جست‌وجوی استاد و پژوهشگر

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

Judith O.E.H. van Laar is an Associate Professor in the Department of Signal Processing Systems at Eindhoven University of Technology (TU/e), and a practicing gynecologist at the Máxima Medical Center. Her research focuses on advancing biomedical technologies to enhance maternal and fetal health monitoring, particularly in detecting congenital defects and labor complications. Education: Master's in Medicine (Radboud University Nijmegen, 2004) PhD in Fetal Autonomic Response During Pregnancy (TU/e, 2012) Specialization in Gynecology (Máxima Medical Center, 2014) Research Interests: Non-invasive fetal monitoring systems Machine learning for cardiotocography analysis Uterine contraction dynamics Artificial intelligence in labor management Technological solutions for preterm birth prediction Key Projects: MEDEIA (2022–2027): Medical Engineering Innovations in maternal-fetal healthcare HASTA (2022–2028): Healthy Ageing through early pregnancy interventions Lab Affiliations: Signal Processing Systems group, Eindhoven MedTech Innovation Center.

• Medical Statistics
• Computer Science
• Artificial Intelligence
• +۶ مورد دیگر

Gerton Lunter is a Professor of Medical Statistics at the University of Groningen's Faculty of Medical Sciences, affiliated with the Department of Epidemiology. He holds a visiting professorship in Computational Biology and Artificial Intelligence at the Weatherall Institute of Molecular Medicine, University of Oxford. His expertise spans Bayesian statistics, statistical genetics, functional genomics, and machine learning applied to evolutionary biology and healthcare challenges. Education: PhD in Mathematics (Dynamical Systems), University of Groningen (1999) MSc Mathematics and Theoretical Physics (cum laude), University of Groningen (1994) Research Focus: Integrates computational methods with medical and genetic data to address complex biological problems. Key areas include: Statistical modeling of genetic variation Machine learning for healthcare diagnostics Evolutionary dynamics of genomic mutations Multi-morbidity and lifestyle epidemiology Recent Work Trends: Publications emphasize interdisciplinary approaches, combining genomic data analysis with clinical applications (e.g., cancer immunotherapy prediction, maternal hypertension patterns, and rare disease diagnostics). His work bridges computational innovation with real-world medical challenges. Affiliations & Roles: Current roles include group leadership at Genomics plc and former positions as a postdoctoral researcher at University of Oxford's Department of Statistics and Philips Research Laboratories. He has contributed to initiatives like the AIHACKCOVID challenge. Team & Infrastructure: Leads research groups focusing on computational biology and statistical learning, collaborating across disciplines to leverage large-scale genomic datasets.

Chris Snijdersمشاهده پروفایل

استاد

• Sociology of Technology
• Sociology of Innovation
• Expertise and Decision-Making
• +۱۰ مورد دیگر

Chris Snijders is a Full Professor at Eindhoven University of Technology, holding the Chair of Sociology of Technology and Innovation. He leads the Human-Technology Interaction group and serves as Research Director of the School of Innovation Sciences, with affiliations to the Eindhoven AI Systems Institute (EAISI) and the Jheronimus Academy of Data Science (JADS). His research bridges sociology, artificial intelligence, and behavioral decision-making, focusing on trust in AI, model-based prediction, digital learning dynamics, and social network influences. Education: MSc in Mathematics (Game Theory) from Utrecht University (1991), PhD in Sociology (1996). Research Themes: Trust in AI systems, self-regulated learning analytics, AI ethics in healthcare, human-AI collaboration in logistics, and behavioral economics under uncertainty. Snijders’ recent publications analyze AI adoption in healthcare and logistics, emphasizing trust dynamics over time, the role of explainability, and ecological validity in human-AI studies. Collaborative projects involve NWO-funded initiatives like SeReNity and INTERSECT. His work integrates formal modeling (e.g., game theory, Markov models) with empirical behavioral data to address real-world challenges in technology and education. Grants from the Netherlands Organization for Scientific Research (NWO), European Supply Chain Forum, and SIA RAAK program underpin his research. He contributes to educational programs such as Supercrunchers and Advanced Research Methods . Snijders’ methodological expertise spans clickstream analysis, longitudinal modeling, and experimental design in digital environments.

• Software Engineering
• Software Design Patterns
• Software Defect Prediction
• +۳ مورد دیگر

Sushant Pandey serves as an Assistant Professor in the Department of Software Engineering at the Faculty of Science and Engineering, University of Groningen, conducting cutting-edge research at the intersection of software engineering and automotive systems. His core research domains include: Design Pattern Detection using machine learning Software Defect Count Estimation techniques Data Leakage Detection in perception systems Large Language Model applications for code analysis Recent publications demonstrate a cohesive focus on empirical validation of novel methodologies, particularly applying computational experiments to software quality challenges in both traditional and automotive contexts with emphasis on practical tool development.

• Code Clones
• Software Development
• Deep Learning
• +۳ مورد دیگر

Dr. Muhammad Hammad is a postdoctoral researcher at Eindhoven University of Technology (TU/e), affiliated with the Department of Mathematics and Computer Science within the School of Software Engineering and Technology. He holds a PhD in Computer Science from TU/e (2022), focusing on visualization and learning applications of code clones. His research interests span code clone analysis, software development tools, deep learning, visualization techniques, and digital twins for smart systems. Hammad has contributed to innovative projects like Clone-Writer and Clone-Seeker, which enhance code development efficiency through clone utilization. His work addresses challenges in API security, predictive analytics for water management, and improving software maintenance practices. Research contributions include systematic reviews on harmful API use repair techniques, digital twin-based smart water management systems, and advancements in code clone detection and visualization. His interdisciplinary approach bridges software engineering with environmental applications, contributing to UN Sustainable Development Goals related to clean water and responsible consumption. Hammad has published widely in reputable journals like IEEE Access and Computer Science Review, with a focus on practical, industry-relevant solutions. Notable press coverage includes a hospital waste monitoring system collaboration. While no grants or formal advisees are highlighted in the provided data, his work emphasizes collaborative research and impactful technological solutions. His lab/team contributions include pioneering tools for code analysis and environmental resource management systems.

Thijs van Laarhovenمشاهده پروفایل

استادیار

• Cognitive Neuropsychology
• Autism
• Multisensory Processing
• +۳ مورد دیگر

Dr. Thijs van Laarhoven is an Assistant Professor at the Tilburg School of Social and Behavioral Sciences and the Department of Cognitive Neuropsychology at Tilburg University. His research focuses on understanding brain-behavior interactions in multisensory processing and social functioning across neurotypes, particularly in neurodivergent populations such as individuals with autism. He emphasizes neurodiversity and challenges neuronormative approaches to address socio-communicative challenges. Education: Master of Science in Medical Psychology, Tilburg University (2015) Bachelor of Science in Psychology and Health, Tilburg University (2013) Bachelor of Science in Industrial Design Engineering, Delft University of Technology (2010) Research Interests: His work explores how neurodivergent individuals (e.g., autism) perceive and interact with their environment, emphasizing multisensory integration deficits, predictive coding abnormalities, and sensory processing differences. Recent studies investigate Zoom fatigue in autism and synaesthesia’s role in autistic symptomatology. Grants & Projects: Involved in projects like “The Art of Perception” , examining emotional dynamics during art experiences using wearable technology. Collaborates on studies linking sensory sensitivity and autistic traits. Labs/Teams: Affiliated with the Academic Collaborative Center for Digital Health & Mental Wellbeing and the Cognitive Neuropsychology research group.

Jeroen Stekelenburgمشاهده پروفایل

استادیار

• Audiovisual Interaction
• Cognitive Neuropsychology
• Autism Spectrum Disorder
• +۳ مورد دیگر

Dr. Jeroen Stekelenburg is an Assistant Professor at Tilburg University's Department of Cognitive Neuropsychology within the Tilburg School of Social and Behavioral Sciences. His research focuses on electrophysiological mechanisms underlying auditory and multisensory processing, with a particular emphasis on autism spectrum disorder (ASD), predictive coding, and speech perception. He utilizes EEG and ERP techniques to investigate how sensory predictions influence perception. Recent work includes studies on selective speech adaptation, predictive coding deficits in ASD, and the role of visual anticipation in auditory processing. He contributes to collaborative projects like the "Charting the Normative EEG in Healthy Aging Population" as a co-investigator. His teaching includes courses such as Social Neuroscience and Experimental Psychology. Key collaborations involve researchers like Jean Vroomen and Mart Eussen, and his work has been presented at conferences like the International Society for Autism Research (INSAR).

Sanne Geeraertsمشاهده پروفایل

استادیار

• Parenting
• Children and Family
• Self-Control
• +۷ مورد دیگر

Sanne Geeraerts is an Assistant Professor in the Youth and Family department at Utrecht University, affiliated with the Social and Behavioral Sciences faculty. Her work focuses on understanding how parenting behaviors are transmitted across generations, particularly examining intergenerational dynamics and their effects on child development. She holds a doctoral degree from Utrecht University and has conducted extensive research on self-regulation in infants and preschoolers, parental interactions, and the role of household chaos and maternal sensitivity in behavioral outcomes. Her expertise includes Parenting, Children and Family dynamics, and Self-Control mechanisms, with a specific emphasis on the Dynamics of Youth (DoY). Dr. Geeraerts has contributed to theoretical frameworks for self-regulation studies and has evaluated methodologies for longitudinal investigations. She has been involved in teaching and mentoring within the Orthopedagogiek Master's program and courses like 'Socialization of Children and Adolescents.' Key awards include the CAS honorable mention (2022), Fulbright Scholarship (2017), and recognition for her Master's thesis (2014). Her research activities span peer reviewing for journals such as 'Self and Identity' and 'Journal of Applied Developmental Psychology,' and she has presented at international conferences including EARA and SRCD. She has also served as a visiting researcher at institutions like Pennsylvania State University.

Laetitia Mulderمشاهده پروفایل

دانشیار

• Moral Behavior
• Sanctions and Norms
• Organizational Behavior
• +۳ مورد دیگر

Dr. Laetitia Mulder is an Associate Professor in the Department of Human Resource Management & Organizational Behavior (HRM & OB) at the University of Groningen's Faculty of Economics and Business. She holds a PhD from Leiden University (2004) and has been at the University of Groningen since 2009. Her research focuses on (im)moral behavior, sanctions, social norms, and the moralization of health topics. She has published extensively in journals like the Journal of Economic Psychology and British Journal of Social Psychology . Education: Bachelor's in Social Psychology, Free University Amsterdam (1999) PhD: "Throwing Light on the Dark Side of Sanctions" (Leiden University, 2004) Research interests include: Ethics in organizations Sanction systems and their societal impacts Moralization of health behaviors (e.g., vaccination mandates) Norm erosion and bystander effects Recent work explores how laws and mandates influence social norm enforcement, and the role of moral appeals in healthcare settings. She has engaged in media discussions about workplace ethics and obesity stigma. Labs/Teams: Active in the HRM & OB research group, collaborating on projects about organizational behavior and ethical leadership.

• Energy-Efficient Computing
• High-Performance Computing
• Compiler Techniques
• +۴ مورد دیگر

Kanishkan Vadivel is a Researcher in the Electronic Systems group at Eindhoven University of Technology (TU/e), specializing in energy-efficient hardware architectures and compiler-based code-generation techniques. He holds a Master’s degree in Embedded Systems from TU/e (2017) and a Bachelor’s from Coimbatore Institute of Technology (India). Prior to academia, he worked in embedded systems at Tata Engineering and Arm Ltd. Research Interests : His work focuses on computation-in-memory architectures using resistive devices, optimal code generation for CGRA (Coarse-Grained Reconfigurable Architecture), and high-performance computing. Key projects include the MNEMOSENE initiative and development of the CIM-SIM simulator for computation-in-memory systems. Awards : HiPEAC collaboration grant (2019) Advising & Grants : His research is supported by grants focused on neuromorphic processors and edge-AI hardware. He collaborates on projects like NEUROKIT2E for embedded deep learning systems. Labs/Teams : Active member of TU/e’s Electronic Systems Center and Efficient Stream Processing Lab, contributing to neuromorphic and energy-efficient computing initiatives.

G. Gousiosمشاهده پروفایل

استادیار

• Software Engineering
• Empirical Software Engineering
• Software Analytics
• +۷ مورد دیگر

G. Gousios is an Assistant Professor in the Department of Software Technology at the Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology. He previously held an assistant professor position at Radboud Universiteit Nijmegen from January 2015 to July 2016. His research focuses on empirical software engineering, software analytics, and big data applications in software development. His research interests include: Empirical analysis of software development practices Software analytics and data-driven development Agile methodologies and sprint planning automation Code review and pull request dynamics Dependency and build system analysis Predictive modeling for project delays The recent publications of G. Gousios reflect a strong trend in applying data science and machine learning techniques to real-world software engineering problems. His work spans automated sprint planning, call graph generation, delay prediction using Bayesian models, and empirical studies on pull request decisions. These contributions highlight a focus on improving software development efficiency, reliability, and maintainability through empirical and analytical methods. His research bridges the gap between theoretical models and practical tooling in software engineering. Scientific awards received: ASE 2024 ACM SIGSOFT Distinguished Paper Award He has supervised four academic works, indicating active involvement in student mentoring and research guidance. Although specific grant details are not mentioned, his consistent publication record in top venues and dataset creation suggest successful engagement in research funding and collaborative projects. He collaborates extensively with prominent researchers such as Arie van Deursen and Emad Shihab. G. Gousios contributes to the software engineering research community through dataset sharing, such as the 'Catcher' dataset for API misuse detection, promoting reproducibility and open science. His work is integrated into both academic and industrial software development contexts, emphasizing practical impact.

Kevin H.A. Verhaeghمشاهده پروفایل

استادیار

• Nuclear Fusion
• Plasma Physics
• Tokamak Device Engineering
• +۴ مورد دیگر

Kevin H.A. Verhaegh is an Assistant Professor at Eindhoven University of Technology (TU/e) in the Science and Technology of Nuclear Fusion group. His research focuses on solving critical power exhaust challenges in tokamak fusion reactors through experimental and theoretical investigations of divertor configurations and plasma-neutral interactions. Academic Affiliation: Eindhoven University of Technology (2024–present) Prior Affiliation: UK Atomic Energy Authority (UKAEA) (2019–2024) Research Interests : Managing extreme heat loads in plasma edge regions Investigating plasma-molecule and plasma-neutral interactions Developing advanced diagnostics for fusion reactors Pioneering Alternative Divertor Configurations (ADCs) Optimizing solutions for compact fusion reactor designs Scientific Contributions : First 2D electron density measurements in MAST-U divertors Key experiments on Super-X divertor at MAST Upgrade Improved predictive models for power exhaust management Scientific Awards : Institute of Physics (IoP) PhD Prize (2019) European Physical Society (EPS) PhD Research Award (2020) EUROfusion Researcher Grant (2020) ITER Expert Group Membership (2022) Collaborative Impact : International collaborations with MAST-U (UK), TCV (Switzerland) Leadership in EuroFusion research on Alternative Divertor Configurations Advancing real-time feedback control systems for fusion reactors

• Neuromorphic Engineering
• Edge AI
• Computer Architecture
• +۴ مورد دیگر

Dr. A. Yousefzadeh is an Assistant Professor in Edge AI at the University of Twente (joined February 2024), affiliated with the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) within the Department of Computer Architecture Design and Test for Embedded Systems. He holds a Ph.D. in Neuromorphic Engineering from IMSE (Instituto de Microelectrónica de Sevilla), where his thesis focused on bio-inspired vision processing. His research specializes in neuromorphic computing systems, with emphasis on: Designing ultra-low-power AI processors and event-based vision systems Developing hardware accelerators for spiking neural networks (SNNs) Edge AI deployment for sensor-based applications Hardware-software co-design for energy-efficient computing His publication trends (2015-2025) reveal core foci on neuromorphic processor architectures (e.g., SENECA, NeuronFlow), event-based vision processing, hardware-aware neural network optimization, and 3D integration techniques. Recent work explores activation sparsification in transformers and hybrid analog-digital neuromorphic systems. Prior to academia, he contributed to industry neuromorphic projects: Architected the NeuronFlow processor at GrAI Matter Labs (acquired by Snap) Led SENECA processor development at imec's Hardware Efficient AI group He currently leads research on next-generation edge AI processors at UT's Embedded Systems lab.

Lina Ochoa Venegasمشاهده پروفایل

استادیار

• Software Engineering
• Cloud Computing
• Software Architecture
• +۳ مورد دیگر

Lina Ochoa Venegas is an Assistant Professor in the Department of Software Engineering and Technology at Eindhoven University of Technology (TU/e), affiliated with the EAISI High Tech Systems initiative. Her research focuses on software architecture, API design, software evolution, and dependency analysis, with a particular emphasis on improving software quality, maintainability, and developer productivity. She holds a Ph.D. in Computer Science and has taught courses such as Programming, Programming for Applied Physics, and Software Evolution. Her work bridges theoretical advancements and practical applications in software engineering, including studies on breaking changes, architecture recovery, and educational technology for young learners. Her research interests span software architecture communication, API security, multi-lingual system dependencies, and the impact of breaking changes in open-source ecosystems. Notable contributions include the BreakBot tool for analyzing breaking changes and systematic reviews on harmful API uses and repair techniques. Awards: Best Teacher Award (Bachelor Data Science 2024), VERSEN PhD Award (2024). Grants & Projects: Supervision of 12 academic works and contributions to datasets on software evolution challenges. Labs & Teams: Active in TU/e’s EAISI initiative, collaborating on high-tech systems research.

Pavol Jancuraمشاهده پروفایل

استادیار

• Reinforcement Learning
• Video Coding
• Computer Vision
• +۲ مورد دیگر

Pavol Jancura is an Assistant Professor in Electrical Engineering at Eindhoven University of Technology, affiliated with the Mobile Perception Systems Lab. His research spans reinforcement learning, video coding architectures, and edge AI systems. Current projects include 5G-MOBIX for connected automated mobility and NXP Smart Mobility. Recent publications focus on multi-agent reinforcement learning coordination, efficient object detection using sensor fusion, and quantization-aware neural networks for edge devices. His work integrates computer vision, multi-agent systems, and optimization for autonomous systems.