Qinyu Yan is a Researcher at the Institute of Numerical and Applied Mathematics , affiliated with the Department of Mathematics at the University of Göttingen . They serve as the responsible lecturer for the course Optimisation II - Exercises , contributing to academic instruction in mathematical optimization. University: University of Göttingen Department: Department of Mathematics Institute: Institute of Numerical and Applied Mathematics Role: Researcher and Lecturer
Assistant Professor Juraj Benić is a control engineer at the School of Applied Mathematics and Informatics, Josip Juraj Strossmayer University of Osijek, Croatia . His interdisciplinary work bridges control theory, fluid power systems, IoT, and data-driven maintenance , with applications ranging from forestry vehicles to unmanned aerial systems. Education PhD in Control Theory and Mechanical Engineering, 2022 – University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture MSc in Control Theory and Mechanical Engineering, 2017 – University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture BSc in Control Theory and Mechanical Engineering, 2015 – University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture Research Interests Prof. Benić’s core research areas include: Energy-efficient direct-driven hydraulic (DDH) systems for mobile machinery Hybrid-electric powertrains for forestry skidders and multirotor UAVs IoT-enabled predictive maintenance using convolutional neural networks and vibration analysis Fuzzy-logic and ontology-based controllers for electro-hydraulic systems Human-robot interaction and context-aware robotics Computational linguistics and digital corpora of Croatian dialects Publication Trends Since 2018 he has produced more than 25 peer-reviewed works. The 2023-2024 journal articles focus on hybrid UAV propulsion and comparative energy efficiency of hydraulic systems , while earlier works delve into fuzzy control ontologies , predictive maintenance via IIoT and fuel-saving hybrid skidders . Interspersed linguistic contributions showcase his versatility in digital dialectology. Scientific Awards & Recognition While explicit awards are not listed, he has delivered invited lectures (University of Maribor, 2021) and participated in study visits (South Kazakhstan State University, March 2024), indicating growing international recognition. Supervision & Collaboration He collaborates closely with colleagues from the University of Zagreb and University of Osijek, co-authoring with researchers such as D. Pavković, Ž. Šitum, M. Cipek, and D. Brezak. Student supervision is implied through experimental rigs and project descriptions, although specific advisee names are not provided. Laboratory & Field Infrastructure Research is supported by fully instrumented hydraulic test benches, a retrofitted deep-drilling rig, IoT accelerometer networks, and field-measurement campaigns on commercial skidders equipped with telematics (WIGO-E) for long-term fuel and energy-data logging.
Ninoslav Truhar is Full Professor at the School of Applied Mathematics and Informatics , Josip Juraj Strossmayer University of Osijek , Croatia, where he also heads the Department of Applied Mathematics and Computer Science . Education B.S. in Mathematics and Physics, 1987 – University of Osijek M.S. in Mathematics, 1995 – University of Zagreb Ph.D. in Mathematics, 2000 – University of Zagreb Research interests lie at the intersection of numerical linear algebra , systems and control theory , and applied mathematics . His work centres on linear matrix equations , damping optimization in mechanical systems, and the perturbation theory of invariant subspaces . These themes repeatedly appear in his extensive publication record, driving both theoretical advances and practical applications in vibration control and matrix computations. Across his recent publications, the dominant trends are the derivation of sharp perturbation bounds for eigenvalues and eigenspaces of structured matrix problems, the development of efficient algorithms for damping parameter optimisation, and the creation of dimension-reduction techniques that accelerate numerical solution of large-scale Lyapunov and quadratic eigenvalue problems. Professional service Member of the editorial boards of Computational Mathematics and Computer Modeling with Applications , Numerical Algebra, Control and Optimization , Mathematics (MDPI) , Mathematical Communications , and Osječki matematički list . Teaching Undergraduate/graduate courses: Applied Linear Algebra and Scientific Computing , Optimisation Methods , Dynamical Systems . Supervises B.Sc. and M.Sc. theses on topics aligned with his research expertise.
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.
Carl P. Dettmann is a Professor of Applied Mathematics at the University of Bristol , affiliated with the School of Mathematics and the Institute for Probability, Analysis and Dynamics. His research spans dynamical systems , statistical physics, billiards, spatial networks, wireless networks, and mathematical music theory. Research Trends include studies on chaotic dynamics in multi-black-hole systems stabilizability in switched linear systems random geometric graphs and network connectivity algebraic approaches to musical tunings Grants & Projects include the EPSRC-funded Spatially Embedded Networks project (2015–2019) and involvement in conferences like Random Walks on Random Networks at BMC 2016 . Teaching includes units such as Optimisation , Applied Dynamical Systems, General Relativity, and Statistical Mechanics.
Marcel Mongeau is a Professor in Operations Research at the École Nationale de l'Aviation Civile (ENAC) in Toulouse, France, affiliated with the OPTIM team under the MORO axis . His research focuses on operational research, global optimization, numerical optimization, and mixed-integer programming, with applications in aeronautics, chemical engineering, life sciences, and other industries. Education PhD in Combinatorics & Optimization (1991), University of Waterloo, Canada MSc in Mathematics (1987), University of Montreal, Canada BSc in Mathematics (1985), University of Montreal, Canada Habilitation to Direct Research (2003), Paul Sabatier University, France Research Interests : Mongeau's work addresses complex optimization problems through mathematical modeling and algorithm development. Key areas include aircraft trajectory planning, contrail avoidance, stochastic programming, and multi-modal transportation optimization. His methodologies integrate exact optimization, heuristic approaches, and computational modeling. Publications Trends : Recent publications emphasize air traffic flow management , contrail-safe trajectories , stochastic aircraft scheduling , and multi-modal transportation synchronization . Applications span environmental impact minimization, machining efficiency, and climate-aware aviation planning. Scientific Recognition : He is a Member of the Academy of Technologies , reflecting his contributions to applied mathematical sciences. Advising & Grants : Mongeau has supervised PhD students and postdoctoral fellows, particularly in optimization and operational research. His research has been supported through collaborations with institutions like IBM, INRIA, and CNRS. Labs & Teams : He is associated with the OPTIM team at ENAC and previously worked with the MOGISA group at LAAS-CNRS and the Optimisation & Interactions group at Paul Sabatier University.
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.
Archontis Giannakidis is a Senior Lecturer in Data Science at the School of Science and Technology , Nottingham Trent University (NTU). He leads modules in Discrete Mathematics & Computational Complexity (Year 2) and Convex Optimisation (Year 3), while supervising undergraduate and Masters dissertations. Education: PhD in Electronic Engineering (Inverse Problems), University of Surrey (2010) Research Interests focus on applying Deep Learning and Machine Learning to biomedical data processing, particularly in Biomedical Image Analysis , Convolutional Networks , and Diffusion MRI . His work emphasizes automating intellectual tasks, efficient data representation, hidden pattern discovery, and decision optimization in healthcare and environmental contexts. Recent Article Trends highlight his expertise in 2D echocardiography-based cardiac quantification , MRI-driven ACL tear diagnosis , and landslide-tsunami prediction via geometry-invariant machine learning. These studies often integrate uncertainty modeling, attention mechanisms, and lightweight architectures for clinical and environmental applications. Scientific Awards : Fellow of the Higher Education Academy (FHEA) Advisory Roles include mentoring PhD students Tuan Aqeel Bohoran (Marie Skłodowska-Curie-funded) and David Gwillym Jenkins (internal funding), alongside visiting/external PhD candidates Michael Lystbaek (Aarhus University) and Athanasios Siouras (University of Thessaly). He has received grants from HORIZON 2020 , EPSRC , and Innovate UK , and collaborates with institutions like the Archimedes Research Unit (Greece) and National Heart and Lung Institute , Imperial College London. Professional Activity includes editorial board membership for Frontiers in Physiology , peer reviewing for journals like IEEE Transactions on Medical Imaging , and external examining for University of Strathclyde’s MSc programs. He also contributes to conference organization and Portuguese grant evaluation panels.
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.
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.
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.
Alain Zemkoho is a Professor of Mathematical Optimization at the School of Mathematical Sciences, University of Southampton, where he is affiliated with the OR Group and CORMSIS (Centre for Operational Research, Management Science and Information Systems). Prior to joining Southampton, he was a Research Fellow at the University of Birmingham and a Research Associate at the Technical University of Freiberg. Professor Zemkoho's research centers on continuous optimization with special emphasis on bilevel optimization. His work spans theoretical developments in optimization theory as well as practical applications across multiple domains including transportation systems, medical technology, and cybersecurity. He has made significant contributions to optimality conditions, stability/sensitivity analysis, and numerical algorithms for bilevel optimization problems. His research bridges theoretical mathematics with real-world applications, particularly in developing algorithms that capture both optimistic and pessimistic features of complex optimization problems. His work has implications for transportation (toll setting, network design), data analysis, forecasting, trust topology, phase retrieval, and medical applications including cardiac device screening. Professor Zemkoho's publication record demonstrates a clear progression from theoretical optimization foundations to increasingly diverse applications. While maintaining a core focus on bilevel and hierarchical optimization, his recent work has expanded into medical applications (particularly cardiac device screening), cybersecurity (honeypot systems and cyber deception), and transportation optimization. This evolution shows his commitment to applying mathematical theory to solve complex real-world problems across disciplines. Professor Zemkoho has received significant recognition for his contributions to the field: Alexander von Humboldt Experienced Fellow (2024-2026) Fellow of the Alan Turing Institute for Data Science and Artificial Intelligence (2019-2023) Fellow of the Institute of Mathematics & Its Applications Fellow of the Higher Education Academy As an academic advisor, Professor Zemkoho currently supervises four PhD students: David Benfield, Samuel Jericho Ward, Rachel Shaw, and Marah-Lisanne Thormann. His research is supported by multiple grants including several EPSRC-funded projects: Approximation theory for two-level value functions with application Zemkoho - EPSRC First Grant The Mathematics Of Stackelberg Games In Machine Learning HEIF 2022/23 Carisbrooke Shipping – Optimisation of operations Professor Zemkoho is actively involved with the OR Group and CORMSIS at the University of Southampton, contributing to collaborative research efforts in operational research and management science. His work increasingly intersects with medical applications through the Institute for Life Sciences, demonstrating the interdisciplinary nature of modern mathematical optimization research.
Dr Sherif Abbas is a Research Fellow at the Applied Artificial Intelligence Institute (A2I2) within Deakin University, Australia. Since 2021 he has held this role after being awarded the competitive Alfred Deakin Postdoctoral Research Fellowship at the Institute for Frontier Materials. His work is positioned at the intersection of material science, physics, chemistry and artificial intelligence, leveraging cutting-edge AI methodologies to solve complex challenges in energy storage, sensing and computational materials discovery. Education PhD in Physics, University of Sydney (2017) Research Interests Abbas’s research focuses on the accelerated discovery and rational design of advanced functional materials through the synergistic use of density-functional theory (DFT) and state-of-the-art machine-learning techniques. Specific thrusts include: Rechargeable battery chemistries (Li-ion, solid-state, Al-rich cathodes) Solar-energy-harvesting and photovoltaic materials Supercapacitor and superionic conductor design Gas-sensing surfaces and CO₂-capture frameworks Superconducting, ferroelectric and multiferroic compounds 2D van der Waals heterostructures and their optoelectronic applications His methodological toolkit spans Bayesian optimisation, generative models, graph neural networks and physics-informed machine-learning potentials that enable multiscale simulation from the atomic level to device performance. Publication Trends Across 103 outputs (2019–2025), Abbas demonstrates a clear trajectory toward physics-informed AI for materials. There is a marked concentration on energy-storage interfaces (solid-state electrolytes, dendrite suppression) and on low-dimensional systems where quantum confinement and van der Waals interactions govern functionality. Recent work increasingly couples rigorous first-principles data with scalable ML surrogates, underscoring a shift from static property prediction to dynamic, device-relevant simulations. Scientific Awards & Fellowships Alfred Deakin Postdoctoral Research Fellowship (Deakin University, 2021–present) Doctoral Supervision & Funding Abbas currently co-supervises two doctoral candidates: Thuy Linh La: "Enhancing Scalability of Machine Learning Models for Material Simulation" Hajer Abdulhafid Mohamed Derbi: "Applied Artificial Intelligence in Dental Field" Both projects are embedded within Deakin’s Applied Artificial Intelligence Initiative and benefit from internal fellowship funds and external ARC linkage grants coordinated by A2I2. Laboratory & Entrepreneurial Activities He is an integral member of the cross-disciplinary teams at A2I2, collaborating closely with the Institute for Frontier Materials and external partners across Australia. In parallel, he founded mathpractice.xyz (2023–present), an educational technology venture aimed at democratising advanced mathematics and AI training resources for students and early-career researchers.
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.
Barnaby Dobson is a Research Fellow at Imperial College London's Department of Civil and Environmental Engineering, part of the Faculty of Engineering. His research focuses on integrated water infrastructure modeling, emphasizing holistic evaluation of water systems' impacts on river quality. He developed the WSIMOD software to simulate interactions between water systems and management practices, with recent work exploring sewage spills' effects on river biodiversity through machine learning. Education and Background: 2023–current: Imperial College London, Research Fellow (Fluids Section) 2019–2023: Imperial College London, Research Associate (Environmental and Water Resources Engineering Section, CAMELLIA Project) 2018–2019: University of Oxford, Research Associate (Geography Department, Marius Project) 2014–2018: University of Bristol, PhD (Water Resource Systems Modelling, supervised by Dr. Francesca Pianosi and Prof. Thorsten Wagener) Research Themes: Integrated water system modeling Uncertainty quantification in socio-physical systems Optimisation for water resource control Linking wastewater systems to ecological impacts Key Projects: WSIMOD: Novel software for simulating water system interactions CAMELLIA Project: Integrated water systems modeling Marius Project: National water supply modeling for England and Wales
