جست‌وجوی استاد و پژوهشگر

Ivana Nikoloskaمشاهده پروفایل

استادیار

• Machine Learning
• Quantum Computing
• Communication Engineering
• +۷ مورد دیگر

Ivana Nikoloska is an Assistant Professor at the Department of Electrical Engineering, Eindhoven University of Technology (TU/e). She is affiliated with the Center for Quantum Materials and Technology Eindhoven and BIASlab (Bayesian Intelligence and Stochastic Agents Lab). Her academic career includes prior roles as a Research Associate at King’s College London and a Visiting Researcher at Aalborg University. PhD: Monash University, Australia (2023) MSc & Dipl.-Ing.: University of Ss. Cyril and Methodius, North Macedonia Research Interests span foundational and applied machine learning, quantum computing, and information/communication engineering. Her work focuses on integrating Bayesian inference, variational methods, and quantum technologies for tasks like signal processing, channel estimation, and power control optimization. Quantum Machine Learning Bayesian Simulation-Based Inference Meta-learning for Wireless Systems Hybrid Quantum-Classical Architectures Stochastic Signal Processing Quantum Sensing & Metrology Notable Trends in Publications include quantum recurrent neural networks with adaptive gating, Bayesian frameworks for quantum sensing, and meta-learning applications in communication systems. She explores variational inference for planning and robust algorithms for channel estimation under non-ideal conditions.

Sofie Haesaertمشاهده پروفایل

استادیار

• Cyber-Physical Systems
• Control Theory
• Formal Verification
• +۳ مورد دیگر

Sofie Haesaert is an Assistant Professor in the Control Systems group at the Department of Electrical Engineering, Eindhoven University of Technology. Her work focuses on formal verification and control synthesis methods for cyber-physical systems, particularly through stochastic simulation relations and temporal logic specifications. Education: BSc (cum laude) and MSc (cum laude) in Mechanical Engineering and Systems & Control from Delft University of Technology; PhD from Eindhoven University of Technology (2017) Experience: Postdoctoral researcher at Caltech (2017-2018), then returned to TU/e as Assistant Professor Her research interests include: Cyber-physical systems verification Stochastic control methods Temporal logic specification Markov decision processes Formal methods in control engineering Model abstractions and simulation relations Recent publications show strong focus on: Stochastic temporal logic control Robust and risk-aware control Multi-agent system verification Formal synthesis via simulation relations AI integration in control systems Software tools for formal control Scientific achievements: Veni Grant recipient (2020) Co-developer of the SySCoRe toolset for stochastic control synthesis Contributor to formal verification benchmarks through ARCH-COMP reports She contributes to education through courses on: Control principles for engineered systems Control challenges in autonomous racing Supervisory control of cyber-physical systems Haesaert collaborates across disciplines including computer science, applied mathematics, and robotics, with over 750 citations and significant contributions to formal control theory for stochastic systems. Her work bridges theoretical developments with practical applications in autonomous systems and complex control architectures.

George Vosselmanمشاهده پروفایل

استاد

• Photogrammetry
• Remote Sensing
• Laser Scanning
• +۷ مورد دیگر

George Vosselman is a Full Professor at the University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), specializing in Geo-Information Extraction with Sensor Systems. Educated with honours at Delft University of Technology (1986) and PhD in Photogrammetry from Rheinische Friedrich Wilhelms University of Bonn (1991), he has held academic roles at the University of Stuttgart, University of Washington, and Delft University of Technology (1993–2004). Since 2004, he has been a key figure at ITC, serving as department head (2012–2018, 2023–). Education: Delft University of Technology (BSc with honours, 1986), Rheinische Friedrich Wilhelms University of Bonn (PhD with honours, 1991) His research focuses on leveraging sensor technology advancements for large-scale geo-information production. Key expertise includes quality analysis of laser altimetry data, point cloud segmentation/classification, 3D building/road modeling, and model-driven imagery analysis. He has published over 220 papers and co-edited the textbook Airborne and Terrestrial Laser Scanning (2010). Recent work integrates deep learning with geospatial data, addressing semantic segmentation, visual question answering, and drone-based mapping. Recent publications (2025–2023) highlight trends in deep learning for remote sensing , including multimodal question answering benchmarks (HRVQA), vectorized building extraction (RoIPoly), latent diffusion for road modeling (LDPoly), and drone obstacle avoidance systems. His work bridges photogrammetry , computer vision , and robotic mapping , with applications in urban planning, disaster management, and informal settlement monitoring. Scientific Awards : Hansa Luftbild (1993), ISPRS Otto von Gruber (2000), Schwidefsky Medal (2012), Karl Kraus Medal (2012), ASPRS Fairchild Award (2015), ISPRS Fellow (2020) As an educator, Vosselman has taught photogrammetry, remote sensing, and laser scanning at Delft University of Technology and globally. He chaired the ITC Examination Board (2015–2023) and modernized geo-information education in Asia/Africa. His software for point cloud processing is commercialized in Europe, and he currently leads ISPRS working groups on point cloud methodologies. Labs/teams include the Earth Observation Science Chair Group at ITC, collaborating on UAV-based datasets (UAVid, UAVPal) and indoor laser scanning systems. Recent activities (2025) involve invited talks on pulse matching limitations in laser scanning and deep learning for point cloud classification.

Anna Vilanovaمشاهده پروفایل

استاد

• Visual Analytics
• Explainable AI
• Biomedical Image Analysis
• +۶ مورد دیگر

Anna Vilanova is a Full Professor in Visual Analytics at the Department of Mathematics and Computer Science, Eindhoven University of Technology (TU/e), and is associated with the Electrical Engineering department's Signal Processing Systems. Previously, she served as Associate Professor at TU Delft (2013-2019) and Assistant Professor at TU/e (2002-2013). Her research focuses on Visual Analytics for high-dimensional data , explainable AI , and biomedical applications including Diffusion Weighted Imaging, 4D Flow, and Pangenomics. Education: Doctorate in Computer Graphics & Visualization (2001) Master in Computer Science (1997), Universitat Politècnica de Catalunya Research Highlights: Vilanova leads work on Visual Analytics systems for biomedical data, with recent publications in Diffusion MRI modeling , Tractography visualization , Explainable AI frameworks , and Pangenomic variant analysis . Her work bridges dimensionality reduction , uncertainty visualization , and medical imaging applications. Scientific Contributions: NWO-Veni grant (2005): "Visualization of global tensor information for diffusion tensor imaging" NWO-Aspasia grant (2013) Best Poster Award EuroVis (2025) Best Demo/Poster Awards (2022) Leadership & Service: Vilanova serves on the IEEE VIS Steering Committee , was EUROGRAPHICS President (2019-2022), and contributes to conferences like IEEE Visualization and EG-EuroVis . She co-founded the EAISI Health research initiative at TU/e.

Irfan Ahmadمشاهده پروفایل

دانشیار

• Pattern Recognition
• Document Image Analysis
• Handwriting Recognition
• +۷ مورد دیگر

Dr. Irfan Ahmad serves as an Associate Professor in the Department of Information and Computer Science at King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia, where he teaches undergraduate and graduate courses in Computer Science and Software Engineering while conducting research and advising graduate students. His academic service includes committee roles on graduate studies, program development, and competitions. His research expertise centers on Pattern Recognition with specialized focus on Document Image Analysis , Handwriting Recognition , and Machine-Printed Text Recognition . He actively explores Machine Learning applications including Deep Learning and Natural Language Processing , with significant contributions across Artificial Intelligence, Computer Vision, Data Mining, Neural Networks, and Computational Linguistics as evidenced by his PeerJ subject area specializations. Recent publications reveal a strategic emphasis on adaptive deep learning architectures for document analysis, particularly generative methods for handwritten text recognition and knowledge distillation techniques. His editorial work on feature extraction and multilingual fake news detection further demonstrates applied research bridging theoretical machine learning with real-world language processing challenges. As an active Academic Editor for PeerJ Computer Science with 1,205 contribution points, Dr. Ahmad provides substantial service to the scholarly community through manuscript evaluation and editorial oversight across emerging technologies in data science and artificial intelligence.

Tom Bootمشاهده پروفایل

دانشیار

• Macroeconomic Forecasting
• High-Dimensional Data Analysis
• Econometric Theory
• +۴ مورد دیگر

Dr. Tom Boot is an Associate Professor at the Department of Economics, Econometrics & Finance at the University of Groningen. He holds a PhD in Econometrics from Erasmus University Rotterdam (2017) and an MSc in Econometrics from the same institution (2012), along with an MSc in Physics from the University of Groningen (2010). His research focuses on econometric theory applied to macroeconomic forecasting, high-dimensional data analysis, and causal inference. He has been recognized with the Veni grant (2021–2024) for his work on forecasting methodologies. Boot’s research interests include improving forecast accuracy through methods like subspace projections, structural break modeling, and privacy-aware marketing analytics. His recent work explores privacy-utility trade-offs in data-driven marketing and unbiased estimation techniques for clustered errors. He has supervised PhD students including Jhordano Aguilar Loyo and Gilian Ponte, whose theses addressed panel data heterogeneity and differential privacy applications. Boot is also a program director for the MSc Econometrics, Operations Research, and Actuarial Studies (since 2024). His contributions to econometrics span over a dozen peer-reviewed publications, with a focus on advanced statistical techniques for economic forecasting and policy analysis. Collaborations include work with institutions like Harvard/MIT and the organization of workshops on causal inference and machine learning.

Ralf Peetersمشاهده پروفایل

استاد

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

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

Anuj Pathaniaمشاهده پروفایل

استادیار

• Low Power Design
• Sustainable Systems
• Electronic Design Automation
• +۴ مورد دیگر

Anuj Pathania serves as an Assistant Professor in the Parallel Computing Systems (PCS) group within the Informatics Institute at the University of Amsterdam's Faculty of Science. His research pioneers sustainable computing systems operating under severe power, thermal, and reliability constraints, with significant contributions to energy-efficient hardware design and embedded systems. Education: PhD in Computer Science (2018), Karlsruhe Institute of Technology MSc in Computer Science (2012), National University of Singapore B.Tech in Computer Science (2009), Maharaja Agrasen Institute of Technology Pathania's research centers on low-power design and sustainable systems for constrained environments, with particular expertise in thermal management of 3D-stacked architectures and energy-efficient machine learning inference . His work bridges electronic design automation with real-world reliability challenges, developing novel power budgeting techniques like T-TSP that incorporate transient temperature effects ignored by conventional methods. Current projects include EU-funded initiatives on energy labeling for digital services, addressing ecological impacts through technological, behavioral, and legal frameworks. His publication trajectory reveals a strategic evolution toward zero-waste computing , with recent work (2023-2025) focusing on hardware-software co-design for edge AI, energy modeling across computing continua, and parameter-efficient neural adaptation. Key themes include thermal-aware scheduling for S-NUCA many-cores, cooperative processor utilization in heterogeneous systems, and sustainability metrics for digital services. Scientific Recognition: Best Paper Award Nomination at IEEE Computer Society Annual Symposium on VLSI 2023 for 3D-TTP power budgeting technique Pathania actively mentors 4 PhD students (Ehsan Aghapour, Saeedeh Baneshi, Sudam Wasala, Yixian Shen) and has successfully supervised 5 Master's theses (including Cum Laude defenses by Joris op ten Berg and Jurre Wolff). His research is supported by major grants including Energy Labels for Ecologically Sustainable Digital Services (2023-2024) and Towards Zero-Waste Computing (2021-2025), developing simulation frameworks like HotSniper and CoMeT for thermal analysis. The PCS group maintains strong industry collaborations with ARM and NVIDIA, particularly through tools like ARM-CO-UP for heterogeneous processor utilization.

Marcus Gerholdمشاهده پروفایل

استادیار

• Model-Based Testing
• Software Reliability
• Critical Infrastructures
• +۷ مورد دیگر

Marcus Gerhold is an Assistant Professor in the Formal Methods and Tools group at the University of Twente's Faculty of Electrical Engineering, Mathematics and Computer Science. His research focuses on model-based testing for software reliability in critical infrastructures, particularly railway systems, alongside significant contributions to game design and programming language analysis. His educational background includes: PhD in Computer Science from University of Twente (2018): Choice and Chance: Model-based Testing of Stochastic Behaviour MSc in Mathematics from Friedrich Schiller Universität Jena (2013): Embeddings of Weighted Morrey Spaces BSc in Mathematics from Friedrich Schiller Universität Jena (2011): Entropy-, Approximation- and Kolmogorov Numbers on Quasi-Banach Spaces Gerhold's research integrates theoretical model-based testing with practical critical infrastructure applications . His work on railway conformance testing addresses EULYNX controller validation, while his game design research explores affective mirroring in NPCs and procedural dungeon generation. The code modernity analysis stream leverages static analysis to quantify legacy code evolution across languages like Python and PHP, revealing version identification challenges through deep learning. Publication trends show consistent focus on model-based testing methodologies (40%), railway safety applications (25%), and innovative game design/code analysis (35%). Recent work increasingly incorporates AI/ML techniques for UML assessment and Python version identification, while maintaining rigorous formal methods foundations. He actively mentors 63 students across all academic levels and contributes to major research initiatives: STORM_SAFE (ERDF, 2024): Daily Supervisor for WP1/WP2 on software reliability for critical infrastructures ZORRO (KIC grant, 2023): Daily Supervisor for WP4 on zero downtime in cyber-physical systems MISSION (MSCA RISE, 2021-2025): Interim coordinator (early 2024) for space systems modeling As part of the Formal Methods and Tools research group, Gerhold participates in European collaborations while serving on SAC-SVT 2024 and FormaliSE 2023 program committees.

A.C.G. (Toine) Egbertsمشاهده پروفایل

استاد

• Clinical Pharmacy
• Adverse Drug Effects
• Pharmacoepidemiology
• +۳ مورد دیگر

Prof. A.C.G. (Toine) Egberts is a Professor of Clinical Pharmacoepidemiology and Clinical Pharmacy at Utrecht University's Faculty of Medicine. He leads the Department of Pharmacoepidemiology & Clinical Pharmacology and serves as Director of Doctoral Education at the Graduate School of Life Sciences. His career includes roles as Chief Pharmacist at UMC Utrecht and Board Member of Pharmacy Division. He earned a PharmD from the University of Groningen and a PhD from Utrecht University, specializing in clinical epidemiology. Egberts focuses on reducing drug-related harm through research in adverse effects, sustainable pharmacy practices, and optimizing medication use. With over 500 publications, he sits on editorial boards and committees like CCMO and EPHOR. His work bridges drug development and patient outcomes, emphasizing translational research. Research interests include clinical pharmacology, pharmacoepidemiology, drug sustainability ('green pharmacy'), and innovations in prescribing/monitoring. Key projects like the ROAD study address medication waste reduction, published in JAMA Oncology . Awards include the Senior Jan Glerum Award (2016) and Huizinga Penning for clinical pharmacy contributions. Teaching and mentorship are central: guided over 70 PhD students, emphasizing education in hospital pharmacy. Grants and collaborations span clinical trials, adverse event detection, and AI applications in healthcare. Active in Dutch medicines policy through advisory roles and public media engagement.

• Cybersecurity
• Data Encryption
• Access Control Systems
• +۳ مورد دیگر

Willem Jonker is a Full Professor at the Digital Society Institute, specializing in Semantics, Cybersecurity & Services. His research focuses on encryption schemes, access control, and privacy-preserving technologies. He has contributed to over 120 publications, with recent work addressing CVE-to-CWE mapping, anomaly detection in network traffic, and functional encryption systems. His expertise aligns with UN Sustainable Development Goals related to secure digital systems and privacy. Jonker has supervised 10 students and actively participates in academic conferences, presenting on topics like secure data management and cryptographic protocols. Research interests include cryptographic protocols, secure data management, and cybersecurity solutions. Notable projects involve developing methods for detecting covert channels, enhancing data privacy in healthcare, and improving secure search over encrypted data. He has also contributed to standards in digital rights management and forensic image recognition.

• Psychometrics
• Network Theory
• Psychopathology
• +۲ مورد دیگر

Denny Borsboom is Professor in the Psychological Methods Group at the University of Amsterdam, where he directs the Social and Behavioural Data Science Centre. His research program develops network theories of psychopathology, novel psychometric methods, and formalized psychological theory through initiatives like the Psychosystems Project and Theory Methods Lab. Research focuses on conceptual foundations of measurement, complex systems approaches to mental disorders, and computational modeling of psychological phenomena. Recent publications advance network psychometrics, validity theory, and integration of intra/interindividual dynamics. Articles demonstrate strong methodological innovation through computational psychiatry applications, network theory development, and philosophical engagement with psychological foundations. Recurring themes include psychopathological networks, measurement realism, system transitions, and collaborative theory-building frameworks.

• Environmental Chemistry
• Mass Spectrometry
• Non-Targeted Analysis
• +۵ مورد دیگر

Dr. Saer Samanipour is a Visiting Professor at the Van 't Hoff Institute for Molecular Sciences, part of the Faculty of Science at the University of Amsterdam. His research focuses on advanced analytical techniques for environmental and biomedical applications, with a strong emphasis on non-targeted analysis, mass spectrometry, and machine learning integration. He leads efforts in developing open-source tools like GcDUO and jHRMSToolBox to enhance data interpretation in complex chemical datasets. Key areas include environmental contaminant detection, chemical exposure assessment via wastewater-based epidemiology, and proteomic analysis of snake venoms. His work bridges computational methods with experimental chemistry to address global challenges in environmental health and toxicology. Primary affiliation: Van 't Hoff Institute for Molecular Sciences Research themes: Non-targeted LC-HRMS workflows, machine learning applications in analytical chemistry, PFAS analysis, and exposome research Software contributions: GcDUO (GC×GC-MS), jHRMSToolBox (HRMS data processing) His publications highlight innovations in data-driven approaches for compound prioritization, toxicity prediction, and method optimization. Recent work explores chemical space exploration and chemometric strategies for complex mixture analysis, with applications to environmental monitoring and forensic science.

• Software Engineering
• Machine Learning
• Software Architecture
• +۴ مورد دیگر

Cagatay Catal is a Lecturer at the Information Technology Department of Wageningen University & Research in the Netherlands. Previously, he served as Associate Professor at Istanbul Kultur University's Department of Computer Engineering (2012–2017), including three years as Department Head (2014–2017). Prior to academia, he worked for 8 years at TÜBİTAK's Information Technologies Institute as Senior Researcher & Project Manager (2002–2012), contributing to large-scale software systems. He holds a BSc/MSc from Istanbul Technical University and a PhD from Yildiz Technical University, all in Computer Engineering. His research focuses on machine learning applications in software engineering, software quality assurance, testing methodologies, and architecture design. He actively reviews for funding bodies like the European Union's EUROSTARS program, Canada's Research Council, and Turkey's Research Council. Catal leads projects such as INREF Smart-In-Ag (SMART farming in Indonesia), VIRAL (ICT in agricultural education), and malware detection in software systems. His work bridges academic research with practical software development challenges in diverse sectors like healthcare and education.

• Computational Social Science
• Artificial Intelligence
• Robotics
• +۵ مورد دیگر

Dr. Yara Khaluf is an Assistant Professor in the Information Technology Group at Wageningen University & Research, Department of Social Sciences. She holds a PhD (2014, Paderborn University, cum laude) on robot swarm task allocation, followed by postdoctoral research at Paderborn University (2014–2015) and Ghent University’s IDLab (2015–2021). Her work focuses on computational social science, hybrid human-agent societies, and distributed artificial cognition, leveraging agent-based modeling and systems dynamics for behavior prediction/modulation. She leads European-funded projects like ChronoPilot (EU Horizon2020 FET) and DELICIOS (FWO, 2019–2022). Her research explores interactions between artificial agents and humans, developing cognitive capacities for seamless interaction via social feedback networks. Notable contributions include collective foraging algorithms, time perception modeling, and agent-based simulations for public health interventions. Awards include competitive IGS and DFG fellowships. She collaborates with leading experts in swarm intelligence (Dorigo, Stuetzle), collective decision-making (Hamann, Marshall), and experimental psychology (Johansson, Vatakis). Current projects investigate modulating human time perception and delegation of conflict-of-interest decisions to AI agents. Teaching includes courses on model thinking, agent-based modeling of complex systems, and data science applications in food/consumer science. Her work bridges computational methods with societal challenges, emphasizing scalable solutions for hybrid systems.