جست‌وجوی استاد و پژوهشگر

Bernd Kaltenhäuserمشاهده پروفایل

استاد

• System Dynamics
• Blockchain in Transportation
• Autonomous Vehicles
• +۷ مورد دیگر

Prof. Dr. Bernd Kaltenhäuser has been Professor of Technical Fundamentals at the Baden-Württemberg Cooperative State University (DHBW) Villingen-Schwenningen since 2014. Affiliated with the Faculty of Economics, he teaches a spectrum of courses spanning project management, quality and process management, financial mathematics, technical mechanics, electrical engineering basics, and physical principles. Education 1994-2003: Studies in Physics at Heidelberg, Ulm and Stuttgart Universities 2003-2007: Doctorate in Natural Sciences (Dr. rer. nat.), University of Stuttgart 2009-2014: M.Sc. in Economics, Fernuniversität Hagen Research Focus Prof. Kaltenhäuser’s research integrates system dynamics, blockchain technology in transportation, predictive modelling for autonomous vehicles, and empirical methods from applied social research and marketing. His interdisciplinary approach leverages physics, economics, and data science to address mobility challenges. He is particularly active in developing algorithms for fleet routing, ride-hailing optimisation, and evaluating market readiness for autonomous driving in Germany. Scientific Awards Artur Fischer Inventor Prize 2009 Heinrich Düker Prize 2004, Robert Bosch Foundation for Education and the Promotion of Disabled People Patents & Impact He holds two patents in flat-structure bioreactor design, demonstrating his earlier engagement with biofuel production technologies. His 2020 and 2018 market studies on autonomous driving provide key data for German transport policy stakeholders.

Andrija Vidosavljevicمشاهده پروفایل

مدرس

• Combinatorial optimization
• Optimization under uncertainty
• Mathematical modeling
• +۵ مورد دیگر

Andrija Vidosavljevic serves as a Lecturer-researcher at Ecole Nationale de l'Aviation Civile (ENAC) in the OPTIM team within the MORO axis. He is affiliated with the Department of Mathematics and Computer Science at ENAC's School of Aeronautics, where he conducts research at the intersection of optimization theory and air traffic management systems. Dr. Vidosavljevic's research spans combinatorial optimization, optimization under uncertainty, and mathematical modeling with specific applications to traffic management and air transport systems. His work addresses critical challenges in urban air mobility, airspace design for high-density environments, and trajectory planning for both manned and unmanned aircraft. He has made significant contributions to understanding how traffic structure affects airspace capacity and safety, particularly in constrained urban environments where traditional air traffic management approaches face limitations. His publication record reveals a clear trajectory toward increasingly complex urban air mobility challenges, with recent work focusing on conflict detection and resolution for constrained urban airspace, centralized separation management for unmanned aircraft systems, and the integration of strategic and tactical approaches to air traffic management. The research demonstrates sophisticated modeling techniques including genetic algorithms, multi-agent systems, and hybrid machine learning approaches to address uncertainty in air traffic systems. Scientific Recognition: Best paper award for the Network Management track at ICRAT 2016 for 'The Influence of Traffic Structure on Airspace Capacity' Dr. Vidosavljevic has been actively involved in major European research initiatives including the Metropolis project (and its successor Metropolis II), Capacity Optimisation in TrajecTory-based OperatioNs, and the REDUCED SEPARATION MINIMA (RESET) project. His collaborative work spans multiple international institutions, reflecting the global nature of air traffic management research. He works within ENAC's Applied Mathematics, Computer Science and Automation for Aeronautics Laboratory (MAIAA), specifically in the OPTIM team that focuses on optimization methods for aeronautical applications. His current research within the MORO axis addresses the mathematical foundations of air traffic flow management and airspace design, with particular emphasis on next-generation urban air mobility systems.

Edilson Arrudaمشاهده پروفایل

دانشیار

• Healthcare modelling and optimisation
• Optimisation under uncertainty
• Markov decision processes
• +۱ مورد دیگر

Edilson Arruda is an Associate Professor in the Department of Decision Analytics and Risk at the University of Southampton's Southampton Business School. His academic career spans Electrical Engineering with specialization in Operational Research and Optimal Control, focusing on interdisciplinary applications at the intersection of artificial intelligence, operations research, and management science. His educational background includes a BSc in Electrical Engineering from the Federal University of Mato Grosso (Brazil), followed by MSc and DSc in Electrical Engineering (Operational Research/Optimal Control) from the University of Campinas. Prior academic appointments include Lecturing/Senior Lecturing positions at the Pontifical Catholic University of Rio Grande do Sul and the Federal University of Rio de Janeiro, plus postdoctoral work at the National Laboratory for Scientific Computation (Brazil) and Cardiff University. Arruda's research centers on optimization under uncertainty using Markov decision processes and stochastic modeling, with significant applications in healthcare resource management, logistics, and supply chain analytics. His work addresses critical challenges such as hospital bed allocation, surgical scheduling for military healthcare, epidemic control, and offshore infrastructure management. He leads the FORecasting Turbulence in Hospitals (FORTH) project and has contributed to mass casualty response modeling for blast events. His publications reveal a strong trend toward healthcare applications of stochastic optimization, particularly in resource-constrained environments. Approximately 60% of his recent work focuses on healthcare modeling, while 30% addresses logistics and supply chain challenges, and 10% explores fundamental methodological advances in Markov decision processes and optimization theory. As an educator, Arruda teaches advanced modules including Markov Decision Processes, Probabilistic Methods in Operational Research, and Foundations of Business Analytics. He actively supervises PhD students across disciplines including Management, Mathematical Sciences, and Human Development. He maintains significant external engagement as Chair at Kerala University of Digital Sciences (2022) and as a speaker on patient pathway modeling (2023). His research is conducted through multiple centers including the Institute for Life Sciences, CORMSIS (Centre for Operational Research, Management Science and Information Systems), and the Centre for Healthcare Analytics.

Jia Guoمشاهده پروفایل

پژوهشگر

• Operations research
• Optimisation
• Modelling and simulation
• +۱۸ مورد دیگر

Dr. Jia Guo is a Researcher at the Research Centre for Integrated Transport Innovation (rCITI) within the School of Civil and Environmental Engineering at the University of New South Wales (UNSW). She received her Ph.D. in Operations Research and Industrial Engineering from the University of Texas at Austin, USA. Her research spans multiple domains including transportation systems, network design, healthcare operations, and power systems optimization. Her primary research interests include: Operations research and optimization methodologies Mathematical modeling and simulation Machine learning applications in transportation Rail and air transportation systems Stochastic analysis and modeling Dr. Guo's work focuses on developing advanced optimization techniques to solve complex problems in transportation planning and logistics. Her research has produced significant contributions in freight train planning, air traffic controller scheduling, vehicle routing, transportation and logistics design, travel behavior analysis, and staff scheduling. She has extensive expertise in mathematical modeling, decomposition algorithms, metaheuristics, machine learning methods, and statistical analysis. Her publications reflect a strong trajectory in applying operations research to real-world transportation challenges, with a particular emphasis on stochastic and optimization approaches across rail, air, and urban transportation systems. Dr. Guo maintains an active research profile with an ORCID identifier (https://orcid.org/0000-0002-5350-2595) and is engaged with the academic community through Google Scholar.

Magdalena Lemańskaمشاهده پروفایل

استادیار

• Graph Theory
• Domination Theory
• Discrete Mathematics
• +۲ مورد دیگر

Dr. Magdalena Lemańska is an Assistant Professor at the Institute of Applied Mathematics within the Faculty of Applied Physics and Mathematics at Gdańsk University of Technology. She has established herself as a dedicated researcher in discrete mathematics with a specialized focus on domination theory in graphs. Dr. Lemańska obtained her doctoral degree (dr) in Mathematics on January 19, 2006, from the Faculty of Mathematics, Physics and Informatics of the University of Gdańsk, marking the beginning of her academic career focused on theoretical aspects of graph structures. Her research centers on advanced topics in graph theory, particularly domination parameters and their variations. She investigates mutual visibility sets, restrained differential, isolation numbers, and super domination across various graph structures including trees, unicyclic graphs, and block graphs. Her work often examines how graph operations like edge subdivision affect these parameters, contributing to the theoretical foundations of combinatorial optimization. Dr. Lemańska's publication record shows consistent scholarly output since 2014, with recent publications in 2024-2025 indicating active ongoing research. Her work appears in reputable journals such as Discrete Applied Mathematics, Discrete Mathematics, and Discussiones Mathematicae Graph Theory, demonstrating her contributions to advancing knowledge in graph theory. She is currently involved in the COVER project (Combinatorial Optimisation for Versatile Applications to Emerging Urban problems), part of the Horizon Europe program (agreement 101182819 — COVER — HORIZON-MSCA-2023-SE-01), which began on September 16, 2024, in the Department of Algorithms and Systems Modelling. Dr. Lemańska maintains substantial teaching responsibilities at Gdańsk University of Technology with 88 teaching entries recorded, indicating significant contribution to academic instruction alongside her research activities. Her office is located in Building B, room 516a, and she can be contacted at magleman@pg.edu.pl.

• Optimization
• Numerical Analysis
• Applied Mathematics
• +۴ مورد دیگر

Chayne Planiden is a Senior Lecturer in the School of Mathematics and Applied Statistics at the University of Wollongong, where he has been appointed since 2022. His research focuses on mathematical optimization, particularly nonsmooth optimization techniques including regularization methods, derivative-free algorithms, and the VU-algorithm. He specializes in proximal mappings, simplex gradient approximations, and Hessian approximation methods. Dr. Planiden's research spans both theoretical and applied mathematics, with significant applications in energy market modeling. His work addresses renewable energy integration, microgrid management, and electricity market design using optimization frameworks. Key methodologies include derivative-free optimization techniques and numerical analysis approaches for complex systems. In terms of research supervision, Dr. Planiden currently mentors three PhD students working on projects involving optimization algorithms, reaction-diffusion equations, and financial option pricing. He has successfully supervised one PhD candidate to completion on decarbonized electric grid frameworks. No scientific awards or honors are mentioned in the available information.

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

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

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

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

Frank Neumannمشاهده پروفایل

استاد

• Artificial Intelligence
• Bio-inspired Computing
• Cybersecurity
• +۳ مورد دیگر

Frank Neumann is a Professor in the School of Computer Science at The University of Adelaide. He leads the Optimisation and Logistics group and holds affiliations with the University of Melbourne as an Honorary Professorial Fellow. His expertise spans artificial intelligence, bio-inspired computing, and optimization. He has held leadership roles, including interim Head of School and Associate Dean (Research) in the Faculty of Engineering, Computer and Mathematical Sciences. Education: PhD in Computer Science (2006), Diplom (2002) from Christian-Albrechts-University of Kiel, Germany. Postdoctoral research at Max Planck Institute for Informatics (2006-2010). Research interests focus on evolutionary computation, cybersecurity, renewable energy optimization, and supply chain management. He has contributed to foundational theory in randomized search heuristics and practical applications in logistics and healthcare. Recent projects include NHMRC grants on personalized time-use interventions and ARC-funded initiatives in optimization and complex resource management. He has authored influential books on bio-inspired computation and theoretical evolutionary algorithms. Scientific Awards: ARC Future Fellowship, Humboldt Fellowship, NHMRC Grants. Grants: ARC Industrial Transformation Training Centre, Research Consortia Program.

Maximilien Gadouleauمشاهده پروفایل

دانشیار

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

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

Maria Garcia De La Banda Garciaمشاهده پروفایل

استاد

• Combinatorial Optimisation
• Program Analysis
• Declarative Programming
• +۲ مورد دیگر

Maria Garcia De La Banda Garcia is a Professor at Monash University's Faculty of Information Technology with over 25 years of academic experience. She currently serves on the ARC College of Experts and is Co-Chair of the Monash-Woodside FutureLab. Her research spans: Combinatorial optimization techniques Program analysis and transformation Constraint programming languages Bioinformatics applications She has secured over $34M in research funding and serves as Area Editor for the Journal of Theory and Practice of Logic Programming. Awards include: Logan Fellowship (1997) International Constraint Modelling Challenge (2005) Monash Honour Roll (2021)

Maria Garcia de la Bandaمشاهده پروفایل

استاد

• Constraint Programming
• Combinatorial Optimization
• Program Analysis
• +۵ مورد دیگر

Maria Garcia de la Banda is a distinguished Professor at Monash University's Faculty of Information Technology, where she serves in the Department of Data Science and Artificial Intelligence (DSAI). With over 25 years of academic experience, she has held significant leadership roles including Deputy Dean (Research) until July 2022, overall Deputy Dean of the Faculty (2013-2016), and Head of the Caulfield School of Information Technology (2009-2011). She is currently a member of the ARC College of Experts and Co-Chair of the Monash-Woodside FutureLab. Her educational background includes a Doctor of Philosophy in Computer Science from the Universidad Politecnica de Madrid (awarded July 7, 1994) and an Ingeniero Informatico degree from the same institution (awarded March 1, 1992). Her PhD received the university's Best PhD Award. Garcia de la Banda's research spans multiple disciplines with a strong focus on constraint programming, combinatorial optimization, program analysis, and bioinformatics. She leads the Optimization research group within DSAI and has made significant contributions to declarative programming languages, parallelism, and automatic parallelization. Her interdisciplinary work bridges computer science with biological applications, particularly in protein structure analysis and computational drug design. Her publication record shows consistent contributions across constraint programming, optimization, and bioinformatics. Recent work demonstrates increasing interdisciplinary collaboration, with a notable expansion into bioinformatics applications alongside her core constraint programming research. She has maintained a strong presence at major conferences like CP (International Conference on Principles and Practice of Constraint Programming) while also building impactful industry collaborations. Her scientific recognition includes: Logan Fellowship (1997) - the first and only prestigious award of its kind in the Faculty of IT International Constraint Modelling Challenge winner (2005, with Peter Stuckey) Universidad Politecnica de Madrid's Best PhD Award (1994) Induction into the Monash Honour Roll (2021) Vice-Chancellor's Diversity and Inclusion Award (2020) As a research leader, Garcia de la Banda has secured over $20M in industry funding and $14M in nationally competitive funding, including $8M as Chief Investigator in 11 ARC grants (5 as lead). She has served as Area Editor of the Journal of Theory and Practice of Logic Programming since 2010 and on the Editorial Board of the Constraints journal since 2019. Her leadership extends to professional organizations, having served on the Executive Committees of both the Association of Logic Programming (2005-2008) and the Association of Constraint Programming (2017-2020), where she was President (2019-2020). She leads the Optimization research group within DSAI and collaborates extensively across Monash University and with industry partners. Her current major projects include HARNESS (Hierarchical Abstractions and Reasoning for Neuro-Symbolic Systems), the ARC Training Centre in Optimisation Technologies, and the Building 4.0 CRC project focused on better buildings through technology. These initiatives demonstrate her commitment to translating theoretical research into practical applications with real-world impact.

Seyedali Mirjaliliمشاهده پروفایل

استاد

• Optimization
• Swarm Intelligence
• Evolutionary Algorithms
• +۶ مورد دیگر

Seyedali Mirjalili is a Professor of Artificial Intelligence and Director of the Centre for Artificial Intelligence Research and Optimization (AIRO) at Torrens University Australia. He holds distinguished professorships at VSB Technical University of Ostrava, Óbuda University, and De La Salle University, and is an adjunct researcher at Griffith University and Yonsei University. His research focuses on optimization algorithms, swarm intelligence, evolutionary computation, and machine learning. Education: PhD in Computer Science (Griffith University, 2016). Research interests include optimization techniques, swarm intelligence algorithms (e.g., Grey Wolf Optimizer, Whale Optimization Algorithm), machine learning applications, and robust optimization frameworks. He has published over 600 papers with an H-index of 110 and is among the most cited researchers in optimization fields globally. Awards include the Pro-Vice Chancellor Research Special Award (2019) and recognition as a top 1% highly cited researcher (since 2019). He serves as an associate editor for journals like Neurocomputing and Applied Soft Computing. Labs/Teams: Leads the AIRO Centre at Torrens University, focusing on AI-driven solutions for complex optimization problems.

• Operational Research
• Combinatorial Optimisation
• Mathematical Programming
• +۷ مورد دیگر

Greet Vanden Berghe is a full professor at the Faculty of Engineering Technology of KU Leuven. She leads the Subdivision Combinatorial Optimisation and Decision Support at Ghent and Aalst Campuses, and contributes to research networks like Leuven.AI and LIM. Her work focuses on industrial combinatorial optimization problems through mathematical programming, heuristics, and decomposition approaches. Affiliations: Numerical Analysis and Applied Mathematics (NUMA), Leuven.AI, LIM Institute Research: Logistics optimization, personnel scheduling, vehicle routing, and algorithm development for industrial decision support Projects: MoVeRS (Modular Vehicle Routing Solver), Trustworthy Algorithms for Industrial Decisions, Fairness in Allocation Problems, and Electric Vehicle Charging Station Optimization Her research emphasizes bridging theoretical advancements with real-world industrial challenges, particularly in maritime logistics, healthcare scheduling, and production planning. She has developed scalable solvers and integrated legal constraints into routing algorithms.

Marthe Bonamyمشاهده پروفایل

پژوهشگر

• Combinatorics
• Graph theory
• Planar graphs
• +۶ مورد دیگر

Marthe Bonamy is a CNRS researcher at LaBRI (Laboratoire Bordelais de Recherche en Informatique) in Bordeaux, France, holding a permanent research position since December 1, 2015. She is a member of the CombAlgo team, head of the Graphes et Optimisation research group, and also a member of the AlgoDist group. Her research focuses on combinatorics and graph theory, particularly planar graphs, algorithms, graph decompositions, and reconfiguration problems. Dr. Bonamy is actively involved in the academic community, serving as vice-Editor-in-chief of the European Journal of Combinatorics since January 2021, a member of the Board of Editors of Discrete Mathematics since January 2021, an editor of Innovations in Graph Theory since August 2023, and an associate editor of the SIAM Journal of Discrete Mathematics since January 2024. She is also part of the scientific board of the GT COA and the steering committee of STACS. Her research interests span various aspects of combinatorics and graph theory, with a particular emphasis on structural graph properties, coloring problems, and reconfiguration processes. She has made significant contributions to understanding graph recoloring, Kempe changes, and various decomposition techniques for graphs. Her work often bridges theoretical computer science and pure mathematics, providing both theoretical insights and algorithmic applications. Dr. Bonamy has supervised numerous PhD students, both currently and in the past, demonstrating her commitment to mentoring the next generation of researchers in combinatorics and graph theory. Her students have gone on to academic positions and industry roles, reflecting the breadth of applicability of their research. She has received recognition through prestigious editorial positions and research grants, including her current involvement in the ANR Project ENEDISC (2024-2027) and previous leadership of multiple research projects. Dr. Bonamy maintains an active research agenda with numerous publications in top-tier journals and conferences, reflecting her standing as a leading researcher in combinatorics and graph theory.