جست‌وجوی استاد و پژوهشگر

• Perovskites
• Machine Learning
• Materials Science
• +۱۱ مورد دیگر

M. Bokdam is an Assistant Professor at the Computational Chemical Physics department within the MESA+ Institute at the University of Twente. His work bridges computational physics and materials science, focusing on advanced simulation techniques and machine learning applied to complex material systems. Research interests include Perovskite materials and their thermal/electronic properties Machine learning force fields for ab initio molecular dynamics Spin dynamics and quantum mechanical modeling Graphene-based heterostructures and interface phenomena Phase transitions and structural distortions in 2D materials Recent publications highlight trends in using AI-driven simulations to study perovskites and graphene interfaces, with keywords spanning computational physics, materials science, and thermodynamics. His work has been recognized through prizes in 2022, including the FAIR DATA Fund and Onderwijsprijs TN (Teaching Award in Natural Sciences). Scientific awards include FAIR DATA Fund 2022 Onderwijsprijs TN 2022 Applied Physics Prize 2022 Key datasets co-authored by Bokdam include force field training databases for CsPbBr3, LaMnO3/SrRuO3, and mixed MAPbI3−xBrx perovskites, emphasizing open science practices.

Anne Opschoorمشاهده پروفایل

دانشیار

• Financial Econometrics
• Time Series Econometrics
• Risk Management
• +۳ مورد دیگر

Dr. Anne Opschoor is an Associate Professor in the Department of Finance at Vrije Universiteit Amsterdam and a research fellow at Tinbergen Institute. She holds a PhD from the Tinbergen Institute/Econometric Institute at Erasmus University Rotterdam (2014) and a master's degree in financial econometrics from Erasmus University Rotterdam. Her research focuses on financial econometrics, time series analysis, risk management, and copula models. She has received notable awards, including the NWO VIDI Grant (2021) and the 2014 Journal of Applied Econometrics Dissertation Prize. Her teaching includes courses such as Empirical Finance, Quantitative Research Methods, and Mathematics for Finance. She has supervised PhD theses and contributes to grants like the NWO VIDI-funded project on extreme risks in high dimensions. Her work frequently addresses volatility modeling, tail risk, and multivariate financial dependencies, leveraging computational tools like the R package MitISEM. She has published extensively in journals such as the Journal of Financial Econometrics and Journal of Applied Econometrics.

• Nonparametric Bayesian inference
• Multiple hypothesis testing
• High-dimensional data
• +۶ مورد دیگر

Professor Subhashis Ghoshal is affiliated with North Carolina State University in the United States. His research focuses on advanced statistical methodologies for analyzing complex data structures. Nonparametric Bayesian inference Multiple hypothesis testing High-dimensional data Bayesian imaging Functional data analysis Nonparametric regression Survival analysis

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.

Udo Böhmمشاهده پروفایل

پژوهشگر

• Bayesian Statistics
• Computational Psychology
• Diffusion Models
• +۲ مورد دیگر

Udo Böhm is a researcher in the Machine Learning group at Centrum Wiskunde & Informatica (CWI) in Amsterdam. His work bridges Bayesian statistics, computational psychology, and numerical mathematics through advanced modeling techniques. Research Focus : Bayesian inference, diffusion models, first-passage time analysis Technical Expertise : Numerical approximation of partial differential equations Collaborations : Extensive collaborations with psychologists and mathematicians His publications demonstrate interdisciplinary applications of machine learning in psychological modeling and mathematical problem-solving. Current work involves developing anytime-valid confidence sequences and improving computational methods for diffusion processes. Notable contributions include: Advancements in hierarchical diffusion decision models Efficient numerical algorithms for non-regular Fokker–Planck equations Foundational guidelines for Bayesian analysis in JASP

• Quantitative Marketing
• Digital Marketing
• Consumer Policy
• +۲ مورد دیگر

Xi Chen is an Associate Professor at the Rotterdam School of Management, Erasmus University, specializing in the Department of Marketing Management. His research bridges quantitative marketing, digital marketing, and social network dynamics, with a focus on how digital platforms influence consumer behavior and inform corporate strategies. Education: PhD in Marketing from Hong Kong University of Science and Technology, supervised by Prof. Ralf van der Lans; earlier studies at Nanjing University. Xi Chen's work examines incomplete information in online social networks, causal inference, and consumer policy. His recent publications highlight advanced methodologies for social network modeling and decision analytics. Articles like Efficient Estimation of Network Games (2021) and Estimating Tie Strength (2014) reflect his expertise in leveraging social network data for marketing insights. His research also intersects with interdisciplinary applications, such as the INSIGHT study (2020) on combined therapies for non-small-cell lung cancer, where he contributed as a co-author. This illustrates his broader engagement with data science methodologies applicable to diverse domains. Xi Chen actively shares insights on digital marketing trends, as seen in media coverage like the Online Social Gaming analysis (2021). His contributions span peer-reviewed journals, book chapters, and working papers, emphasizing empirical rigor and policy relevance.

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.

• Bayesian Networks
• Probabilistic Graphical Models
• Sensitivity Analysis
• +۴ مورد دیگر

Silja Renooij is an Associate Professor at Utrecht University's Department of Information and Computing Sciences, specializing in Artificial Intelligence and Intelligent Systems . Her work bridges Bayesian networks , probabilistic graphical models , and human-centered AI , with a focus on uncertainty quantification, sensitivity analysis, and legal reasoning applications. Current affiliation: Utrecht University Department: Information and Computing Sciences Academic rank: Associate Professor Contact: s.renooij@uu.nl Research interests include: Bayesian network construction and sensitivity analysis Probabilistic reasoning in legal and medical domains Interpretable AI through scenario-based modeling Conflict detection in black-box systems Hybrid human-AI reasoning frameworks Probability elicitation and evidence evaluation Recent publications demonstrate her focus on explainable AI through MAP-independence analysis, legal evidence modeling , and robust decision support systems . Her work often combines argumentation theory with probabilistic graphical models , emphasizing human-AI collaboration in critical domains like healthcare and law.

Daan Meerburgمشاهده پروفایل

استادیار

• Cosmology
• Cosmic Microwave Background
• Inflationary Model Building
• +۴ مورد دیگر

Daan Meerburg is Assistant Professor at the University of Groningen , Faculty of Science and Engineering, embedded in the Van Swinderen Institute for Particle Physics and Gravity . He directs the VIDI programme ‘the cosmic laboratory’ and holds leadership roles in the Simons Observatory, CCAT-prime and REACH collaborations. Education PhD (cum laude) in Cosmology, University of Amsterdam, 2011 – advisors Jan Pieter van der Schaar & Ralph Wijers Rubicon Fellow, Princeton University CITA Fellow, University of Toronto Senior Kavli Fellow, University of Cambridge Research Interests Meerburg’s research straddles theoretical and observational cosmology . Core themes include the physics of the cosmic microwave background , primordial non-Gaussianity , inflationary model building , gravitational waves and the epoch of reionisation . Recently he has pioneered the use of artificial intelligence to extract faint cosmological signatures from large data sets, always cross-validated against conservative statistical methods. Recent Publication Trends Over 2022-2025 his output focuses on next-generation CMB experiments (Simons Observatory, CCAT-prime), global 21-cm observations with REACH, and neural-network-assisted inference of primordial non-Gaussianity and reionisation history. The work integrates instrument design, advanced statistical techniques and forefront theory. Honours & Awards Templeton Grant (2012) VIDI Grant, Dutch Research Council (2017) Rubicon Fellowship (Princeton) Kavli Senior Fellowship (Cambridge) CITA Fellowship (Toronto) Grants & Leadership Principal Investigator , VIDI project ‘the cosmic laboratory’ Science Leadership , Simons Observatory & CCAT-prime consortia Instrument Team , REACH 21-cm experiment Labs & Teams Meerburg leads the Cosmic Frontier research group within the Van Swinderen Institute. The team contributes to hardware development, data pipelines and theoretical interpretation for Simons Observatory, CCAT-prime and REACH, fostering close collaboration between cosmologists, instrumentalists and data scientists.

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.

Irene Klugkistمشاهده پروفایل

استاد

• Bayesian Statistics
• Circular Data Analysis
• Multivariate Statistics
• +۳ مورد دیگر

Prof. Dr. Irene Klugkist is a Professor of Methodology and Statistics at Utrecht University's Faculty of Social and Behavioural Sciences, serving as Vice Dean for the Social and Behavioral Sciences. Her research focuses on Bayesian statistics, circular data analysis, and informative hypotheses evaluation, with recent emphasis on Bayesian research synthesis methods for conceptual replications. She holds a PhD in Bayesian Analysis (2005) and has contributed to over 90 peer-reviewed publications. Klugkist's work bridges statistical methodology with applications in psychology, education, and health sciences, emphasizing reproducibility and transparent research practices through preregistration and open data advocacy. She leads the Methodology and Statistics department, guiding interdisciplinary research projects and graduate training. Research Interests: Bayesian inference methods, circular statistics, evidence synthesis, statistical modeling for behavioral and health sciences, and methodological innovations in hypothesis testing. Her work often addresses translational challenges, such as integrating qualitative and quantitative data in mixed methods reviews or applying expert priors in veterinary epidemiology. Recent Contributions: Her 2024 papers advance Bayesian alternatives to meta-analysis and explore student-program matching dynamics. Ongoing work includes developing circular statistical tools for behavioral measurements and improving Bayesian approaches to latent class models in diagnostic testing. Klugkist also advocates for transparent research practices through guidelines on preregistration and data sharing in psychopathology studies.

• Advanced Feedforward Control
• Learning Control
• Repetitive Control
• +۴ مورد دیگر

Lennart L.G. Blanken is a part-time Assistant Professor in the Control Systems Technology group at the Mechanical Engineering Department of Eindhoven University of Technology (TU/e). He also works at Sioux Technologies in the Mechatronics department since 2019, focusing on projects in the semiconductor and printing industries. His research emphasizes advanced control strategies including feedforward control, learning control, repetitive control, and system identification, with applications in mechatronic systems. Academically, he earned his MSc (cum laude) in Mechanical Engineering from TU/e in 2015 and his PhD in 2019, both under the supervision of Oomen, with a thesis on learning and repetitive control for complex systems applied to large format printers. His work has been published in journals like Meathctronics and Mechanical Systems and Signal Processing , focusing on frequency-domain methods, iterative learning control, and multirate systems. Key collaborations include projects with Philips Innovation Services, Océ Technologies (Canon Production Printing), and international researchers. His contributions span theoretical control development and industrial applications, with a focus on enhancing system performance through innovative control methodologies.

• Neuroscience
• Psychology
• Cognitive Psychology
• +۷ مورد دیگر

Johannes Fahrenfort is an Assistant Professor at the Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, with a focus on cognitive psychology and neuroscience. His research explores the neural mechanisms underlying perception, decision-making, and consciousness. Key Research Areas: Metacognition, Working Memory, Electroencephalography (EEG), Functional MRI, and the interplay between conscious and unconscious processing. Recent Projects: Templeton Grant on statistical frameworks for unconscious cognition, Advanced Human Capital Program grant on cognitive neuroscience, and PERISCOPE on visual search and peripheral vision. Research Trends: His recent articles investigate decision-making confidence, neural decoding of working memory, catecholamine effects on perception, statistical rigor in consciousness studies, and arousal-dependent cognitive performance. Keywords span Neuroscience , Psychology , and Neural Oscillations . Scientific Awards: A.F. Sandersprice (2007), Best Lecturer for the Research Master Psychology (2019), and Open Science Award FBMS (2024). Academic Contributions: Frequent lecturer in Cognitive Neuroscience, co-supervisor of PhD theses, and creator of open-access datasets and software tools for EEG analysis.