جست‌وجوی استاد و پژوهشگر

J. van den Bergمشاهده پروفایل

استاد

• Cybersecurity
• Machine Learning for Security
• Intrusion Detection
• +۹ مورد دیگر

J. van den Berg is a Professor in the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology, specializing in Cyber Security. He has authored over 100 research outputs and supervised multiple students, contributing significantly to cybersecurity science, education, and policy. His work spans technical and human aspects of cyber defense. Research interests: Cybersecurity and cyber operations Machine learning for intrusion and phishing detection Risk modeling and mental models in security Digital trust and access control Cybersecurity education and ethics His recent publications (2020–2025) focus on AI-driven detection of phishing, tax fraud, and online manipulation, using machine learning and data reduction techniques. These works appear in top venues like Springer LNCS and the Journal of Network and Computer Applications, highlighting trends in behavioral AI, anomaly detection, and lightweight security algorithms. Scientific Awards: BEST PAPER AWARD, NATO IST-122 Cyber Security Science and Engineering Symposium (2015) He has advised several researchers in cybersecurity and machine learning, and his collaborative projects include international supply chain security and cyber crisis management. He is involved in developing frameworks for cyber operations, risk evaluation, and secure data pipelines. His work is associated with labs and teams focused on cyber operations, secure logistics, and intelligent data analysis, contributing to both theoretical and applied advancements in cybersecurity.

Marielle Stoelingaمشاهده پروفایل

استاد

• Predictive Maintenance
• Formal Methods
• Fault Tree Analysis
• +۷ مورد دیگر

Prof. Marielle Stoelinga is a Professor at the University of Twente, Netherlands, working within the Electrical Engineering, Mathematics and Computer Science faculty in the Formal Methods and Tools department. She leads significant research initiatives in formal methods with applications to safety, security, and reliability engineering. Her research spans predictive maintenance , fault tree analysis , attack tree modeling , and the integration of safety and security through formal methods. She focuses on applying big data analytics to predict system failures, with particular emphasis on critical infrastructure including railway systems, satellite missions, and nuclear reactors. Her work bridges theoretical formal methods with practical applications in asset management. Analysis of her recent publications reveals a strong trend toward integrating safety and security analysis through attack-fault-defense trees, developing formal frameworks for risk assessment, and applying model checking techniques to real-world maintenance problems. Her research increasingly addresses the human and organizational aspects of predictive maintenance systems while maintaining rigorous formal foundations. 5 million euros research grant from Dutch National Organization for Scientific Research (NWO) for PrimaVera project Prof. Stoelinga leads the PrimaVera research project ( Predictive maintenance for Very effective asset management ), which takes a holistic approach to predictive maintenance spanning sensor systems, data science, maintenance optimization, and human factors. Her research group actively contributes to formal methods applications in critical infrastructure sectors including energy, transportation, and aerospace.

• Hybrid Systems
• Networked Control Systems
• Model Predictive Control (MPC)
• +۵ مورد دیگر

Prof. W.P.M.H. (Maurice) Heemels is a Full Professor at the Eindhoven University of Technology in the Mechanical Engineering department, leading the Control Systems Technology group. His work focuses on hybrid systems , networked control , and model predictive control (MPC) with applications in cyber-physical systems , precision agriculture , and cancer treatment optimization . PhD in Mathematics (1999) and MSc in Mathematics (1995) from TU/e Assistant Professor at TU/e Electrical Engineering (2000-2004) Research Fellow at Embedded Systems Institute (2004-2006) His research addresses resource-aware control strategies to replace traditional time-triggered systems with event-triggered schemes , enabling efficient use of computational and communication resources. Applications span automated driving , precision agriculture , thermal control in lithography , and nuclear fusion reactors . He has developed innovative hybrid integrator-gain systems (HIGS) and reset controllers to overcome limitations of linear time-invariant controllers. Model predictive control is a core methodology, applied to hyperthermia treatments, wafer scanner thermal control, and drone-based farming systems. His 2025 publications emphasize networked control for nuclear fusion, safety-critical MPC in autonomous vehicles, and data-driven control barrier functions for nonlinear systems. Key trends include hybrid systems theory , resource-aware control , and scalable optimization for large-scale applications. 2025: DISC PhD Award for Stefan Heijmans 2019: EECI European PhD Award for Victor Dolk 2017: Best Paper Award @ EBCCSP Conference 2014: Automatica Outstanding Service Award 2000: ASML-award for best fundamental PhD thesis He has supervised 88+ students and secured over 7 MEuro in research funding from NWO, EU, and industry. His Control Systems Technology group operates labs including the Automotive Engineering Science Laboratory and Robotics Laboratory , contributing to the UN Sustainable Development Goals for sustainable cities and climate action through cyber-physical systems.

Alina Rweiمشاهده پروفایل

استادیار

• Drug Delivery
• Wearable Bioelectronics
• Precision Medicine
• +۱۴ مورد دیگر

Dr. Alina Rwei is an Assistant Professor in the Department of Chemical Engineering at Delft University of Technology (TU Delft), Faculty of Applied Sciences. She leads the Rwei Group, focusing on innovative biomedical technologies at the intersection of materials science, engineering, and biology. Her research aims to bridge clinical translation with scientific discovery through smart therapeutic and diagnostic platforms. Academic Background: Bachelor’s and Ph.D. in Chemical Engineering and Materials Science and Engineering from the Massachusetts Institute of Technology (MIT) Ph.D. supervised by Prof. Robert Langer (MIT) and Prof. Daniel Kohane (Harvard Medical School) Postdoctoral training in the lab of Prof. John Rogers at Northwestern University Research Interests: Dr. Rwei’s work focuses on precision medicine through externally triggerable drug delivery systems and wearable biosensors. Her group develops liposome-based platforms activated by light, ultrasound, and magnetic fields for targeted cancer and pain therapies. Concurrently, they engineer wearable bioelectronic sensors for real-time, non-invasive monitoring of biomarkers in sweat and physiological states, particularly in pediatric and neonatal care. Publication Trends: Her recent publications reflect a strong emphasis on translational biomedical engineering, combining nanotechnology with clinical applications. Key themes include ultrasound- and light-responsive drug carriers, wearable biosensors for cerebral hemodynamics, and aptamer-based detection systems. The integration of modeling, in vitro/in vivo validation, and device engineering underscores a systems-level approach to therapeutic innovation. Scientific Awards: Postdoctoral Fellowship Research Training Award (TL1) – NIH/NCATS Clinical and Translational Science Awards Program Advising and Grants: Dr. Rwei actively mentors a diverse team of postdocs, PhD candidates, and master’s and bachelor’s students. She supervises numerous MEP and BEP projects, fostering early-career development in interdisciplinary research. While specific grants are not listed, her NIH-supported postdoctoral fellowship and high-impact publications in journals like Nature Medicine and Science Advances suggest a strong grant funding profile. Labs and Teams: The Rwei Group operates at TU Delft, conducting cutting-edge research in precision therapeutics. The team is structured around two main tracks: externally triggerable nanoparticles and wearable sensor technology. They collaborate with clinical and engineering partners to develop closed-loop therapeutic systems and multiplexed sensing platforms for real-world healthcare applications.

Elisabetta Chiccaمشاهده پروفایل

استاد

• Neuromorphic Engineering
• Spiking Neural Networks
• Memristive Systems
• +۶ مورد دیگر

Elisabetta Chicca is a Professor of Bio-Inspired Circuits and Systems at the University of Groningen's Faculty of Science and Engineering. Her work bridges neuromorphic engineering, spiking neural networks, and bio-inspired sensing. University: University of Groningen Department: Bio-Inspired Circuits and Systems Email: e.chicca@rug.nl Research Interests: Focus on developing CMOS models of cortical circuits for brain-inspired computation, combining spiking neural networks with memristive systems. Key areas include bio-inspired vision, olfaction, touch, and motor control to create agents that operate in real-world environments. Recent Article Trends: Explore neuromorphic processors with hybrid CMOS-memristor architectures, event-based vision for motion detection, tactile sensing in robotics, and benchmarking frameworks for neuromorphic algorithms. Scientific Contributions: She co-founded the Neuromorphic Computing and Engineering journal and serves on its Executive Editorial Board. Her team has received EU Horizon 2020, NWO, and DFG grants.

Mohsen Alirezaeiمشاهده پروفایل

استادیار

• Automated Vehicles
• Cooperative Systems
• Control Systems
• +۳ مورد دیگر

Mohsen Alirezaei is a part-time Assistant Professor at Eindhoven University of Technology's Mechanical Engineering department, specializing in vehicle dynamics and control. He concurrently serves as a Fellow Scientist at Siemens Industry Software and Services. His academic background includes a PhD in Mechanical Engineering (2011) from an unspecified institution, followed by a postdoc at Delft University of Technology (2012). Previously, he held roles as Senior Scientist at TNO Automotive (2012–2019) and part-time Assistant Professor at TU Delft (2015–2019). Research interests focus on automated and cooperative vehicles, including adaptive cruise control, collision avoidance, and state estimation. His work aligns with UN Sustainable Development Goals, emphasizing safer transportation systems. Key collaborations include studies on neural network-based behavior estimation during occlusion and criticality measures for automated vehicle testing. Education: PhD in Mechanical Engineering (Robotics & Control), 2011 Postdoc, Delft University of Technology, 2012 Awards: No explicitly stated scientific awards found. Grants/Advising: Supervised 8 works but no student names listed. Active in industry-academia collaborations via Siemens and TNO. Labs/Teams: Research involves Siemens' automotive software teams and TU/e's vehicle dynamics group.

Michele Cucuzzellaمشاهده پروفایل

دانشیار

• Power Systems
• Control Systems
• DC Microgrids
• +۳ مورد دیگر

Prof. Michele Cucuzzella is an Associate Professor at the University of Groningen’s Faculty of Science and Engineering, Department of Discrete Technology and Production Automation. He holds a visiting professorship at Hiroshima University. His research focuses on control systems, DC microgrids, and renewable energy integration. He has contributed to over 65 publications, with notable work in IEEE Transactions on Control Systems Technology and Automatica. His research addresses challenges in distributed control, passivity-based methods, and robust stability of power networks. He received the IEEE Outstanding Paper Award (2020) for his work on consensus algorithms in DC microgrids. His work bridges theoretical control advancements with practical applications in smart grids and district heating systems. Research Interests: - DC Microgrid Control (voltage regulation, distributed optimization) - Passivity-Based Control (for nonlinear systems, sampled-data stabilization) - Energy Systems (district heating, renewable integration) - Robust Control (sliding mode techniques, uncertain dynamics) - Networked Systems (multi-agent consensus, game-theoretic approaches) Awards: IEEE Transactions on Control Systems Technology Outstanding Paper Award (2020) Key Contributions: Prof. Cucuzzella’s research emphasizes scalable solutions for modern energy networks. He has developed frameworks for voltage control in DC microgrids, robust stability under ZIP loads, and adaptive control for district heating systems. His work on passivity and sliding mode control addresses practical challenges in energy systems, balancing theoretical rigor with real-world applicability.

Ahmadreza Marandiمشاهده پروفایل

استادیار

• Data-driven decision making
• Robust Optimization
• Resilient Engineering
• +۵ مورد دیگر

Dr. Ahmadreza Marandi is a tenured Assistant Professor at Eindhoven University of Technology (TU/e), affiliated with the Operations, Planning, Accounting, and Control (OPAC) group, and an assistant professor at the Eindhoven Artificial Intelligence Systems Institute (EAISI). He holds an adjunct role at the University of British Columbia. His research focuses on data-driven robust optimization, addressing nonlinearity and uncertainty in complex systems, particularly in high-tech industries and supply chains. Education: PhD in Operations Research from Tilburg University (2017). Research Interests: Robust Optimization, Resilient Engineering, Convex Optimization, and applications in supply chain networks and high-tech systems. He emphasizes mathematical tools to solve real-world decision-making challenges, including inventory management, intervention planning, and adaptive treatment protocols in radiotherapy. Key Contributions: Developed scalable optimization algorithms for interconnected infrastructure systems and robust spare parts inventory management. His work on intermediate imaging in radiotherapy planning has advanced treatment resistance management. He also co-chaired ISMP2027 and is part of the 4TU Resilience Engineering Center’s steering group. Awards & Recognition: Co-authored a paper winning the College of Supply Chain Management’s best student paper at the 2023 Annual POMS Conference. His research has been published in top journals like *Annals of Operations Research*, *Reliability Engineering & System Safety*, and *Journal of Global Optimization*. Advising & Collaborations: Supervises four PhD students (Sina Shahri Majarshin, Alireza Yazdani Esfidvajani, Marloes Remijnse, Zhao Kang). Collaborates with industry partners on projects like cold chain optimization and resilient supply chain design. Active in organizing academic events, including the Robust Optimization Webinar series. Labs & Teams: Involved with EAISI High Tech Systems and the 4TU Resilience Engineering Center. His work bridges theoretical optimization with practical applications in high-tech systems and resilient infrastructure.

Marco de Baarمشاهده پروفایل

استاد

• Nuclear Fusion
• Plasma Control
• Plasma Physics
• +۵ مورد دیگر

Marco de Baar is a Full Professor (Part-time) and Chair of Plasma Fusion Operation and Control at the Mechanical Engineering Faculty of Eindhoven University of Technology (TU/e). He concurrently serves as Head of Fusion Research at DIFFER (Dutch Institute for Fundamental Energy Research) and represents the Netherlands in the EUROfusion consortium. His research focuses on plasma control in fusion reactors, including magnetohydrodynamic (MHD) instabilities, plasma stability optimization, and remote maintainability of fusion systems. He has extensive experience leading operations at JET (Europe’s largest fusion experiment) and contributes to ITER-NL as a program leader for MHD stabilization. Education: PhD in Physics (TU/e), MSc in Experimental Physics (Utrecht University) Affiliations: Member of DIFFER’s management team, Technical Advisory Panel for Fusion for Energy His work integrates physics and control engineering to address challenges in managing plasma heat loads and achieving optimal reactor performance. He also explores tele-operated maintenance techniques for fusion reactors, as evidenced by his collaboration on the Upper Port Launcher project. Labs/Teams: Part of the Control Systems Technology Group and Group Heemels at TU/e, collaborating closely with DIFFER’s fusion research initiatives.

• Animal Genomics
• Livestock Conservation
• Dairy Cattle Genetics
• +۴ مورد دیگر

Ina Hulsegge is a Researcher in the Animal Breeding & Genomics group at Wageningen University & Research, specializing in livestock genomics and conservation. Her work focuses on genomic diversity analysis, inbreeding estimation, and precision livestock farming technologies, particularly for dairy cattle conservation. Her research spans Genomic profiling of livestock diversity Development of real-time animal identification systems using computer vision Conservation genomics for Dutch cattle breeds Deep learning applications in genotype data analysis with strong emphasis on practical implementation in breeding programs and gene banks. Recent publications highlight trends in Automated phenotyping through ear-tag recognition (2024) SNP array-based genomic diversity assessment (2024) Aneuploidy detection via deep learning (2023) Sub-population analysis in red-pied cattle (2023) demonstrating her dual focus on computational innovation and conservation applications. Scientific contributions include: Management systems for genomic information in gene banks (2024) EUGENA database updates for multi-species conservation (2024) Analysis of genetic changes in Dutch black-and-white cattle (2022) She actively engages in knowledge transfer through professional presentations at precision livestock farming conferences and collaborates with institutions like CGN (Centre for Genetic Resources, the Netherlands) on conservation initiatives. Her PhD thesis 'Genomics applied to conservation of genetic diversity in Dutch livestock' (2023) established foundational work for national conservation strategies.

Nicu N. Gogaمشاهده پروفایل

پژوهشگر

• Computer Simulation
• Molecular Dynamics
• Robotics
• +۵ مورد دیگر

Nicu N. Goga is an external researcher at the University of Groningen's Faculty of Science and Engineering, specializing in computational methods with applications spanning transportation systems, robotics, and renewable energy. His work integrates advanced simulation techniques across multiple engineering disciplines to solve complex real-world problems. Dr. Goga's research demonstrates exceptional interdisciplinary breadth, with core expertise in computer simulation and molecular dynamics. He has pioneered adaptive route optimization solutions for commercial fleets, developed affordable humanoid robotics platforms like Miha, and engineered EEG-based brain-computer interfaces for consumer electronics. His molecular dynamics work focuses on photoelectrochemical water splitting for sustainable hydrogen production, directly contributing to UN Sustainable Development Goals in clean energy and industry innovation. Analysis of his 2021-2025 publications reveals a strategic evolution from fundamental computational chemistry toward applied engineering solutions. While maintaining molecular dynamics as a foundational methodology, his recent work increasingly targets transportation logistics and human-robot interaction, showcasing a deliberate shift toward high-impact applications in commercial and assistive technologies. Affiliated with the Molecular Dynamics research group at Groningen, Dr. Goga operates within a robust collaborative network evidenced by international co-authorship patterns. His technical fingerprint spans computer simulation engineering, model checking, multiscale modeling, and distributed systems architecture, reflecting a versatile computational toolkit applied across diverse problem domains.

• Process Mining
• Process Modelling and Simulation
• Distributed Computing
• +۵ مورد دیگر

Claudio di Ciccio is an Associate Professor at the Department of Information and Computing Science within the Faculty of Science at Utrecht University, Netherlands. Previously, he worked with the Department of Computer Science of Sapienza University of Rome (Italy) and the Institute for Information Business of the Vienna University of Economics and Business (WU Vienna), Austria. He received his PhD in Computer Science and Engineering in 2013 from Sapienza University. His research interests span across Process Mining, Formal Methods, Automated Reasoning, and Blockchain & Distributed Ledger Technologies. He has developed expertise in Process Modelling and Simulation, Distributed Computing, Logic, and Artificial Intelligence. His work bridges theoretical formal methods with practical applications in business process management and decentralized systems. His recent publications focus on advancing Process Mining techniques while addressing critical challenges in data privacy and security, particularly in decentralized environments. His research explores the intersection of visual analytics and process mining, developing innovative approaches for multi-faceted process information analysis through time and space. He has made significant contributions to formal methods for process specification verification and constraint satisfaction. Member of the Steering Committee of the IEEE Task Force on Process Mining General Chair of the Conference on Process Mining (ICPM) in 2023 PC chair of ICPM in 2021 PC chair of the Conference on Business Process Management (BPM) in 2022 PC chair of the Blockchain Forum at BPM in 2019 and 2024 Di Ciccio has been actively involved in numerous significant research projects related to process mining, blockchain applications, and formal methods. His work demonstrates a consistent focus on developing practical tools and frameworks that address real-world challenges in business process management while maintaining strong theoretical foundations. His research group appears to focus on the intersection of process science with emerging technologies, particularly in secure and decentralized environments.

• Emotion
• Interpersonal Relationships
• Psychophysiology
• +۵ مورد دیگر

Dr. Monika Donker serves as Assistant Professor in the Department of Youth and Family within Utrecht University's Faculty of Social and Behavioural Sciences. Her academic base is the Martinus J. Langeveld Building (Heidelberglaan 1, Room E2.28) in Utrecht, Netherlands. As an active researcher, she contributes to the Dynamics of Youth (DoY) initiative and leads investigations within the InTransition project focusing on parent-adolescent interactions and adolescent autonomy development. Her research expertise spans emotion dynamics, interpersonal relationships, and psychophysiological measurement techniques. Specializing in longitudinal and dynamical systems approaches, she examines teacher-student relationships, parent-child interactions, and adolescent development through multimodal methodologies including heart rate monitoring and behavioral coding. Her work integrates personality theory with developmental frameworks to understand stress responses in educational and family contexts. Analysis of her recent publications reveals strong thematic continuity in educational and developmental psychology, with increasing emphasis on pandemic-related family dynamics and physiological assessment methods. Her scholarly output demonstrates methodological sophistication through integration of self-report, observational, and physiological data streams, particularly in classroom and family settings. Professional activities include presentations at major international conferences (EARLI, SAA, ISPA), service on the PhD council of the Faculty of Social Sciences, and membership in the Educational Committee of the Interuniversity Centre of Educational Sciences (ICO). She completed a visiting research period at the University of Konstanz (Germany) under Prof. Thomas Goetz in 2018. Her technical proficiency encompasses advanced statistical software (MLwiN, Mplus, HLM, AMOS, SPSS, R) for multilevel and structural equation modeling. Current media engagements include expert commentary on teacher emotional experiences and research dissemination regarding parent-adolescent relationships during the COVID-19 pandemic.

• Machine Learning
• Neural Networks
• Neuromorphic Computing
• +۳ مورد دیگر

Marcel van Gerven serves as Professor of Artificial Intelligence at Radboud University, leading the Artificial Cognitive Systems laboratory within the Donders Institute for Brain, Cognition and Behaviour. He holds dual Principal Investigator roles at both the Donders Centre for Cognition and the Donders Institute, while directing the ELLIS Unit Nijmegen as an ELLIS Fellow. His research program bridges artificial and natural intelligence through machine learning and neuromorphic computing, with core expertise in neural networks, brain-computer interfaces, and neuroprosthetics for vision restoration. Current work develops brain-inspired AI systems that enhance computational efficiency while modeling biological neural processes, particularly focusing on cortical stimulation safety and real-time adaptive systems. Analysis of his 2025 publications reveals dominant themes in reinforcement learning for neuroprosthetics, anomaly detection frameworks, and spiking neural network applications. His work consistently integrates medical applications including epilepsy regulation, immunotherapy diagnostics, and prosthetic vision enhancement, demonstrating strong translational impact from fundamental AI research to clinical solutions. Scientific recognition includes: Vidi laureate from the Dutch Research Council ELLIS Fellowship for European AI leadership Professor van Gerven directs significant research funding through national and international grants, including the Vidi award. His laboratory develops specialized frameworks like Abmax and Kozax for agent-based modeling while mentoring students in cognitive AI systems, though specific advisees aren't documented in available sources. The Artificial Cognitive Systems lab operates within the Donders Institute ecosystem, collaborating closely with the ELLIS Unit Nijmegen to advance European neuromorphic computing research. Current initiatives focus on biologically plausible learning rules, efficient neural network architectures for embedded systems, and closed-loop neuroprosthetic control systems.

Martijn Goordenمشاهده پروفایل

استادیار

• supervisory control theory
• synthesis-based engineering
• formal methods in control
• +۲ مورد دیگر

Martijn Goorden is an Assistant Professor at the Department of Mechanical Engineering, Eindhoven University of Technology (TU/e), Netherlands, since October 2024. He previously served as an Assistant Professor at Aalborg University (2022-2024) and was a postdoc there starting in 2020. He also serves as a senior advisor on synthesis-based engineering within Rijkswaterstaat, part of the Dutch Ministry of Infrastructure and Water Management, since 2020. Education: Ph.D. in Mechanical Engineering, Eindhoven University of Technology (2019) Martijn Goorden's research focuses on supervisory control theory, synthesis-based engineering, and formal methods in control. He is particularly interested in applying these approaches to complex systems like infrastructural systems and lithography. His work bridges theoretical foundations with practical applications, developing methodologies that can be implemented in real-world scenarios. His research often involves the development of algorithms and tools for automated controller synthesis. His recent publications demonstrate a strong focus on water management systems, particularly stormwater detention ponds, where he applies formal methods and reinforcement learning to develop optimal control strategies. His work spans both theoretical advancements in supervisory control and practical implementations in infrastructure management. The research shows a progression from foundational theoretical work to specific applications in critical infrastructure. Scientific Awards: Best Paper Award (2025) in Control Systems Technology Martijn Goorden leads the open-source Eclipse project ESCET (Eclipse Supervisory Control Engineering Toolkit), which is used both in academia and industry for developing supervisory controllers in a synthesis-based approach. As project lead, he actively contributes to the development of ESCET with new algorithms and code implementations as well as reporting and solving issues. His work with Rijkswaterstaat since 2020 demonstrates strong industry-government collaboration. He is affiliated with the Control Systems Technology group at TU/e, specifically within Group Reniers, focusing on applying formal methods to real-world control problems. His work bridges the gap between theoretical computer science and practical engineering applications, particularly in infrastructure systems.