جستجوی استاد راهنمای دانشگاهی

• Optimisation Under Uncertainty
• Discrete Choice
• Social Choice
• +۴ مورد دیگر

Chung Piaw Teo is the Stephen Riady Professor and Executive Director of the Institute of Operations Research and Analytics (IORA) at the National University of Singapore (NUS) Business School. He has held significant academic roles including Head of Department, Acting Deputy Dean, Vice-Dean of Research and Ph.D. Programs, and Chair of the Ph.D. Committee at NUS. PhD in Operations Research from MIT (1996) Bachelor of Science (Honors) in Mathematics from NUS (1990) Research Interests: Optimisation Under Uncertainty Discrete Choice Modeling Social Choice Theory Inventory Theory Supply Chain Management Combinatorial Optimisation Operations Research Publications focus on stochastic optimization, supply chain resilience, and network design, with recent works spanning 2020–2015 in journals like Management Science , Operations Research , and Mathematical Programming . Key themes include urban logistics, risk mitigation, and decision analytics. Scientific Awards: Stephen Riady Professor (2024) Provost’s Chair (2014) Faculty Outstanding Researcher Awards (2014, 2006, 2003) Nominee for University Outstanding Researcher Award (2006) Grants & Leadership: Served on international committees (INFORMS, LANCHESTER Prize, Fudan Prize) and held visiting/fellow positions at MIT, Northwestern, and Sungkyunkwan University. Currently a department editor for Management Science and associate editor for multiple journals.

• Parallel and Distributed Systems
• Graph Optimization
• Big Data
• +۷ مورد دیگر

Tomaž Dobravec is an Assistant Professor at the Faculty of Computer and Information Science, University of Ljubljana, Slovenia. He is actively involved in research and teaching in computer science, with a focus on system software, algorithms, and distributed computing. His work is supported by national research programmes and structural funds projects. His research interests include: Parallel and Distributed Systems Graph Optimization and Big Data Combinatorial Scientific Computing Mobile and Industrial Operating Systems Interactive Educational Software Development The trend in his research projects indicates a strong emphasis on algorithmic efficiency, large-scale data processing, and practical software solutions for both industrial and educational contexts. His involvement in long-term ARRS programmes (e.g., P2-0095) highlights sustained contributions to core computing disciplines. He has participated in several research and development projects, including: P2-0095 - Parallel and distributed systems (2020–2026) N2-0171 - Graph Theory and Combinatorial Scientific Computing (2021–2023) PKP6 - Mobile Operating System for Industrial Use (MobiIOS) (2020) Graph Optimisation and Big Data (2016–2019) Development of the Visual Assistant project (2016–2017) Design and programming of interactive electronic textbooks (2017) He teaches key undergraduate courses such as System Software Programming 2 , Algorithms and Data Structures 2 , and The C Programming Language , indicating a strong foundation in core computer science education. While no formal advisees are listed, his role in research projects suggests mentorship of students and junior researchers. There is no mention of scientific awards in the provided text.

• Mathematical Modelling
• Transportation Systems
• Optimisation
• +۲ مورد دیگر

Lucy Maybury serves as a Graduate Tutor in the School of Mathematics at Cardiff University while pursuing her PhD in the School of Computer Science and Informatics through an interdisciplinary project with the School of Engineering. Her role encompasses comprehensive mathematics support across computer science curricula. Her research specializes in mathematical modelling of transportation systems, with core expertise in optimisation and simulation techniques applied to the UK's transition from conventional to electric vehicles. This work bridges computational mathematics, environmental policy, and sustainable infrastructure development. Maybury's 2022 publication in Transportation Research Part D establishes her authority in systematic analysis of electric vehicle adoption models, contributing to interdisciplinary scholarship at the intersection of transportation engineering and mathematical sciences. Teaching responsibilities include: Maths Support Tutor for foundational courses Tutorials for CM2104: Computational Mathematics Support for CM3109: Combinatorial Optimisation Instruction for CM1208: Maths for Computer Science Group project supervision for CM2305 Her academic activities foster collaboration between the School of Mathematics and School of Engineering, advancing sustainable transportation research through computational methodologies.

• Genetic Programming
• Evolutionary Algorithms
• Combinatorial Optimisation
• +۶ مورد دیگر

Dr. Edgar Galván is an Associate Professor in the Department of Computer Science at Maynooth University's Faculty of Science & Engineering. He is a leading expert in Genetic Programming (GP) and Evolutionary Algorithms, with a focus on semantic-based approaches, neutrality, and multi-objective optimization. His work spans applications in combinatorial optimization, gaming (e.g., Carcassonne), and software engineering, including neuroevolution for deep learning architectures. Current Affiliation: Maynooth University Previous Roles: Senior Researcher at University College Dublin, Trinity College Dublin, and INRIA Paris-Saclay Research interests include: Semantic-based Genetic Programming Multi-objective Evolutionary Algorithms Monte Carlo Tree Search Circular Economy Applications Privacy-Preserving Optimization Neuroevolution in Autonomous Systems His recent publications analyze semantic diversity in GP, neural architecture search, and privacy-aware swarm optimization. Key awards include being ranked among the top 1% of GP researchers by University College London (2020), a Marie Curie Fellowship (2014), and a Best Paper Award at ECTA 2015. Current Projects: REBUILD (Circular Economy Buildings, 2024-2027), VISION (Circular Business Models, 2023-2026) Previous Grants: Stochastic Bio-inspired Algorithms (2014-2017, €267k), circAI (2022-2023, €142k) Dr. Galván serves on program committees for IEEE, ACM, and Springer conferences, and as Scientific Adviser for institutions in Ireland, France, and Mexico. His work bridges theoretical GP analysis with real-world applications in energy optimization and AI.

Yingqian Zhangمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Deep Reinforcement Learning
• +۷ مورد دیگر

Yingqian Zhang is an Associate Professor in the Information Systems group at the Industrial Engineering and Innovation Sciences department of Eindhoven University of Technology (TU/e). She is affiliated with the Eindhoven Artificial Intelligence Systems Institute (EAISI), specifically with the EAISI High Tech Systems and EAISI Foundational groups. Her research focuses on applying Artificial Intelligence to solve complex decision-making problems across various domains including logistics, transportation, manufacturing, and e-commerce. Dr. Zhang received her PhD in Computer Science from the University of Manchester, UK. Prior to joining TU/e, she served as an Assistant Professor in the Econometrics Institute at Erasmus University Rotterdam and as a postdoc researcher in the Algorithmics group at TU Delft. She was also a visiting professor at the Institute for Advanced Computer Studies at University of Maryland, College Park, USA. Her research expertise lies at the intersection of Artificial Intelligence and optimization, with particular focus on machine learning, deep reinforcement learning, and trustworthy data-driven optimization. Dr. Zhang develops socially aware algorithms that can optimize decisions in data-rich environments. Her work bridges the gap between theoretical AI advancements and practical applications in industrial settings, addressing real-world challenges through innovative algorithmic solutions. She is particularly interested in how AI can support human decision-making while maintaining transparency and trustworthiness. Dr. Zhang's recent publications reveal a strong trend toward applying graph neural networks and reinforcement learning to complex scheduling and optimization problems. Her work demonstrates increasing sophistication in handling stochastic elements in decision-making processes, with applications spanning healthcare diagnostics, logistics, transportation, and manufacturing. She has made significant contributions to the field of neural combinatorial optimization, particularly for job shop scheduling problems and vehicle routing. Dr. Zhang has received several prestigious awards recognizing her contributions to the field: Winner of the MLVRP2023 GECCO competition (2023) Best Paper Award from Omega-International Journal of Management Science (2017) Best Industrial Paper Award (2020) Best Student Paper Award (2019) Best Student Paper Award of ICAART 2022 (2022) As a dedicated mentor, Dr. Zhang supervises numerous PhD students including Mohsen Abbaspour Onari, Abdo Abouelrous, Luca Begnardi, Xia Jiang, Chengpeng Hu, Minshuo Li, Robbert Reijnen, Jesse van Remmerden, Bart von Meijenfeldt, Ya Song, and Igor Smit. Her research is supported by various grants, including the LEO (Learning and Explaining Optimization) project co-funded by Holland High Tech | TKI HSTM via the PPP allowance scheme for public-private partnerships. Dr. Zhang actively contributes to the academic community as the Chair of the Benelux Association for Artificial Intelligence (BNVKI) and as a member of the Technical Board for the European Big Data Value Association (BDVA). She serves as an associate editor for the "Annals of Mathematics and Artificial Intelligence" journal and participates in the technical Program Committee for major AI conferences such as IJCAI, AAAI, AAMAS, and ECAI. She is also on the executive committee of the Data Science meets Optimisation (DSO) working group of EURO to promote collaboration between AI and Operations Research communities.

Daniel Herringمشاهده پروفایل

پژوهشگر ارشد

• Multi-objective Optimisation
• Evolutionary Computation
• Machine Learning
• +۵ مورد دیگر

Dr Daniel Herring is a Research Fellow in Industrial Mathematics at the School of Mathematics, University of Birmingham, working within Professor Fabian Spill's research group. His position focuses on applied mathematical research bridging theoretical computation and real-world industrial applications. His academic qualifications include: MSci in Natural Sciences from the University of Exeter (2017), with thesis work on optimisation methods for Alzheimer's EEG data modeling PhD in Computer Science from the University of Birmingham (2023), completed under the Priestley Scholarship Award with extended research at the University of Melbourne Dr Herring specializes in dynamic multi-objective optimisation for both continuous and combinatorial problems, advancing evolutionary computation frameworks. His work integrates evolutionary machine learning with novel classification algorithms, targeting applications in healthcare diagnostics, energy infrastructure optimization, and social network dynamics. Current projects address building occupancy optimization and dissociation analysis in adolescent populations, while upcoming research explores clean-air turbulence prediction and information diffusion in graph-based social networks. Scientific recognition includes: Priestley Scholarship Award for doctoral research excellence He actively contributes to the international computer science community through presentations at premier conferences including GECCO and CEC. Dr Herring's research group collaborates on cross-disciplinary industrial mathematics challenges with strong emphasis on translational impact in public health and infrastructure systems.

• Discrete Optimisation
• Scheduling Theory
• Operations Research
• +۷ مورد دیگر

Frank Werner is apl. Professor (Adjunct Professor) of Mathematical Optimisation at the Otto von Guericke University Magdeburg, Faculty of Mathematics, Institute for Mathematical Optimization. Since 1989 he has been a permanent member of this institute, leading DFG- and EU-funded projects, supervising international doctoral students and post-doctoral researchers, and serving on the editorial boards of more than a dozen journals. Education 1961–1973: Nordpark-Oberschule, Clara-Zetkin-Oberschule, EOS Geschwister Scholl, Magdeburg (Abitur ‘Ausgezeichnet’) 1975–1980: Diploma in Mathematics, Technische Hochschule Magdeburg (Dipl.-Math., grade ‘Ausgezeichnet’) 1980–1983: Doctoral studies, TH Magdeburg (Dr. rer. nat., Summa cum laude) 1987–1989: Habilitation, TH Magdeburg (Dr. rer. nat. habil.) Research Interests Werner’s work lies at the intersection of applied mathematics, operations research and computer science. He designs exact and heuristic algorithms for NP-hard scheduling and sequencing problems, analyses the stability of solutions under interval data uncertainty, and applies these techniques to real-world domains such as machine scheduling, vehicle routing, train timetabling, supply-chain coordination, and healthcare logistics. Recent activities extend to the use of machine-learning techniques to enhance optimisation algorithms. Scientific Awards & Honours Best Paper Award, International Journal of Production Research Best Paper Award, IISE Transactions NOC Prize 2009, INCOM Symposium Moscow, for work on hierarchical mobile-robot scheduling Supervision, Grants & Service Since 1992 Werner has coordinated numerous DFG and EU projects and acted as principal investigator for international collaborations involving Belarus, Russia, Poland, the USA, Mexico, Iran, Nepal, China and Finland. He has mentored Alexander von Humboldt fellows and long-term visiting scholars, and currently serves as Editor-in-Chief of the MDPI journal Algorithms , Associate Editor for Journal of Scheduling and International Journal of Production Research , and on the editorial or advisory boards of more than ten further journals. Laboratory & Teams He heads the Scheduling & Discrete Optimisation group within the Institute for Mathematical Optimization, maintaining active partnerships with Tribhuvan University (Nepal), Autonomous University of Baja California (Mexico), Belarusian State University, École des Mines de Saint-Étienne (France), University of Alabama in Huntsville (USA), and many others.

• Computer Science
• Algorithmic Game Theory
• Combinatorial Optimization
• +۵ مورد دیگر

Professor Piotr Krysta is a faculty member in the Department of Computer Science, affiliated with the research groups on Algorithms, Complexity Theory and Optimisation, and Economics and Computation. His work spans theoretical and applied domains in algorithmic design and economic systems. University of Liverpool (implied by context, though not explicitly stated) His research interests include: Approximation Algorithms Combinatorial Optimization Algorithmic Game Theory Computational Complexity Optimization in Network and Economic Systems His recent publications focus on blockchain mechanisms, combinatorial constraints in delegation, and optimization techniques in graph theory. Funded by UK and German research councils, his work bridges computational hardness with economic applications. Scientific awards include: Best paper award for Track C of 39th ICALP (2012) Emmy Noether Fellow (DFG, 2004–2008) He has served on program committees for major conferences, collaborated internationally, and contributed to journals like Electronic Commerce Research and Applications . His teaching involves supervising theses on algorithmic challenges and mechanism design.

• Combinatorial Optimization
• Operations Research
• Artificial Intelligence
• +۳ مورد دیگر

Corinne LUCET-VASSEUR is a University Professor at Université de Picardie Jules Verne (UPJV), leading Research Unit UR 4290 (OCIA - Optimisation Combinatoire, Images et Applications). Her office (Room 302, Tel: 5900) serves as the hub for her research group focused on combinatorial optimization and artificial intelligence applications. Her research spans: Combinatorial Optimization : Developing metaheuristics for NP-hard problems Healthcare Logistics : Patient flow optimization, facility location, simulation training Logistics Engineering : Parcel distribution, vehicle routing with time windows Algorithm Design : Ant Colony Optimization, Adaptive Large Neighborhood Search, portfolio methods She applies these methodologies to solve complex real-world problems, particularly in healthcare systems where resource constraints and scheduling complexity demand innovative optimization approaches. Her work bridges theoretical advances with practical implementation through industrial partnerships. Current research projects include: SMILE PICK UP (CIFRE industrial partnership) Simusanté (healthcare simulation) LORH (logistics optimization) These projects secure ongoing funding and provide doctoral training opportunities through industry collaboration. Her publication record demonstrates consistent methodological innovation applied to healthcare and logistics challenges across multiple European conferences and journals. Professor Lucet-Vasseur actively mentors junior researchers through co-authorship on conference papers and journal articles. Her supervision style emphasizes practical problem-solving with industry relevance, preparing students for both academic and industrial careers in optimization. The OCIA research unit provides a collaborative environment for tackling complex combinatorial problems with real-world impact. The OCIA laboratory serves as UPJV's center for combinatorial optimization research, specializing in metaheuristic development for healthcare and logistics applications. The lab maintains strong industry connections through CIFRE contracts and applied projects, ensuring research relevance while providing students with exposure to real business challenges. Current focus areas include adaptive algorithm selection using reinforcement learning and fitness landscape analysis for optimization problems.

John McCallمشاهده پروفایل

استاد

• Data Science
• Artificial Intelligence
• Nature-inspired Computing
• +۵ مورد دیگر

Professor John McCall is a distinguished academic and researcher at Robert Gordon University's School of Computing, Engineering & Technology, where he previously served as Head of School. He currently serves as Director of the National Subsea Centre, leading initiatives to accelerate energy transition through smart technologies applied to industrial and environmental challenges in subsea and related marine sectors. With over 25 years of research experience in nature-inspired computing and artificial intelligence, Professor McCall has established himself as a leading expert in optimization algorithms and explainable AI. Professor McCall's research interests span data science, artificial intelligence, nature-inspired computing, and optimization, with significant applications in energy transition and subsea technologies. His work bridges theoretical foundations with practical implementations, having founded two spinout companies that deliver real-world optimization solutions to industry. He leads both the Complex Optimisation Research Group and the Computational Intelligence Research Group, where his team explores cutting-edge approaches to solving complex computational problems. Analysis of Professor McCall's recent publication record (2023-2025) reveals a strong focus on explainable AI, particularly in the context of evolutionary computation and metaheuristics. His research demonstrates an increasing emphasis on practical applications in energy systems, transportation, and subsea technologies, reflecting his commitment to addressing real-world challenges related to climate change and industrial transformation. The interdisciplinary nature of his work is evident in publications spanning computer science, operations research, renewable energy, and transportation planning. Lead of the Computational Intelligence Research Group ResearcherID: G-1423-2011 Scopus Author ID: 36797474900 ORCID: https://orcid.org/0000-0003-1738-7056 Professor McCall is actively involved in mentoring the next generation of researchers, currently supervising multiple PhD students across diverse topics including explainability of non-deterministic solvers, optimization of electrical machines, and computational intelligence applications in hydrocarbon systems. His research is supported by numerous grants from industry and government sources, with projects totaling millions of pounds focused on solving challenges in energy transition and smart technologies. At the National Subsea Centre, Professor McCall leads a multidisciplinary team working on digital twin technologies, subsea AI applications, and data-driven solutions for the energy sector. His work emphasizes collaboration between academia and industry to develop transformative solutions that address both current challenges and future opportunities in the subsea domain.

• Dynamic Optimisation
• Optimisation under Uncertainty
• Combinatorial Optimisation
• +۷ مورد دیگر

Dr. Joan Alza Santos is an early-career Research Fellow at the School of Computing, Engineering, and Technology within Robert Gordon University (RGU). She is affiliated with the Complex Optimisation Research Group and contributes to RGU's research themes in "Environment, Energy & Sustainability" and "Living in a Digital World." Her work bridges academic research in dynamic optimization with practical applications in sustainable logistics and other real-world domains. Dr. Alza Santos completed her educational journey with: BSc (Hons) in Computer Engineering from Universidad del País Vasco (University of the Basque Country), 2013-2017 Postgraduate Certificate from Robert Gordon University, 2018-2021 PhD in Computing from Robert Gordon University, completed in January 2025 with no corrections required - a rare distinction Her research focuses on addressing fundamental challenges in dynamic optimization. Dr. Alza Santos has made significant contributions to the field through three key innovations: developing a fitness landscape rotation framework for dynamic benchmarking, introducing the concept of "elusivity" to quantify algorithmic adaptability over complete restart, and creating methods for generating realistic synthetic instances from real-world logistic data. Her work aims to bridge the gap between academic research and practical applications, particularly in sustainable logistics where optimization under uncertainty is critical. Dr. Alza Santos's publication record shows a consistent focus on dynamic optimization problems, particularly in combinatorial spaces. Her research trajectory demonstrates progression from theoretical foundations of dynamic permutation problems to increasingly practical applications, culminating in methods for generating realistic benchmarks from real-world data. A notable trend in her work is the development of metrics like "elusivity" that provide quantitative ways to evaluate the true adaptive challenge in dynamic optimization problems, moving beyond simplistic representations of dynamism. Her scientific recognition includes: Membership in the International Graduate Student Research Cohort (2024) Presentation at the Arctic Circle Assembly (2024) PhD defense with no corrections required (2025) - noted as a rare distinction As an early-career researcher, Dr. Alza Santos actively supervises research projects and serves as a Demonstrator for "Applied Data Science" and "Data Visualisation" modules. Her industrial collaborations span multiple sectors including energy, transportation, and environmental sustainability. She has worked on projects evaluating offshore wind farm impacts, developing visualization tools for fleet management, simulating transportation demand, optimizing cargo transportation, creating VR flooding simulations, reviewing childhood obesity data, and developing predictive maintenance platforms for the oil and gas industry. Dr. Alza Santos leads research within the Complex Optimisation Research Group at RGU, where she continues to develop innovative approaches to dynamic optimization problems. Her current work focuses on applying simulation-optimization techniques to address real-world challenges, particularly in sustainable logistics. She is actively seeking new collaboration opportunities with both academic researchers and industry partners to expand the impact of her work on society and the economy.

• Combinatorial Optimisation
• Explainable AI
• Metaheuristics
• +۷ مورد دیگر

Dr. Lee Christie is a Research Fellow at Robert Gordon University's School of Computing, Engineering & Technology, where he conducts research in optimization and artificial intelligence. He is affiliated with the Complex Optimisation Research Group and maintains connections with the National Subsea Centre through his research on net-zero operations. His educational background includes: BSc (Hons) in Computer Science from Robert Gordon University (2003-2007) MSc in Information Engineering (Distinction) from Robert Gordon University (2009-2011, part-time) PhD in Computational Intelligence from Robert Gordon University (2011-2016) Dr. Christie's research primarily focuses on combinatorial optimization, structure learning, and blockchain technologies. He investigates how to make non-deterministic solvers more transparent through trajectory mining and feature extraction. His work bridges theoretical optimization techniques with practical applications in transportation systems (particularly connected autonomous vehicles), renewable energy (wind farm optimization), and supply chain management. He has published extensively on explainable metaheuristics, with a growing emphasis on making optimization algorithms interpretable for end-users while maintaining effectiveness. His recent publications (2021-2025) demonstrate a clear research trajectory toward explainable AI for optimization algorithms, with applications spanning transportation systems, renewable energy infrastructure, and complex supply chains. These works showcase his ability to translate theoretical advances into practical solutions for real-world problems with societal impact. Dr. Christie has secured research funding for projects including the A.R.T. Forum NSR (2019-2022), which developed implementation roadmaps for automated road transport in the North Sea Region. He teaches programming for business analytics courses and actively contributes to the Aberdeen Python User Group as a steering committee member. His academic service includes supervision of PhD students, with Dr. Martin Fyvie recently completing a dissertation on 'Explainability of Non-Deterministic Solvers' under his guidance as second supervisor. Dr. Christie maintains active collaborations with researchers including John McCall, A.-C. Zăvoianu, and A.E.I. Brownlee, resulting in consistent publication output across reputable venues in evolutionary computation and artificial intelligence.