جست‌وجوی استاد و پژوهشگر

Max Hinneمشاهده پروفایل

استادیار

• Bayesian Statistics
• Machine Learning
• Neuroscience
• +۶ مورد دیگر

Max Hinne is an assistant professor at the Department of Artificial Intelligence, Radboud University, Nijmegen, The Netherlands, where he leads the Uncertainty in Complex Systems research group. His work bridges artificial intelligence, neuroscience, and statistics through advanced Bayesian methodologies. Dr. Hinne's research focuses on Bayesian modeling of brain networks using neuroimaging data. His primary interests include: Bayesian nonparametric models, particularly Gaussian processes Structural and functional brain connectivity analysis Predictive modeling of neural systems Causal inference frameworks Uncertainty quantification in complex systems Development of computational tools for neuroscience His approach emphasizes how probabilistic methods can address uncertainty in complex biological systems while providing interpretable models of brain function. Analysis of Dr. Hinne's publication trajectory reveals a consistent focus on Bayesian methods applied to increasingly diverse domains. Starting with foundational work in brain connectomics, his research has expanded to include applications in developmental psychology, medical genetics, and educational technology. His most recent work demonstrates sophisticated integration of nonparametric Bayesian methods with domain-specific challenges, particularly in handling uncertainty in complex, high-dimensional data across multiple scientific fields. Dr. Hinne actively mentors students and invites master's thesis projects focused on Bayesian nonparametric methods for neuroimaging data. He has developed several software tools including the Bayesian Connectomics Toolbox (BaCon), latent space modeling code, and GP CaKe for causal inference. His research group maintains strong connections with the Donders Institute for Brain, Cognition and Behaviour, facilitating interdisciplinary collaborations between statisticians, neuroscientists, and domain experts.

Francisco Blasquesمشاهده پروفایل

استاد

• Econometrics
• Statistics
• Data Science
• +۵ مورد دیگر

Francisco Blasques is a Full Professor of Econometrics and Data Science at the Vrije Universiteit Amsterdam (VU) and a Research Fellow at the Tinbergen Institute. He holds leadership roles, including Co-founder of Churned (QuantIQ) and datastuff BV, and served as TI Doctoral Director for the School of Business and Economics. His research focuses on econometrics, statistics, and data science, with expertise in time-series analysis, financial econometrics, and nonlinear dynamics. Teaching includes advanced econometrics courses at VU and Tinbergen Institute, covering topics like time-series modeling and financial econometrics. His PhD supervision spans over 15 students since 2012, focusing on econometric methods and applications. Research contributions emphasize score-driven models, volatility analysis, and spatial econometrics. Key publications address topics such as natural disaster impacts on economies, robust volatility estimation, and fusion of experimental/observational data. Awards include the NWO-VIDI Grant (2019) for methodological innovations in econometrics. Professional activities include ancillary roles as Partner at QuantiQ BV and Director at Metyis, alongside academic duties. His work contributes to UN Sustainable Development Goals through economic resilience and policy analysis.

Clara Stegehuisمشاهده پروفایل

دانشیار

• Random Graphs
• Network Science
• Theoretical Computer Science
• +۴ مورد دیگر

Clara Stegehuis serves as Associate Professor in Mathematics of Operations Research, specializing in the mathematical foundations of complex networks. Her research establishes critical connections between theoretical graph models and real-world applications in wireless communications and chemical reaction systems, with 68 research outputs including 34 articles and 16 preprints. Her primary research domains include: Random Graph Theory and Scale-Free Networks Hypergraph Modeling and Sampling Algorithms Geometric Network Analysis Stochastic Processes on Networks Combinatorial Optimization for Network Structures Network Applications in Wireless Systems Recent publications (2023-2025) demonstrate methodological innovation in hypergraph sampling (Hypercurveball algorithm) and geometric network detection, while addressing practical challenges in mmWave networks, cellular resilience, and reaction network analysis. Her work consistently bridges abstract mathematical frameworks with engineering applications through rigorous probabilistic modeling. Dr. Stegehuis has supervised two graduate students and contributed two significant datasets to the research community. Though no specific awards are documented in the provided materials, her h-index of 9 and consistent publication output (10 papers in 2025) reflect substantial scholarly impact. Current research initiatives focus on multi-connectivity optimization for wireless networks, geometric structure detection in scale-free graphs, and applying random graph theory to model out-of-equilibrium chemical systems, often through interdisciplinary collaboration with domain experts.

Joris Bierkensمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Markov Chain Monte Carlo
• Piecewise Deterministic Markov Processes
• +۷ مورد دیگر

Joris Bierkens is an Associate Professor in Statistics at the Delft Institute of Applied Mathematics, Delft University of Technology (TU Delft), and a Visiting Associate Professor at the University of Warwick. His research focuses on computational challenges in Bayesian statistics and statistical physics, particularly through the development and analysis of Markov Chain Monte Carlo (MCMC) methods based on Piecewise Deterministic Markov Processes (PDMPs). His research interests include Bayesian inference, stochastic processes, probabilistic computation, and Monte Carlo methods. He is particularly known for his work on the Zig-Zag and Boomerang samplers, which offer efficient alternatives to traditional MCMC in high-dimensional and complex settings. These methods are applied in statistical learning, physics, and industrial R&D. The recent publications highlight a strong trend in theoretical and applied aspects of PDMP-based sampling, including scaling analysis, boundary conditions, infinite-dimensional extensions, and federated computation. Keywords across articles include Bayesian statistics, applied probability, and computational mathematics, with subfields ranging from high-dimensional inference to molecular dynamics and diffusion bridges. Associate Editor, Applied Probability Trust (Journal of Applied Probability, Advances in Applied Probability) Joris Bierkens advises BSc and MSc students on research projects in MCMC, stochastic processes, and probabilistic computation. He collaborates with industry on Bayesian modeling and MCMC applications. His research is supported by academic collaborations and public dissemination through arXiv, GitHub, and open-access journals. He is a key member of the applied mathematics team at TU Delft, contributing to both theoretical developments and practical implementations of novel sampling algorithms. His work bridges applied mathematics, statistics, and machine learning, with software implementations available on GitHub for reproducibility.

Johannes Schmidt-Hieberمشاهده پروفایل

استاد

• Statistics
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Johannes Schmidt-Hieber is a Professor of Statistics at the University of Twente, Department of Applied Mathematics. He has held positions at Leiden University (2014-2018) and conducted postdoctoral research at Vrije Universiteit Amsterdam and ENSAE Paris. His work bridges deep learning theory with nonparametric statistics, focusing on convergence rates, regularization techniques, and Bayesian inference. Education: PhD from Universität Göttingen and Bern (2010), studies at Freiburg and Göttingen Research: Specializes in statistical learning theory, neural network analysis, and high-dimensional inference His 15 most recent publications address topics like GCN convolutions in regression, implicit regularization in deep networks, and posterior contraction in Gaussian processes. Key contributions include theoretical guarantees for optimization algorithms and bias-variance trade-off analysis. Scientific recognition includes: ERC Consolidator Grant (2024-2029) IMS Fellow (2024) Van Dantzig Award (2025) Frontiers of Science Award (2025) NWO Vidi grant (2019-2024) He served as associate editor for leading journals and organized multiple international workshops. Current projects include mathematical foundations of machine learning and statistical uncertainty quantification.

Thijs W. van de Laarمشاهده پروفایل

استادیار

• Probabilistic Programming
• Active Inference
• Bayesian Signal Processing
• +۴ مورد دیگر

Thijs W. van de Laar is an Assistant Professor at the Eindhoven University of Technology in the Department of Electrical Engineering , affiliated with the Bayesian Intelligent Autonomous Systems lab (BIASlab) and the EAISI Foundational and High Tech Systems groups. His research focuses on intelligent systems for decision-making under dynamic conditions, particularly through scalable probabilistic programming implementations of Active Inference. Academic Background : PhD (2019, TU/e) in automated Bayesian signal processing algorithm design; MSc in natural sciences (biophysics, science communication, Radboud University). Van de Laar's research integrates probabilistic programming with active inference to develop autonomous agents capable of real-time adaptation. Key methodologies include variational message passing , factor graph representations , and free energy minimization . His recent publications examine active inference formalisms, factor graph applications in AI, and uncertainty reduction strategies. Collaborative work spans hearing aid technology, autonomous navigation, and synthetic intelligence development.

• Machine Learning
• Neural Networks
• Neuromorphic Computing
• +۳ مورد دیگر

Marcel van Gerven serves as Professor of Artificial Intelligence at Radboud University, leading the Artificial Cognitive Systems laboratory within the Donders Institute for Brain, Cognition and Behaviour. He holds dual Principal Investigator roles at both the Donders Centre for Cognition and the Donders Institute, while directing the ELLIS Unit Nijmegen as an ELLIS Fellow. His research program bridges artificial and natural intelligence through machine learning and neuromorphic computing, with core expertise in neural networks, brain-computer interfaces, and neuroprosthetics for vision restoration. Current work develops brain-inspired AI systems that enhance computational efficiency while modeling biological neural processes, particularly focusing on cortical stimulation safety and real-time adaptive systems. Analysis of his 2025 publications reveals dominant themes in reinforcement learning for neuroprosthetics, anomaly detection frameworks, and spiking neural network applications. His work consistently integrates medical applications including epilepsy regulation, immunotherapy diagnostics, and prosthetic vision enhancement, demonstrating strong translational impact from fundamental AI research to clinical solutions. Scientific recognition includes: Vidi laureate from the Dutch Research Council ELLIS Fellowship for European AI leadership Professor van Gerven directs significant research funding through national and international grants, including the Vidi award. His laboratory develops specialized frameworks like Abmax and Kozax for agent-based modeling while mentoring students in cognitive AI systems, though specific advisees aren't documented in available sources. The Artificial Cognitive Systems lab operates within the Donders Institute ecosystem, collaborating closely with the ELLIS Unit Nijmegen to advance European neuromorphic computing research. Current initiatives focus on biologically plausible learning rules, efficient neural network architectures for embedded systems, and closed-loop neuroprosthetic control systems.

Igor Borisovمشاهده پروفایل

استاد

• Probability Theory
• Mathematical Statistics
• Stochastic Processes
• +۴ مورد دیگر

Prof. Igor Borisov is a distinguished academic affiliated with Novosibirsk State University (NSU) as a Professor in the Chair of Probability Theory and Mathematical Statistics. He also holds the position of Top-level Research Fellow at the Sobolev Institute of Mathematics, Russia. His career spans over five decades, with a Doctor of Science degree (1988) and a Ph.D. in Probability and Statistics (1978). Educational Background: Novosibirsk State University (1969-1974), followed by a Post Graduate Course at Sobolev Institute of Mathematics (1974-1978). His research focuses on Probability Theory, Mathematical Statistics, and Stochastic Processes, with significant contributions to Poisson approximation, limit theorems, and asymptotic analysis of random walks. His recent work explores kernel-type estimators, exponential inequalities for dependent data, and canonical statistics in nonlinear regression models. The 15 most recent articles highlight his expertise in Poisson approximation, canonical processes, and random walk dynamics. Topics include asymptotic behavior, exponential inequalities, and statistical inference for dependent observations. He has supervised 11 Ph.D. students, including Evgenii Shefer (2021) and Vasilii Zhechev (2018), and contributes to teaching through materials like Probability and Statistics lectures at NSU.

James Townsendمشاهده پروفایل

پژوهشگر

• deep generative models
• lossless compression
• unsupervised learning
• +۴ مورد دیگر

James Townsend, also known as Jamie, is a machine learning researcher at the Amsterdam Machine Learning Lab (AMLab) within the Informatics Institute at the University of Amsterdam. He completed his PhD in 2020 at the UCL AI Centre in London under the supervision of Professor David Barber. His educational background includes a PhD in lossless compression with latent variable models from University College London, with prior research contributions to the Autograd library and early development of JAX during a Google Brain internship in 2018. Townsend's research centers on deep generative models and lossless compression, extending to unsupervised learning, approximate inference, Monte Carlo methods, optimization, and machine learning software systems. His work bridges theoretical information theory with practical implementation, particularly in neural compression techniques. He has significantly contributed to open-source tools including Autograd and JAX, demonstrating expertise in automatic differentiation systems. His publication record spans high-impact venues like NeurIPS, ICLR, and ICML, with recent work focusing on innovative compression paradigms for complex data structures including graphs and multisets. Key contributions include shuffle coding, reversible programming for compression verification, and multiset compression techniques that challenge conventional approaches. Scientific recognition includes: Best Paper Award at Deep Generative Models and Downstream Applications Workshop (2021) Townsend actively participates in the research community through invited talks at Stanford's Information Theory Forum and the Languages for Inference workshop. His collaborations span academic institutions and industry partners like Google Brain, with current work centered on advancing lossless compression through deep learning at the AMLab. He maintains an active open-source presence via GitHub (@j-towns) and technical discourse on Twitter (@_j_towns), while publishing through Google Scholar under his formal name James Townsend.

M.B. Eichlerمشاهده پروفایل

دانشیار

• Time Series Analysis
• Econometrics
• Causal Inference
• +۳ مورد دیگر

Dr. M.B. Eichler is an Associate Professor of Econometrics at the QE Econometrics Department within the School of Business and Economics at Maastricht University. His research focuses on advancing methodologies for causal inference in time series analysis, particularly addressing limitations in Granger causality and developing algorithms for multivariate time series with latent variables. He collaborates with institutions like Raytheon BBN Technologies on projects involving heterogeneous data sources for predicting rare events. His expertise spans econometrics, functional time series analysis, and dynamic factor models. Notable projects include electricity spot price modeling, semi-parametric approaches for non-stationary processes, and applications in neuroscience and energy economics. He advises PhD students such as Carlos A. Moreno, Dennis Tuerk, and Anne van Delft. Research Themes: Causal inference, Granger causality, time series econometrics, dynamic factor models Labs/Teams: QE Econometrics Research Group Grants: Open Source Indicator (OSI) Program funded by IARPA Publications highlight contributions to spectral analysis, graphical modeling of time series, and applications in energy markets. His work bridges theoretical advancements with practical challenges in economics, neuroscience, and engineering.

Paulus Bekkerمشاهده پروفایل

استاد

• Econometrics
• Statistical Modeling
• Financial Economics
• +۲ مورد دیگر

Paulus Bekker is a Professor in the Department of Economics, Econometrics and Finance at the University of Groningen's Faculty of Economics and Business. His research specializes in advanced econometric methodologies and financial modeling techniques. Research focuses on developing statistical methods for economic and financial applications. Primary interests include econometric theory, instrumental variable techniques, dynamic financial modeling, and arbitrage-free pricing frameworks. Recent work examines concentrated instrument methodologies and generalized yield curve modeling. Publications demonstrate consistent methodological innovation in econometrics. Recent articles feature applications in panel data modeling, dynamic mean-variance analysis, and robust estimation techniques. Research frequently addresses challenges in financial model specification and statistical inference.

Katharina Prokschمشاهده پروفایل

پژوهشگر

• Statistics
• Biomedical Imaging
• Data Analysis
• +۴ مورد دیگر

Katharina Proksch is a researcher specializing in advanced statistical methodologies and their applications across diverse scientific domains. Her work focuses on regression models, simultaneous inference, and transforms, with notable contributions to nonparametric testing, inverse problems, and quantitative imaging techniques. She explores interdisciplinary applications in biomedical imaging, astrophysics, and biomechanical analysis, emphasizing rigorous statistical frameworks to ensure methodological robustness. Her research interests include developing statistical tools for super-resolution microscopy, fatigue detection through biomechanical data, and analyzing complex datasets from fluorescence microscopy and X-ray astronomy. Proksch's methodologies often address challenges in handling measurement errors, outliers, and multiscale data structures. Key contributions include a two-sample test using Wilcoxon rank sums, quantitative super-resolution microscopy techniques with statistical guarantees, and fatigue detection algorithms leveraging sequential testing. Her work bridges theoretical statistics with practical applications in health monitoring and astrophysical phenomena modeling.

Alexander Van Werdeمشاهده پروفایل

پژوهشگر

• Random Matrix Theory
• Spectral Algorithms
• Stochastic Processes
• +۵ مورد دیگر

Alexander Van Werde is a doctoral researcher at Eindhoven University of Technology, affiliated with the Department of Mathematics and Computer Science. He is supervised by Jaron Sanders and recently defended his PhD thesis titled Latent structures in Markovian processes and associated random matrices . He will transition to a postdoctoral researcher role at the Cluster of Excellence Mathematics Münster. Education: Current PhD candidate at TU/e Research interests span random matrix theory , Markovian processes , algebraic geometry , and applications of spectral algorithms . His work explores spectral characterization of networks, latent structures in stochastic processes, and intersections with algebraic combinatorics. Additionally, he contributes to theoretical computer science through Lean4-based proof formalization . Recent articles focus on dependent random matrices , semipermeable barriers in Brownian motion , and algebraic properties of Bernstein-Sato ideals . These works emphasize asymptotic behavior , leading-order term analysis , and spectral concentration phenomena . Scientific awards include: 2023 Best Poster Award for universality-based concentration in dependent random matrices Projects include OCENW.KLEIN.324 on Markov chain clustering (2021–2025). He also engages in teaching (Probability and Stochastics 1) and public engagement through blogs and art creation.

• Theoretical Community Ecology
• Evolutionary Ecology
• Phylogenetic Community Ecology
• +۵ مورد دیگر

Rampal S. Etienne is a Full professor in theoretical and evolutionary community ecology at the University of Groningen, affiliated with the Faculty of Science and Engineering and the Groningen Institute for Evolutionary Life Sciences (GELIFES). He serves as Scientific Director of GELIFES and is a member of the editorial board of the Journal of Theoretical Biology . His research focuses on understanding community assembly processes, particularly through integrating ecological and evolutionary dynamics using stochastic models. Key areas include phylogenetic community ecology, diversification models, and experimental evolution with mites and bacteria. He has developed influential software tools such as DAISIE and DDD for analyzing biodiversity patterns and evolutionary processes. His work bridges theoretical frameworks with empirical data from microorganisms, land snails, and other organisms to address fundamental questions in ecology and evolution. Education and training details are not explicitly stated, but his academic contributions highlight expertise in theoretical ecology and evolutionary biology. Research interests emphasize the interplay between ecological processes (e.g., drift, competition) and evolutionary mechanisms (e.g., speciation, extinction), with a focus on island biogeography and macroevolutionary patterns. Collaborative projects include experimental evolution studies and large-scale biodiversity modeling. His publications span topics like phylogenetic tree analysis, species distribution modeling under climate change, and microbiome-host interactions. He has supervised numerous PhD students and postdocs, contributing to a vibrant research group. Notably, Professor Etienne’s awards and grants are not detailed here, but his leadership roles and extensive publication record reflect significant contributions to the field. His research group develops open-source software (e.g., treestats, nLTT) to facilitate data analysis in evolutionary and ecological studies. Current projects explore the impacts of climate change on biodiversity, evolutionary rescue mechanisms in canids, and the dynamics of microbial communities.

Maria Vlasiouمشاهده پروفایل

استاد

• Stochastic processing networks
• Queueing theory
• Energy networks and EV charging
• +۶ مورد دیگر

Maria Vlasiou is a Full Professor of Mathematics of Operations Research at the University of Twente, an Associate Professor at Eindhoven University of Technology, and a Research Fellow of EURANDOM. Her work focuses on stochastic processing networks, particularly layered architectures arising in energy systems, high-tech manufacturing supply chains, and bandwidth-sharing networks. Education & Qualifications UTQ (University Teaching Qualification) diploma obtained in 2010; continuous professional-education training thereafter. Research Interests Vlasiou’s research spans the performance analysis of stochastic networks, with special emphasis on: Electric-vehicle charging networks and congestion control. Extreme-value theory for fork-join queues inspired by ASML/Philips/Boeing supply chains. Lévy-driven queues, heavy-tailed risk models, and large-deviations asymptotics. Resource-allocation problems involving non-convex optimisation and heavy-traffic limits. She combines rigorous probabilistic methods (fluid & diffusion approximations, perturbation analysis) with practical applications in energy, logistics and manufacturing. Scientific Awards & Recognition AMSI-ANZIAM Lecturer 2017 Marcel Neuts Student Paper Award (MAM8) 3rd Prize, 8th Conference in Actuarial Science & Finance 2014 Best Paper Award, ICORES 2013 Excellent Course Prize, University of Twente 2021 Grants & Projects She has attracted over €4.5 million in external funding from NWO, Australian Research Council, 4TU.CEE, and industry, including current NWO LTP ROBUST programme on “AI for Energy Grids” (2023-2032). Teaching & Supervision Vlasiou teaches Probability, Stochastic Processes, Performance Modelling and Infectious-Disease Epidemiology. She has supervised nine PhD dissertations and numerous BSc/MSc theses, internships and PDEng projects, and leads educational-innovation grants on mini-lectures and student engagement. Editorial & Leadership Roles Associate editor for OR Spectrum, IISE Transactions, Math. Methods of OR, and INFOR. She represents the Netherlands at the IMU Committee for Women in Mathematics and serves on advisory boards for national women-in-math organisations in Cyprus and Greece.