جست‌وجوی استاد و پژوهشگر

• Fracture mechanics
• Acoustic emission monitoring
• Hybrid composite-concrete structures
• +۳ مورد دیگر

Sven De Sutter is a Visiting Professor (Paid guest professor) in the Mechanics of Materials and Constructions research group at Vrije Universiteit Brussel (VUB) , Belgium. His affiliation with VUB centres on advancing structural engineering solutions for lightweight, high-performance composite and concrete systems. Research Focus: Fracture mechanics and damage characterisation of cementitious and composite materials Acoustic emission monitoring and digital image correlation for real-time structural health assessment Development of hybrid textile- or fibre-reinforced concrete beams for lightweight construction His investigations integrate experimental validation with analytical modelling, aiming to optimise safety and durability while reducing material usage in civil infrastructure. Publication Trends: Across 26 outputs captured between 2013 and 2019, De Sutter’s work consistently targets composite-concrete hybrid beams , employing acoustic emission as a primary non-destructive evaluation tool. Key themes include fracture monitoring, damage source identification, and the structural performance of carbon- and textile-reinforced lightweight systems, reflecting a trajectory towards sustainable, resilient construction technologies. Scientific Recognition: While the provided text does not list any specific awards or honours, his publications have attracted 230 citations (Scopus) and an h-index of 6, indicating growing impact within the engineering community. Supervision & Grants: De Sutter has co-supervised at least one Master’s thesis (2012) entitled “Analyse en haalbaarheidsstudie van een innovatief lichtgewicht composiet bekistingssyteem voor betonnen balken” . Further doctoral supervision or grant details are not disclosed in the text. Laboratory & Collaboration: He conducts research within the VUB Mechanics of Materials and Constructions laboratories, collaborating closely with colleagues such as Prof. T. Tysmans, Dr. S. Verbruggen, Dr. D. Angelis, and others, forming an active network around advanced composite and concrete experimentation.

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.

Nicolai Siim Larsenمشاهده پروفایل

استادیار

• Statistics
• Data Science
• Stochastic Processes
• +۳ مورد دیگر

Nicolai Siim Larsen is an Assistant Professor (Tenure track) in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), specializing in statistical modeling with applications in finance and healthcare analytics. His work bridges theoretical statistics and real-world data challenges. Education : Ph.D. in Stochastic Financial Models based on Matrix-Analytic Methods, Technical University of Denmark (2022) Dr. Larsen's research centers on multivariate phase-type distributions and matrix-analytic methods , with significant contributions to financial risk modeling and healthcare analytics . His expertise spans survival analysis, sensor data processing, and price optimization frameworks, addressing complex stochastic phenomena in insurance and disease progression monitoring. Analysis of his publications reveals a cohesive focus on advancing numerical computational methods for multivariate data structures. His 2022 Ph.D. thesis established foundations for joint density functions and infinitely divisible distributions, with subsequent work directly applied to commercial pricing strategies and edema patient monitoring systems. Statistical analysis and price optimisation of DJ rental services (2022) AI Denmark: Monitoring disease progression in Oedema patients (2022) KomDigital: Optimal pricing strategies for price monitoring (2022) Stochastic Financial Models based on Matrix-Analytic Methods (PhD project 2018-2023) He actively contributes to academic service as an internal examiner for Statistical Genetics (2025) and instructor for R-based data science workshops, demonstrating commitment to both research leadership and pedagogical development within DTU's Statistics and Data Analysis section.

Michael Feluxمشاهده پروفایل

استاد

• Aviation Communication, Navigation and Surveillance (CNS)
• Global Navigation Satellite Systems (GNSS)
• Ground-Based Augmentation System (GBAS)
• +۵ مورد دیگر

Prof. Dr. Michael Felux is full Professor and team leader of the Aviation Infrastructure group at the ZHAW School of Engineering , Zurich University of Applied Sciences. He also co-founded and co-owns the Estonian consultancy Navaid OÜ , providing GNSS/CNS expertise while ensuring non-conflict with his academic role. Education Dr.-Ing. in Mechanical Engineering, TU München (2012 – 2018) Dipl.-Tech. Math. in Mathematics, TU München (2003 – 2009) CAS Hochschuldidaktik (Higher-Education Didactics), PHZH (2021) Research Focus Michael Felux’s research centres on safe, secure and efficient aviation communication, navigation and surveillance (CNS) . He investigates GNSS-based augmentation systems (GBAS, SBAS) for precision approach and landing, develops real-time interference detection & localization techniques to counteract jamming and spoofing, and explores high-integrity navigation solutions for unmanned aerial vehicles (UAVs). Additional interests include environmental optimisation of flight procedures and multi-constellation, multi-frequency signal processing . Across more than 50 peer-reviewed publications since 2015, his work consistently targets the intersection of technical robustness and operational feasibility . Recent papers map GNSS disruption events across European airspace, quantify fuel-burn reductions enabled by GBAS-guided continuous-descent approaches, and introduce cost-efficient machine-learning frameworks for real-time localisation of malicious radio-frequency interference. Scientific Awards & Recognition (no specific awards listed in supplied material) Research Funding & Projects Spoofer Localization – Swiss project leader, ongoing EGNSS DFMC for GBAS based operations – EU project leader, ongoing Making I-CNS A Reality – integrated CNS technology, project leader, ongoing High Integrity Satellite Navigation for UAV using Galileo HAS – project leader, ongoing LINA – Shared large-scale infrastructure for safe testing of autonomous systems, team member, ongoing Collision avoidance system for manned & unmanned aircraft via SDR – completed Emission Reduction using Satellite Navigation for Approach Guidance – completed Laboratory & Team As head of the Aviation Infrastructure team at ZHAW, Prof. Felux directs a multidisciplinary group developing next-generation CNS technologies. The team operates dedicated GNSS/GBAS testbeds, flight-trial aircraft, and spectrum-monitoring networks to validate concepts from simulation through to real-world deployment.

Nikolas Kantasمشاهده پروفایل

دانشیار

• Computational Statistics
• Stochastic Processes
• Numerical Methods
• +۷ مورد دیگر

Dr. Nikolas Kantas is a Reader in Statistics at Imperial College London's Department of Mathematics. He completed his undergraduate studies and PhD at the University of Cambridge's Signal Processing Group. His research focuses on developing numerical methods for complex problems in inference, optimisation, filtering, and control. Research Interests: Kantas specializes in computational statistics and stochastic processes, with expertise in particle filtering, Sequential Monte Carlo, and Markov Chain Monte Carlo methods. His work bridges theoretical foundations with applications in data assimilation, optimization under uncertainty, and high-dimensional statistical modeling. Publication Trends: Recent work (2022-2025) demonstrates strong focus on optimization algorithms, stochastic differential equations, and Monte Carlo methods. Key themes include multi-objective optimization, privacy-preserving algorithms, Langevin dynamics, and distributed computing. Methodological innovations frequently address high-dimensional and real-time computational challenges. Student Advising & Grants: Currently supervises 4 PhD students and has graduated 9 doctoral candidates. Research funding includes JP Morgan AI Faculty Research Awards and support from the National Physical Laboratory (NPL). Academic Leadership: Co-organizes the annual Greek Stochastics workshop on Statistics and Applied Probability. Coordinates PhD programs through the Mathematics Research program, MFC CDT, and Statistics and Machine Learning CDT.

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.

Alessandro Di Giorgioمشاهده پروفایل

دانشیار

• Smart grids and micro-grids control
• Model Predictive Control for energy systems
• Integration of renewable generation and storage
• +۳ مورد دیگر

Alessandro Di Giorgio is Associate Professor of Automatic Control at the Department of Computer, Control and Management Engineering Antonio Ruberti (DIAG) , Sapienza University of Rome, where he teaches courses on Automation, Network Control and Handling, and Modeling & Simulation. He coordinates the Smart Energy Research Group of the Consorzio per la Ricerca nell’Automatica e nelle Telecomunicazioni and acts as scientific referent of the university start-up Applied Research to Technologies . Education Ph.D. in Systems Engineering, Sapienza University of Rome, 2010 M.Sc. in Physics (110/110 cum laude), Sapienza University of Rome, 2005 Research interests revolve around advanced control architectures for future power systems, with particular emphasis on smart-grid planning and real-time operation , model-predictive control of micro-grids , large-scale integration of renewable generation and storage , stochastic optimisation of electric-vehicle charging infrastructures , and cyber-physical security against malicious attacks . His work combines systems theory with practical implementations validated within several European and national projects. Recent publications (2023-24) demonstrate a clear trend toward data-driven and stochastic model-predictive strategies that coordinate millions of grid-edge resources—electric vehicles, batteries, renewable plants—while guaranteeing economic efficiency, reliability and resilience of distribution networks. Projects & Technology Transfer Scientific responsible in 10 EU-funded research projects on smart grids and electromobility Principal Investigator of Italian academic and Industria 2015 programmes Co-founder & scientific reference, start-up Applied Research to Technologies (technology transfer on EV-charging optimisation) He is member of the Networked Systems Cybersecurity initiative and serves as reviewer and organiser for major IEEE conferences on power systems and control.

• 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.

• Business Analytics
• Optimization
• Decision Science
• +۷ مورد دیگر

Xin Fei is a Lecturer in Business Analytics at the University of Edinburgh Business School since 2022. Previously, she held the same role at the University of Bristol and was a fellow at the Bristol Digital Futures Institute . Her academic foundation includes a PhD in Operations Research & Management Science from Warwick Business School , supervised by Professor Juergen Branke and Professor Nalan Gulpinar. Current affiliation: University of Edinburgh Business School Prior affiliations: University of Bristol, Bristol Digital Futures Institute Education: PhD in Operations Research & Management Science (Warwick Business School) Xin Fei's research bridges business analytics , optimization , and decision science , focusing on stochastic programming , simulation optimization , and reinforcement learning . Her work develops computationally efficient methods for complex decision problems under uncertainty, emphasizing scenario generation and information collection procedures . Her recent publications in European Journal of Operational Research (ABS 4), IEEE Transactions on Evolutionary Computation (ABS 4), and INFORMS Transportation Science (ABS 3) highlight applications in transportation systems , industrial engineering , and pharmaceutical portfolio planning . Collaborative research includes air traffic management innovations recognized by the Jane's Innovation Award (2019). Teaching highlights include UG: Business Analytics and Information Systems (BUST08032) (2021-present), PG: Principles of Data Analytics (CMSE11432) (2022), and advanced PG courses in Applied Decision Optimisation (2023-present) and Online Learning and Decision Making (2023-present). Administrative roles include PGR Representative for the Management Science and Business Economics Group (2022-2025). Professional Affiliations: Fellowship of the Higher Education Academy The Operational Research Society The Institute for Operations Research and the Management Sciences (INFORMS)

• 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.