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Carolin BauerView profile

Senior Lecturer

• Computer Networks
• Wireless Communication
• Cellular Communication
• +5 more

Carolin Bauer is a Senior Lecturer in the Department of Computing at Staffordshire University. Her expertise spans Computer Networks, Wireless Communication, and Artificial Intelligence, with a focus on Genetic Algorithms and Neural Networks. She holds a PhD in Cellular Communication (2010) and certifications including Cisco CCNA/CCNP Academy Instructor and AWS Academy roles. Education: 1999: Diplom der Technischen Informatik (FH)/BSc, HTWG Konstanz, Germany 2001: MSc by Research in Distributed Systems, Staffordshire University 2010: PhD in Cellular Communication, Staffordshire University Research focuses on evolving network communication technologies, including Cloud Technology and IoT, aiming to enhance real-world applications. Her work bridges theoretical advancements with practical implementation in education and industry. Publications highlight contributions to neural network optimization, cellular handover prediction, and genetic algorithm applications. Awards include the 2013 Fellowship of the Higher Education Academy (FHEA). Teaching spans undergraduate to postgraduate levels, emphasizing Cisco Networking and AWS Cloud Computing. She actively engages in professional development initiatives to integrate cutting-edge technologies into curricula.

D. Marc KilgourView profile

Professor

• Game Theory
• Decision Analysis
• Conflict Resolution
• +5 more

D. Marc Kilgour is a Professor of Mathematics at Wilfrid Laurier University, affiliated with the Faculty of Science. His research focuses on multiple-person decision analysis, game theory, fair division, voting systems, and environmental management. He has contributed to resolving complex conflicts through mathematical models, including water resource allocation disputes and strategic negotiations. His expertise spans international strategy, arms control, and the strategic analysis of voting systems. Kilgour’s work integrates theoretical rigor with practical applications, such as improving fairness in sports (e.g., soccer shootouts and tennis tiebreakers) and optimizing resource distribution algorithms. He has published extensively on conflict resolution frameworks, stability definitions, and multi-criteria decision analysis. Recent research highlights include redefining sequential stability in multi-decision-maker conflicts, analyzing environmental misalignment in logistics, and exploring fair division algorithms for indivisible items. His methodologies often involve graph models, fuzzy preferences, and matrix representations to address uncertainty and complexity in decision-making scenarios. Kilgour’s academic contributions are reflected in over 150 peer-reviewed articles and collaborations on real-world policy issues, such as the US-China trade dispute and urban planning in Brazil. His work bridges theoretical game theory with actionable solutions for environmental, economic, and social challenges.

Nadia PapamichailView profile

Professor

• Decision analysis
• Decision behaviour
• Decision support systems
• +5 more

Nadia Papamichail is a Professor of Decision Systems at Alliance Manchester Business School, The University of Manchester, and a Fellow of the Alan Turing Institute. She holds roles including Chair of the Decision Analysis Special Interest Group (DASIG) and Secretary/Treasurer of the INFORMS Multiple Criteria Decision Making (MCDM) Section. Her research focuses on improving organizational decision-making through technology, with projects funded by EPSRC, Innovate UK, and Horizon2020. She has authored a Cambridge University Press book on decision behavior and analysis, and teaches across MBA, Executive MBA, and specialist programs. Education & Background: PhD research presented at the House of Commons 'Special Reception for Britain's Top Younger Scientists' Previous roles include research posts at the University of Leeds and University of Manchester's School of Computer Science, and a visiting lectureship at Warwick Business School. Research Interests: Decision analysis, decision support systems, crisis management, environmental decision-making, and digital strategy. Her work addresses practical challenges such as emergency resource allocation (via the REMESH EU project) and AI-driven service encounters. Awards & Recognition: 2020 Outstanding Teaching Award (Faculty of Humanities) Fellowship at Alan Turing Institute (2018–2021) Leadership roles in professional bodies including INFORMS and Women in OR and Analytics (WORAN) Activities & Engagement: Organizes conferences/workshops on decision analysis and AI External examiner at LSE and Loughborough University (2019–2023) Academic director of the Law and Technology Initiative (LaTI) Key Projects: EPSRC project on 'good practice in decision making' with British Telecom Innovate UK-funded KTP project with DWF Horizon2020 REMESH initiative for emergency resource supply chains

Hanna KurniawatiView profile

Professor

• Robotics
• Decision-Making Under Uncertainty
• Motion Planning
• +4 more

Hanna Kurniawati is a Professor at the ANU School of Computing and holds the SmartSat CRC Professorial Chair for System Autonomy, Intelligence, and Decision-Making. She leads the ANU Node and Planning & Control theme of the ARIAM Hub, and founded the Robot Decision Making group. Her research focuses on scalable algorithms for decision-making under uncertainty, particularly in robotics and autonomous systems. She has received prestigious awards, including the RSS 2021 Test of Time Award and multiple best-paper recognitions. She is an active conference organizer and editorial board member for journals like IEEE Transactions on Robotics. Education: BSc Computer Science, University of Indonesia PhD Computer Science (Robot Motion Planning), National University of Singapore (2008) Research Interests: Planning under uncertainty and POMDP frameworks Robot motion planning and computational geometry Integration of planning and reinforcement learning Autonomous system assurance and safety Service and Leadership: Former President of the Australian Robotics and Automation Association (2019–2020) Keynote speaker at ICRA 2025, IROS 2018, and ICAPS 2025 Program Co-Chair for ICRA 2022 and Editor roles for IEEE TRO and RA-L Teaching: Advanced Topics in AI (COMP4620/8620) Advanced Topics in Machine Learning (COMP4680/8650) Algorithms (COMP3600/6466)

Mohsen AfsharianView profile

Professor

• Operations Research
• Data Envelopment Analysis (DEA)
• Supply Chain Optimization
• +5 more

Prof. Mohsen Afsharian is a Professor of Production and Logistics at Leibniz Fachhochschule Hannover, Germany. He specializes in Operations Research with focus on Data Envelopment Analysis (DEA), incentive regulation mechanisms, and performance analytics. His research bridges theoretical advancements in DEA methodologies with practical applications in supply chain optimization, facility location, and regulatory policy design. Education: Holds a Habilitation in Prescriptive Analytics (2022) and a Doctorate in Operations Research. Notable academic contributions include monographs on cutting-stock problems with defects (2013) and prescriptive analytics frameworks (2022). Research interests span efficiency evaluation systems, productivity measurement over time, and data-driven decision models. Recent work emphasizes incentive mechanisms for regulated sectors and multi-criteria optimization under centralized management structures. Publications (2020–2024) highlight contributions to DEA flexibility control, metafrontier analysis, and emerging data science applications in business. He co-edited special issues on incentive regulation analytics (2022) and public sector DEA applications (2018). Active in editorial roles for Decision Analytics Journal and Socio-Economic Planning Sciences. His work integrates mathematical programming with real-world challenges in logistics, education systems, and banking efficiency analysis.

Serigne GueyeView profile

Lecturer

• Operational Research
• Combinatorial Optimization
• Urban Planning
• +3 more

Serigne Gueye is a Lecturer in Computer Science at Avignon Université, affiliated with the Research and Innovation Support Department (DARI). His research focuses on operational research, combinatorial optimization, and their applications in urban planning and transportation systems. Key interests include graph partitioning, mathematical optimization, and multi-agent simulation for solving real-world problems with societal impact. Notable contributions include winning the 2015 Data Crossing Prize for work on mobile phone data exploitation and co-authoring impactful studies on urban planning optimization and air passenger flow estimation. His work bridges theoretical research and practical applications, often collaborating with international teams (e.g., Senegal, Brazil). Projects include OPTIDENS (urban accessibility modeling) and mobility analysis in smart cities. Technical expertise spans hybrid algorithms, quadratic programming, and network flow analysis. His research narrative emphasizes interdisciplinary approaches, combining optimization techniques with computational methods to address complex societal challenges. Awards: Data Crossing Prize (Data for Development Challenge, 2015). Key Collaborations: Multi-national teams in Senegal, Brazil, and France. Labs/Platforms: Contributed to Avignon Université’s Metaboscope Platform and Science Ouverte initiatives.

Juan Carlos Vidal AguiarView profile

Assistant Professor

• Knowledge Discovery
• Semantic Annotation
• Workflow Modeling
• +7 more

Juan Carlos Vidal Aguiar is an Assistant Professor in the Department of Electronics and Computing at the University of Santiago de Compostela (Spain), affiliated with the Higher Technical School of Engineering and the Center for Research in Intelligent Technologies (CITIUS). His research focuses on knowledge discovery, semantic technologies, process mining, and AI-driven business intelligence. He holds a PhD from the University of Santiago de Compostela (2010) and a Bachelor's in Computer Science from the University of La Coruña (2000). His work integrates formal methods like Petri nets with machine learning and semantic web technologies, addressing challenges in workflow modeling, predictive monitoring, and healthcare analytics. Key projects include Predictive monitoring and causality for cardiac rehabilitation , Responsible AI for Process Mining 2.0 , and AQUATECHInn 4.0 (digital aquaculture training). He has led research on glucodensity analysis for diabetes monitoring and developed frameworks like OPENET for Petri net-based workflow execution. His contributions span 200+ publications in venues like IEEE Transactions, ACM T-KDD, and Advances in Data Analysis and Classification. Juan Carlos collaborates with institutions across Europe and Latin America on learning analytics, smart manufacturing, and health informatics. His lab's work emphasizes practical applications of AI in education and industry, including gamification analytics and cloud-based process monitoring tools.

• Computational Neuroscience
• Decision-Making
• Neuroeconomics
• +4 more

Sophie Bavard is a post-doctoral researcher at LNC², part of the Human Reinforcement Learning team. She holds a PhD in cognitive and computational neuroscience from École Normale Supérieure (ENS) and prior degrees in mathematics and cellular neuroscience from Université Pierre et Marie Curie (Paris 6). Her work focuses on computational models of decision-making, emphasizing reinforcement learning mechanisms, inter-individual variability, and neuropathological implications. Her research bridges cognitive neuroscience with neuroeconomics, exploring how contextual factors influence decision strategies and reward processing. Key contributions include studies on value normalization processes, cross-cultural economic preferences, and decision-making deficits in depression. Her educational background spans fundamental mathematics (Bachelor's), cellular neuroscience (Master's), and computational neuroscience (PhD). Current research interests include developing neural models to explain irrational preferences arising from adaptive learning processes, as well as investigating how contextual and cultural factors shape decision-making across global populations. Publications highlight her work on reinforcement learning dynamics, neuroimaging applications for assessing individual differences, and the cognitive mechanisms underlying economic choices. Notable studies examine the dual effects of range adaptation in learning and the role of contextual influences in depression-related decision-making deficits. As part of the LNC² team, she collaborates on interdisciplinary projects combining computational modeling with experimental neuroscience to advance understanding of human learning and decision systems.

Afshin RahimiView profile

Professor

• Aerospace Engineering
• Control Systems
• Machine Learning
• +3 more

Dr. Afshin Rahimi is a Professor of Engineering at the University of Windsor's Faculty of Engineering, specializing in aerospace systems, machine learning applications, and fault diagnosis. His research focuses on enhancing reliability in satellite systems, renewable energy infrastructure, and smart manufacturing through advanced control systems and AI-driven solutions. He has won prestigious awards including gold medals at the Seoul International Invention Fair (2022) and the International Competition for Inventors (2021) for his work on helicopter engine diagnostics and satellite fault detection. Dr. Rahimi's research interests include: Machine learning for fault detection/prognosis in aerospace and energy systems Control systems design for satellites, UAVs, and offshore wind turbines Optimization of energy efficiency in cloud data centers and renewable systems Smart manufacturing applications using computer vision and object detection His recent work emphasizes applying AI to solve challenges in: Real-time satellite fault diagnosis using hybrid frameworks PV-integrated robotic systems for sustainable energy management Thermal regulation in smart buildings via LSTM networks Data scarcity mitigation in space systems using GANs Dr. Rahimi collaborates with industry partners and student teams like the WinSAT satellite design group. His research bridges theoretical advancements with practical implementation in critical infrastructure sectors.

Hasnaa ZidaniView profile

Professor

• Optimal Control
• Applied Mathematics
• Numerical Analysis
• +3 more

Hasnaa Zidani is a Professor in applied mathematics at INSA Rouen Normandie, affiliated with the Laboratoire de Mathématiques de l'INSA (LMI), CNRS EA-3226. She holds the Chaire d'excellence 'COPTI' (2021–2025), funded by ANR, Région Normandie, and the EU, focusing on optimal control applications in environmental modeling, transportation, and image processing. Her research emphasizes optimal control theory, Hamilton-Jacobi equations, numerical methods, and stochastic systems. Recent activities include organizing conferences like the “On the Road to New Horizons” event (June 2024) celebrating Witold Respondek and serving as a speaker at the 3rd International Conference on Variational Analysis (2024). She co-leads the Workshop on Optimal Control Theory (2023) and has contributed to the ROC-HJ solver for optimal control problems. Key research interests include multi-objective optimization, stochastic control, and applications in aerospace engineering. Her work bridges theoretical advancements with practical tools like the ROC-HJ software. Awards include the COPTI excellence chair, recognizing her contributions to applied mathematics and control theory. Grants: COPTI Project (ANR, 2021–2025) Labs/Teams: Laboratoire de Mathématiques de l'INSA (LMI), collaboration with CMM (Chile) and Thales Alenia Space

Germano VeigaView profile

Assistant Professor

• Industrial Robotics
• Robotics and Automation
• Human-Robot Collaboration
• +3 more

Germano Veiga is an Assistant Professor at the Faculty of Engineering of the University of Porto and Senior Researcher at INESC TEC in Porto. He holds a PhD in Mechanical Engineering (Robotics and Automation) from the University of Coimbra (2010). His research focuses on future industrial robotics, including plug-and-produce technologies, mobile manipulators, and human-robot interfacing. He has led major EU projects such as H2020 ScalABLE4.0 and coordinated teams in FP7 initiatives like CARLoS and STAMINA. His work emphasizes automation in automotive manufacturing, sensor integration, and collaborative robotics. Education: PhD in Mechanical Engineering (Robotics and Automation), University of Coimbra, 2010 Mechanical Engineering Degree Research Interests: Industrial robotics, human-robot collaboration, automated manufacturing systems, 3D sensing, and quality control. His recent projects address challenges in automotive assembly lines, bin-picking robotics, and ergonomic laboratory automation. Recent Projects: Coordinator of H2020 ScalABLE4.0 (2017-?) FP7 ECHORD Executive Committee (2009-2012) FP7 SMErobotics (part of coordination team) Advising & Grants: Supervised 6+ graduate theses in robotics and automation. Active in EU-funded research coordination. Labs & Teams: Leads robotics teams at INESC TEC focusing on Industry 4.0 solutions and cyber-physical systems integration.

Martin HitzView profile

Professor

• Human-Computer Interaction
• Usability Engineering
• Smart Home Systems
• +5 more

Martin Hitz is a Full Professor for Interactive Systems at the University of Klagenfurt , where he founded the Interactive Systems research group (IAS) in 2000. He serves as Chairman of the University Senate (2022-2025) and previously as Vice Rector (2001-2006, 2012-2020) and Dean of the Faculty of Technical Sciences (2007-2012). His academic career includes assistant/associate professor roles at the University of Vienna and visiting positions at Politecnico di Milano, University of Ottawa, and University of Linz.

• Optimization applied to complex problems
• Predictive modeling in healthcare
• Interpretable Machine Learning
• +9 more

Sara Lumbreras Sancho is a Senior Associate Professor at the ICAI School of Engineering, Universidad Pontificia Comillas, where she graduated in 2006 as the Graduation Prize Winner. She serves as Deputy Director of Research Results at the Technological Research Institute and co-leads the Chair of Science, Technology, and Religion with Jaime Tatay. Her research focuses on decision support techniques for complex problems in energy, healthcare, and finance, leveraging optimization methods like Benders decomposition, heuristics, and AI. She also explores the philosophy of technology, transhumanism, and AI's impact on anthropology. With over 70 academic publications and leadership in over 20 projects, she bridges technical innovation with ethical and anthropological considerations. Education: Bachelor’s Degree in Engineering from ICAI School of Engineering (2006) PhD in Decision Support Methods for Large-Scale Flexible Transmission Planning (2014) Research Interests: Optimization techniques (stochastic, Benders, metaheuristics) Predictive healthcare modeling Interpretable machine learning AI ethics and responsible science Energy systems planning (wind farms, transmission networks) Philosophical implications of technology Grants & Projects: Lead researcher in EU-funded initiatives like DIAMOND and OPEN_ENTRANCE Collaborations with industries like Iberdrola, Enel, and Pharoes Advisory Transnational projects addressing energy transition challenges Awards: Global Shaper (World Economic Forum) Marshall Memorial Fellow Outreach: Chair of Cátedra de Ciencia, Tecnología y Religión Keynote speaker at conferences like EURO 2025 and Science and Religion Forum Author of books and book chapters on AI ethics, transhumanism, and science-religion dialogue

• Process Systems Engineering
• Optimization
• Chemical Engineering
• +6 more

Matthew J. Realff is a Professor in the School of Chemical and Biomolecular Engineering at the Georgia Institute of Technology. With an extensive publication record spanning over three decades, his work focuses on the intersection of chemical engineering, process systems engineering, and advanced computational methods. His research has made significant contributions to optimization techniques, supply chain management, and sustainable engineering practices. Dr. Realff's research interests span a broad range of topics in process systems engineering, with particular emphasis on optimization under uncertainty, supply chain modeling, and sustainable engineering practices. His work combines rigorous mathematical approaches with practical engineering applications, particularly in the areas of biorefinery systems, carbon capture technologies, and renewable energy integration. He has pioneered methodologies that bridge traditional chemical engineering with modern computational techniques including machine learning and Bayesian statistics. Analysis of his recent publications reveals a consistent focus on addressing complex engineering challenges through advanced computational methods. His work demonstrates a strong trajectory toward integrating data-driven approaches with traditional process engineering, particularly evident in his recent papers on Bayesian experimental design, uncertainty quantification, and machine learning applications in chemical processes. The interdisciplinary nature of his research connects chemical engineering with operations research, environmental science, and computer science. Throughout his career, Dr. Realff has maintained a productive research program with consistent publication output in top-tier chemical engineering and operations research journals. His collaborations span multiple institutions and disciplines, reflecting the interdisciplinary nature of modern engineering research. He has supervised numerous graduate students and contributed significantly to the education and training of future engineers through his academic appointments and research mentorship.

Gao JieView profile

Professor

• Algorithms
• Wireless Networks
• Social Networks
• +3 more

Jie Gao is a Professor of Computer Science at Rutgers University, serving as the Graduate Program Director. Her research focuses on algorithms, computational geometry, wireless networks, and social networks. She holds an office in Hill 411 and can be reached at jg1555@rutgers.edu. Her work emphasizes geometric algorithms, privacy-preserving techniques, and network optimization. Professor Gao teaches advanced courses such as Computational Geometry (CS529), Design and Analysis of Data Structures and Algorithms (CS513/CS514), and Introduction to Discrete Structures (CS205). Her courses blend theoretical foundations with practical applications in geometric computing and algorithm design. Her research group explores geometric algorithms, distributed systems, and privacy-preserving data analysis. Notable projects include developing efficient algorithms for unit-disk graphs, security game theory for patrol scheduling, and methodologies for interpreting health data from wearable devices. She actively contributes to the field through interdisciplinary collaborations, addressing challenges in both theoretical and applied computing domains.