جست‌وجوی استاد و پژوهشگر

• Image Sensor Engineering
• CMOS Image Sensors
• Sensor Design
• +۳ مورد دیگر

Albert Theuwissen is a Professor in the Faculty of Electrical Engineering, Mathematics and Computer Science, specializing in the Electronic Instrumentation department. His career spans decades of pioneering work in image sensor technology, with over 120 research publications and 27 recorded academic activities including keynote speeches at major international conferences. His research dominates Image Sensor Engineering, focusing on CMOS Image Sensors, Sensor Design, Analog-to-Digital Converters, and Thermal Sensors. He has developed breakthrough techniques for dark current compensation using in-pixel temperature sensors, linearity enhancement in column-parallel SAR ADCs, and global shutter characterization. His work bridges semiconductor physics and circuit design to solve critical challenges in high-speed imaging, dynamic range limitations, and thermal management. Analysis of his 2022-2024 publications reveals a concentrated effort on practical circuit innovations for CMOS image sensors, particularly calibration methods for column-parallel ADCs and thermal compensation systems. These contributions address fundamental bottlenecks in sensor performance, reflecting a strategic shift toward system-level optimization of imaging pipelines for scientific and industrial applications. No specific scientific awards were documented in the source material, though his status as a frequent invited speaker and editorial contributor indicates significant peer recognition within the sensor community. Professor Theuwissen has supervised 11 graduate students according to institutional records. His sustained research output and conference engagements suggest consistent grant funding, though specific awards aren't detailed in the available information. He operates within the Electronic Instrumentation research group, which provides the collaborative framework for his sensor development work. This group specializes in measurement systems and electronic instrumentation, directly enabling his innovations in image sensor architecture and performance characterization.

• Radar Engineering
• Antennas
• mm-Wave Technology
• +۶ مورد دیگر

Alexander Yarovoy is a Full Professor at the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology (TU Delft), specializing in Radar Systems, Antenna Design, and mm-Wave Technology. His research bridges theoretical and applied domains, with a focus on automotive radar, weather radar, and machine learning integration in radar signal processing. Active in radar, antennas, and microwave engineering Key contributions to automotive radar and human activity recognition Collaborates on datasets like RaDelft for autonomous driving Recent work explores OTFS radar for communication integration, polarimetric calibration, and high-resolution imaging algorithms. His research often addresses challenges in real-world applications, such as urban meteorology and vehicular safety. In 2023, he received the outstanding paper award at IEEE MetroAeroSpace for radar waveform coexistence studies. He participates in conferences and editorial activities, advancing radar metrology and phased array technologies.

• Photovoltaics
• Solar Cell Engineering
• Thin Film Technology
• +۵ مورد دیگر

Prof. Miro Zeman serves as a Professor in the Photovoltaic Materials and Devices research group within the Department of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology. With an extensive publication record of 559 research outputs including articles, conference contributions, patents, and datasets, he maintains active leadership in photovoltaic research. His research spans photovoltaic materials science , solar cell engineering , and renewable energy systems . Specializing in crystalline silicon solar cells, thin-film technology, and tandem cell configurations, his work addresses fundamental semiconductor physics alongside practical implementation challenges. Recent research focuses on impedance spectroscopy of PN junctions, defect analysis in transition metal oxides, and integration of power electronics with photovoltaic modules. Zeman's publication portfolio reveals consistent contributions to high-impact journals including Solar Energy Materials and Solar Cells , ACS Applied Energy Materials , and EPJ Photovoltaics . The research demonstrates strong interdisciplinary connections between semiconductor physics, materials engineering, and energy system integration, with particular emphasis on improving efficiency metrics and practical implementation of photovoltaic technologies across diverse environmental conditions. His research group has produced 37 supervised academic works and maintains active collaboration with industry and academic partners globally, as evidenced by the extensive network visualizations in his profile. The Photovoltaic Materials and Devices laboratory at TU Delft serves as the primary research environment for his team's investigations into next-generation solar technologies.

Alain Hecqمشاهده پروفایل

استاد

• Econometrics
• Time Series Analysis
• Financial Economics
• +۵ مورد دیگر

Alain Hecq is a Full Professor in the department of QE Econometrics at the School of Business and Economics, Maastricht University. His research focuses on econometric methodologies, particularly in time series analysis, noncausal models, and financial econometrics. He has contributed significantly to the understanding of volatility dynamics, cryptocurrency markets, and inflation targeting regimes. His work often addresses policy-relevant questions in macroeconomics and financial markets. Key research interests include mixed causal-noncausal autoregressive models, volatility modeling with MARMA-GARCH frameworks, and the application of these techniques to real-world phenomena such as oil price bubbles and cryptocurrency volatility. He has also explored the credibility of central banking policies during crises, such as the Brazilian inflation-targeting regime during the pandemic. His recent work emphasizes methodological advancements in high-dimensional time series analysis, including spectral estimation, hierarchical regularizers for mixed-frequency data, and reduced-rank matrix autoregressive models. These contributions reflect a blend of theoretical rigor and practical applicability in addressing complex economic and financial problems. While no formal awards are listed, his extensive publication record and focus on cutting-edge econometric techniques underscore his scholarly impact. Advising and grant activities are not detailed in the provided information, but his research demonstrates sustained engagement with both academic and policy-oriented audiences.

Victorita Dolean-Mainiمشاهده پروفایل

استاد

• Domain Decomposition Methods
• High Performance Computing
• Wave Propagation
• +۲ مورد دیگر

Victorita Dolean-Maini is Professor at University of Strathclyde and Visiting Professor at University of Strathclyde (2023-2026). She specializes in domain decomposition methods for PDEs, high-performance computing, and scientific machine learning. Her work develops fast algorithms for wave propagation problems and complex systems. She co-authored the reference work 'Domain Decomposition Methods' (SIAM 2015) and received the Bull-Joseph Fourier Prize for stroke simulation using HPC. Current research explores physics-informed neural networks with domain decomposition architectures and their application to medical digital twins. She serves on the editorial board of SIAM Journal on Scientific Computing and collaborates with industrial partners on scientific machine learning projects. Recent publications focus on neural network architectures combining domain decomposition with physics-informed learning, multilevel methods, and efficient coarse spaces for partial differential equations.

• Stochastic Processes
• Operations Research
• Data-Driven Decision Making
• +۴ مورد دیگر

Stella Kapodistria is an Associate Professor at Eindhoven University of Technology's Department of Mathematics and Computer Science, specializing in Stochastic Operations Research. She holds roles as EAISI High Tech Systems Associate Professor and editorial board member of journals like MCAP and PEIS. Her research focuses on data-driven decision-making, stochastic systems optimization, and maintenance policies, with applications in renewable energy, critical infrastructure, and cryptocurrency networks. She has secured grants including NWA-ORC, NWO Big Data, and TKI WoZ, and collaborates with industry partners in the Brainport region. Education: BSc (2003), MSc (2006, Hons.), and PhD (2009, summa cum laude) in Mathematics from the University of Athens. Postdoc at TU/e, followed by roles at Groningen University and TU/e's Stochastic Operations Research group. Teaching includes courses on Optimal Decision Making, Stochastic Performance Modeling, and Financial Mathematics. Research interests emphasize real-time learning, system resilience, and scalable algorithms for complex networks. Recent work addresses maintenance logistics, blockchain confirmation times, and wind energy prediction. She has published over 40 peer-reviewed articles and contributed to the 4TU Resilience Engineering Center. Awards include editorial leadership roles and grant funding. Advised 32 academic works and oversees industrial projects bridging theory and practice. Her labs and collaborations focus on adaptive systems, predictive analytics, and sustainable engineering solutions.

Sanne Muisمشاهده پروفایل

استادیار

• Climate Change
• Coastal Flood Risk Assessment
• Extreme Sea Levels
• +۶ مورد دیگر

Dr. Sanne Muis is an Assistant Professor at the Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, specializing in large-scale coastal flood risk assessments. She is also employed at Deltares in Delft, focusing on hydrodynamics and forecasting. Her research integrates climate change impacts, extreme sea levels, and hydrodynamic modeling to improve coastal resilience. She holds an MSc in Hydrology (2013) and a BSc in Earth Sciences and Economics (2009), both from VU Amsterdam. Research Interests: Her work emphasizes global coastal flood risk, extreme sea level projections, and dynamic modeling of compound flooding events. She employs methods like hydrodynamic modeling, extreme value analysis, and high-performance computing. Her current projects, such as the MOSAIC and CHANCE initiatives, aim to enhance flood risk frameworks and reduce uncertainties in sea level projections. Awards: She has received the 2019 Natural Hazards Section Award, 2nd place Lloyd's Science of Risk Prize (2021), and the Dutch Data Prize (2020). These recognize her contributions to natural hazards research and data-driven approaches in disaster risk reduction. Advising & Grants: She supervises PhD students exploring topics like tropical cyclones and climate change impacts. She leads the NWO-ENW CHANCE project and collaborates with TU Delft and the Dutch eScience Center on scalable flood risk frameworks. Labs/Teams: Her work involves interdisciplinary teams at IVM, Deltares, and international partners, focusing on global coastal risk assessment and climate adaptation strategies.

Alessandro Corbettaمشاهده پروفایل

استادیار

• Pedestrian Dynamics
• Machine Learning
• Fluid Turbulence
• +۳ 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.

• Cryogenics
• Heat Transfer
• Thermal Management
• +۴ مورد دیگر

Dr. S. Vanapalli is an Associate Professor in the Energy Materials Systems department at the University of Twente, specializing in cryogenics, heat transfer, and microcooling technologies. With over 920 Scopus citations and a 16 h-index, their research focuses on cryogenic systems, phase change materials, and thermal management solutions for biomedical and industrial applications. Research Highlights: Developed cryogen-free snap-freezing devices for tissue preservation Innovated gas-gap heat switches for thermal control systems Investigated Leidenfrost dynamics in liquid nitrogen and dry ice systems Optimized microchannel cooling architectures with isotropically etched pillars Explored additive manufacturing applications in cryogenic components Scientific Impact: Vanapalli's recent work spans cryogenic cold chain logistics, direct-contact freeze concentration, and sublimation temperature modeling. Their research bridges fundamental thermodynamic studies with practical applications in pharmaceuticals and precision oncology, with recent emphasis on eliminating sacrificial cryogens in biomedical freezing protocols.

Mitra Nasriمشاهده پروفایل

استادیار

• Real-Time Systems
• Embedded Systems
• Scheduling Algorithms
• +۴ مورد دیگر

Mitra Nasri is an Assistant Professor at the Eindhoven University of Technology, affiliated with the College of Engineering's Department of Electrical Engineering. She contributes to the High Tech Systems Center and EAISI Foundational, focusing on interconnected resource-aware intelligent systems. Research Focus: Real-Time Systems, Scheduling Algorithms, Embedded Systems, Fault-Tolerant Computing, and Cyber-Physical Systems. Key Contributions: Development of scheduling frameworks for multi-rate task chains, response-time analysis techniques, and containerization strategies for real-time distributed applications. Her recent work includes advancements in weakly-hard timing constraints, parallel global scheduling, and cloud integration for embedded systems. She actively collaborates on projects like SAM-FMS and COMP4DRONES. Scientific Awards: Best Paper Award - RTAS 2022 Best Paper Award - RTNS 2016 Outstanding Paper Awards at RTAS 2017, 2022 and RTSS 2020 She teaches courses in Real-Time Systems, Operating Systems, and Automotive Software, and participates in organizing conferences like Embedded Systems Week and CompSys.

• Data Science
• Veterinary Epidemiology
• Precision Livestock Farming
• +۷ مورد دیگر

Dr. Miel Hostens is an Assistant Professor in Farm Animal Health at Utrecht University's Faculty of Veterinary Medicine, with additional affiliation at Ghent University. His work bridges dairy science and data science, focusing on precision livestock farming to monitor sustainable food production systems from a global perspective. He holds a 0.9 FTE position at Utrecht University and a 0.1 FTE at Ghent University, coordinating courses on data-driven precision agriculture at both institutions. Hostens received his MSc in Veterinary Medicine from Ghent University in 2006 and completed his PhD in 2012 on 'Health and Fertility Challenges in High Yielding Dairy Cows during the Transition Period and the Use of Dietary Fatty Acids as an Optimization Strategy.' His academic journey includes extensive training in epidemiology, statistics, and data science, with over 15 post-graduate educations completed. He has attended more than 50 national and international trainings between 2007 and 2020, demonstrating deep commitment to continuous learning in his interdisciplinary field. His research interests focus on applying data science methodologies to veterinary medicine, particularly in dairy cow health monitoring. He specializes in precision livestock farming, big data analytics, and herd health management. His work integrates sensor technology, machine learning, and traditional veterinary approaches to develop predictive models for health monitoring. He has established a new research focus area at the intersection of dairy and data science, creating innovative methodologies for monitoring sustainable food production systems. Analysis of his recent publications reveals a strong trend toward applying artificial intelligence and machine learning to dairy farming challenges. His work spans predictive modeling for transition period success, hypocalcaemia prediction, behavioral pattern analysis, and milk yield monitoring. The research increasingly integrates multi-source data streams including sensor data, milk composition, and behavioral observations to develop comprehensive health monitoring systems for dairy cows. Microsoft Research Grant (€150,000) Microsoft Research Grant (€25,000) Ambassador of the Faculty of Veterinary Medicine within Utrecht University's Applied Data Science focus area Member of the Dairy Cattle Milk Recording Working Group of the International Committee for Animal Recording Hostens has guided over 70 MSc students in Veterinary Medicine and currently supervises multiple PhD candidates. His research is supported by significant funding including a €9,000,000 European gpluse.eu consortium (FP7) project, a €1,300,000 VLAIO project, and a ZonMW research grant. He leads the 'Sense of Sensors' research-industry consortium focused on prediction of health indicators using common sensor technology in the Netherlands. His work has practical applications with industry partners including Delaval.com and DairyDataWarehouse.com. Hostens established a research group that has grown through attracting MSc and PhD students, resulting in the completion of Jenne de Koster's PhD in 2016 and supervision of five ongoing PhD projects. His team frequently expands with visiting researchers, enhancing international collaboration. He serves as an advisor for 'De Tolakker' research farm, supporting Utrecht University's investment to transform it into a research innovation center for circular agriculture. His work connects with the Utrecht Platform for Applied Data Science and the Future Food Utrecht initiative.

• Wind Energy
• Wind Turbine Engineering
• Condition Monitoring
• +۳ مورد دیگر

Simon J. Watson is a Professor in the Faculty of Aerospace Engineering at Delft University of Technology, specializing in Wind Energy through the TU Delft Wind Energy Institute (DUWIND). His work focuses on advancing wind turbine technology, wind farm optimization, and renewable energy integration within the university's aerospace framework. His research spans wind turbine engineering, condition monitoring systems, atmospheric effects on energy production, and wind farm design. Key investigations include damage detection in turbine components (blades, drivetrains), simulation of atmospheric gravity waves for improved energy forecasting, and hybrid wind-storage systems for grid stability. Recent work emphasizes machine learning applications for predictive maintenance and high-fidelity modeling of boundary layer conditions. Professor Watson's 2025 publications reveal a strong trend toward AI-driven condition monitoring and refined atmospheric simulations, with consistent focus on operational reliability and damage detection across wind energy systems. His work bridges computational fluid dynamics, structural health monitoring, and energy storage integration. He actively supervises students and leads the €4.2M MERIDIONAL project (2022-2026) on multiscale wind farm modeling, developing advanced toolchains for performance assessment and load prediction. This EU-funded initiative involves collaboration with Siemens Gamesa, Vestas, and ENEL. As a core member of DUWIND, he co-develops industry partnerships and experimental facilities including wind tunnel testing and field monitoring systems for offshore wind farms. His team maintains close ties with the Netherlands Wind Energy Association and European Wind Energy Technology Platform.

• Nuclear materials chemistry
• Experimental thermodynamics
• Computational thermodynamics
• +۹ مورد دیگر

Dr. Anna Louise Smith is an Associate Professor in the Reactor Physics and Nuclear Materials section at the Radiation Science and Technology department of Delft University of Technology (TU Delft). She is a physical chemist and materials scientist specializing in nuclear materials, with expertise in both experimental and computational approaches to studying advanced nuclear fuel systems. Dr. Smith earned her "diplôme d'ingénieur" from Chimie ParisTech in 2011, conferring a Master of Science in Chemistry and Chemical Engineering. She simultaneously completed an M.Phil. in Advanced Chemical Engineering from the University of Cambridge. She received her PhD in Materials Science and Metallurgy from Cambridge in 2015, after spending three years at the Joint Research Centre in Karlsruhe, Germany, where she developed expertise in nuclear fuel chemistry and chemical thermodynamics of actinide materials. Her research focuses on the chemistry of advanced nuclear fuels, including ceramics (oxides) and molten salts (fluorides and chlorides), with particular interest in structure-property relationships. She combines experimental and computational studies to investigate physico-chemical properties from atomic to macroscopic scales, with applications to next-generation nuclear reactor technologies emphasizing safety and sustainability. Her current interests include the chemistry of fission products in oxides and molten salts, interaction chemistry between fuel, coolant, and cladding materials, and corrosion issues at high temperatures in nuclear reactor environments. Analysis of Dr. Smith's recent publications (2022-2025) reveals a strong focus on thermodynamic properties of molten salt systems, particularly those relevant to nuclear fuel cycles. Her work spans fundamental studies of actinide chemistry in molten fluorides and chlorides, corrosion mechanisms between molten salts and structural materials, and the development of thermodynamic models for complex multi-component systems essential for molten salt reactor design. She has made significant contributions to understanding the behavior of thorium-based fuel systems and corrosion products in high-temperature environments. Dr. Smith has established a research team and laboratory at TU Delft specifically designed for handling uranium and thorium materials. Her work contributes to several major European projects focused on advanced nuclear technologies, including the MIMOSA and ENDURANCE projects that explore the safety and performance of molten salt reactors for deployment in the European Union. She collaborates extensively with researchers across Europe and has published over 50 research outputs including articles, book chapters, and review papers.

Daan Crommelinمشاهده پروفایل

استاد

• Uncertainty Quantification
• Stochastic Modeling
• Data-Driven Modeling
• +۶ 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.

• Surface Physics
• Thin Film Technology
• Materials Science
• +۳ مورد دیگر

Andrey Yakshin is an Assistant Professor at the University of Twente’s MESA+ Institute, specializing in XUV Optics. His research focuses on surface physics, thin film interfaces, and materials characterization using techniques like Low-Energy Ion Scattering (LEIS). He has contributed to understanding W/Si and W/Al multilayer systems, interface dynamics, and nanoscale material behavior. Key research areas include interface characterization, nanotechnology, and optical properties of advanced materials. He collaborates internationally on projects involving XUV applications and thin film engineering. His work aligns with UN Sustainable Development Goals, particularly in advancing clean energy technologies through material innovation. Yakshin has supervised 10 research projects and actively participates in academic activities, including invited talks and international conferences.