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Junlei HuView profile

Lecturer

• Actuarial Science
• Risk Management
• Insurance Mathematics
• +3 more

Dr. Junlei Hu is a Lecturer in Actuarial Science at the University of Essex, affiliated with the School of Mathematics, Statistics and Actuarial Science (SMSAS) within the Department of Mathematical Sciences. He is based at the Colchester Campus. His expertise spans actuarial science, risk management, and insurance mathematics, with a focus on numerical optimization and robust methods in reinsurance. PhD in Actuarial Science , Cass Business School, City, University of London (2018) MSc in Actuarial Science , Cass Business School, City, University of London (2013) MPhil in Economics , University of Oxford (2012) BSc in Economics and Statistics , University College London (2010) His research interests include reinsurance optimization, robust contract design, and numerical techniques for solving complex actuarial problems. He has contributed to advancements in Pareto-optimal insurance contracts, solvency regulations (e.g., Solvency II), and non-convex optimization in risk transfer mechanisms. His work bridges theoretical actuarial models with practical applications in regulatory compliance and risk mitigation. Publications highlight trends in addressing uncertainty in reinsurance, leveraging homotopy optimization methods, and designing contracts under budget constraints. His recent work (2025) explores multivariate risk analysis with dependence uncertainty, reflecting cutting-edge developments in actuarial science. No scientific awards or grants are explicitly mentioned in the provided materials. His teaching and supervision roles are part of his academic responsibilities at the University of Essex, though specific student advisees are not listed.

• 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.

Abdellah SalhiView profile

Professor

• Optimization
• Metaheuristics
• Operations Research
• +3 more

Abdellah Salhi is a Professor at the School of Mathematics, Statistics and Actuarial Science (SMSAS) at the University of Essex. His academic qualifications include a PhD from Aston University and a BSc from the University of Constantine. He specializes in optimization, metaheuristics, and operations research, with applications in logistics, port operations, and mathematical modeling. His research addresses complex problems such as container port scheduling, vehicle routing, and numerical solutions for biological systems. Salhi's work spans optimization algorithms like the Plant Propagation Algorithm, Genetic Algorithms, and hybrid methods for solving real-world challenges. His articles focus on multi-objective optimization, logistics efficiency, and algorithmic advancements. He has supervised numerous PhD students, contributing to academic and industrial research. While grants and funding sections are listed, specific details are not provided in the text. No dedicated labs or teams are mentioned, though collaborations are implied through his extensive publication record.

Xinan YangView profile

Professor

• Operations Research
• Optimization
• Logistics and Supply Chain Management
• +4 more

Professor Xinan Yang is a faculty member at the University of Essex, affiliated with the School of Mathematics, Statistics and Actuarial Science. He holds a PhD in Optimization from the University of Edinburgh (2011), an MSc in Operational Research (with distinction) from the same university (2007), and a BSc in Applied Mathematics from Fudan University (2006). His research focuses on optimization, logistics, and operations research, with applications in energy systems, drone/robot-assisted delivery, and stochastic programming. Prior to Essex, he worked as a Senior Research Associate at Lancaster University Management School. Research interests include combinatorial optimization, metaheuristic algorithms, and their applications in transportation, energy management, and healthcare logistics. His recent work explores AI-driven solutions for surgery scheduling and dynamic routing in last-mile delivery systems. He also contributes to energy systems modeling, particularly in integrating renewable energy sources. Publications highlight advancements in collaborative caching, container terminal operations, and volcano eruption algorithms for optimization. His academic support hours follow an open-door policy, and he is based at STEM 5.17 on the Colchester Campus.

• Large-scale optimization
• Humanitarian logistics
• Emergency network design
• +3 more

Dr. Jyotirmoy Dalal is a Lecturer in Decision Sciences at the Sheffield University Management School (University of Sheffield, UK) since December 2022. He previously served at the Indian Institute of Management (IIM) Lucknow (2017–2022) and IIM Udaipur (2014–2017), focusing on Operations, Quantitative Methods, and Information Systems. He holds a PhD in Industrial and Systems Engineering from Texas A&M University. His research emphasizes large-scale optimization techniques applied to humanitarian logistics, emergency network design, food insecurity systems, and supply chain management. His work has been published in prestigious journals like Transportation Science , IISE Transactions , and Naval Research Logistics . Research Trends : Recent publications focus on humanitarian operations (e.g., foodbank network design, emergency response systems), stochastic programming, and case studies in disaster management and social initiatives. His work bridges theoretical optimization models with real-world applications in crises and resource allocation. Teaching : Teaches courses like MGT3010 (Applications of Operations and Supply Chain Management) and MGT138 (Analysis for Decision Making). He emphasizes practical applications of operations research in logistics and decision-making. Labs/Teams : Engaged in collaborative projects involving humanitarian logistics, though no specific lab names are mentioned. Active in PhD supervision focusing on emergency logistics, food bank operations, and supply chain resilience.

Amin VafadarnikjooView profile

Lecturer

• Decision Analysis
• Multiple Criteria Decision Making
• Circular Economy
• +5 more

Dr. Amin Vafadarnikjoo is a Lecturer in Operations Management and Decision Sciences at the Sheffield University Management School (University of Sheffield). He holds a PhD from the Norwich Business School, University of East Anglia, and previously worked as a Research Associate at Manchester Metropolitan University Business School. His research focuses on decision analysis, supply chain management, sustainability, and Industry 4.0 technologies. He has published in journals like Annals of Operations Research and International Journal of Management Reviews. Research Interests: Decision analysis, multiple criteria decision making, circular economy, supply chain risk management, smart manufacturing, and Industry 4.0/5.0. Key Publications: Includes works on supply chain effectiveness frameworks, risk assessment models, and decision-making under uncertainty. His 2021 paper on neutrosophic decision-making and 2020 study on blockchain barriers are notable contributions. Scientific Award: Third Best Graduate Paper Award (IEOM 2016) PhD Supervision: Focuses on multiple criteria decision making, data-driven decisions, circular economy, and emerging technologies. Current advisees include Fasha and Johanis (focusing on Circular Economy & Sustainable Supply Chain). Teaching: Courses include MGT138 (Analysis for Decision Making) and MGT6085 (Global Supply Chain Leadership).

• Applied Mathematics
• Optimization
• Autonomous Agents and Multiagent Systems
• +4 more

Thomas Evans is a Lecturer in Quantitative Methods at the Centre for Advanced Spatial Analysis (CASA) at University College London (UCL), a position he has held since January 2019. He holds a PhD in Mathematics (2015) from UCL, supervised by Professors Steven Bishop and Frank Smith, with research focusing on agent-based modelling, game theory applications in search/patrol models, and mathematical foundations of these systems. Education: MSci Mathematics with a Year in Europe, Imperial College London (2003–2007) PGCE Postgraduate Certificate of Education, Canterbury Christ Church University (2009–2010) PhD Mathematics, UCL (2011–2015) Research Expertise: Dr. Evans specializes in applied mathematics, optimization techniques, and the development of models for complex systems. His work includes optimizing land use around Heathrow Airport using multi-objective linear programming, modeling international trade via input-output tables, and applying the sterile insect technique (SIT) to control dengue-carrying mosquitoes. He has contributed to projects such as ENFOLD-ing (EPSRC-funded), 3cixty (EU), and HARMONY (EU). Teaching and Experience: Prior to UCL, he taught mathematics in the UK, France, Kenya, and Ghana (2009–2011). His academic roles at UCL include Research Assistant (2014–2015), Research Associate (2015), and Teaching Fellow (2015–2019). Labs/Teams: Active within CASA, where he collaborates on interdisciplinary projects blending mathematical modeling with real-world urban and environmental challenges.

Arina BuzdalovaView profile

Lecturer

• Reinforcement Learning
• Evolutionary Algorithms
• Algorithm Optimization
• +3 more

Arina Buzdalova is a Lecturer in Computer Science at the Department of Computer Science, Aberystwyth University. Her research focuses on evolutionary algorithms, reinforcement learning, and optimization techniques, particularly in the context of dynamic selection of auxiliary objectives and runtime analysis. She has authored over 27 research outputs since 2011, with notable contributions to theoretical and applied aspects of evolutionary computation. Her work explores hybridization of clonal selection algorithms with local search, analysis of mutation rate control in evolutionary algorithms, and the use of reinforcement learning to enhance algorithmic efficiency. Key research themes include adaptive systems, stochastic optimization, and theoretical bounds on algorithm performance. Collaborations span international conferences in computational intelligence and evolutionary computation. Publications span topics such as re-optimization under frequent changes, fixed-target runtime analysis, and trade-offs in heuristic search. Her research bridges theoretical foundations with practical applications in optimization and artificial intelligence.

Navid SahebjamniaView profile

Senior Lecturer

• Sustainable and Resilient Operation Management
• Supply Chain and Logistics
• Multi-Objective and Stochastic Programming Methods
• +6 more

Dr Navid Sahebjamnia is a Senior Lecturer in Operations and Information Management at Brunel Business School, Brunel University London. He earned his PhD in Management Science and Operations Research from the College of Engineering at the University of Tehran and has held academic positions at Middlesex University London and Brunel University London. His work bridges operations research, sustainability, and digital transformation in supply chains. Research Interests: Dr Sahebjamnia specializes in sustainable and resilient operations management, with a focus on supply chain and logistics optimization. He develops advanced mathematical models, including multi-objective and stochastic programming, to enhance decision-making under uncertainty. His research supports industries such as transportation, agri-food, and manufacturing in achieving environmental sustainability and operational resilience. Publication Trends: His recent publications reflect a strong trend toward integrating operational research with digital technologies such as AI, big data analytics, and digital twins to address global challenges like pandemic response, green port operations, and humanitarian logistics. There is a consistent emphasis on resilience, sustainability, and quantitative modeling across diverse industrial contexts. Scientific Awards: No specific awards are mentioned in the provided text. Advising and Grants: Dr Sahebjamnia supervises PhD students in areas including resilient supply chains and AI-driven risk mitigation. He is actively involved in securing research funding, notably as a key participant in the NARRATE project funded by the European Commission (2023–2026), which focuses on building regenerative resilient smart manufacturing networks using AI and IoT. He has extensive experience in forming European research consortia and submitting proposals to major funders like UKRI and ERC. Labs and Research Groups: He is affiliated with the OISM (Operations and Information Systems Management) Research Group at Brunel Business School and collaborates closely with Prof Afshin Mansouri and other researchers in innovation and sustainability. His work involves co-author networks and interdisciplinary teams focused on smart manufacturing and supply chain transformation.

• Combinatorial Optimization in Logistics and Production Systems
• Resilience in Cyber-Physical and Collaborative Networks
• Machine Learning/AI in OR

Dr Fatima Ezzahra Achamrah serves as a Lecturer in Operations and Supply Chain Management at the Sheffield University Management School, University of Sheffield, where she contributes to advancing research at the intersection of operations research and artificial intelligence. Her work focuses on developing innovative methodologies to solve complex logistical challenges in modern supply chains. Education: Ph.D. in Industrial Engineering from Paris Saclay University, CentraleSupelec, France Engineering degree (MEng/CEng) in Process Engineering from the National School of Applied Sciences, Morocco Research Interests: Dr Achamrah's research centers on Combinatorial Optimization in Logistics and Production Systems , Resilience in Cyber-Physical and Collaborative Networks , and Machine Learning/AI in OR . She pioneers interdisciplinary approaches that integrate reinforcement learning with exact and meta-heuristic methods to address vehicle routing, inventory management, and 3D bin packing problems. Her groundbreaking work on flow management within cyber-physical networks, particularly the 'Physical Internet' concept, aims to revolutionize supply chain resilience and efficiency through AI-driven solutions. Publication Trends: Her recent publications (2022-2025) reveal a dominant focus on deep reinforcement learning applications for combinatorial optimization under uncertainty, with significant contributions to sustainable cargo-bike delivery systems, selective maintenance in cyber-physical manufacturing, and Physical Internet protocols. These works consistently address stochastic demands and dynamic environments, demonstrating how AI-enhanced optimization can transform logistics operations while enhancing environmental sustainability. Grants and Supervision: Principal Investigator for The Grantham Centre for Sustainable Futures (GCSF) GO Fund (2024, £10,000) Co-Investigator for APR&D programme (2021-2023, £420,000) She actively supervises PhD research in operations and supply chain efficiency, resilience, sustainability, and cyber-physical network management, guiding next-generation researchers in developing intelligent logistics solutions. Research Group: Dr Achamrah is a core member of the Operations Management and Decision Sciences (OMDS) research group, driving collaborative projects that bridge theoretical innovation with practical industry applications.

• Astrodynamics
• Space Mission Design
• Asteroid Deflection
• +3 more

Dr. Joan Pau Sanchez Cuartielles is a Lecturer in Space Engineering at Cranfield University's School of Aerospace, Transport and Manufacturing. He holds a PhD from the University of Glasgow and has extensive experience in postdoctoral research, including a Marie Curie Fellowship at the Universitat Politècnica de Catalunya. His expertise spans small body missions, asteroid deflection, and space mission design. He has contributed to high-profile projects such as the ESA's Comet Interceptor mission and has pioneered methods in trajectory optimization using dynamic programming and graph-based algorithms. Education Background: Bachelor's Degree in Physics, Universitat de Barcelona MSc in Mission Analysis and Design, University of Glasgow PhD in Space Engineering, University of Glasgow (2009) Research Interests: Focuses on complex space trajectory design, including gravity-assist techniques, asteroid exploration, and autonomous navigation systems. His work integrates advanced optimization algorithms with practical mission constraints, enabling low-cost CubeSat missions and innovative sample-return architectures. Grants & Collaborations: Supported by ESA, UK Space Agency, and European Commission. Active in interdisciplinary projects involving spacecraft navigation, planetary defense, and interplanetary mission planning. Labs/Teams: Core member of the Visionary Space Systems theme within the ERC-funded VISIONSPACE project, and collaborator on international missions like the ESA F-Class Comet Interceptor.

• Machine Learning Safety
• Autonomous Systems Engineering
• System Safety
• +3 more

Dr Colin Paterson is a Senior Lecturer in Machine Learning Safety at the University of York's Department of Computer Science, working within the Institute for Safe Autonomy (ISA). His research focuses on ensuring safety in autonomous systems, particularly addressing uncertainties in deployed systems. He holds a PhD in Computer Science (2018, York) and a prior PhD in Control Systems Engineering (1993, Coventry), alongside qualifications in education and business. Education: PhD: Computer Science, University of York (2018) PhD: Control Systems Engineering, Coventry University (1993) PGCE: Secondary Education, Leeds Trinity University (2012) BA: Business, Finance, Mathematics, Open University (2011) BEng: Computer & Control Systems Engineering, Coventry University (1990) His research interests span autonomous systems engineering, machine learning assurance, ethical AI design, and verification methodologies. He contributes to the development of safety frameworks for self-adaptive systems and has published extensively on topics like runtime safety monitors, hazard mitigation in autonomous vehicles, and transfer assurance in machine learning. Dr Paterson currently serves as GTA Coordinator and Training Officer within the department. His work emphasizes bridging theoretical safety models with practical implementation in real-world autonomous systems.

Xiang SongView profile

Senior Lecturer

• Logistics and Transportation
• Supply Chain Management
• Cutting and Packing Problems
• +3 more

Dr. Xiang Song is a Senior Lecturer at the Department of Mathematics, University of Portsmouth, within the Faculty of Technology and School of Mathematics and Physics. He is affiliated with the Centre for Operational Research & Logistics and the Portsmouth AI and Data Science Centre. His research focuses on cutting and packing problems, operational research, logistics optimization, and artificial intelligence applications in supply chain management. He has held EPSRC-funded projects, including the LANCS Initiative in Foundational Operational Research (2008-2011) and contributed to projects like automated algorithm selection for cutting/packing problems (2004-2008). His research interests include unmanned vehicle routing for offshore inspections, maintenance planning for renewable energy infrastructure, stochastic supply chain modeling, and heuristic algorithms for complex optimization problems. He has published extensively in journals like European Journal of Operational Research and Expert Systems with Applications. His work bridges theoretical advancements with real-world applications in logistics, healthcare, and manufacturing. PhD: Jointly awarded by CIMS, Shenyang Institute of Automation (Chinese Academy of Sciences) and Université de Technologie de Troyes (2004) EPSRC Projects: Including strategic maintenance planning for offshore wind farms and blood supply network optimization in disaster scenarios His research outputs span 27 peer-reviewed articles, with recent work addressing carbon cost impacts on supply chains, multi-modal logistics in medical supply networks, and UAV/USV coordination for offshore inspections. He supervises PhD students and collaborates with industry partners on practical operational research challenges.

• Revenue Management and Pricing
• Reinforcement Learning
• Operations Research
• +5 more

Prof. Dong Li is a Professor of Operational Research at Lancaster University Management School (LUMS). He previously held academic roles at Loughborough Business School and the University of York, alongside industry experience at Avis Budget Group and Intel Corp. His research focuses on Revenue Management, Reinforcement Learning, and Resource Allocation, with methodological expertise in Markov Decision Processes and Stochastic Programming. Education: BEng/MEng in Mechanical Engineering (Xi’an Jiaotong University), MEng in Industrial Engineering (National University of Singapore), and PhD in Management Science (Lancaster University). Research Interests include: Revenue Management & Dynamic Pricing in Sharing Economies Discrete Choice Modelling Healthcare Operations Management Reinforcement Learning and Approximate Dynamic Programming He leads the STOR-i Doctoral Training Centre and serves as Associate Editor of Computational Management Science . Notable grants include a UKRI/EPSRC-funded project on medical resource rationing during public health emergencies (2020–2022). External Roles: External examiner at Strathclyde University (2020–present) and Xi’an Jiaotong Liverpool University (2020–2024). Research Groups: Centre for Transport & Logistics (CENTRAL), Health Systems Optimisation, and STOR-i.

Wei PanView profile

Senior Lecturer

• Robotics
• Machine Learning
• Control Theory
• +3 more

Wei Pan is a Senior Lecturer (Associate Professor) in Machine Learning at the Department of Computer Science, University of Manchester. He previously held positions as Assistant Professor at TU Delft and Project Leader at DJI. He received his PhD from Imperial College London. His research focuses on robot control using Bayesian machine learning and dynamic control principles, with contributions to robotics, control theory, and reinforcement learning. He leads the UKRI AI CDT on Decision Making and co-directs the Manchester Centre for AI Fundamentals. Education: PhD in Machine Learning from Imperial College London; Master's and Bachelor's degrees from Harbin Institute of Technology and University of Science and Technology of China. Research Interests: Bayesian Reinforcement Learning, Multi-Agent Systems, Autonomous Robotics, and Safe Control. His work bridges theoretical foundations with practical applications in robotics and AI, emphasizing real-world deployment and safety. Recent Research Trends: His articles emphasize scalable multirobot control, safe reinforcement learning, and Bayesian methods for system identification. Notable contributions include hierarchical physics-informed neural networks and distributed MPC frameworks. Awards: Outstanding Associate Editor Award (IEEE RA-L). Projects: Co-PI on MCAIF (Centre for AI Fundamentals) and RAI (Robotics & AI) initiatives. Advising & Teams: Supervises over 20 PhD students and leads research groups at Manchester and TU Delft. Collaborates widely in academia and industry, including DJI and Alibaba. Labs: Former co-director of Delft SELF AI Lab; active in Manchester’s robotics facilities and computational AI labs.