جست‌وجوی استاد و پژوهشگر

Aswani Kumar Cherukuriمشاهده پروفایل

استاد

• Information Security
• Machine Learning
• Data Mining
• +۵ مورد دیگر

Dr. Aswani Kumar Cherukuri is a Professor of Information Technology at Vellore Institute of Technology (VIT), India, with research spanning information security, machine learning, and quantum computing. He serves as a Senior Member and distinguished speaker of the Association for Computing Machinery (ACM) and as Vice-Chair of the IEEE Taskforce on Educational Data Mining. Education: PhD in informational retrieval, data mining, and soft-computing techniques from Vellore Institute of Technology His research focuses on information security, machine learning, data mining, artificial intelligence, quantum computing, and security/privacy with significant contributions to neural networks, computer networks, and IoT. His work bridges theoretical foundations with practical applications in emerging technologies, emphasizing real-world implementation. Recent publications reveal strong trends in AI ethics (accuracy-bias trade-offs in text detection), autonomous systems (aerial vehicle surveillance), quantum computing (Qinterpreter platform development), and cybersecurity (terrorism prediction using BiGRU, internet anomaly detection via GANs). His editorial work on video conferencing security during the pandemic highlights practical societal impacts. Scientific Awards: Young Scientist Fellowship from Tamilnadu State Council for Science and Technology Inspiring Teacher Award from The Indian Express He has secured major research funding from India's Department of Science and Technology, Department of Atomic Energy, and Ministry of Human Resources Development. With over 150 refereed publications and editorial board memberships at international journals including PeerJ Computer Science, he actively shapes academic discourse through 1,375 editorial contributions.

Pan Wangمشاهده پروفایل

استادیار

• Generative AI
• Human-machine Co-creativity
• Brain Computer Interface
• +۳ مورد دیگر

Pan Wang is an Assistant Professor at the Faculty of Industrial Design Engineering (IDE) at TU Delft. Her research focuses on AI for design, particularly Human-machine (AI) co-creativity, integrating human-computer interaction, brain-computer interfaces, and AI-driven creative processes. She developed the 'symbiotic creativity' framework, an interactive system enabling human-AI collaboration throughout creative workflows. Education: PhD from the Dyson School of Design Engineering and Data Science Institute at Imperial College London. Research emphases include generative AI, brain-computer interfaces, and AI-generated artworks. She explores innovative methods for human-AI interaction in design contexts. Publications (2024–2025) highlight advancements in AI-co-creation tools, generative models for typography, and evaluation frameworks for AI art. Current work addresses interdisciplinary applications of AI in creative industries. No secondary employment declared (2023–2026). Teaches research modules (3 & 5 ECTS) at IDE, contributing to both academic and applied design education.

M.H.M. Winandsمشاهده پروفایل

استاد

• Heuristic Search
• Monte-Carlo Tree Search
• Artificial Intelligence
• +۳ مورد دیگر

M.H.M. Winands is a Professor in Machine Reasoning and Chair of the Department of Advanced Computing Sciences at Maastricht University's Faculty of Science and Engineering. He received his PhD in Artificial Intelligence from Maastricht University in 2004. His research focuses on heuristic search algorithms, Monte-Carlo Tree Search (MCTS), and artificial intelligence in games, with significant contributions to General Game Playing and adaptive search methods. Winands leads research on: Monte-Carlo Tree Search variants and hybridization Online learning of search-control knowledge Game-solving algorithms (Proof-Number Search) Self-adaptive AI systems for games His work has been applied to diverse domains including board games (Lines of Action, Clobber), video games (StarCraft, Ms. Pac-Man), and structural engineering. Analysis of his publications shows strong emphasis on: Enhancing MCTS robustness through parameter randomization Developing hybrid algorithms combining MCTS with classical search Creating adaptive systems for general game playing Hardware acceleration of game AI components Awards include: ChessBase Best-Publication Award (2004, 2008) Multiple Computer Olympiad wins for game-playing programs He has supervised 3 PhD students and 22 MSc students, with research supported by NWO grants (GoGeneral, Go4Nature). Leads the DKE Games and AI group and serves as Editor-in-Chief of the ICGA Journal.

George Azzopardiمشاهده پروفایل

دانشیار

• Pattern Recognition
• Image Processing
• Machine Learning
• +۵ مورد دیگر

George Azzopardi is an Associate Professor in Pattern Recognition at the University of Groningen, affiliated with the Bernoulli Institute. He leads the PRISMA team, focusing on robust AI, medical imaging, and interdisciplinary applications in astronomy and forensics. His roles include coordinating the Applied AI theme at the Jantina Tammes School and leading WG4 of the EU GLITSS COST Action. Education: PhD in Computer Science (University of Groningen, 2013), MSc in Advanced Methods in Computer Science (Queen Mary University London, 2008), BSc in Computer Science (Goldsmiths College London, 2006). Research Interests: Brain-inspired algorithms, image processing, predictive analytics, and applications in radio astronomy, forensics, and medical diagnostics. His work emphasizes robustness in AI models and cross-domain collaborations. Publications span advancements in COSFIRE filters, deep learning robustness, and interdisciplinary applications like radio galaxy classification and biometric systems. Recent trends include enhancing model explainability and combating adversarial attacks. Awards: Ben Feringa Impact Award (2023), ENLIGHT Impact Award (2024) Advising: Supervises PhD students in AI-driven medical imaging, radio astronomy, and computer vision. Collaborates with institutions like ASTRON, UMCG, and industry partners Ziel NL and KAIOS. Labs/Teams: PRISMA team develops robust AI tools for medical and astrophysical data analysis, with projects on biostructure segmentation and camera forensics.

Vasilis Tsouvalasمشاهده پروفایل

پژوهشگر

• Federated Learning
• Data Privacy
• Audio Recognition
• +۶ مورد دیگر

Vasilis Tsouvalas is a Researcher in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e), affiliated with the Interconnected Resource-aware Intelligent Systems research group. His work centers on advancing federated learning architectures with emphases on communication efficiency and privacy preservation. His core research domains include: Federated Learning Data Privacy and Security Audio and Speech Recognition Systems Communication Cost Optimization Model Compression Techniques Distributed Machine Learning Analysis of his recent publications reveals a strong trajectory in solving heterogeneity challenges in federated systems, with innovative approaches to functional encryption for unlearning compliance and vision model fine-tuning. His work consistently addresses real-world constraints like communication bottlenecks and edge device limitations while maintaining model accuracy. Contact: v.tsouvalas@tue.nl Dr. Tsouvalas has supervised 2 students according to institutional records, though specific names aren't publicly listed. His research is conducted within the Interconnected Resource-aware Intelligent Systems framework at TU/e, focusing on practical implementations for resource-constrained environments.

• Solar Energy
• Photovoltaics
• Renewable Electricity
• +۴ مورد دیگر

Dr. Sara Mirbagheri Golroodbari serves as an Assistant Professor in the Energy & Resources department at Utrecht University's Copernicus Institute of Sustainable Development, Faculty of Geosciences. She specializes in the integration of photovoltaic solar energy systems, with particular expertise in offshore and floating solar technologies. Her research interests span multiple dimensions of solar energy systems, including solar PV integration feasibility studies, local agrivoltaic systems in the Netherlands, responsible deployment of floating PV systems (both onshore and offshore), land restoration potential through solar installations, offshore floating PV data analysis, offshore solar energy transmission methods, solar energy forecasting using advanced techniques like All Sky Imager and machine learning algorithms, and big data analysis of large-scale PV systems. Dr. Golroodbari's publication record shows a consistent focus on solar energy technologies, particularly floating photovoltaic systems, with numerous high-impact publications in leading energy journals. Her recent work has examined global offshore floating photovoltaics system assessment, enhanced water cooling models for offshore PV, and advanced solar forecasting techniques incorporating artificial intelligence. Graduated with distinction (2013) Grand Cru 2019 award (November 13, 2019) Scholarship awarded (October 2019) Scholarship awarded (October 2018) She actively contributes to international collaborations as a member of the ETIP PV solar integration working group and IEA_PVPS task 13. Dr. Golroodbari teaches courses including 'Introduction to Energy and Material Analysis' and 'Photovoltaics: basics and integration' within the Energy Science and Environmental Sciences programs.

Anouk Waeijen-van Diepenمشاهده پروفایل

پژوهشگر

• Machine Learning
• Biomedical Engineering
• Mechanical Ventilation

Anouk Waeijen-van Diepen is a PhD candidate at the Biomedical Diagnostics Lab within the Signal Processing Systems group at Eindhoven University of Technology (TU/e), focusing on integrating machine learning with mechanical ventilation systems. Her work aims to address patient-ventilator asynchrony through advanced algorithms and simulated clinical data . Education : MSc in Electrical Engineering (2017, TU/e) BSc in Bioinformatics/Biostatistics (2014, TU/e) Pre-Master in Electrical Engineering (2015, TU/e) Her research spans biomedical signal processing , machine learning applications in healthcare, and ventilator system optimization . She specializes in asynchrony detection using clinical and simulated data , with a focus on intensive care unit (ICU) environments. Key trends in her publications include adversarial learning for waveform generation, model-based approaches for patient effort estimation, and automated detection algorithms validated through peer-reviewed journals like Computer Methods and Programs in Biomedicine and Heliyon . She has contributed to IEEE symposia and European Signal Processing Conferences . Projects include: STW Zero 15-06 P2 (2017–2024): Autonomous Acoustic Systems Smart Monitoring (2015–2020): Real-time healthcare data analysis

Pierre H. Woerleeمشاهده پروفایل

مدرس

• Patient Monitoring
• Clinical Measurements
• Perioperative Care
• +۳ مورد دیگر

Pierre H. Woerlee is a University Lecturer in the Department of Electrical Engineering, Signal Processing Systems at Eindhoven University of Technology (TU/e). He previously held roles at Philips Research (1980–2017), including Research Fellow (1998–2017), and was a part-time Professor at the University of Twente’s Faculty of Electrical Engineering (1992–2001). His research focuses on healthcare technology, particularly patient monitoring, clinical measurements, and perioperative care, with contributions to resuscitation care, automated CPR, and physiologic modeling. Education: Doctorate in Physics (University of Amsterdam, 1979) Master’s in Physical Chemistry (University of Amsterdam, 1975) Research at FOM Institute for Atomic and Molecular Physics (1975–1979) Research Interests: Woerlee specializes in low-acuity patient monitoring, new clinical measurements, and prediction of patient deterioration. His work integrates machine learning and engineering principles to improve healthcare technologies, such as pressure support ventilation systems and patient-ventilator asynchrony detection. Notable Projects: Member of the PISANO SPS project (2021–2026), focusing on perioperative innovations and newborn health. Grants & Labs: Active in translational research within the SPS group, collaborating on hemodynamic parameter analysis and medical device development. His work aligns with UN Sustainable Development Goals for health and innovation.

• Machine Learning
• Civil Engineering
• GIS-BIM Integration
• +۳ مورد دیگر

Özgün Balaban is a Researcher in the Department of Information Systems in the Built Environment at Eindhoven University of Technology. Their work focuses on innovative applications of machine learning and information systems within the built environment sector, particularly in BIM-GIS integration and generative AI for architectural design. They have collaborated on interdisciplinary projects involving civil engineering, disaster management, and structural design automation. Research interests include leveraging generative adversarial networks (GANs) for automated design solutions and improving flood risk management through advanced data integration frameworks. Their recent publications address both technical implementation and stakeholder engagement challenges in smart infrastructure systems. No scientific awards are explicitly mentioned in the provided data. Current research activities involve collaborations with institutions in structural planning and environmental risk assessment. Contact: o.balaban@tue.nl .

• Electric Power Distribution
• Load Profile Analysis
• Distribution Systems
• +۵ مورد دیگر

Mauricio Salazar Duque serves as a University Researcher in the Department of Electrical Energy Systems within the School of Electrical Engineering at Eindhoven University of Technology (TU/e). His work focuses on modernizing power distribution infrastructure through advanced computational techniques. His research expertise spans critical domains in electrical grid modernization: Electric Power Distribution & Load Profile Analysis Distribution System Optimization & Photovoltaics Integration Community Energy Storage & Reinforcement Learning Applications His publications consistently address UN Sustainable Development Goals related to affordable clean energy and climate action. Recent work demonstrates strong emphasis on data-driven modeling for grid resilience. Analysis of his 2023-2025 publications reveals a strategic shift toward generative AI for load profile synthesis and tensor-based mathematical formulations for multidimensional grid analysis. Key themes include handling PV integration challenges in low-voltage networks and developing visualization techniques for medium-voltage load anomalies. No scientific awards are documented in available records. He contributed to the 2018-2022 project 'Decentrale Optimalisatie en Sturing van Elektriciteitsdistributienetten' as a research member and supervised at least one Master's thesis on transfer learning for medium-voltage network measurements. His research collaborations extend through TU/e's press engagements including National Growth Fund initiatives and mobility systems workshops. Salazar Duque operates within TU/e's Electrical Energy Systems research group, collaborating with Prof. J.G. Slootweg and international partners on smart grid innovation projects.

Momen Abayazidمشاهده پروفایل

استادیار

• Robotic Image-Guided Needle Insertion
• Motion Compensation Using Machine Learning
• Soft Robotics
• +۳ مورد دیگر

Momen Abayazid is an Assistant Professor in the Robotics and Mechatronics group (RaM) at the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente. He holds a PhD in Surgical Robotics from the University of Twente and an M.Sc. in Biomedical Engineering from Delft University of Technology. His research focuses on robotic image-guided interventions, soft robotics, and machine learning applications in medical systems. Education: M.Sc. in Biomedical Engineering (Delft University of Technology), PhD in Surgical Robotics (University of Twente) His expertise includes haptic feedback, needle insertion control, and organ motion compensation. Recent projects involve AI-driven medical imaging, soft robotics for rehabilitation, and sensorized medical devices. Collaborations span institutions like Harvard Medical School and Brigham and Women’s Hospital. ResearchGate and Google Scholar profiles highlight his contributions to medical robotics. Scientific Awards: No explicit awards mentioned in the provided text.

• Machine Learning
• Critical Care Medicine
• Medical Informatics
• +۳ مورد دیگر

Ashley de Bie serves as a University Researcher at Eindhoven University of Technology within the Department of Electrical Engineering's Signal Processing Systems group. She is deeply embedded in the Eindhoven MedTech Innovation Center, a strategic partnership between TU/e and Catharina Hospital that drives medical technology translation into clinical practice. Her work directly addresses UN Sustainable Development Goals for health and well-being through engineering-clinical collaborations. Her research centers on machine learning applications for critical care, with three core thrusts: (1) developing and validating early warning systems like the Advanced Alert Monitor for patient deterioration detection; (2) creating computational tools for mechanical ventilation optimization, particularly in identifying patient-ventilator asynchrony; and (3) designing clinical decision support systems such as the TraceBook tool for individualized patient management. This work consistently bridges algorithm development with real-world hospital implementation. Analysis of her 2022-2025 publications reveals a strong trend toward context-specific validation of medical algorithms in Dutch healthcare settings. Key patterns include local optimization of international models, integration of clinician feedback in design cycles, and rigorous multicenter trials like the PRECISe study on critical care nutrition. Her publications demonstrate exceptional translational impact, with several tools already deployed in hospital workflows. With 349 Scopus citations and significant media attention (8 press mentions, 121 X posts, 101 Mendeley readers), her work resonates across academic and clinical communities. The PlumX metrics show particularly strong clinical uptake, with news coverage and social media engagement indicating real-world relevance. Dr. de Bie has supervised 5 students and maintains a robust collaborative network spanning TU/e's engineering departments and Catharina Hospital's clinical units. Her projects consistently involve multidisciplinary teams of data scientists, biomedical engineers, intensivists, and nurses, reflecting the complex nature of healthcare innovation. As a core contributor to the Signal Processing Systems group and Eindhoven MedTech Innovation Center, she operates within a dynamic ecosystem focused on rapid prototyping and clinical validation of medical technologies. Her current work with the Advanced Alert Monitor team exemplifies this environment, where algorithms move from simulation to hospital wards within months.

Luis A. Zavala Mondragonمشاهده پروفایل

پژوهشگر ارشد

• Medical Imaging
• Signal Processing
• Machine Learning
• +۴ مورد دیگر

Luis A. Zavala Mondragon is a postdoctoral researcher at the Department of Electrical Engineering , Eindhoven University of Technology , with a focus on medical imaging and signal processing. He is affiliated with the Center for Care & Cure Technology Eindhoven and actively contributes to research in deep learning, wavelet-based denoising, and X-ray imaging systems. Education : PhD in Electrical Engineering (2023), TU/e. His research explores the intersection of convolutional neural networks (CNNs) and medical imaging , particularly for noise reduction in CT scans. Recent work integrates wavelet transforms and spectral decomposition into deep learning architectures. He has published extensively in journals like IEEE Transactions on Medical Imaging and presented at conferences including EMBC. Key scientific contributions include: CT denoising using learned wavelet-frame shrinkage networks Signal processing interpretations of CNNs Conditional GANs for low-dose CT Fixed wavelet frame integration with CNNs He is involved in teaching courses on neural networks for computer vision and has developed open-access datasets and codebases for medical imaging research.

• Semantic Web
• Linked Data
• Ontology Engineering
• +۷ مورد دیگر

Dr. Stefan Schlobach is an Associate Professor at the Faculty of Science, Vrije Universiteit Amsterdam, affiliated with the Artificial Intelligence department, Network Institute, and Knowledge Representation and Reasoning group. His research focuses on Semantic Web, Linked Data, Ontology Engineering, and Hybrid Intelligence systems. He has published over 116 peer-reviewed works and supervised 6 PhD theses. Recent projects include robotic knowledge acquisition (ORKA ontology), explainable AI for security systems, and knowledge engineering for virtual heritage applications. He co-developed the LOTUS search framework for big linked data and contributed to the CoPla ontology for agriculture. His work bridges theoretical foundations (e.g., argumentation systems) with applied domains (e.g., cyber-physical systems, cultural heritage digitization). Research Interests: Semantic Web Technologies Knowledge Graphs & Ontology Engineering Hybrid Intelligence (Human-AI Collaboration) Robotic Perception Systems Explainable AI Entity Linking & Identity Resolution Key Contributions: Developed LOTUS adaptive search system (2016 prize-winning work) Created ORKA ontology for robotics knowledge acquisition Pioneered perceived-entity linking methodologies Advocated knowledge engineering principles for hybrid systems Teaching: Leads courses in Artificial Intelligence, Knowledge Representation, and Semantic Technologies at both undergraduate and graduate levels.

• Computer Science
• Model Checking
• Performance Evaluation
• +۴ مورد دیگر

Professor Boudewijn R.H.M. Haverkort is affiliated with the University of Twente's Digital Society Institute and Faculty of Electrical Engineering, Mathematics, and Computer Science. His research focuses on formal methods, performance evaluation, and thermal management in computing systems. He has contributed to advancing model checking techniques, data center workload modeling, and temperature-aware scheduling algorithms. His work intersects computer science, embedded systems, and cyber-physical systems. Research interests include: - Model Checking and Formal Verification - Performance Evaluation of Distributed Systems - Thermal Management in Real-Time Systems - Machine Learning Applications for System Workload Analysis Notable contributions include developing statistical model checking frameworks and temperature prediction methods recognized by the Harvey Rosten Award in 2020. He has supervised 27 research projects, though student names are not listed here. His collaborations span thermal investigations in ICs, cyber-physical system design, and digital communication systems in conflict resolution contexts. Labs/Teams: Active in the Digital Society Institute and MESA+ Institute, collaborating with TechMed Centre on interdisciplinary projects.