holds the academic rank of Professor and is a Principal Investigator at the Insight Centre for Data Analytics and the CTVR (Telecommunications Research Centre) . BSc (Hons) Mathematics, University of Glasgow, 1986 MSc Mathematical Logic and the Foundations of Computation, University of Manchester, 1987 PhD Artificial Intelligence in Engineering, University of Bristol, 1991 His research focuses on Artificial Intelligence, constraint programming, optimisation, and distributed reasoning , with applications in wireless networking, sensor networks, and dynamic resource management . He also works on smart energy systems, data analytics, and human-centric applications. His recent work includes projects under the SFI-funded Insight Centre and Horizon Europe initiatives like GLACIATION and SEISMEC. His publications span topics from wireless sensor network optimization and cognitive radio to constraint-based decision support and AI in emergency management. He has led and contributed to over 15 recent publications in top-tier journals and conferences, showing a strong trend in AI-driven solutions for networked and intelligent systems. IEEE SECON 2015 Best Demonstration 2014 TAOS Best Paper Award in Access Networks and Systems Best paper, SMARTGREENS 2013 Enterprise Ireland Lifescience and Food Commercialisation Award Best Application Paper, AI2008 Best Application Paper, AI2006 Best paper nomination, ECAI 2004 Best Paper, Intl Conf AI in Design Professor Brown has supervised numerous PhD and MSc students in areas including constraint programming, sensor networks, evacuation modeling, and smart buildings. He has secured significant research funding from SFI, Enterprise Ireland, and IRCSET. He is actively involved in research leadership, serving as Deputy Director of Insight@UCC and PI in multiple national and international projects. He is a member of research groups involved in GLACIATION (green, responsible data operations), SEISMEC (human-centric industry), and SMARTeBuses . His lab supports a team of doctoral students and research staff working on AI, networking, and data analytics for real-world applications.