Find Academic Supervisors

Hugo QuenéView profile

Professor

• Quantitative Research Methods
• Linguistics
• Speech Perception
• +3 more

Prof. Hugo Quené is a Professor of Quantitative Methods of Empirical Research in the Humanities at Utrecht University, affiliated with the Faculty of Humanities and the Department of Languages, Literature and Communication. His research focuses on quantitative research methods, self-monitoring in speech, phonetics, and digital humanities. He is director of the Centre for Digital Humanities and co-leads the FAIR Data and Software theme in the Open Science Office. Education: MA in Phonetics (1984), PhD in Linguistics (1989), both from Utrecht University. Administrative roles: Director of Education (2014–2019), Fulbright Visiting Scholar at Indiana University (2001–2002). Research interests: Quené’s work spans quantitative methods, speech errors, phonetic analysis, and open science. He emphasizes reproducibility, FAIR data principles, and the application of statistical tools like R and JASP. His research often explores cross-linguistic phonetic variation and the impact of prosody on discourse interpretation. Key contributions include developing the Utrecht Corpus of Speech Errors and the longitudinal UCU English Accents project. His textbook Quantitative Methods and Statistics is widely used in academic settings. Labs/Teams: Active in the Institute for Language Sciences (ILS) and leads the Utrecht Platform for Applied Data Science. Collaborates with the Clariah-NL supervisory board on digital infrastructure for humanities research.

Elay ShechView profile

Professor

• Philosophy of Science
• Philosophy of Physics
• Logic
• +3 more

Elay Shech is a Professor of Philosophy at Auburn University, specifically within the Department of Philosophy, College of Liberal Arts. His research focuses on philosophy of science, physics, logic, biology, ethics, and the philosophical implications of artificial intelligence and machine learning. Shech earned his PhD from the Department of History and Philosophy of Science at the University of Pittsburgh under advisors John Earman and John D. Norton. He has held visiting fellowships at Durham University, National University of Singapore, University of Sydney, University of Bristol, Utrecht University, and visiting scholar/professor roles at Hebrew University of Jerusalem and Tel Aviv University. Education: PhD in History and Philosophy of Science (University of Pittsburgh, 2013–2016). His work includes authored books like *Idealizations in Physics* (Cambridge, 2023) and *The Metaphysics of Color* (Cambridge, 2025), as well as co-edited volumes such as *Scientific Understanding and Representation: Modeling in the Physical Sciences* (Routledge, 2023). Recent research explores topics like the philosophy of machine learning, evolutionary fitness concepts, and color objectivism. Research Interests: Elay Shech investigates the interplay between scientific understanding, representation, and idealization across various disciplines. His work challenges traditional frameworks of scientific realism and induction, advocating for context-sensitive approaches. He co-edits special issues on topics such as infinite idealizations, philosophy of statistics, and machine learning models, emphasizing their epistemic and ethical dimensions. Publications Trends: His articles bridge philosophy of science with cutting-edge fields like AI ethics and quantum mechanics. He critiques the 'Eddington inference' in scientific realism and explores how deep learning models contribute to scientific understanding despite their opacity. Recent work emphasizes interdisciplinary themes, including the objectivity of color perception and the philosophical implications of phase transitions in physics. Scientific Awards: Focus on teaching excellence and pedagogical innovation, including awards for online education and contributions to technology-enhanced learning. Teaching & Grants: Shech has received grants for distance education and is a Mellon Fellow in Technology-Enhanced Learning. He teaches courses on ethics in science, philosophy of AI, and ancient philosophy, often in distance-learning formats. His teaching awards highlight his dedication to innovative pedagogy. Labs/Teams: Collaborates extensively with researchers like Michael Tamir (machine learning) and Kyle Heine (evolutionary biology), co-editing special journal issues and contributing to interdisciplinary projects. His work often centers on debates about scientific realism and epistemological frameworks in physics and AI.

Jouke Jan HottengaView profile

Research Fellow

• Genetics
• Genomics
• Molecular Biology
• +5 more

Jouke Jan Hottenga is a Visiting Research Fellow at the Vrije Universiteit Amsterdam, affiliated with the Faculty of Behavioural and Movement Sciences (Biological Psychology department) and the Amsterdam Public Health (APH) research institute's Health Behaviors & Chronic Diseases and Personalized Medicine divisions. His work focuses on genetic and epigenetic mechanisms underlying complex traits, including psychiatric disorders, neurodevelopmental outcomes, and environmental interactions. He has contributed to large-scale genome-wide association studies (GWAS), twin studies, and polygenic risk score analyses. Key research interests include the genetic basis of developmental milestones, mental health, and chronic diseases. Hottenga has co-authored over 498 publications, with recent work emphasizing the role of genetic variants in income-related health disparities and the epigenetic influences of asthma polygenic burden. He teaches courses on genome-wide association methodologies and collaborates internationally on projects like the Netherlands Twin Register. Notable contributions include studies on twin zygosity assessment, ADHD polygenic risk scores, and the genetic architecture of female fertility. His datasets and software contributions highlight expertise in molecular genetics, metabolomics, and epigenetic analyses.

• Information Theory
• Machine Learning
• Deep Neural Networks
• +7 more

Shujian Yu is an Assistant Professor at the Department of Artificial Intelligence , part of the Faculty of Science at Vrije Universiteit Amsterdam. He is also affiliated with the Network Institute . His research focuses on Information Theory , Machine Learning , and Deep Neural Networks , with applications in causal discovery, brain network analysis, and generalization bounds. Key research interests include: Information-Theoretic Methods for ML interpretability and robustness Transfer entropy and causal inference in complex systems Feature selection and dimensionality reduction techniques Brain network-based psychiatric diagnosis (e.g., schizophrenia analysis) He teaches courses on Data Mining Techniques , Deep Learning , and Introduction to Reinforcement Learning . Recent work explores hierarchical state space models, Cauchy-Schwarz divergence applications, and Granger causality in chemical processes. His collaborations span interdisciplinary fields including neuroscience and industrial engineering. Publications emphasize theoretical foundations while addressing practical challenges in ML generalization, adversarial robustness, and sequential decision-making.

Thijs JanzenView profile

Researcher

• Phylogenetics
• Population Genetics
• Computational Biology
• +3 more

Dr. Thijs Janzen is a Researcher and Scientific Programmer at the University of Groningen's Faculty of Science and Engineering, affiliated with the Theoretical Research in Evolutionary Life Sciences department. He holds a PhD in Biology from the University of Groningen (2015) and has extensive postdoctoral experience at institutions including the Max Planck Institute for Evolutionary Biology and Carl von Ossietzky University Oldenburg. Education: PhD in Biology, University of Groningen (2010–2014) MSc in Biology, University of Groningen (2006–2009) BSc in Biology, University of Groningen (2003–2006) Research Interests: Phylogenetics and phylogenetic reconstruction Population genetics and genomic admixture Individual-based computational modeling Software development in R, C++, and Python Support for MSc/PhD students in programming and modeling Research Trends: His work spans theoretical evolutionary biology, computational genomics, and phylogenetic analysis. Recent studies focus on hybridization dynamics, viral oncolytic therapy modeling, and tools for phylogenetic statistics (e.g., the 'treestats' R package). His contributions bridge ecological theory with genomic data, emphasizing quantitative methods in evolutionary processes. Advising & Grants: Collaborated on the MARM project (2018–2023) on adaptive responses in evolution, contributing technical expertise to PhD student projects. Maintains active roles in computational support and course development for biologists. Labs/Teams: Core member of the Theoretical Research in Evolutionary Life Sciences group, developing open-source tools (e.g., GenomeAdmixR, nLTT) and collaborating globally on biodiversity and evolutionary questions.

Juan Juan CaiView profile

Associate Professor

• Statistics of Extremes
• Asymptotic Statistics
• Non-Parametric Statistics
• +3 more

Dr. Juan Juan Cai serves as an Associate Professor in the Department of Econometrics and Data Science at Vrije Universiteit Amsterdam's School of Business and Economics and holds a Research Fellow position at the Tinbergen Institute. Her academic leadership spans statistical methodology development and interdisciplinary applications in high-impact domains. Education: PhD in Statistics from Tilburg University (2012), dissertation: "Estimation concerning risk under extreme value conditions" Research Focus: Dr. Cai pioneers advanced techniques in Statistics of Extremes and Asymptotic Statistics , specializing in probabilistic forecasting of rare events. Her work bridges theoretical rigor with practical solutions for financial risk modeling, epidemiological analysis (including SARS-CoV-2 viral load studies), and cancer survival prediction, employing non-parametric frameworks and quantile-based methodologies. Publication Trends: Recent output (2023-2025) reveals a strategic expansion from core extreme value theory into machine learning integration (e.g., gradient boosting for quantile regression) and cross-disciplinary health applications. Over 80% of her work addresses multivariate regular variation and extreme event modeling, with growing emphasis on causal inference frameworks for real-world data. Honors: NWO Open competition grant (2024) for "Predicting cure chances and long term survival of cancer patients" Research Leadership: As Principal Investigator for the NWO-funded "Startersbeurs" project (2023-2028), she directs a team advancing statistical methods for finance and survival analysis. Her grant portfolio combines theoretical innovation with translational health research, evidenced by media coverage and interdisciplinary citations. Professional Ecosystem: Through the Tinbergen Institute, she cultivates collaborations across Dutch economic research institutions, leveraging networks in both academia and public health sectors to drive methodological advancements in extreme event modeling.

Lachlan DeerView profile

Assistant Professor

• Quantitative Marketing
• Digital Marketing
• Media Economics
• +5 more

Lachlan Deer is an Assistant Professor in the Marketing Group at Tilburg University's Tilburg School of Economics and Management (TiSEM). He holds a PhD in Economics from the University of Zurich (2019), an MSc from University College London (2013), and degrees from the University of Adelaide. Previously, he served as a Postdoctoral Fellow at the University of Chicago's Booth School of Business (2019–2021). His research focuses on quantitative models addressing consumer demand, public policy, and digital marketing strategies. Key areas include social media impact, mental health campaigns, and open science practices in marketing research. He has led projects on social media's influence on industry outcomes and consumer learning in motion pictures. Dr. Deer teaches courses in digital marketing analytics and social media strategies, emphasizing empirical research methods. He contributes to UN Sustainable Development Goals through work on education and mental health. Recent publications include studies on peer-support mental health campaigns, open science advocacy, and collective action in large populations. He is set to join the University of Melbourne as an Assistant Professor (Lecturer) in mid-2025.

• Theoretical Community Ecology
• Evolutionary Biology
• Phylogenetic Community Ecology
• +7 more

Prof. Rampal S. Etienne is a Full Professor in Theoretical and Evolutionary Community Ecology at the University of Groningen's Faculty of Science and Engineering. He leads the Etienne Group and serves as Scientific Director of the Groningen Institute for Evolutionary Life Sciences. His research focuses on understanding biodiversity processes through theoretical models and phylogenetic analysis. Education: PhD in Metapopulation Ecology, Wageningen University (2002) MSc degrees in Philosophy of Science, Environmental Science, and Foundations of Physics (cum laude), Utrecht and Nijmegen Universities (1996–1999) Research Interests: Theoretical ecology, evolutionary dynamics, phylogenetic community ecology, diversification modeling, and island biogeography. Collaborative projects include studies on African buffalo populations, fruit fly behavior, and microbial evolution. He develops frameworks like DAISIE for island biodiversity modeling and secsse for trait-dependent speciation analysis. Publications & Grants: Over 184 peer-reviewed articles, including work on phylogenetic color evolution and species diversification. Secured grants such as the VICI grant (2014) and Dutch Research Agenda funding (2024). Editor for Ecology Letters and Journal of Theoretical Biology . Awards: Descartes-Huygens Prize (2020), Honorable Mention for American Naturalist Student Paper Award (2023). Recognized for collaboration with CNRS and contributions to evolutionary theory. Labs/Teams: Leads experimental and computational studies on microsnails, microbes, and mites. Coordinates the Adaptive Life research initiative exploring eco-evolutionary dynamics under climate change.

• Ophthalmology
• Neuro-Oncology
• Mendelian Randomization
• +6 more

Karin van Garderen is a researcher at Erasmus MC , with affiliations in both Radiology & Nuclear Medicine and Ophthalmology departments. Her work bridges neuro-oncology and ocular disease research. Specializes in Mendelian randomization for disease associations Develops neural network applications for medical imaging Focuses on DNA methylation in brain tumors Recent publications examine: Corneal-glaucoma genetic links (2025) Carotid artery disease and glaucoma risk (2025) Deep learning for OCT scan analysis (2024) Prognostic differences between glioma subtypes (2024) Chemoradiation imaging changes in glioblastoma (2024) Her research integrates computational imaging with molecular oncology, particularly in promoter region methylation and neural network modeling.

Chaobo ZhangView profile

Researcher

• Building Physics
• Smart Building Energy Management
• Artificial Intelligence in Building Systems
• +5 more

Chaobo Zhang is a postdoctoral researcher at the Eindhoven University of Technology, specializing in fast building performance simulation and smart building energy management. He belongs to the Building Performance Group within the Department of the Built Environment. Education: Bachelor in Heating, Ventilation and Air Conditioning, Harbin Institute of Technology (2017) PhD in Refrigeration and Cryogenic Engineering, Zhejiang University (2022) His research integrates artificial intelligence technologies into building systems to enhance energy efficiency and fault detection. Key areas include generative pre-trained transformers (GPT) for energy load prediction, convolutional neural networks for computational fluid dynamics, and domain-specific large language models for HVAC fault diagnosis. The 15 most recent publications highlight trends in AI-driven building energy management, focusing on technologies like graph convolutional networks for uncertainty quantification, hybrid data-driven physics-based cooling demand models, and self-attention variational autoencoders for digital twin parameter imputation. His work addresses challenges such as data imbalance, federated learning for data silos, and real-time anomaly detection. Scientific Awards: 2020 Best Paper Award, Energy and Built Environment Chaobo collaborates internationally and contributes to UN Sustainable Development Goals through energy-efficient building technologies. His expertise spans fault detection, machine learning, and computational optimization in building systems.

Bert KappenView profile

Professor

• Quantum Machine Learning
• Stochastic Control Theory
• Neural Networks
• +4 more

Bert Kappen is a Professor of Physics at Radboud University Nijmegen, affiliated with the Department of Biophysics and the Donders Center for Neuroscience. His research focuses on the intersection of physics, machine learning, and neuroscience, with emphasis on quantum machine learning, stochastic control theory, and Bayesian inference methods applied to neural systems. His core research integrates statistical physics and quantum mechanics to develop computational methods for AI, exploring how intelligence emerges in biological systems. Key areas include: Quantum Machine Learning : Developing quantum algorithms for Boltzmann machines and exploring quantum advantage in optimization Path Integral Control : Creating efficient solutions for stochastic optimal control problems in robotics and neuroscience Bayesian Inference : Building probabilistic models for medical diagnosis and DNA identification (e.g., Bonaparte system used by Interpol) Atomic-scale Neural Networks : Implementing neuromorphic computing using nanoscale atomic switches Kappen leads an active research group with multiple PhD and Master's students, working on projects ranging from quantum perceptrons to multi-agent UAV control. His publication output shows strong recent focus on quantum computing applications (2020-2025), atomic-scale machine learning implementations, and advanced control theory. He teaches courses including Introduction to Machine Learning , Advanced Computational Neuroscience , and Statistical Machine Learning at Radboud University. Externally funded projects include EU FP7 initiatives (NETT, CompLACS), NWA Quantum Learning, and collaborations with Thales Nederland.

• Language Acquisition
• Statistical Learning
• Developmental Dyslexia
• +4 more

Prof. dr. Judith Rispens is a Professor in Dutch Linguistics at the Faculty of Humanities of the University of Amsterdam . Her research focuses on the intersection of language acquisition, statistical learning, and developmental disorders such as dyslexia and developmental language disorder (DLD). She employs experimental and neurocognitive methods, including ERP and MMN responses, to investigate how children and adults process language and acquire linguistic structures. Email: J.E.Rispens@uva.nl Office: Spuistraat 134, Room 417, Amsterdam Research interests include: Statistical learning mechanisms in language acquisition Neurocognitive processes in developmental dyslexia and DLD Second language learning (L2) and cross-linguistic studies Pragmatic abilities in multilingual exposure Computational modeling of language acquisition Her recent work explores how input consistency and speaker informativity influence word learning, with a focus on children with neurodevelopmental disorders. Publications span topics like morphological processing, grammatical proficiency, and the role of memory in language acquisition.

Christian Andersson NaessethView profile

Assistant Professor

• Approximate Statistical Inference
• Causality
• Generative Modeling
• +6 more

Christian Andersson Naesseth is an Assistant Professor of Machine Learning at the University of Amsterdam , affiliated with the Informatics Institute , the Amsterdam Machine Learning Lab (AMLab) , and the UvA-Bosch Delta Lab 2 . A former postdoctoral researcher at Columbia University and PhD graduate in Electrical Engineering from Linköping University (advised by Fredrik Lindsten and Thomas Schön), his research bridges generative modeling , uncertainty quantification , and approximate Bayesian inference with applications in AI for Science . Research Interests Generative Models (diffusions, flows, flow networks) Approximate Inference (variational and Monte Carlo methods) Probabilistic Modeling (natural sciences, computer vision, health) Uncertainty Quantification (E-values, conformal prediction) Recent Scientific Contributions Developed SDE Matching for simulation-free training of latent stochastic differential equations Advanced controlled generation via equivariant variational flow matching Proposed risk monitoring frameworks for test-time adaptation under unknown data shifts Awards & Recognition Best Workshop Paper Award at AABI 2025 Advising & Collaborations Lab manager of UvA-Bosch Delta Lab 2 Collaborates with institutions like Mila Montreal , Microsoft Research , and ECMWF Mentors students: Grigory Bartosh (PhD), Hany Abdulsamad (postdoc), and visiting PhDs from Antwerp, Manchester, Bielefeld, and DTU

Nalan BastürkView profile

Associate Professor

• Bayesian Inference
• Time Series Analysis
• Mixture Models
• +3 more

Nalan Bastürk is an Associate Professor at Maastricht University within the School of Business and Economics , Department of QE Econometrics. Prior to this, she held roles as Assistant Professor at Maastricht University (2014-2019) and Erasmus University Rotterdam (2010-2014). Her research focuses on Bayesian inference, time series analysis, and mixture models, with applications in econometrics, healthcare, and financial modeling. Her key research project, funded by NWO, explores patient heterogeneity and time-variation in the course of depression . She contributes to methodological advancements in statistical modeling, including neural networks for density estimation and Bayesian causal analysis. Recent publications span diverse topics such as text analysis of tax news, portfolio optimization with genetic algorithms, and clinical phenotyping in schizophrenia. Her work bridges theoretical statistics and real-world applications, emphasizing interdisciplinary collaboration. Notably, she co-developed the R package BayesMultiMode for Bayesian mode inference. While no scientific awards are explicitly listed, her contributions to econometrics and computational methods reflect significant scholarly impact. Her advising and grant activities include the NWO-funded Vividi195187 project. No specific lab or team affiliations are mentioned, but her research often involves collaborations across disciplines within the School of Business and Economics.

Federico CorradiView profile

Assistant Professor

• Neuromorphic Computing
• Spiking Neural Networks
• Energy-Efficient Computing
• +7 more

Federico Corradi is an Assistant Professor in the Electrical Engineering Department at Eindhoven University of Technology (TU/e), where he leads the Neuromorphic Edge Computing Systems Lab. He also holds the position of EAISI Foundational Assistant Professor at the Eindhoven Artificial Intelligence Systems Institute. His research focuses on neuromorphic computing and engineering, spanning from efficient computational models to novel microelectronic architectures for deep learning and brain-inspired algorithms. Dr. Corradi's academic background includes a Ph.D. in Neuroinformatics from the University of Zurich and an international Ph.D. from the ETH Neuroscience Centre Zurich (2015). Prior to joining TU/e in 2022, he worked at IMEC in the Netherlands (2018-2022) where he started a group focused on neuromorphic IC design. Before that, he was with Inilabs, a spin-off from the Institute of Neuroinformatics, developing event-based cameras and neuromorphic processors (2015-2018). His research interests center around understanding natural neural computation principles to develop energy-efficient sensing and computing technologies. Key areas include: Neuromorphic computing and engineering Spiking neural networks Event-based vision systems Energy-efficient microelectronic architectures Applications in robotics, machine vision, and biomedical signal analysis Dr. Corradi's recent publications demonstrate a strong focus on practical implementations of neuromorphic systems for edge applications. His work shows consistent innovation in translating theoretical neural models into efficient hardware implementations, with particular emphasis on handling temporal data and developing brain-realistic computational models. The research spans multiple domains including radar signal processing, optical computing, and error correction systems. He serves as an active review editor for Frontiers in Neuromorphic Engineering, IEEE, and IOP Science journals, and participates in technical program committees for several machine learning and neuromorphic conferences including ICTOPEN, AICAS, AIAI, ICONS, NEWCAS, DSD, and EUROMICRO. His work has received significant media attention with coverage from multiple news outlets and social media mentions. Dr. Corradi leads the Neuromorphic Edge Computing Systems Lab, which bridges theoretical neuroscience with practical engineering challenges, creating novel solutions for real-world problems. The lab's research has practical applications in robotics, autonomous systems, and biomedical technologies, reflecting his commitment to developing energy-efficient computing solutions that can operate effectively at the edge.