جست‌وجوی استاد و پژوهشگر

Tristan van Leeuwenمشاهده پروفایل

استاد

• Imaging Reconstruction
• Inverse Problems
• Scientific Computing
• +۴ مورد دیگر

Tristan van Leeuwen is a Professor of Computational Inverse Problems at Utrecht University's Faculty of Science, within the Mathematical Institute's Department of Mathematical Modeling. He holds a MSc in Computational Science (2006) and a PhD in Geophysics (2010). His career includes postdoctoral roles at the University of British Columbia and Centrum Wiskunde & Informatica (CWI), followed by faculty positions at Utrecht University and group leadership at CWI. His research focuses on inverse problems, scientific computing, imaging reconstruction, and computational methods for geophysics and medical imaging. Key areas include wave-equation inversion, tomographic reconstruction, and uncertainty quantification. Notable contributions span seismic inversion techniques, convex optimization frameworks for tomography, and deep learning applications in imaging. His publications emphasize methodologies like wavefield reconstruction inversion, convex programming for shape sensing, and Bayesian approaches for uncertainty analysis. He collaborates with institutions like CWI and the University of British Columbia, contributing to open-source tools like Tomosipo for tomography. His work bridges theoretical mathematics with practical applications in geophysics, medical diagnostics, and industrial inspection.

Maryam Tavakolمشاهده پروفایل

استادیار

• Uncertainty in Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +۳ مورد دیگر

Maryam Tavakol is an Assistant Professor at Eindhoven University of Technology, affiliated with the Uncertainty in AI group within the Mathematics and Computer Science department. Her expertise bridges artificial intelligence, machine learning, and computational modeling for sequential systems. PhD in Machine Learning, TU Darmstadt B.Sc. & M.Sc. in Computer Science, University of Tehran Her research focuses on uncertainty estimation , reinforcement learning , and transformer architectures applied to domains like drug-target interaction prediction, soccer analytics, and music generation. Recent work explores activation sparsity in LLMs and robust VAE frameworks. Key trends in her publications (2012–2024) span reinforcement learning , neural network architectures , and applications in healthcare and creative domains . Notable themes include contextual bandits, variational inference, and energy-efficient computing. Contributed to UN Sustainable Development Goals (Education/Academic Qualification) She has supervised multiple research projects and served as corresponding author in peer-reviewed conferences. Collaborations include TU Dortmund, Leuphana University, and industry internships at Criteo.

Dmitry Bagaevمشاهده پروفایل

پژوهشگر

• Bayesian Inference
• Probabilistic Programming
• Reactive Message Passing
• +۲ مورد دیگر

Dmitry Bagaev is a Researcher at Eindhoven University of Technology's Signal Processing Systems department, specializing in Bayesian inference and probabilistic programming. His work develops computational tools like RxInfer.jl and ReactiveMP.jl for scalable probabilistic modeling. Research spans Bayesian inference frameworks, reactive programming architectures, and applications in computational immunology. Recent publications focus on probabilistic programming languages (GraphPPL.jl), multi-agent trajectory planning, and SARS-CoV-2 T-cell receptor databases. Articles demonstrate consistent innovation at the intersection of computational statistics, biological data analysis, and real-time systems engineering. Awards and advising roles are not documented in available records. Research occurs within the BIASLab group, focusing on signal processing systems and probabilistic AI.

• Neuroscience
• Neurophysiology
• Computational Neuroscience
• +۳ مورد دیگر

Marcel Oberlaender is an Associate Professor in the Faculty of Science at Vrije Universiteit Amsterdam, holding joint appointments in the Integrative Neurophysiology department and Amsterdam Neuroscience (Cellular & Molecular Mechanisms and Systems & Network Neuroscience programs). He also serves as a Medewerker at the Max Planck Institute for Neurobiology of Behavior in Bonn since 2016. His research focuses on understanding neural circuit organization and function in mammalian cerebral cortex, combining experimental and computational approaches. Key research interests include in vivo neurophysiology, synaptic connectivity, and computational modeling of neural networks. He investigates topics such as corticothalamic neuron regulation, sparse connectivity's role in information processing, and glial-neuronal interactions in thalamic circuits. His work bridges molecular, cellular, and systems levels to uncover principles of sensory processing and network dynamics. Dr. Oberlaender has published 40+ peer-reviewed articles, with recent contributions appearing in iScience , Frontiers in Neural Circuits , and PLoS Computational Biology . His research has been recognized through collaborations with institutions globally and open-access publication practices.

Tom Oomenمشاهده پروفایل

استاد

• Control Systems
• System Identification
• Machine Learning
• +۷ مورد دیگر

Tom Oomen is a full professor in the Department of Mechanical Engineering at Eindhoven University of Technology. He specializes in control systems, system identification, and mechatronics, with applications in precision engineering, semiconductor technology, and healthcare. His research focuses on data-driven control strategies, integrating machine learning and artificial intelligence to enhance system performance. He has held academic positions at KTH Royal Institute of Technology, The University of Newcastle, and Delft University of Technology. Recipient of the 7th Grand Nagamori Award and NWO Veni/Vidi grants. Editor roles: Senior Editor of IEEE Control Systems Letters and Co-Editor-in-Chief of IFAC Mechatronics. Research interests include advanced motion control, iterative learning control, and fault detection. He teaches courses like Advanced Motion Control and organizes post-academic courses through the Mechatronics Academy. Collaborates with industries in semiconductor equipment, printing, space technology, and healthcare. Recent articles explore topics such as random learning in ILC, nonlinear control for ventilators, and gravitational wave detection systems. Advises PhD students including Max van Meer, Max van Haren, and Koen Classens.

Raaja Ganapathy Subramanianمشاهده پروفایل

پژوهشگر

• Bayesian Inference
• Control Theory
• System Identification
• +۳ مورد دیگر

Raaja Ganapathy Subramanian is a PhD candidate and guest researcher at the Bayesian Intelligent Autonomous Systems Lab (BIASlab) within the Signal Processing Systems group at Eindhoven University of Technology. He concurrently serves as an architect in the Metrology department at ASML, specializing in modeling and controlling spatial-temporal systems for semiconductor manufacturing equipment since November 2017. His academic qualifications include: Master of Science in Systems and Control (2017) from Eindhoven University of Technology Bachelor of Engineering in Instrumentation and Control Systems (2015) from PSG College of Technology Raaja's research bridges Bayesian inference theory with industrial control applications, focusing on system identification for large-scale high-precision machinery. Under Professor Bert de Vries' supervision, his work targets practical implementation gaps in semiconductor lithography systems, leveraging ASML's real-world metrology challenges to advance control theory. He operates at the intersection of academia and industry through BIASlab's intelligent autonomous systems research and ASML's Metrology department, where his architectural role directly informs his doctoral work on spatial-temporal system dynamics.

Suzanne van der Veldtمشاهده پروفایل

استادیار

• Neuroscience
• Behavioural Neuroscience
• Anxiety Disorders
• +۵ مورد دیگر

Suzanne van der Veldt serves as an Assistant Professor at the University of Groningen's Faculty of Science and Engineering within the Groningen Institute for Evolutionary Life Sciences (GELIFES), specializing in the behavioural neuroscience cluster. Her research focuses on how the brain distinguishes between safe and threatening contexts, particularly examining failures in this process that lead to anxiety disorders, PTSD, and other fear-related conditions. PhD in Neuroscience from McGill University (2016-2021) as a Vanier Canada Graduate Scholar Postdoctoral fellowship at Université de Montréal and CHU Sainte-Justine (2021-2025) supported by CIHR and FRQS fellowships MSc in European Master in Neuroscience (Neurasmus program) (2013-2015) Her research integrates traditional laboratory approaches with innovative studies of free-roaming mice in semi-naturalistic environments to bridge the gap between controlled experiments and real-world complexity. She specializes in the septohippocampal system, employing cutting-edge techniques including multi-region neural recordings, cell-type specific manipulations, and miniaturized calcium imaging in freely behaving animals. Her work examines how neural circuits assign emotional value to places and experiences, with particular attention to sex differences in emotional processing and vulnerability to anxiety disorders. Analysis of her publication record reveals a consistent focus on hippocampal-septal circuitry, with significant contributions to understanding spatial memory coding, neural oscillations (particularly theta and gamma rhythms), and the neural basis of fear conditioning. Her research increasingly incorporates sex as a biological variable and examines circuit dysfunctions in psychiatric conditions. Vanier Canada Graduate Scholarship (prestigious Canadian doctoral award) CIHR Fellowship (Canadian Institutes of Health Research) FRQS Fellowship (Fonds de recherche du Québec - Santé) Dr. van der Veldt leads the Van der Veldt Lab for Behavioural Neuroscience, which investigates emotional processing through innovative approaches combining circuit neuroscience with ethologically relevant behavioral paradigms. Her lab receives funding through tenure-track mechanisms at the University of Groningen and previously secured competitive fellowships during her postdoctoral training. The lab actively collaborates with international neuroscience groups, particularly with researchers at McGill University and Université de Montréal.

Bella Struminskayaمشاهده پروفایل

دانشیار

• Survey Methodology
• Smartphone Surveys
• Data Donation
• +۶ مورد دیگر

Bella Struminskaya is an Associate Professor at Utrecht University’s Department of Methodology & Statistics, Faculty of Social and Behavioural Sciences. She is also an affiliated researcher at Statistics Netherlands and previously held a senior researcher role at GESIS - Leibniz Institute for the Social Sciences. Her work bridges survey methodology with digital data collection techniques, focusing on smartphone sensors and passive data donation. Education: PhD in Survey Methodology, Utrecht University M.A. in Sociology, University of Mannheim B.A. in Sociology, Novosibirsk State University Her research explores innovative data collection methods, including smartphone surveys, mixed-mode designs, and ethical frameworks for data donation. She investigates how mobile technologies can reduce survey burden while addressing nonresponse bias and measurement errors. Recent work emphasizes integrating digital trace data with traditional surveys to enhance accuracy and scope. Key article trends highlight applications of smartphone sensors in health studies, GDPR compliance for data access, and methodological advancements in smart surveys. Her contributions span technical tools like the Port software and theoretical insights on panel conditioning and ethical data augmentation. Scientific Awards: GOR Thesis Award 2015 She serves as a board member for the German Society for Online Research, Program Chair for the General Online Research Conference (GOR), and holds advisory roles in organizations like the European Social Survey, SHARE ERIC, and ODISSEI. She is an associate editor for multiple journals, including the Journal of Survey Statistics and Methodology and Survey Research Methods.

• Astrophysics
• Gravitational Waves
• Binary Evolution
• +۳ مورد دیگر

Dr. Marc van der Sluys van der Sluijs is a researcher at Utrecht University's Department of Gravitational and Subatomic Physics (GRASP) and the Dutch National Institute for Nuclear and High Energy Physics (Nikhef) in Amsterdam. His academic focus spans gravitational-wave detection, binary evolution, and computational astrophysics, with active roles in the Virgo, LIGO, and Einstein Telescope collaborations. Research Interests: His work centers on gravitational-wave data analysis, neutron star and black hole coalescences, common-envelope evolution, and multi-messenger astronomy. He employs heavy computing and Bayesian statistics for empirical modeling of astrophysical phenomena. Teaching: He teaches Introduction to Astrophysics and Stellar Evolution in Utrecht University's physics bachelor program. Publications: His recent articles (2019–2025) predominantly explore gravitational-wave detection methodologies, dark matter searches, and solar position algorithms. Key themes include machine learning applications in astrophysics, multi-instrument data analysis, and open-source software development for scientific computation. Ancillary Activities: He founded hemel.waarnemen.com , a popular Dutch astronomy website with 1–2 million annual visits, providing observational guides for celestial phenomena in Belgium and the Netherlands.