جست‌وجوی استاد و پژوهشگر

Marcel Mongeauمشاهده پروفایل

استاد

• Operations Research
• Global Optimization
• Numerical Optimization
• +۵ مورد دیگر

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 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.

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.

Li Xiaoliمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۷ مورد دیگر

Professor Li Xiaoli is the Head of the Information Systems Technology and Design (ISTD) Pillar at Singapore University of Technology and Design (SUTD), effective 15 August 2025. An internationally recognised AI researcher with over 30 years in academia and industry, he was previously Department Head of Machine Intellection at A*STAR I²R and Technical Director of the S$35.8 million national AIMfg centre. He holds adjunct professorial appointments at NUS and NTU and has advised Singapore government agencies including MTI, MOE, MOH and SNDGO. Education: PhD, Institute of Computing Technology, Chinese Academy of Sciences, 2001 Research Interests: Prof Li’s work straddles machine learning, data mining, graph learning and time-series analytics, with high-impact applications in smart manufacturing , semiconductors and the digital economy . He pioneered sensor feature learning with deep neural networks and co-coined the term positive-unlabelled (PU) learning ; his 2015 IJCAI paper alone exceeds 1,600 citations. Additional contributions span social & biological network mining, NLP & text analytics, and AI sustainability. Recent Publication Landscape (2022-2025): His latest 15 papers reveal a dual focus on time-series intelligence (domain adaptation, self-supervised representation, sensor alignment, remaining useful-life prediction) and efficient AI deployment (green AI, hardware-aware deep learning, game-theoretic NAS). Interdisciplinary threads link biomedical informatics (sleep stage, gene networks) and advanced NLP (preference optimisation, video grounding), underscoring a strategy of algorithmic innovation coupled with real-world validation . Honours & Awards: IEEE Fellow (2024) Fellow, Asia-Pacific Artificial Intelligence Association (2023) Clarivate Highly Cited Researcher Listed among World’s Top 2% Scientists (Stanford University) Three IEEE/Conference Best Paper Awards for sensor-data and network-mining research Grants, Labs & Industry Collaboration: Prof Li has secured and led more than 10 major collaborative grants with industry partners such as DBS, Singtel and KPMG, including the S$35.8 million AIMfg programme. He directs joint research labs, translating AI advances into aerospace, telecom, insurance and aviation solutions. His group maintains cutting-edge GPU clusters and sensor test-beds for manufacturing analytics, and he currently supervises a large multi-disciplinary team of research staff and graduate students at SUTD.

• 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.

• Control Theory
• Artificial Intelligence
• Robotics
• +۵ مورد دیگر

Dr. Hamid Abdi is a Senior Lecturer in Engineering Management at the School of Engineering, Deakin University. He holds a PhD from Deakin University (2011) and has over 20 years of combined academic and industrial experience in control systems, robotics, and engineering education. His research focuses on control theory (time-delay systems, fault tolerance), robotics (dynamics, teleoperation), and industrial automation (SCADA, PLC systems). He has supervised 12+ doctoral/master students and led projects in energy systems, medical robotics, and IoT applications. Education: BEng/MEng (Control Engineering, Sharif University, 1998-2000), PhD (Deakin University, 2011). Professional affiliations include Senior Membership in IEEE and collaboration with Colorado State University. Research interests span AI on hardware systems, dynamical systems modelling, entrepreneurship in engineering, and optimisation. Recent projects include robotic ultrasound systems (HaptiScan), energy storage control software, and haptic teleoperation for medical diagnostics. His work has led to multiple awards, including the Alfred Deakin Best Thesis Medal (2013) and VC Staff Award (2015). Teaching areas: Process control, robotics, entrepreneurship, signal processing Grants: $542,594 in industry-funded research (e.g., Telstra, Energys Australia) Labs/Teams: Institute for Intelligent Systems Research and Innovation, Centre for Intelligent Systems Research

Wolfram Wiesemannمشاهده پروفایل

استاد

• Decision-making under uncertainty
• Supply chain management
• Healthcare optimization
• +۱ مورد دیگر

Wolfram Wiesemann is Professor of Analytics & Operations and Head of the Analytics, Marketing & Operations Department at Imperial College Business School. His research focuses on decision-making under uncertainty with applications in supply chain management, healthcare, and energy. Research Focus: Development of tractable approximation schemes for decision-making under uncertainty with rigorous error bounds. Applications span operations management, energy systems, and financial engineering. Teaching: BA1803 (Optimisation and Decision Models) and BA1806 (Introduction to Machine Learning) for MSc programs. Received Imperial College Business School Dean's Teaching Awards and nominations for Student Academic Choice Awards. Editorial Roles: Editor-in-Chief of Operations Research Letters and department co-editor for Management Science. Previously served on editorial boards of multiple leading journals. Research Impact: COVID-19 research on hospital care prioritization featured in Financial Times, Daily Mail, and France24.

Xin Maمشاهده پروفایل

دانشیار

• Platform Operations
• Behavioral Operations
• Data-Driven Prescriptive Models
• +۴ مورد دیگر

Dr. Xin Ma is an Associate Professor of Operations and Supply Chain Management at Monash University's Department of Management. He previously held a Lecturer position at the University of Exeter (2017–2018). He obtained his PhD in Operations Management from The Hong Kong Polytechnic University in 2017. His research focuses on platform operations, behavioral operations, data-driven prescriptive models, and sustainable operations, with notable contributions to journals like Manufacturing & Service Operations Management and European Journal of Operational Research . He has received prestigious awards including the 2021 Dean's Award for Early Career Research and the 2018 Decision Sciences Journal's Most Significant Contribution Award. Dr. Ma leads collaborative research projects such as 'Optimising Australia’s Electricity System: The Role of Blockchain Technology' (2024–2025) and 'Building Cyber Resilience in Fiji' (2025–2026). He serves as Department Editor for IEEE Transactions on Engineering Management and editorial roles for multiple journals. His work addresses UN Sustainable Development Goals related to sustainable cities and communities, climate action, and responsible consumption. Major grants include CRC-P industry grants and leadership in Algorand Centre of Excellence projects. He advises PhD students in operations research, management science, and industrial engineering, emphasizing technical rigor and 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.

• Bayesian Search and Optimization
• Evolutionary Algorithms
• Multi-objective Optimization
• +۴ مورد دیگر

Dr Alma Rahat is an Associate Professor of Data Science at Swansea University, affiliated with the School of Mathematics and Computer Science. He specializes in Bayesian search and optimization, evolutionary algorithms, and multi-objective optimization. His work focuses on solving computationally expensive problems with applications in engineering, healthcare, and education. Education: BEng (Hons) in Electronic Engineering from the University of Southampton (UK), PhD in Computer Science from the University of Exeter (UK), and a Postgraduate Certificate in Teaching in Higher Education from Swansea University. He is a Fellow of the Higher Education Academy (FHEA). Research Interests: Dr Rahat’s expertise includes data-driven evolutionary optimization, surrogate-assisted methods, and active learning. He has contributed to pandemic response modeling for the Welsh Government and the UK Health Security Agency, leveraging machine learning and parameter optimization. His work on healthcare decision-making systems and educational assessment tools demonstrates a commitment to interdisciplinary applications of optimization. Key Contributions: He leads the Surrogate-Assisted Evolutionary Optimization (SAEOpt) workshop at GECCO and is a member of the IEEE Computational Intelligence Society Task Force on Data-Driven Optimization. His research bridges academic theory and industry needs, with patents and publications in top journals and conferences like IEEE Transactions and ACM. Grants: £750k from Welsh Government (Co-PI/Co-I), £230k EPSRC grant (EP/W01226X/1 as PI). Awards: Best Paper in Real-World Applications Track at GECCO, Patent for industrial fluid separation technology. Supervision: Currently guiding PhD students across AI, healthcare, education, and environmental modeling. His supervision emphasizes Bayesian methods and human-in-the-loop systems. Lab/Teams: Active in Swansea’s Computational Foundry, collaborating on projects like beach change forecasting and clinical decision support systems.

Simon Moakesمشاهده پروفایل

پژوهشگر ارشد

• Sustainable Agriculture
• Life Cycle Assessment
• Farm System Modelling
• +۱۲ مورد دیگر

Simon Moakes is a Research Fellow at the Institute of Biological, Environmental & Rural Sciences (IBERS), Aberystwyth University, and holds a Doctor of Professional Studies in the Department of Life Sciences. He is actively engaged in advancing sustainable agricultural systems through interdisciplinary research and external collaborations. Institution: Aberystwyth University School: Institute of Biological, Environmental & Rural Sciences (IBERS) Department: Department of Life Sciences Academic Rank: Research Fellow Email: sim44@aber.ac.uk, srm21@aber.ac.uk ORCID: https://orcid.org/0000-0002-0519-2254 His research focuses on improving the sustainability of farm systems through methods such as farm system modelling, life cycle assessment (LCA), carbon footprinting, nutrient dynamics, and economic analysis. With over 20 years of experience in the UK, Switzerland, and internationally, he contributes to key global challenges including climate change mitigation, biodiversity conservation, and Net Zero agriculture. His work aligns with several UN Sustainable Development Goals, particularly those related to climate action, responsible consumption, and life on land. The recent trend in his publications shows a strong emphasis on harmonizing LCA methodologies for agricultural and livestock systems, assessing climate resilience in European dairy farms, and evaluating economic and environmental trade-offs in sustainable farming practices. His work integrates modelling, empirical data, and policy-relevant insights to support transitions toward more resilient and sustainable food systems. He contributes to the academic community as a member of the editorial advisory board for the journal Agricultural Systems . PATHWAYS: Pathways for transitions to sustainability in livestock husbandry and food systems (Principal Investigator, 2022–2026) Grassland Resilience for Net Zero (Co-Investigator, 2024–2027) Optimising nitrogen from legumes to reduce emissions in grassland farming (NUE-Leg) Land Use for Net Zero (LUNZ Hub) The Potential for Extending Economic Farm-level Benchmarking to Environmental Aspects (completed, 2010–2011) He has been involved in multiple externally funded research initiatives, including EU- and UKRI-supported projects, demonstrating a strong track record in securing and leading impactful research. His collaborative network spans Europe and beyond, contributing to transdisciplinary efforts in agroecology and sustainable land use. His research is supported by active grants from Horizon Discovery (UK), UK Research and Innovation, and the Department for Environment, Food and Rural Affairs (Defra). Simon Moakes is affiliated with research teams and hubs focused on sustainable agriculture, including the LUNZ Hub and the PATHWAYS project consortium, which bring together experts from multiple institutions to develop integrated solutions for sustainable food systems.

• 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.

• Computational Structural Mechanics
• Non-linear Finite Element Analysis
• Structural Optimisation
• +۶ مورد دیگر

Dr. Stanyslav Grosman is a Research Associate in the Computational Structural Mechanics group within the Department of Civil and Environmental Engineering at Imperial College London's Faculty of Engineering. He holds the position of Academic Visitor and has been working at Imperial since December 2019. His educational background includes: PhD in Engineering from Imperial College London (2015-2019) MSc in Structural Engineering from Prydniprovs’ka State Academy of Civil Engineering and Architecture, Dnipro, Ukraine (2012-2013) BSc in Civil Engineering from Pridneprovs’k State Academy of Civil Engineering and Architecture, Dnipro, Ukraine (2008-2012) Dr. Grosman's research focuses on computational structural mechanics, particularly non-linear finite element modeling of structures under extreme loading. His work includes the development of algorithms for assessing structural robustness, topology optimization, parametric design, and meta-modelling techniques. He also applies digital image correlation and programming in his research. His recent publications (2017-2022) demonstrate a strong emphasis on masonry structures, especially arch bridges and floor slabs, under various loading conditions including earthquakes, fatigue, and extreme events. The research employs advanced numerical modeling techniques to address challenges in structural safety and durability. Dr. Grosman is currently involved in a Network Rail funded project titled "Realistic Assessment of Masonry Railway Viaducts", which involves high-fidelity modeling of masonry arch bridges under transient traffic loading. He is part of the Computational Structural Mechanics research group, collaborating with experts such as Professor B. Izzuddin and Dr. L. Macorini.

• Computer Science
• Probabilistic Programming
• Semantics
• +۶ مورد دیگر

Fredrik Dahlqvist is a faculty member at the University College London , Department of Computer Science , focusing on theoretical and applied aspects of probabilistic programming , semantics , and formal verification . His work bridges computer science with mathematical logic , machine learning , and programming language theory . Education: PhD in Coalgebraic Logics from Imperial College London (2014) His recent publications (2016–2025) explore model pruning , reparameterisation invariance , probabilistic numerical analysis , and categorical approaches to machine learning . Key themes include optimisation , cosine similarity , and omega-complete cone duality in probabilistic systems. Contact: f.dahlqvist@ucl.ac.uk (institutional) or f.p.h.dahlqvist@gmail.com (private), located at Gower Street, London WC1E 6BT, United Kingdom .

Acho Zuppa, Leonardoمشاهده پروفایل

استاد

• Control Systems
• Nonlinear Systems
• Engineering Applications
• +۳ مورد دیگر

Leonardo Acho Zuppa is a Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Mathematics and the Escola Superior d'Enginyeries Industrial, Aeroespacial i Audiovisual de Terrassa (ESEIAAT). His research focuses on Control Systems, Nonlinear Dynamics, and their applications in biomedical engineering, structural control, and robotics. He leads the OMGRAPH research group (Optimisation Methods on Graphs) and has contributed extensively to projects in EIT device development for medical imaging, vibration control in mechanical systems, and renewable energy systems. Key research interests include the design of advanced control algorithms for nonlinear systems, optimization methods on graphs, and interdisciplinary applications in healthcare and civil engineering. His work spans theoretical contributions to practical implementations, such as the development of electrical impedance tomography (EIT) devices for early breast cancer detection and robust control strategies for wind turbines. Dr. Acho has collaborated with institutions like the MAPTHE group and has secured multiple competitive R+D+i grants. His recent projects emphasize innovation in biomedical technology and sustainable energy solutions. He is actively involved in mentoring students and has co-advised theses on adaptive control and nonlinear systems.