جستجوی استاد راهنمای دانشگاهی

Bruna Goveia da Rocha is an Assistant Professor at the Department of Industrial Design, Eindhoven University of Technology. Her research focuses on advancing research-through-making practices, particularly through textile innovation and material experimentation from a more-than-human perspective. She explores how samples and sample-making can drive creative and technological breakthroughs in digital craftsmanship. Education: PhD in Industrial Design, TU/e (2023), thesis: 'Becoming Travelers: Reflecting on the Emerging Practices of Sample Making in Digital Craftsmanship' MSc in Industrial Design, TU/e (2015), thesis: 'Flow: In the path towards embodied guidance' BSc in Product Design, Universidade Positivo (2012) Research interests include: Material agency and serendipity in design Documentation strategies for digital craftsmanship Embodied interaction through textiles and smart materials Sample archives as dynamic knowledge repositories Her recent work investigates how 'diffractive re-interpretations' of material samples can uncover hidden potentials, as seen in projects like 'Making in the Dark' (2024). She also explores negotiations between designers and fabrication machines, such as in 'Entering the 3D printer' (2024). Lab affiliations include the 'Making With…' group at TU/e, focused on collaborative material exploration. Her work bridges traditional craftsmanship with digital fabrication techniques, emphasizing iterative and embodied design processes.

• Textile Innovation
• Digital Craftsmanship
• Research-Through-Making
• +۳ مورد دیگر

Nikolaos Paterakis is an Assistant Professor of Power System Optimization and Electricity Markets with the Electrical Energy Systems research group at the Department of Electrical Engineering, Eindhoven University of Technology (TU/e). He is the founder and principal investigator of the Electricity Markets & Power System Optimization Laboratory (EMPSOLab) established in 2019, and a member of the Cyber-Physical Systems Center Eindhoven (CPSe). His research focuses on applying optimization and machine learning techniques to power system and electricity market problems, particularly regarding renewable energy integration and smart grid technologies. Dr. Paterakis received his Dipl.Eng. from Aristotle University of Thessaloniki in 2013, followed by a PhD in Industrial Engineering and Management (cum laude) from the University of Beira Interior in 2015. After serving as a post-doctoral fellow at TU/e from 2015-2017 and working as a consultant for the Energy Market Regulatory Authority of Turkey, he was appointed Assistant Professor at TU/e in April 2017. His research spans power system optimization, electricity market design, renewable energy integration, and the application of machine learning techniques to grid management problems. Recent work emphasizes distributed energy resource integration, local electricity markets, congestion management in low-voltage grids, and real-time grid control using advanced optimization techniques. His publications demonstrate a clear trajectory toward increasingly sophisticated methods for managing grid constraints while enabling market participation of distributed energy resources. Dr. Paterakis has received several prestigious awards including IEEE SEGE'15, SEST 2019, and SEST 2020 Best Paper Awards, and recognition as a Best Reviewer for IEEE Transactions on Smart Grid (2015, 2017) and IEEE Transactions on Sustainable Energy (2016). He serves as Associate Editor for multiple journals including IET Renewable Power Generation, IEEE Systems Journal, IEEE Transactions on Intelligent Transportation Systems, and Elsevier's e-Prime. He leads multiple research projects including MEGAMIND (NWO-funded), P2P-TALES (NWO-funded), and the Electricity Markets Game series (TU/e BOOST!-program). His educational contributions include teaching courses on power system analysis and optimization, electricity markets modeling, and developing innovative educational tools for power systems education. In 2021, he was elevated to Senior Member of the IEEE Power & Energy Society.

• Power System Optimization
• Electricity Markets
• Renewable Energy Integration
• +۵ مورد دیگر

Daan Crommelin holds a part-time professorship in Numerical Analysis and Dynamical Systems at the KdV Institute for Mathematics, University of Amsterdam, and is a senior researcher at CWI Amsterdam's Scientific Computing group. He serves on CWI's management team and previously led its Scientific Computing group (2013–2021). His research focuses on stochastic modeling of multiscale systems, uncertainty quantification, and rare event analysis, with applications in climate science, renewable energy, and fluid dynamics. Crommelin combines methods from scientific computing, applied probability, and dynamical systems to address challenges in atmosphere-ocean-climate modeling. He has contributed to projects like the EU-funded VECMA initiative for exascale computing and collaborated on superparameterization techniques for climate models. His work also extends to epidemic modeling and computational chemistry. Crommelin earned his PhD in 2003 from Utrecht University, with a thesis co-supervised by KNMI, and holds an MSc in theoretical physics and an MA in philosophy from the University of Amsterdam.

• Uncertainty Quantification
• Stochastic Modeling
• Data-Driven Modeling
• +۶ مورد دیگر

Bahar Haghighat is a Tenure Track Assistant Professor in Robotics and Automation at the Faculty of Science and Engineering, University of Groningen. She leads the Distributed Autonomous Intelligent Systems (DAISY) Lab as Principal Investigator and contributes to academic governance as a Member of the Faculty Council. Her professional affiliations include the Royal Netherlands Institute of Engineers (KIVI), the Institute of Electrical and Electronics Engineers (IEEE), and editorial roles with Nature Portfolio Journal Robotics and Springer Nature Journal Autonomous Robots. Her educational background includes: PhD in Robotics, Control, and Intelligent Systems from the Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland (2018) Master's degree in Electrical Engineering/Digital Electronics from Sharif University of Technology (SUT), Tehran, Iran Bachelor's degree in Electrical Engineering/Physics (double major) from Sharif University of Technology (SUT), Tehran, Iran Dr. Haghighat's research focuses on building novel miniaturized robotic swarms and algorithmic frameworks for sensing, surveying, and inspection applications. Her work spans mechatronics, electronics, embedded systems, embedded artificial intelligence and machine learning, and distributed systems. She envisions developing surface, aquatic, and aerial miniaturized robot swarms and small-scale intelligent devices for basic research and commercial applications including inspection of complex structures, environmental monitoring, space exploration, and search-and-rescue operations. Her recent publications demonstrate a strong focus on swarm robotics, particularly using particle swarm optimization techniques for multi-robot coordination, surface inspection tasks, and spacecraft hull inspection. Her research shows an interdisciplinary approach combining mechatronic design with advanced algorithms for self-assembly and collective decision-making in resource-constrained robotic systems. Her notable scientific achievements include: EPFL's PhD research award of Gilbert Hausmann for the best PhD thesis in mechanical engineering, electricity, and physics (2019) EPFL distinction of excellence for a PhD thesis in Robotics, Control, and Intelligent Systems (2018) Swiss National Science Foundation Postdoc Mobility Fellowship (2019) Swiss National Science Foundation Early Postdoc Mobility Fellowship (2017) Third place in EPFL's "My Thesis in 180 Seconds" competition (2017) EECS Rising Star recognition (2021 at MIT and 2019 at UIUC) Dr. Haghighat has served as Program Co-Chair for The International Symposium on Distributed Autonomous Robotic Systems (DARS) and has held visiting scholar positions at MIT and Harvard University. Her research has received media attention for applications in Mars rover technology and drone swarms for defect detection. She leads the DAISY Lab, which focuses on distributed autonomous intelligent systems for various inspection and monitoring applications.

• Robotics
• Control Systems
• Artificial Intelligence
• +۱۱ مورد دیگر

Chiel van Heerwaarden is an Associate Professor of Meteorology at Wageningen University & Research. His work focuses on cloud dynamics, radiative transfer, and their impacts on climate processes. He leads projects on solar irradiance variability, convective cloud organization, and atmospheric modeling. He has received a NWO Vidi grant for his research on cloud shadows and radiative effects. His research integrates machine learning, large-eddy simulations, and observational data to advance understanding of boundary layer processes and climate feedbacks. Key research areas include: irradiance variability under broken clouds, 3D radiative effects in shallow cumulus clouds, and the interaction between clouds and land surface processes. He collaborates on field experiments like CloudRoots-Amazon22 and Fesstval, advancing multiscale modeling approaches. His team addresses challenges in kilometer-scale Earth system modeling and wildfire-atmosphere interactions. Recent achievements include developing efficient radiative transfer parameterizations and analyzing extreme weather phenomena like heatwaves linked to soil drought. He advises five PhD candidates on topics ranging from convective self-aggregation to forest fire plumes. His work bridges computational science and observational data to improve climate projections and atmospheric understanding. Grants: NWO Vidi Grant (2019) Labs/Teams: Meteorology and Air Quality Group at Wageningen University Key Projects: CloudRoots-Amazon22, Shedding Light on Cloud Shadows, Real-Weather Large Eddy Simulation

• Meteorology
• Climate Modeling
• Radiative Transfer
• +۷ مورد دیگر

Wouter van Toll is a Lecturer at the Academy for AI, Games & Media, specializing in crowd simulation and real-time systems. His research focuses on path planning, crowd behavior modeling, and fluid dynamics in agent-based simulations. He has contributed to advancing algorithms for microscopic crowd simulation and integrating techniques like Smoothed Particle Hydrodynamics (SPH) to handle extreme crowd densities. Key research interests include sketch-based interaction design for steering behaviors, navigation mesh optimization, and topological strategies for agent coordination. His work bridges computational methods with creative applications in game development and artificial intelligence. Received Best Paper Award Honorable Mention (2022) for his work on sketch-based steering behaviors in crowd simulation. Active collaborations in Europe and North America, particularly in crowd simulation software development. Publications span algorithmic advancements in crowd simulation, navigation systems, and interdisciplinary applications combining physics-based methods with agent-based models. Current research emphasizes real-time simulation efficiency and human-centered design tools for behavior specification.

• Crowd Simulation
• Path Planning
• Real-time Simulation Software
• +۳ مورد دیگر

Dr. V. Menkovski serves as an Associate Professor in Data Mining at Eindhoven University of Technology's Department of Mathematics and Computer Science. He also holds associate professor positions with EAISI Health and EAISI High Tech Systems, and is an ICMS Affiliated member. His work spans multiple domains of artificial intelligence and computational physics, with significant contributions to fusion energy research. Mathematics and Computer Science, Data Mining (Primary Appointment) EAISI Health (Associate Professor) EAISI High Tech Systems (Associate Professor) ICMS (Affiliated Member) Menkovski's research focuses on Graph Neural Networks, Machine Learning, Deep Learning, and their applications in diverse fields from plasma physics to metamaterials. His work demonstrates strong interdisciplinary connections, particularly between computer science and fusion energy research. He has developed novel approaches for crowd simulation, tokamak plasma monitoring, and metamaterials homogenization using advanced neural architectures. His fingerprint reveals expertise in Quality-of-Experience, Autoencoders, Neural Networks, Annotation, Graph Neural Networks, Video Streaming, Adversarial Machine Learning, and Anomaly Detection. Analysis of his recent publications (2023-2025) shows a clear trend toward applying Graph Neural Networks to complex physical systems, particularly in fusion energy research and materials science. His work increasingly integrates symmetry principles with neural architectures, as seen in his research on equivariant networks for metamaterials and symmetry-informed networks for zeolite analysis. There's also significant focus on practical applications in fake news detection, anomaly detection, and plasma state monitoring. Best Paper Award ICPM 2021 (with Sommers and Fahland) Best Paper Award of LoG 2022 (with multiple co-authors including Huang, Chen, Fang, Zhao, Yin, Pei, Mocanu, Wang, Pechenizkiy, and Liu) Menkovski teaches several advanced courses including Deep Learning, Advanced Topics in Artificial Intelligence, and Sociophysics 2, which runs through August 2025. His supervised work portfolio includes 79 projects, indicating substantial mentorship activity. He has received significant media attention for his research, including coverage by 11 news outlets, blog posts, and mentions on social media platforms. His work on 'Supervised Learning of Process Discovery Techniques Using Graph Neural Networks' was particularly noted in media coverage. His research involves collaboration with multiple institutions and teams, particularly in fusion energy research (Eurofusion Tokamak Exploitation Team, ASDEX-Upgrade team, EUROfusion MST1 Team). He works closely with researchers across disciplines, including physicists working on tokamak plasma and materials scientists studying metamaterials and zeolites.

• Graph Neural Networks
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Lonneke Roelofs is a Researcher in the Department of Physical Geography at Utrecht University, within the Faculty of Geosciences. She specializes in studying debris flows and mass movements on Earth and Mars, focusing on CO2-driven processes under Martian conditions. Her research bridges planetary geology and geomorphology, with a focus on landscape evolution under varying planetary conditions. Education: MSc Earth Surface and Water (Utrecht University, 2017–2019) – Cum Laude BSc Earth Sciences (Utrecht University, 2014–2017) – Cum Laude Geosciences Honors College (Utrecht University, 2014–2017) Research Interests: Her work explores debris flow dynamics across planetary bodies, particularly comparing water-driven flows on Earth with CO2-driven flows on Mars. Key areas include: CO2 sublimation mechanisms in Martian gullies Bedrock erosion by debris flows under varied conditions Remote sensing of planetary surfaces Geo-computation for planetary geomorphology Key Contributions: Her experiments in facilities like the Aarhus Mars Simulation Wind Tunnel and the Open University’s Mars Chamber have advanced understanding of Martian surface processes. Recent work highlights CO2’s role in shaping Martian landscapes and informing Earth hazard prediction under climate change. Awards and Recognition: Escher Award (2019) – Best Master’s Thesis in Earth Sciences, Netherlands Teacher Talent Award (2020) – Top Emerging Lecturer, Faculty of Geosciences Member, Young Women of Geoscience (YWOG) Committee (2019–2023) – Chair since 2021 Teaching & Outreach: Previously served as a lecturer and teaching assistant for Earth Sciences courses. Developed the Summer School Junior program for primary school students, fostering early STEM engagement. Committed to improving diversity and inclusion in academia through initiatives like YWOG. Labs & Facilities: Active in experimental facilities simulating Martian environments and terrestrial fluvial-tidal systems. Collaborates with international teams on planetary geomorphology projects.

• Planetary Geomorphology
• Debris Flow Dynamics
• Martian Surface Processes
• +۴ مورد دیگر

Bram Droppers is a Researcher and ICT developer at the Department of Physical Geography, Utrecht University. His work focuses on modeling hydrological systems to address water scarcity, climate adaptation, and sustainable agriculture. He holds a PhD in Earth and Environmental Sciences from Wageningen University (2022), with prior degrees from the same institution (BSc 2014, MSc 2017). His research emphasizes large-scale hydrological models like PCR-GLOBWB and VIC, integrating them with agricultural models and applying deep learning techniques to improve accuracy and computational efficiency. His expertise spans Human Impact on Global Water Systems, Integrated Water Management, and Climate Modeling. Key themes include the interplay between socio-economic factors, climate change, and water resources. He has developed frameworks to assess water constraints on crop production and pioneered high-resolution global hydrological modeling approaches. Recent work explores deep-learning surrogates for process-based models to enhance scalability. Publications highlight analyses of water scarcity hotspots in Pakistan, California, and global basins, alongside model improvements for irrigation management and lake system dynamics. His research supports Sustainable Development Goals, particularly in balancing agricultural productivity with ecosystem protection. Current projects aim to refine global hydrological models for hyper-resolution applications and multi-model ensemble approaches. Bram’s career includes roles as ICT Manager and developer, ensuring technical infrastructure supports his research. He has secured grants through Wageningen’s WIMEK institute and collaborates internationally on initiatives like the ISIMIP Lake Sector. His work bridges computational innovation with environmental sustainability, addressing critical water challenges at planetary scales.

• Climate Change
• Water Resource Management
• Hydrological Modeling
• +۴ مورد دیگر

Alessandro Corbetta is an Assistant Professor in the Department of Applied Physics and Science Education at Eindhoven University of Technology (TU/e). He leads the 'AI for Traffic and Complex Flows' group, focusing on pedestrian dynamics, machine learning in fluid mechanics, and active flowing matter. His work integrates empirical data, statistical physics, and computational methods to model crowd behavior and optimize pedestrian environments. Education & Academic Background: MSc (cum laude) in Mathematical Engineering, Polytechnic University of Turin (2011) PhD in Applied Mathematics, TU/e (2016) PhD in Structural Engineering, Polytechnic University of Turin (2016) Research Interests: His research spans pedestrian dynamics, machine learning for fluid mechanics, turbulence modeling, and high-performance computing. Key areas include real-world crowd tracking, AI-driven crowd management, and statistical mechanics applied to big data analytics. Awards & Grants: 2021 Ig Nobel Prize in Physics for studying pedestrian collision avoidance 2018 VENI Grant (NWO) for 'Understanding and Controlling Human Crowd Flows' Teaching & Academic Contributions: Responsible for courses like 'Machine Learning in Science' and 'Machine Learning for Fluid Mechanics' Editor-in-Chief of Collective Dynamics Labs & Collaborations: Collaborates with municipalities, museums, and festivals to deploy real-time crowd management systems. Active in TU/e's Intelligent Lighting Institute and Fluids and Flows research groups.

• Pedestrian Dynamics
• Machine Learning
• Fluid Turbulence
• +۳ مورد دیگر

Caspar A.S. Pouw is a Research Fellow in the Department of Applied Physics and Science Education at Eindhoven University of Technology (TU/e). He holds a dual role as a Postdoc researcher and Data Scientist at ProRail. His work focuses on advancing human crowd flow monitoring, modeling, and nudging technologies, particularly within the HTCrowd project. Pouw’s research integrates fluid dynamics principles to analyze pedestrian behavior in crowded environments, aiming to enhance safety and efficiency in urban spaces. Educated at TU/e, he earned his Master’s in Applied Physics (specializing in Fluids and Flows) and a Bachelor’s in Combustion Science. He has taught courses on sociophysics, covering crowd dynamics analysis, modeling, and nudging strategies. His contributions align with UN Sustainable Development Goals related to safe cities and resilient infrastructure. Recent research emphasizes data-driven modeling of pedestrian dynamics, psychological influences on train boarding efficiency, and real-time monitoring systems. His work bridges physics, computer science, and urban planning, with applications in transportation and public safety. Collaborations include ProRail and interdisciplinary teams at TU/e. Pouw’s datasets and software tools, such as those for pedestrian trajectory analysis, are openly available. His media coverage highlights innovations in crowd management post-COVID-19. Future work involves expanding predictive models for crowd behavior and optimizing transport infrastructure design.

• Pedestrian Dynamics
• Crowd Flow Modeling
• Fluid Dynamics
• +۳ مورد دیگر

SG Oei is a Professor at Eindhoven University of Technology in the Department of Electrical Engineering, affiliated with the Eindhoven MedTech Innovation Center and Biomedical Diagnostics Lab. His research focuses on developing novel technologies for maternal-fetal health monitoring, particularly in obstetric care settings. Research interests center on: Advanced fetal monitoring techniques including electrohysterography and non-invasive fetal electrocardiography Biomedical signal processing applications in perinatal care Development of AI-driven diagnostic tools for fetal distress detection Simulation-based obstetric training systems for low-resource settings Publication analysis reveals consistent focus on improving fetal monitoring through: Novel signal processing algorithms for cardiotocography interpretation Validation of non-invasive electrophysiological monitoring techniques Application of speckle tracking echocardiography in fetal assessment AI-powered diagnostic systems for high-risk pregnancies Extensive student supervision includes 28 doctoral candidates since 2010, with dissertations covering fetal cardiac function monitoring, electrohysterography applications, and obstetric simulation training. Research is conducted through the Biomedical Diagnostics Lab and Center for Care & Cure Technology Eindhoven.

• Biomedical Engineering
• Fetal Monitoring
• Signal Processing
• +۴ مورد دیگر

Richard Loendersloot is an Associate Professor specializing in dynamics-based maintenance, focusing on structural integrity diagnostics and prognostics. His work integrates advanced sensors and data-driven methods for predictive maintenance in composite materials and infrastructure systems. Research interests include structural health monitoring (SHM), vibration analysis, and damage accumulation modeling. He has contributed to methodologies like Bayesian filtering for prognostics and non-collinear wave mixing for material aging assessment. Recent publications (2024-2025) emphasize sensor technology comparisons, ML-assisted frameworks for cyber-physical systems, and scaled bridge testing for vehicle-bridge interaction models. He collaborates widely, with activities including invited talks on smart maintenance and presentations at international conferences. Supervised 13 academic works, though student names are not listed. Labs/Teams: Part of dynamics-based maintenance research groups, contributing to datasets like motor current/vibration monitoring for e-motor-driven pumps.

• Structural Health Monitoring
• Composite Materials
• Vibration Analysis
• +۳ مورد دیگر

Felipe Martins is a Senior Lecturer-Researcher at Hanze University of Applied Sciences, active in both teaching and the Sensors and Smart Systems research group. He holds a PhD in Electrical and Electronic Engineering (2009) and has a background in industrial automation engineering (1999-2003). His work contributes to UN Sustainable Development Goals through educational robotics and automation. PhD: Modeling and Dynamic Compensation of Mobile Robots (Federal University of Espirito Santo) MSc: Control of Induction Generators via Fuzzy Logic BSc: Microcontroller-based Motor Control Research spans mobile robot control , educational robotics , autonomous navigation , and sensor networks . Recent publications focus on LiDAR data processing (2024), reinforcement learning for robotic applications (2022), and machine learning-driven localization (2023). He received Best Paper Awards at OL2A 2023 and WRE 2018. As a part-time teacher at University of Groningen (since 2022) and visiting researcher at CeDRI (2023), he bridges academia and industry. His editorial roles include guest editorships at Automation (MDPI) and Sensors journals. He co-organized RoboCup Junior events (2013-2021) and developed open-source robotics education tools like RoSoS A free simulator (2016).

• Mobile Robot Control
• Educational Robotics
• Robotics Competitions
• +۶ مورد دیگر

Moez Louati is an Adjunct Associate Professor in the Department of Civil and Environmental Engineering at The Hong Kong University of Science and Technology (HKUST). His expertise spans fluid mechanics of waves, pipeline hydraulics, and smart water infrastructure. He holds a PhD from HKUST, supported by scholarships and the SENG PhD Fellowship Award. His research focuses on pipeline defect detection, hydraulic transients, and smart urban water systems, with notable contributions including the discovery of Bragg resonance connections in pressurized pipes. He has led projects funded by RGC, ITF, and international collaborations, and co-founded Hyele Limited, a startup developing pipeline condition assessment technologies. Education: B.Eng. (Electro-Mechanical Engineering) and MPhil (Renewable Energy) from Tunisia’s National School of Engineering of Sfax (ENIS). Professional credentials include HKIE chartered engineer status and roles as Chair of IAHR’s Africa Division and Assistant Editor of the Journal of Hydraulic Research. Research Interests: Fluid mechanics of waves, computational fluid dynamics (CFD), machine learning for infrastructure optimization, and sustainable water management. Key projects include Smart Urban Water Supply Systems and high-frequency wave-based defect detection methods. Awards: Geneva Innovations Expo 2023/24 awards, Straub Award nomination, and HKUST’s Tech-Ship startup funding. Active in international organizations and advisory roles for Hong Kong’s WSD and DSD. Labs & Facilities: Designed three water lab facilities at HKUST and collaborated globally on projects like the FREJLEV Facility’s air-water stratified flow studies. Holds five patents in pipeline diagnostics and HPC applications.

• Fluid Mechanics of Waves
• Pipeline Hydraulics
• Water Loss Management
• +۵ مورد دیگر