Find Academic Supervisors

• Optimisation
• Operational Research
• Applied Probability
• +4 more

Jamie Fairbrother is a Lecturer in Operational Research (Optimisation) at the Data Science Institute , Lancaster University , within the Department of Management Science . His research integrates optimisation, applied probability, and statistics to solve real-world problems in logistics and telecommunications, with a particular focus on stochastic programming and scenario generation. Education: Jamie Fairbrother completed his PhD research in scenario generation for stochastic programming, which laid the foundation for his subsequent work in optimisation under uncertainty. Research Interests: His expertise spans several domains: Optimisation & Operational Research: Development of mathematical models and algorithms for complex decision-making problems. Applied Probability & Statistics: Use of probabilistic methods to model uncertainty in planning and operations. Logistics & Telecommunications: Practical applications in mail centre staffing, airport slot scheduling, and wireless communication networks. Stochastic Programming: Scenario generation and robust optimisation techniques for strategic planning under uncertainty. Research Funding & Projects: Dr Fairbrother has led or contributed to multiple collaborative projects, including: Coordination of Strategic and Tactical Interventions for Reducing Air Traffic Delays (2023–2024) – a case study at Heathrow Airport. STOR-i: Optimising In-Store Price Reductions (2022–2025) – with PhD student Katie Howgate. Resource Allocation under Uncertain Demand in Royal Mail Mail Centres (2020–2023). Industrial Mathematics KTP with BT Research (2013) – modelling TV whitespace interference. PhD Supervision: He currently supervises Katie Howgate , a PhD student in Bayesian and Computational Statistics within the STOR-i Centre for Doctoral Training . Professional Affiliations: Dr Fairbrother is affiliated with the STOR-i Centre for Doctoral Training , the Centre for Transport & Logistics (CENTRAL) , and the OR-MASTER project on airport resource allocation.

Tim StojanovicView profile

Senior Lecturer

• Marine Planning
• Ecosystem Services
• Coastal Management
• +5 more

Tim Stojanovic serves as Senior Lecturer in Sustainable Development and Geography at the University of St Andrews' School of Geography & Sustainable Development. His research directly contributes to UN Sustainable Development Goals through marine conservation and climate adaptation work. Active in multiple funded projects including the NERC-backed Resilient Coasts: Optimising Co-benefit Solutions (Co-Opt) until 2025, he maintains strong institutional ties within UK environmental policy circles. Stojanovic's research spans marine planning, ecosystem services valuation, and climate adaptation with particular expertise in coastal flood risk management and marine protected area governance. His fingerprint reveals strong concentrations in Marine Planning (100%), Ecosystem Services (91%), UK/Scotland coastal systems (72%), and Climate Change (50%). Recent work explores diverse perspectives on nature-based solutions, saltmarsh carbon storage preferences, and legal frameworks for estuaries. Analysis of his 15 most recent publications shows consistent focus on stakeholder engagement in marine governance, with increasing emphasis on climate adaptation since 2020. His work integrates environmental economics, spatial planning, and social science to address coastal vulnerability, particularly examining socio-demographic dimensions of climate impacts and community-led resilience. University of St Andrews Golden Dandelion Award (2022) for holistic teaching excellence in Environmental Management University of St Andrews Teaching Excellence Award (2019) As principal investigator for eight projects totaling over £70,000 in Scottish Funding Council grants, Stojanovic leads international collaborations in Kenya on marine protected area governance while directing UK coastal resilience initiatives. His external examination of six PhD theses demonstrates significant academic leadership. Current work with the Royal Geographical Society and community resilience events like Airth Community Resilience Event (2025) shows active knowledge mobilization. Stojanovic's research team operates through the Co-Opt project consortium, engaging coastal communities in participatory decision-making about flood risk management. His work with the MASTS network facilitates science-policy collaboration, while Scottish Government-funded initiatives focus on scaling community capabilities for marine conservation.

• Particle Technology
• Pharmaceutical Engineering
• Process Modeling
• +5 more

Bilal Ahmed is a Research Fellow at the Strathclyde Institute Of Pharmacy And Biomedical Sciences, University of Strathclyde, UK. His work merges advanced process modeling with experimental techniques in pharmaceutical engineering, focusing on industrial applications. Education: MChem Chemistry for Drug Discovery, University of Bradford (2010-2014) PhD in Particle Engineering (2019), supervised by Professors Alastair Florence and Jan Sefcik Research Focus: Specializing in particle technology, Ahmed develops methodologies for optimizing pharmaceutical manufacturing processes. His expertise spans crystallization, granulation, and continuous direct compression, with emphasis on: Designing industrial-scale particle processes Application of inline sensing and modeling Multi-objective optimization of drug formulation Collaborative industry-academia-government projects Scientific Contributions: Key outputs include mechanistic models for twin screw granulation and data fusion techniques for particle size distribution analysis. His work aligns with UN Sustainable Development Goals through process innovation. Awards: Best Poster: Runner-up (2015) Collaborations: Active in international conferences like Industrial Crystallisation (2017), I2APM Symposium (2016), and involved in 3 major research projects.

• Machine Learning
• Data Analytics
• Blockchain Technology
• +7 more

Prof. Tahar Kechadi is a Principal Investigator at the Insight Centre for Data Analytics , specializing in Machine Learning & Statistics and Optimisation & Decision Analytics . His research spans interdisciplinary domains including agriculture, healthcare, and cybersecurity. University : Insight Centre for Data Analytics Role : Principal Investigator Ranks : Professor His current research focuses on applying machine learning to smart agriculture (e.g., crop yield prediction, data engineering), blockchain technologies (e.g., energy trading, e-voting), and privacy-preserving systems in healthcare and cybersecurity. Publications highlight advancements in multi-modal analysis , deep learning architectures , and game-theoretic clustering . Recent work explores data contamination in LLMs , privacy-aware blockchain systems , and distributed reputation management . His team develops tools for agro-climate modeling , medical diagnostics , and cloud forensic readiness . Contact: tahar.kechadi@insight-centre.org

• Kansei informatics
• Soft computing
• Colour emotion
• +6 more

Keiichi Muramatsu is Associate Professor (tenure-track since 2025) at Waseda University’s Global Education Center, Japan. Holding a Doctor of Human Sciences from Waseda, he has successively served as Assistant Professor at Saitama University and as part-time Lecturer at Bunkyo University. He is an active member of the Japan Society of Kansei Engineering, the Japanese Society for Artificial Intelligence and the Color Society of Japan. Education: Doctoral Program, Human Sciences, Waseda University (2009-2014) Master’s Program, Human Sciences, Waseda University (2007-2009) Bachelor, Department of Human Informatics and Cognitive Sciences, Waseda University (2003-2007) Research interests: Muramatsu integrates Kansei informatics and soft computing to quantify human emotional and aesthetic responses. His work spans colour-emotion modelling for lighting and butterfly aesthetics, bio-signal driven emotion recognition using chaotic fingertip plethysmography, and gait classification via deep learning for personalised rehabilitation and stumble prevention. He also develops ontological frameworks to share emotional knowledge across educational and product-service system design domains. Publication trends: Across 62 Scopus papers (h-index 7), Muramatsu’s recent outputs demonstrate a convergence of affective engineering and AI: convolutional networks visualise gait features for training feedback, neural predictors estimate learner states in intelligent tutoring systems, and psychophysiological models link colour, fragrance and 3-D sound to emotional dimensions. Studies consistently combine subjective evaluations with objective indices such as fNIRS, skin conductance and chaotic exponents. Grants & industry links: He has led JSPS KAKENHI grants and internal Waseda special research projects, collaborated with Toyota on driver affective scales, and contributed to exoskeleton development with Saitama medical device firms. His work on LED automotive lighting and mobility-scooter stress evaluation reflects ongoing industry knowledge transfer. Advising & labs: While specific student names are not disclosed, Muramatsu supervises graduate research on gait-assistive devices, emotion recognition wearables, and ITS architectures within the Global Education Center’s interdisciplinary environment.

Yi MeiView profile

Associate Professor

• Evolutionary Computation
• Combinatorial Optimisation
• Genetic Programming
• +10 more

Dr. Yi Mei is an Associate Professor and Associate Dean (Research) at the Faculty of Engineering, Victoria University of Wellington, New Zealand. He serves as Programme Director for Computer Science & Computer Graphics within the School of Engineering and Computer Science and is affiliated with the Centre for Data Science and Artificial Intelligence (CDSAI). Dr. Mei's research focuses on evolutionary computation for combinatorial optimisation , with specific expertise in genetic programming , automatic algorithm design , explainable AI , and multi-objective optimisation . His work bridges theoretical foundations with practical applications in transportation, healthcare, manufacturing, and aquaculture. His publication portfolio shows a clear trajectory toward increasingly complex real-world applications of evolutionary computation, with recent work focusing on emergency medical dispatch optimization , dynamic transportation systems , and explainable AI for routing problems . The research demonstrates strong interdisciplinary connections between evolutionary computation, machine learning, and domain-specific optimization challenges. Victoria University of Wellington Ki te Pae - Research Excellence Award 2024 Multiple Best Paper Awards at ACM Genetic and Evolutionary Computation Conference (GECCO) from 2022-2024 HUMIES Silver Award at GECCO 2023 IEEE Transactions on Evolutionary Computation Outstanding Associate Editor (2024, 2025) Fellow of Engineering New Zealand and IEEE Senior Member Dr. Mei has successfully supervised numerous PhD and Master's students whose research has received recognition, including the ACM SIGEVO Dissertation Award. He leads significant research projects funded by MBIE Endeavour Smart Idea Fund ($1M NZD), New Zealand Royal Society Catalyst Leaders Fund ($150K NZD), and industry partners like Honda Research Institute Europe. His service contributions include editorial roles for top journals including IEEE Transactions on Evolutionary Computation and leadership positions in IEEE Computational Intelligence Society. Dr. Mei leads the Evolutionary Computation for Combinatorial Optimisation Group (ECCO) and the Evolutionary Computation and Machine Learning Research Group (ECRG) at Victoria University of Wellington. He also chairs the IEEE Taskforce on Evolutionary Scheduling and Combinatorial Optimisation (TESCO), fostering international collaboration in evolutionary computation applications.

• Transport Planning
• Sustainable Infrastructure
• Resilient Infrastructure
• +4 more

Fulvio Lopane serves as Assistant Professor in Transport Planning at Northeastern University London within the Centre for Apprenticeships, holding academic credentials spanning civil and environmental engineering disciplines. His educational foundation includes: Undergraduate degree in Civil and Environmental Engineering MSc in Civil Engineering PhD in Civil Engineering with specialisation in multi-objective spatial optimisation of infrastructure services Research focuses on urban science and mobility systems through sustainable infrastructure networks, human mobility patterns, and urban modelling. His work develops spatial optimisation frameworks and land use analysis tools specifically targeting resilient, equitable transport networks responsive to urban population dynamics. Current investigations integrate spatial interaction platforms with infrastructure service evaluation methodologies. Professional engagement includes teaching comprehensive transport planning curricula across theoretical and applied domains. No formal advisee relationships or research grants are documented in available materials. Operational activities occur within Northeastern University London's Centre for Apprenticeships framework, though specific laboratory facilities or research teams remain unmentioned in source documentation.

Jonas SchnidrigView profile

Lecturer

• Sustainable Energy System Modelling
• Life Cycle Assessment
• Renewable Energy Planning
• +7 more

Jonas Schnidrig is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Engineering (STI) and the School of Architecture, Civil and Environmental Engineering (ENAC). He works as an External Employee in the SCI STI FM Group at EPFL Valais Wallis, based at the IPESE institute in Sion. His research focuses on sustainable energy policy and planning, specializing in energy system modeling, optimization, and decision support for the energy transition. He actively bridges research, industry, government, and society by developing strategies that balance ecological, social, and economic dimensions of energy transitions. Dr. Schnidrig's educational background includes: PhD in Mechanical Engineering (Energy and Technologies) from EPFL-HES-So Valais Wallis (2020-2024) Master's in Mechanical Engineering (Thermodynamics & Energetics) from EPFL (2018-2020) Bachelor's in Mechanical Engineering from EPFL (2013-2018) His research interests span sustainable energy system modeling, life cycle assessment, renewable energy planning, rational use of energy, and thermoeconomic optimization. He specializes in energy system integration, incorporating sustainability metrics into energy planning, policy support for sustainable energy transitions, and renewable energy finance and infrastructure planning. His work emphasizes a life-cycle perspective to evaluate the ecological, social, and economic dimensions of energy systems. Analysis of his recent publications reveals a strong focus on decentralized energy systems, multi-actor energy planning, and the integration of environmental metrics into energy modeling. His research demonstrates how strategic decentralization can reduce system costs by up to 10% while increasing self-consumption by up to 68%. He has pioneered methodologies that integrate life-cycle impact assessment into energy system modeling, showing potential for 15-47% cost reductions alongside 31-81% reductions in environmental impacts. His notable scientific achievement includes: Zanelli Prize for outstanding contribution to sustainable development (2020) Dr. Schnidrig coordinates the EnergyScope Community, which brings together over 50 researchers and practitioners, and contributes to major international projects including EnergyScope Community, Net Zero Valais, and collaborations with Polytechnique Montréal, Hydro Québec, and Swiss energy institutions. He supervises semester and master projects at EPFL and teaches the course "Sustainability, climate and energy" (ENV-421). He leads research on digital twins for cities within the Blue City project and focuses on understanding the role of infrastructure in high share renewable energy systems, investigating synergies across different scales, and developing sustainable energy systems that align economic efficiency with environmental sustainability.

Xiaohui LiuView profile

Professor

• Intelligent Data Analysis
• Deep Learning
• Artificial Intelligence
• +7 more

Professor Xiaohui Liu is a distinguished Professor of Computing at Brunel University London, serving within the Computer Science department of the College of Engineering, Design and Physical Sciences. He maintains his office in the Wilfred Brown Building (Room 218) and has established himself as a leading figure in intelligent data analysis and artificial intelligence research. With over 20 years of academic leadership, Professor Liu has held significant visiting appointments including Honorary Pascal Professor at Leiden University (2004), Visiting Scientist at Harvard Medical School (2005), and Visiting Professor at the Chinese Academy of Sciences (2010). Professor Liu's research spans intelligent data analysis, deep learning, dynamical systems, human factors, innovative AI applications, optimisation, statistical pattern recognition, and trustworthy decision making. His work bridges theoretical advances with practical implementations across various industries, demonstrating exceptional translational impact. He has pioneered approaches that integrate artificial intelligence with data science to enable effective data interpretation and trustworthy decision-making systems, with applications spanning healthcare, manufacturing, and business domains. Analysis of Professor Liu's recent publications reveals a strong focus on transformer architectures, transfer learning, and optimization techniques applied to real-world problems. His work demonstrates consistent innovation in neural network architectures, particularly for anomaly detection, fault diagnosis, and recommendation systems. The publications show interdisciplinary applications spanning manufacturing, healthcare, digital marketing, and network science, reflecting his commitment to solving practical challenges through advanced computational methods. Clarivate Highly Cited Researcher for 11 consecutive years (2014-2024) World's top 2% of scientists by Stanford University (2020-2024) ScholarGPS Highly Ranked Scholar – Lifetime: Neural Network (2022-2024) Daniel Berg Award (2023) Research.com United Kingdom Leader Award in Computer Science (2023-2025) IDA Founders Award (2025) Professor Liu has secured substantial research funding from diverse sources including the European Commission, Innovate UK, Royal Society, and EPSRC. His current projects include AI-assisted tax assessment, intelligent data-driven pipelines for manufacturing certified metal parts, and maintenance models for zero-unexpected-breakdowns. He leads collaborative efforts through knowledge transfer partnerships with industry partners like Veritas Advisory Limited and has directed multiple European Commission-funded initiatives focused on IoT platforms, water resource management, and predictive maintenance systems. His research group actively mentors PhD students and collaborates with international partners across multiple continents. Professor Liu leads research activities within the IEHS and CSSB research groups at Brunel University, fostering interdisciplinary collaboration between computer scientists, engineers, and domain experts. His teams integrate expertise in neural networks, optimization algorithms, and statistical pattern recognition to develop innovative solutions for complex real-world problems. The research environment emphasizes both theoretical rigor and practical application, with strong industry partnerships ensuring that research outputs deliver tangible societal and economic impact.

Micah NehringView profile

Senior Lecturer

• Mine Planning
• Production Scheduling Optimisation
• Optimal Ultimate Pit Limit (UPL) determination
• +4 more

Micah Nehring is a Senior Lecturer at the School of Mechanical and Mining Engineering , The University of Queensland . He leads the High Performance Surface Mining Research Group , focusing on optimizing In-Pit-Crusher-Conveyor (IPCC) systems and advancing mine planning methodologies. His work bridges mathematical programming with practical mining challenges, particularly in ultimate pit limit determination and production scheduling . Research interests include: Mine Planning Production Scheduling Optimization IPCC Systems Integration Cut-off Grade Methodologies ESG Risk Incorporation Recent publications highlight trends in machine learning applications for coal mine roof characterization, electrification of surface mining fleets, and multi-objective optimization for sustainable underground operations. His work often combines mixed integer programming with discrete event simulation to address complex mining scenarios. Advising spans projects like: Forecasting Market Capitalisation via Neural Networks Battery Trolley Haulage Systems Hydro-Economic Modeling for Water Management Funding collaborations include Anglo American , NewCrest Mining , and Caterpillar Global Mining , with grants focused on carbon tax impacts , roof displacement prediction , and rock characteristics research.

• structural optimisation
• computational mechanics
• high performance computing
• +7 more

Associate Professor Vivien Challis is a leading academic in Applied and Computational Mathematics at Queensland University of Technology (QUT), affiliated with the Faculty of Science and the School of Mathematical Sciences . She earned her PhD in Mathematics from The University of Queensland in 2009 and has since built an international reputation in structural optimisation, computational mechanics, and high-performance computing. Her research focuses on piezoelectric metamaterials , topology optimization , and GPU-accelerated computational methods , with recent work advancing robotics applications and biomedical prosthetics. She leads the 2022 Australian Research Council Discovery Project DP220102759 on optimising piezoelectric materials for robotics and contributed to ARC projects DP240102104 and DP170104307. 2025: GridapTopOpt.jl (Julia toolbox for level set optimization) 2025: Wave power absorption by floating plates (piezoelectric energy harvesting) 2025: FF-PINNTO framework (neural network-based optimization) In 2024, she received the JH Michell Medal from ANZIAM, recognizing her contributions to industrial and applied mathematics. Her teaching includes first-year calculus (MXB105) and second-year computational mathematics (MXB226), and she directed QUT's Mathematics Summer School (2022-2024).

• Stochastic Optimization
• Risk-Averse Decision-Making
• Mathematical Programming
• +7 more

Darinka Dentcheva is a Professor in the Department of Mathematical Sciences at Stevens Institute of Technology, affiliated with the Charles V. Schaefer, Jr. School of Engineering and Science. She holds a PhD in Mathematics from Humboldt University (2006) and an earlier PhD in Mathematics (1989) and MS in Mathematics and Computer Science (1981), also from Humboldt University. Her research focuses on stochastic optimization, risk-averse decision-making, and their applications in machine learning, finance, healthcare, and energy systems. Dr. Dentcheva’s work emphasizes developing mathematical frameworks for managing risk under uncertainty, including stochastic dominance constraints and risk-averse control of Markov systems. She has authored influential books such as *Risk-Averse Optimization and Control: Theory and Methods* (2024) and co-edited volumes on stochastic optimization theory. Her research has been supported by grants from the National Science Foundation (NSF), Office of Naval Research, and Air Force Office of Scientific Research, totaling over $3 million in funding. Her honors include the Davis Memorial Research Award (2007), recognition from the Stevens Board of Trustees for research excellence, and awards from the Bulgarian Ministry of Education. She has held leadership roles, including Chair of the Department of Mathematical Sciences (2018–2021) and co-director of the CRAFT Center (Center for Research toward Advancing Financial Technologies). Her teaching spans courses in nonlinear optimization, stochastic control, and data science. Dr. Dentcheva’s contributions to stochastic optimization theory and applications have been published in over 100 peer-reviewed articles and book chapters. Her recent work explores risk-averse machine learning models, dynamic resource allocation, and bias reduction in sample-based optimization. She actively collaborates with institutions like Rutgers University and serves on editorial boards for journals such as *SIAM Review* and *ESAIM: Control, Optimisation and Calculus of Variations*.

• Healthcare Logistics
• Operations Research
• Supply Chain Optimization
• +3 more

Issam NOUAOURI is an Associate Professor with HDR (Habilitation à Diriger des Recherches) specializing in healthcare logistics, operations research, and disaster management. His research focuses on optimizing patient flows, material resources, and supply chain networks in dynamic and uncertain environments. Key areas include emergency medical services, sustainable supply chains, and AI-driven healthcare solutions. Education: Ph.D. Thesis: 'Gestion hospitalière en situation d’exception: optimisation des ressources critiques' (2006-2010) HDR: 'Contribution à l’optimisation des flux de patients et matières dans un environnement incertain et dynamique' (2023) Research interests span healthcare logistics, optimization algorithms (metaheuristics, genetic algorithms), disaster response planning, and sustainable supply chain design. Notable work includes modeling ambulance routing, emergency department resource allocation, and collaborative hospital networks. Publications emphasize practical applications in healthcare systems, such as reducing patient waiting times, optimizing vehicle routing for hazardous waste, and integrating AI in disease diagnosis. His work bridges theoretical optimization and real-world healthcare challenges. Grants and collaborations include projects on hospital logistics within Groupements Hospitaliers de Territoire (GHT), disaster management systems, and rail safety co-simulation. He has supervised seven Ph.D. students focusing on healthcare scheduling, supply chain optimization, and hyperheuristics. Labs/Teams: Active contributor to the LGI2A laboratory, focusing on interdisciplinary research in intelligent systems and healthcare operations.

Melih OzlenView profile

Associate Professor

• Operations Research
• Combinatorial Optimisation
• Mathematical Programming
• +1 more

Associate Professor Melih Ozlen serves as Deputy Head of Department (Academic Operations) in the Department of Mathematical and Geospatial Science within RMIT University's School of Science at the City Campus in Australia. With a strong academic background in operations research and optimization, he has established himself as a leading researcher in combinatorial optimization, mathematical programming, and multi-objective optimization. His research interests focus on practical applications of optimization techniques across various domains including healthcare logistics, wildfire management, network optimization, and power systems. Ozlen's work bridges theoretical optimization methods with real-world challenges, particularly in resource allocation problems under uncertainty. The publication record reveals a consistent research trajectory with emphasis on decomposition methods, evolutionary algorithms, and matheuristic approaches for solving complex optimization problems. His recent work shows increasing focus on healthcare applications while maintaining strong contributions to wildfire management and network optimization problems. As an active reviewer for prestigious journals including European Journal of Operational Research and Computers and Operations Research, as well as the Australian Research Council, Ozlen contributes significantly to the academic community. His professional memberships span key organizations including INFORMS, Australian Mathematical Society, and Australian Society for Operations Research. Ozlen actively supervises graduate students with current projects focusing on home care optimization, multi-objective mixed integer programming, and vehicle routing problems with applications to wildfire management. His teaching interests align with his research expertise in operations research, combinatorial optimization, mathematical programming, and multi-objective optimization.

Ripon Kumar ChakraborttyView profile

Researcher

• Artificial Intelligence
• Decision Analytics
• Operations Research
• +8 more

Dr Ripon Kumar Chakrabortty is a Decision Scientist at the School of Systems & Computing, UNSW Canberra . His research focuses on applying Artificial Intelligence and Optimization Models to complex systems, particularly in Global Supply Chains, Logistics, and Medical Informatics . He coordinates the Master of Decision Analytics program and leads the Artificial Intelligence Community of Practice at the Australian Institute of Project Management. PhD in Computer Science, UNSW (2017) M.Sc. & B.Sc. in Industrial Engineering, BUET Diploma in Logistics, Monarch Institute Certified by Chartered Institute of Logistics and Transport Australia His research portfolio includes over 5 million AUD in Category 1 grants from Australian Research Council , Department of Defence , and other government agencies. He has supervised 24 PhD/Master's students to completion or ongoing supervision, with expertise in Explainable AI , Healthcare Analytics , and Smart Manufacturing . Recent scientific recognitions include a Highly Cited Paper , Best Paper at IEEE IEEM , and Champion at IEEE CEC Competition . His editorial roles span prestigious journals like IEEE Transactions on Industrial Informatics and Mathematics (MDPI) .