جستجوی استاد راهنمای دانشگاهی

• Autonomous Systems
• Robotics
• Motion Planning
• +۳ مورد دیگر

Javier Alonso-Mora is a Professor in the Department of Mechanical Engineering at Delft University of Technology, specializing in Learning & Autonomous Control. His research focuses on autonomous systems, robotics, motion planning, and transportation logistics, with applications in mobile manipulation, dynamic environments, and urban mobility. He leads key projects such as INTERACT (Intuitive Interaction for Robots among Humans) and ACT (Perceptive Acting Under Uncertainty), exploring human-robot interaction, autonomous vehicles, and healthcare robotics. Notable achievements include an ERC Starting Grant (2022) and a Veni Grant (2017). His work addresses challenges in robot navigation, control systems, and fleet optimization, with contributions to both theoretical advancements and practical implementations. Projects like TRiLOGy focus on sustainable water transportation, while HARMONY advances assistive robotics in healthcare. Alonso-Mora’s research leverages geometric fabrics for motion planning, probabilistic modeling for dynamic environments, and multi-agent coordination. He collaborates internationally and contributes to open-source frameworks for robotics. His recent publications emphasize safety-aware control, instance-aware semantic mapping, and adaptive systems for cluttered environments.

Massimo Mischiمشاهده پروفایل

استاد

• Biomedical Signal Processing
• Diagnostic Imaging
• Ultrasound Technology
• +۴ مورد دیگر

Massimo Mischi is a Full Professor at the Faculty of Electrical Engineering of the Eindhoven University of Technology (TU/e) and chairs the Signal Processing Systems (SPS) Division , the largest division at TU/e with over 250 researchers. He founded the Biomedical Diagnostics (BM/d) Lab in 2012, which now includes 180 researchers and clinical/industrial advisors, focusing on biomedical signal processing for diagnostics and monitoring.

• Evolutionary Computation
• Machine Learning
• Natural Computing
• +۵ مورد دیگر

Thomas Bäck is a Professor at the Leiden Institute of Advanced Computer Science (LIACS) , Leiden University , Netherlands, and a member of the interdisciplinary programme Society, Artificial Intelligence and Life Sciences (SAILS) . His academic career spans roles at Leiden University (1996–present) and leadership positions at the Center for Applied Systems Analysis in Dortmund (1994–2000). Education : Diplom-Informatiker (Computer Science), Technische Universität Dortmund (1990) Dr. rer. nat. (Computer Science), Technische Universität Dortmund (1994) Research Interests : Dr. Bäck specializes in evolutionary computation , machine learning , and their applications in sustainable smart industry and healthcare . Recent work focuses on integrating large language models (LLMs) and quantum computing into optimization frameworks, with projects like CIMPLO (predictive maintenance), ECOLE (experience-based optimization), and SAPPAO (airline operations optimization). Scientific Contributions : His 526+ publications cover evolutionary algorithms, quantum optimization, and LLM-driven design, with recent trends including: Quantum computing (e.g., quantum approximate optimization, quantum advantage challenges) LLM integration (e.g., hyperparameter tuning, mutation control, code evolution graphs) Healthcare and industry (e.g., predictive maintenance, anomaly detection, melt quality prediction) Algorithm benchmarking (e.g., IOHprofiler, MA-BBOB, explainable benchmarking) Scientific Awards : IEEE Fellow (2022) Royal Netherlands Academy of Arts and Sciences (KNAW) member (2021) Academia Europaea member (2022) IEEE Computational Intelligence Society Evolutionary Computation Pioneer Award (2015) Fellow, International Society of Genetic and Evolutionary Computation (2003) Best Ph.D. thesis award, German Society of Computer Science (GI) (1995) Advising and Grants : He supervises Ph.D. candidates in evolutionary computation and machine learning and has secured 7 major grants from organizations like the Dutch Research Council , European Commission , and The Research Council of Norway . His editorial roles include Editor-in-Chief of the Evolutionary Computation Journal and associate editorships in leading AI journals.

• Power Electronics
• Power Quality and Stability
• Solid-State Transformers
• +۴ مورد دیگر

Zian Qin serves as an Associate Professor in the Department of Electrical Sustainable Energy at Delft University of Technology, Netherlands. His academic journey includes a B.Sc. from Beihang University (2009), M.Sc. from Beijing Institute of Technology (2012), and Ph.D. from Aalborg University (2015), all in Electrical Engineering, with a Visiting Scientist stint at RWTH Aachen University (2014). B.Sc., Electrical Engineering, Beihang University (2009) M.Sc., Electrical Engineering, Beijing Institute of Technology (2012) Ph.D., Electrical Engineering, Aalborg University (2015) His research centers on power electronics-based grid stability, solid-state transformers, and battery energy storage systems. Key contributions span DC microgrid control, magnetic material optimization, and EV charging infrastructure, with fingerprints highlighting expertise in power quality ( 100% ), voltage stability ( 87% ), and grid-forming applications ( 79% ). Current projects like ECS4DRES and GROW focus on resilient renewable energy systems and African energy storage solutions. His 154+ publications reveal strong emphasis on power electronics control ( 83% ), grid integration ( 79% ), and EV technologies ( 42% ), with recent work targeting microgrid vulnerability reduction and data-driven magnetic loss modeling. Awards include IEEE Prize Paper Awards (2023), World Top 2% Scientist recognition, and IEEE Innovation Awards (2024). IEEE International Challenge Excellent Innovation Award (2024) IEEE Open Journal Prize Paper Award (2023) World's Top 2% Scientist (2022-2024) Featured IES Journal Articles (2023) IEEE TIE Distinguished Reviewer (2020) As Founding Chair of IEEE Transportation Electrification Council Benelux Chapter and Dutch National for Cigre WG B4.101, he leads critical industry collaborations. His editorial roles in IEEE TPEL/TIE/JESTPE and projects like PROGRESSUS demonstrate significant grant leadership in next-generation power infrastructure. Lab activities focus on DC systems, energy conversion, and storage validation through Delft's Electrical Sustainable Energy facilities.

• Neuromorphic Computing
• Memristor-Based Architectures
• Energy-Efficient AI Hardware
• +۷ مورد دیگر

Said Hamdioui serves as a full Professor in the Department of Computer Engineering within the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology. His research focuses on cutting-edge hardware architectures for neuromorphic computing and energy-efficient AI acceleration, with particular emphasis on memristor-based systems, emerging memory technologies, and fault-tolerant designs for edge applications. His research interests span Neuromorphic Computing , Memristor-Based Architectures , and Energy-Efficient AI Hardware , addressing critical challenges in hardware security, computation-in-memory, and reliable edge AI deployment. Recent work demonstrates significant advancements in RRAM/FeFET testing methodologies, spiking neural network implementations, and spin wave computing alternatives to traditional CMOS. His publications reveal strong trends toward real-world deployment of brain-inspired hardware with practical constraints like power efficiency, testability, and security. Award highlights include: DATE'20 Best Paper Award DFT'21 Outstanding Student Paper ETS 2021 Best Paper Award LATS 2018 & 2022 Best Paper Awards Professor Hamdioui actively contributes to the research community through editorial roles at IEEE Transactions on VLSI Systems , IEEE Design & Test , and Journal of Electronic Testing from 2017-2018. His leadership in multi-partner projects like CONVOLVE and NEUROKIT2E demonstrates strong industry-academia collaboration for edge AI solutions. Current work shows increasing focus on practical deployment challenges including in-field fault monitoring, security vulnerabilities in neuromorphic systems, and realistic brain simulation frameworks.

• Cyber-Physical Systems
• Machine Learning
• Self-Adaptive Systems
• +۴ مورد دیگر

Dr. Ilias Gerostathopoulos is an Assistant Professor at the Faculty of Science, Vrije Universiteit Amsterdam, affiliated with the Network Institute and the Department of Information Management & Software Engineering. He specializes in self-adaptive systems, cyber-physical systems, and machine learning operations (MLOps). His work focuses on software architectures for autonomous systems, decision-making under uncertainty, and experiment-driven adaptation frameworks. He teaches courses such as 'Fundamentals of Adaptive Software' and 'Information Management', emphasizing practical applications of adaptive systems and data-driven decision-making. Gerostathopoulos has been awarded the Best Presentation Award (2021) for contributions to evaluating self-adaptive systems. His research addresses challenges in industrial self-adaptation, MLOps architectures, and robotics. Key research themes include: Architecture-based self-adaptation in robotics and CPS MLOps frameworks and systematic analysis of AI systems Uncertainty management in autonomous systems Experiment-driven learning and tool development Notable contributions include the ExpEngine tool for workflow optimization and the ReBeT framework for robotic systems. His work bridges theoretical software engineering with practical industrial implementations.

Nathan van de Wouwمشاهده پروفایل

استاد

• Dynamics and Control
• Mechatronics and Robotics
• Smart Manufacturing
• +۶ مورد دیگر

Nathan van de Wouw is a Full Professor at the Mechanical Engineering Department of Eindhoven University of Technology (TU/e), affiliated with ICMS, EAISI Mobility, EAISI High Tech Systems, EAISI Foundational, and EIRES. He also holds an adjunct Full Professor position at the University of Minnesota and a part-time Full Professorship at Delft University of Technology. His research focuses on dynamics and control of mechanical systems, including mechatronics, robotics, smart manufacturing, energy systems, and networked control. He has supervised over 150 students and led numerous projects funded by industry partners like ASML, Philips, and Shell. Education: M.Sc. (with Honors) in Mechanical Engineering, TU/e (1994) Ph.D. in Mechanical Engineering, TU/e (1999) Research Interests: Nonlinear systems and control Model reduction and complexity analysis Data-driven and networked control strategies Applications in high-tech systems, autonomous vehicles, and energy systems Awards: IEEE Control Systems Technology Award (2015) for variable-gain control in motion systems Grants & Projects: Lead projects on mechatronic design, lithography systems, and thermodynamic optimization Collaborations with TNO, ASML, and industrial partners Labs & Teams: Member of TU/e’s Dynamics and Control group Affiliated with EAISI (Eindhoven AI Systems Institute)

• Hybrid Systems
• Networked Control Systems
• Event-Triggered Control
• +۷ مورد دیگر

Maurice Heemels is a Full Professor at Eindhoven University of Technology (TU/e), leading the Control Systems Technology group. He holds additional professorships in EAISI Mobility, EAISI Foundational, EAISI Health, and EAISI High Tech Systems. His research focuses on hybrid and networked systems, emphasizing resource-aware control, event-triggered strategies, and cyber-physical systems integration. He is an IEEE Fellow and chairs the IFAC Technical Committee on Networked Systems. Academic Background: MSc and PhD in Mathematics (TU/e, 1995 and 1999, both summa cum laude ) Visiting Professorships: ETH Zurich (2001), UC Santa Barbara (2008) Industry Experience: Research & Development at Océ NV Research Interests: Hybrid Systems, Networked Control, Event-Triggered Control Model Predictive Control (MPC) in healthcare and high-tech systems Cyber-Physical Systems for applications like lithography and precision agriculture Key Contributions: Developed Hybrid Integrator-Gain (HIGS) systems and Projection-Based Control methodologies Recipient of a VICI Grant for wireless control systems research Oversaw over €7M in research funding from NWO, EU, and industry Awards & Recognition: Automatica Outstanding Service Award (2014) Best Paper Awards (EBCCSP 2017, etc.) Invited Keynote Speaker at ECC, CDC, and others Grants & Projects: Current Projects: COMEDI (Cost-effective Mechatronics), PROACTHIS (Projection-based Control) Past Projects: Fault Detection in Wafer Scanners, Drone-based Farming Labs & Teams: Active in TU/e’s Cyber-Physical Systems and Systems Engineering research groups, collaborating globally on nonsmooth dynamics and hybrid systems.

• Robotics
• Control Systems
• Multi-Agent Systems
• +۶ مورد دیگر

Herman Bruyninckx is a Part-Time Full Professor at Eindhoven University of Technology (TU/e) in the Mechanical Engineering department, specifically within the Control Systems Technology group and EAISI High Tech Systems initiative. He also serves as a professor (Hoogleraar) at KU Leuven in Belgium. Academic focus on robotics, control systems, and multi-agent coordination Active research in model predictive control , semantic mapping , and dynamic constraint algorithms Recent publications address industrial automation , agro-food robotics , and haptic technology Research Highlights : Developed hybrid decision-making frameworks for multi-agent navigation Innovated swing-free control methods for robotic pick-and-place operations Formulated constrained dynamics algorithms with LQR-Gauss principle integration Created ExoTen-Glove for haptic feedback in virtual environments Collaborative Projects : Coordinated with researchers like René van de Molengraft , Elena Torta , and Koen de Vos Contributed to NWO/TTW FlexCRAFT project for cognitive robotics in agro-food technology

• Emotion Regulation
• Mental Health
• Positive Psychology
• +۳ مورد دیگر

Tajda Laure is a Researcher at the Erasmus School of Social and Behavioural Sciences , Erasmus University Rotterdam. Her work focuses on emotion regulation , mental health , and digital interventions for university students. Specializes in mobile mental health applications Active in mixed methods research design Collaborates with experts like Dr. M. Boffo and Prof. R.C.M.E. Engels Research Trends : Recent articles highlight her development of the ROOM mental health app, optimization of transdiagnostic emotion regulation interventions, and methodologies involving microrandomized trials and mixed method studies . Her work intersects digital health , positive psychology , and mindfulness practices .

Sara Magliacaneمشاهده پروفایل

استادیار

• Causality
• Causal Representation Learning
• Causality-inspired Machine Learning
• +۴ مورد دیگر

Sara Magliacane is an Assistant Professor at the University of Amsterdam , affiliated with the Amsterdam Machine Learning Lab (AMLab) and the Informatics Institute . She also holds a Research Scientist position at the MIT-IBM Watson AI Lab and has been an ELLIS Scholar since 2022. Education PhD in Artificial Intelligence (2017), VU Amsterdam MSc in Computer Engineering (2011), Politecnico di Milano BSc in Computer Engineering (2008), Università degli Studi di Trieste Research Focus : At the intersection of Causality and Machine Learning , her work addresses Causal Representation Learning from high-dimensional data (images, sequences) Causal Discovery in latent confounder scenarios Causality-inspired Reinforcement Learning for robustness and adaptability Neurosymbolic AI for theoretical guarantees Publication Trends : Her recent work explores Factored adaptation in non-stationary environments (NeurIPS 2022) Temporal causal identifiability (ICML 2022) Binary interaction-based causal discovery (UAI 2023) Safe exploration in visual RL (HSCC 2021) Structure learning lower bounds (NeurIPS 2020) Scientific Recognition : ELLIS Scholar (2022–present) Spotlight presentations at ICML 2022 and ICLR 2022 Advising & Collaborations : Currently supervising 6 PhD students at the University of Amsterdam and AUMC, with 12 alumni advisees. Collaborates with researchers at MIT-IBM Watson AI Lab, Simons Institute, and TUM.

• Soft Robotics
• Actuators
• Self-Healing Materials
• +۴ مورد دیگر

Julie Legrand is an Assistant Professor in the Mechanical Engineering department at Eindhoven University of Technology , affiliated with the Group Van de Molengraft. Her work focuses on soft robotics , self-healing materials , and medical robotics applications . She designs actuators and sensors for adaptive robotic systems, emphasizing resilience through self-healing mechanisms and embodied intelligence. She teaches courses including Control of a Flexible Robot System , Haptics and Soft Robotics , and Robot-Arm , reflecting her expertise in both theoretical and applied robotics. Her research spans actuator design , material science integration , and minimally invasive surgical robotics , with notable contributions to self-healing actuator validation and continuum robot end-effectors for surgical applications. Legrand collaborates internationally on topics like shape memory alloys and anisotropic materials , and her work has been featured in media for breakthroughs in self-healing polymer limitations in soft robots. She actively contributes to the Medical Robotics research theme at TU/e, advancing interdisciplinary approaches to robotic systems in healthcare.

Ralf Peetersمشاهده پروفایل

استاد

• Signal and Image Processing
• Systems and Control Theory
• Mathematical Modelling
• +۷ مورد دیگر

Ralf Peeters is a Full Professor in Mathematics of Knowledge Engineering at Maastricht University's Faculty of Science and Engineering , Department of Advanced Computing Sciences. He serves as Vice-Dean of Research and Director of the STEM Graduate School, while leading the university's team at the inter-university research school DISC and co-chairing the Mathematics Centre Maastricht. Education: PhD in Mathematics (Free University, Amsterdam, 1994) Technical Mathematics (Delft University of Technology, 1988) Research Interests span applied mathematics, systems and control theory, signal/image processing, artificial intelligence, and biomedical engineering applications. His work bridges mathematical techniques with real-world challenges in healthcare and industrial systems. Recent Publications highlight advancements in deep learning for cardiac signal reconstruction, tensor-based signal decomposition, and recurrence plot analysis. These works integrate machine learning with clinical diagnostics, particularly in electrocardiographic imaging and arrhythmia characterization. Key Collaborations: Mathematics Centre Maastricht Dutch Mathematics Platform Dutch Institute of Systems and Control Leadership Roles: Vice-Dean of Research (FSE), Director of STEM Graduate School, Head of DISC-affiliated team, and Co-Chair of Mathematics Centre Maastricht. He has supervised over 25 PhD projects, emphasizing applied research across health and industrial domains.

Remco M. Dijkmanمشاهده پروفایل

استاد

• Business Process Management
• Data-Driven Process Optimization
• Supply Chain Management
• +۳ مورد دیگر

Remco M. Dijkman serves as Full Professor in Information Systems at Eindhoven University of Technology (TU/e), chairing the Information Systems group within the Industrial Engineering and Innovation Sciences school. He additionally holds a Full Professor position at EAISI High Tech Systems and acts as research director for high-tech supply chains at the European Supply Chain Forum—a network of over 50 multinational companies. His research centers on Business Process Management with emphasis on data-driven optimization of business processes. His academic background includes both PhD and Master's degrees in Computer Science from the University of Twente. Publications span Information Systems, Computers in Industry, and Transactions on Software Engineering and Methodology, with over 100 papers and service on the editorial board of Information Systems. He has held visiting positions at New York University, Hasso Plattner Institute, IBM Zurich Research Lab, Humboldt-University Berlin, and Queensland University of Technology. Dijkman's research interests focus on detecting, diagnosing, and predicting optimal execution scenarios in business processes, developing mathematical models for quantitative process analysis , and resource assignment optimization . These are primarily applied in transportation logistics and high-tech supply chains, where he investigates data-driven predictions for transport order assignment and supply chain planning. His work bridges artificial intelligence with practical business applications. Recent publications (2024-2025) reveal concentrated efforts in deep reinforcement learning for resource allocation, process pattern discovery, and software library development (GymPN, SimPN). Key trends include predictive process monitoring for healthcare applications, event data enrichment frameworks, and uncertainty handling in logistics planning—demonstrating strong interdisciplinary integration. Scientific recognition includes: Best Demo Award (2019) Best Reviewer Award (2016) Test of Time Award (2019) He has supervised 150 students, including Lotte Vugs who received the Dow Chemical Best OML Master Thesis Award in 2020. Grant leadership spans eight projects: NXTGEN Smart Industry (2023-2030), CollChain (2023-2029), CERTIF-AI (2020-2025), FENIX (2019-2023), and DynaPlex (2021-2024), focusing on digital twins, federated networks, and AI-driven supply chain solutions. Dijkman directs the Information Systems group at TU/e and leads the European Supply Chain Forum's high-tech supply chain research. His work integrates with semiconductor manufacturing and transportation logistics through collaborations with industry partners, while his 2023 invited talks at Technical University of Munich and Humboldt University Berlin highlight his international engagement.

• Smart Energy Systems
• Multi-agent Systems
• Computational Intelligence
• +۷ مورد دیگر

Han La Poutré is a Professor at Delft University of Technology and a Senior Researcher/Manager at CWI's Intelligent and Autonomous Systems group. His research focuses on multi-agent systems, computational intelligence, and optimization techniques for Smart Energy Systems (SES), integrating electrical engineering and economics frameworks. M.Sc. in Mathematics (1986, TU Eindhoven) Ph.D. in Computer Science (1991, Utrecht University) His work spans game theory , reinforcement learning , and market mechanism design for energy grid optimization, addressing challenges in congestion management, demand-side bidding, and AI-driven media sector innovation. Recent projects include AI, Media & Democracy Lab and RESCUE (Cyber-Physical Energy Security). Key trends in his 2020-2025 publications include: Hybrid fairness/welfare frameworks for grid congestion Double-sided auction mechanisms for heat/power markets Deep reinforcement learning in discrete action spaces Game-theoretical cybersecurity modeling Multi-temporal demand response coordination Scientific Awards 2015: Best paper award at GECCO-2015 He has led NWO-funded projects like Computational Capacity Planning in Electricity Networks and served as Vice President of ERCIM. Current affiliations include ACM Transactions on Intelligent Systems editorial board.