Find Academic Supervisors

• Statistical Physics
• Network Theory
• Complex Systems
• +9 more

Diego Garlaschelli is a Professor of Theoretical Physics at Leiden University, affiliated with the Leiden Institute of Physics (LION) and the Biological, Soft and Complex Systems department within the Faculty of Science. His research focuses on the structure, dynamics, and physics of complex networks in financial, economic, social, neural, and biological systems. Combining statistical physics, information theory, and data science, his group explores interdisciplinary topics such as systemic risk in financial networks, mesoscopic organization in neural systems, and mathematical modeling of networks using maximum-entropy ensembles. Garlaschelli’s work emphasizes collaboration across fields like mathematics, computer science, economics, and neuroscience. Recent grants include NWO Open Competition funding for projects on network theory and systemic risk. He advises several PhD candidates, including Alessio Catanzaro, Francesca Giuffrida, and Jingjing Wang. His publications span high-impact journals like Nature Physics , Nature Reviews Physics , and Science , addressing topics from ensemble equivalence in networks to cultural diversity models. Key research themes include: (1) statistical physics of constrained systems, (2) financial network reconstruction from limited data, (3) early-warning signals for economic instabilities, and (4) information-theoretic bounds for large data structures. Garlaschelli co-leads the Leiden Complex Network Network (LCN2), fostering Dutch network science collaboration.

Noela MüllerView profile

Assistant Professor

• Probability Theory
• Random Matrices
• Sparse Matrices
• +5 more

Noela Müller is an Assistant Professor in the Mathematics and Computer Science school at Eindhoven University of Technology . Her research focuses on Probability Theory , Random Matrices , and Random Graphs , with significant contributions to understanding the rank of sparse matrices and clique factors in probabilistic settings. Research Outputs : Published 22 works including journal articles and preprints. Collaborations : Active in international networks, particularly in sparse matrix analysis and probabilistic combinatorics. Her recent work explores sparse pooled data algorithms , random 2-SAT models , and sharp thresholds in random graphs , showcasing interdisciplinary applications in computer science, mathematics, and theoretical physics.

Jop BriëtView profile

Researcher

• Theoretical Computer Science
• Quantum Information Theory
• Combinatorics
• +3 more

Jop Briët is a Researcher at the Department of Algorithms and Complexity at Centrum Wiskunde & Informatica (CWI) in the Netherlands. His work focuses on theoretical computer science, quantum information theory, combinatorics, and tensor analysis. He has held grants including the Veni Innovational Research Grant from NWO and a Rubicon fellowship. He has authored over 50 publications in leading venues, exploring topics such as Grothendieck inequalities, quantum computing, and additive combinatorics. His research interests span the interplay between combinatorics and computational complexity, with particular emphasis on tensor analysis, probabilistic methods, and algorithm design. Recent work includes studies on Szemerédi’s theorem with random differences and the application of quantum query algorithms to entanglement-based problems. Awards: Outstanding paper award TQC (2020), Andreas Bonn medal (2013), Stieltjesprijs (2011). Professional Activities: Editor for ERCIM News, Board Member of Koninklijk Wiskundig Genootschap, and frequent invited speaker at workshops on quantum computing and combinatorics. Grants: Veni Grant (2014), Rubicon Fellowship (2012). Current teaching includes courses on Additive Combinatorics and Quantum Information Processing, reflecting his commitment to bridging foundational theory with advanced applications in computing and mathematics.

Peter van der HeijdenView profile

Professor

• Categorical Data Analysis
• Data Science
• Multivariate Statistics
• +3 more

Prof. Peter van der Heijden is a Professor of Statistics for the Social and Behavioural Sciences at Utrecht University's Department of Methodology and Statistics. He also holds a professorship in Social Statistics at the University of Southampton. His roles include chairing the Ethical Review Board and the Committee for Policy on Integrity at Utrecht's Faculty of Social and Behavioural Sciences. He chairs the Advisory Council on Methodology and Quality of Statistics Netherlands and serves on the Executive Board of the European Statistical Advisory Committee (ESAC). Since 2017, he has led Utrecht's Applied Data Science focus area, focusing on human-centered AI and data-driven solutions. His research emphasizes population size estimation, fraud detection, and categorical data analysis, with applications for Dutch ministries and international bodies like the UN. He has pioneered methods for estimating human trafficking victims and optimizing healthcare treatments using multilevel models and neural networks. Key projects include the AI for Health initiative with Utrecht Medical Center and Wageningen University. His work bridges statistical rigor with societal impact, addressing challenges in criminal justice, public health, and policy-making through innovative methodologies. Universities: Utrecht University (Primary), University of Southampton Key Committees: European Statistical Advisory Committee, UN Human Trafficking Monitoring Research Themes: Multiple Systems Estimation, Data Science for Social Issues Research interests span statistical methods for complex societal problems, including: Register linkage and fraud detection Machine learning applications in healthcare Human trafficking prevalence estimation His publications (2019-2023) highlight advancements in multilevel modeling, randomized response techniques, and AI-driven clinical data classification. He has advised on policy frameworks for official statistics and contributed to global initiatives like the UN Sustainable Development Goals (Target 16.2). Grants and collaborations include projects with Dutch ministries, the EU, and international organizations. Current initiatives involve optimizing Hepatitis C treatment networks and improving criminal recidivism prediction models. His leadership in interdisciplinary teams ensures methodological innovation addresses real-world challenges.

• High-dimensional probability theory
• Data Science
• Machine Learning
• +5 more

Sjoerd Dirksen is a Professor of Mathematics for Data Sciences at Utrecht University since May 2025, having previously served as an Associate Professor for Applied Mathematics (2019-2025) and Junior Professor at RWTH Aachen University (2014-2019). He is affiliated with the Mathematical Institute within the Faculty of Science at Utrecht University, where his office is located in the Hans Freudenthal Building. His research interests focus on high-dimensional probability theory and its applications in data science, machine learning, and signal processing. Specifically, he investigates randomized data dimension reduction methods using structured random matrices, theory for deep learning including random neural networks, high-dimensional covariance estimation for wireless communication systems, and statistical postprocessing of weather forecasts in collaboration with the Royal Netherlands Meteorological Institute (KNMI). Previously, he worked on compressed sensing, sharp estimates for stochastic processes in Banach spaces, and noncommutative analysis. Analysis of his recent publications (2018-2024) reveals a strong focus on quantization effects in high-dimensional data processing, particularly one-bit compressed sensing and covariance estimation under coarse quantization. His work bridges theoretical mathematics with practical applications in signal processing, wireless communications, and meteorological forecasting, demonstrating a consistent trajectory from foundational mathematical research to applied data science problems. Dirksen's academic career shows progression from postdoctoral work at the Hausdorff Center for Mathematics in Bonn to independent research positions. His publication record demonstrates significant contributions to the mathematics of data science, with papers appearing in top journals across mathematics, statistics, and signal processing. His research combines deep theoretical insights with practical applications, particularly in the areas of dimensionality reduction and high-dimensional statistics.

• Veterinary Pharmacology
• Antibiotic Resistance
• Food Safety
• +3 more

Larissa Jansen serves as a Research Officer at Wageningen University & Research within the Team Animal Drugs, focusing on veterinary drug residues and antimicrobial resistance in animal production systems. Her work bridges laboratory analysis, field surveillance, and food safety policy to address critical challenges in agricultural pharmacology. Her research centers on veterinary pharmacology and food safety, with specialized expertise in antibiotic residue monitoring across livestock systems (veal calves, broilers, hens) and novel food matrices like edible insects. Key investigations include transmission pathways of antimicrobial residues, ecological impacts of antibiotics in aquatic environments, and development of rapid screening methodologies for veterinary drugs. This work directly informs surveillance strategies to mitigate antimicrobial resistance risks in the food chain. Analysis of her recent publications reveals a cohesive research trajectory emphasizing practical monitoring solutions for antibiotic use in agriculture. Her 2024-2025 output demonstrates strong focus on veal calf production systems, analytical validation for emerging food sectors (edible insects), and environmental consequences of antibiotic pollution. The work consistently integrates pharmacological testing with agricultural practice data to model residue transmission and resistance development. Jansen operates within Wageningen University & Research's Team Animal Drugs, which develops evidence-based tools for veterinary drug residue detection. The team collaborates extensively with Dutch food safety authorities on national surveillance frameworks, contributing to risk-based monitoring systems that protect consumer health while supporting sustainable livestock production.

Alexander Van WerdeView profile

Researcher

• Random Matrix Theory
• Spectral Algorithms
• Stochastic Processes
• +5 more

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.

Robbie van AertView profile

Assistant Professor

• Meta-analysis
• Publication bias
• Questionable research practices
• +3 more

Robbie van Aert is an Assistant Professor at the Department of Methodology and Statistics, Tilburg University, and a member of the Meta-Research Center. He holds editorial roles at Research Synthesis Methods and Neuropsychology Review . His research focuses on statistical methods for meta-analysis, correcting publication bias, and research transparency, supported by an NWO Veni Grant (2022). Key research interests include meta-analytic techniques, detection and correction of bias in research, and reproducibility in psychological science. Notable contributions include methods for multivariate meta-analysis and addressing outcome reporting bias. Recent work spans topics like the effects of exercise on mental health, personality's role in social status, and the relationship between social capital and economic growth. His articles emphasize methodological innovation and practical applications in diverse fields. Grants and Awards: NWO Veni Grant (2022). Editorial activities include roles at leading journals in research synthesis and psychology.

• Quantum Physics
• Topological Superconductors
• Majorana Zero-Modes
• +3 more

Yaroslav Herasymenko is a Guest Researcher affiliated with the Leiden Institute of Physics (LION) under the Quantum Matter and Optics Tura Group at Leiden University. His research focuses on quantum physics, topological superconductors, and Majorana zero-modes, with expertise in statistical mechanics and condensed matter physics. Academic Rank: Researcher Contact: herasymenko@lorentz.leidenuniv.nl Key research themes include adiabatic pumping, quantum noise, superconductivity, and applications in quantum computing. His work explores topological effects in quantum systems and non-Abelian exchange statistics. Recent publications (2018-2019) analyze eigenvalue distributions in Berezin-Toeplitz matrices and electrical detection methods for Majorana fusion rules in topological superconductors.

Floske SpieksmaView profile

Associate Professor

• Operations Research
• Stochastic Processes
• Decision Theory
• +4 more

Floske Spieksma is an Associate Professor at the Mathematical Institute (Probability Theory) of Leiden University , where she has worked since 1985 in various roles including PhD researcher, Postdoc, Assistant Professor, and Associate Professor. Her research focuses on Stochastic Processes , Markov Decision Processes , and Operations Research , particularly in Queueing Theory and Network Optimization . She has supervised multiple PhD students including Herman Blok and Laurens Smit and co-organized international workshops. Education : PhD in Mathematics (1990), Leiden University MSc (cum laude) in Mathematics (1985), Leiden BSc in Mathematics (1981), Leiden BA in Spanish (1981), Leiden Research Trends : Her recent publications analyze unbounded jump rate Markov processes , graph resistance metrics , and stochastic decision frameworks . Key collaborations include work with M.N. Katehakis, L. Smit, and H. Blok. Scientific Awards : KNAW 5-year fellowship (1993) C.J.Kok prize (1991) Shell travel grant (1991) Two book grants (1984) Advising : Supervises PhD students and research projects, with past advisees including Herman Blok and Laurens Smit. Organized the H.E.T. Symposium (2012) connecting students with alumni in industry.

Martin HarmsenView profile

Professor

• Regenerative Medicine
• Cardiovascular Disease
• Biomaterials
• +3 more

Dr. Martin Harmsen is a Full Professor at the Faculty of Medical Sciences, University of Groningen. His research focuses on Regenerative Medicine, Cardiovascular Disease, Biomaterials, and Molecular Biology. He leads studies on stem cells, extracellular matrices, and tissue engineering, with applications in liver fibrosis, cardiovascular regeneration, and wound healing. His work integrates biochemical and biomechanical approaches to understand cellular behavior in disease contexts. Education: Doctorate in Medical Sciences (not explicitly stated, inferred from title). Research Interests: Regenerative Medicine: Developing scaffolds for tissue repair. Biomaterials: Designing hydrogels for cell support. Cardiovascular Biology: Investigating endothelial dysfunction and stem cell therapies. His studies often involve extracellular vesicle interactions and epigenetic regulation in diseases like fibrosis. Publications reflect a focus on biomaterials, stem cell applications, and cellular crosstalk mechanisms. He serves on committees like the Tekke Huizingafonds evaluation board (2022-2024) and contributes to peer review for journals like Nature Cell Biology and PLOS One.

Thomas P.A. van VurenView profile

Researcher

• Stochastic Operations Research
• Machine Learning
• Quantum Gravity
• +3 more

Thomas P.A. van Vuren is a researcher at the Eindhoven University of Technology , affiliated with the School of Mathematics and Computer Science and the Stochastic Operations Research department. His work bridges disciplines such as Stochastic Operations Research , Machine Learning , Quantum Gravity , and Chemical Engineering . His recent research explores Markov Chains and their applications in clustering (2024), Effective Field Theory and Symmetry Breaking in Type IIB string theories (2023), and Turbulent Emulsions with a focus on surfactant dynamics and viscosity asymmetries (2022). These works highlight his interdisciplinary approach to problems in Computer Science , Physics , and Chemical Engineering . Email: t.p.a.v.vuren@tue.nl Collaborations include projects with J. Sanders and S.F.M. van Kempen on AI-driven research (2021-2026), and co-authored works with L. Yi , D. Lohse , and C. Sun on fluid dynamics.

• Analytical Chemistry
• Mass Spectrometry
• Pesticide Analysis
• +4 more

Rosalie Nijssen is an active Researcher at Wageningen University & Research specializing in analytical toxicology within Team Pesticides 2. Her work focuses on developing advanced methodologies for pesticide detection and risk assessment across food safety, environmental health, and human biomonitoring domains. Her primary research interests include: High-Resolution Mass Spectrometry (HRMS) applications Pesticide metabolite identification and quantification Food and feed safety control systems Biological marker development for exposure assessment Veterinary drug analysis in complex matrices Nijssen's publication record demonstrates consistent innovation in harmonized analytical workflows, particularly for suspect screening of pesticide mixtures. Her recent work emphasizes multi-laboratory validation, European collaborative studies, and novel approaches to uncovering hidden risks in food chains through metabolite tracking and urinary biomonitoring. As a core member of Team Pesticides 2, she contributes to Wageningen University & Research's leadership in analytical toxicology through method development, inter-laboratory studies, and European regulatory support initiatives focused on pesticide exposure assessment.

Palina SalanevichView profile

Assistant Professor

• Signal Processing
• Data Analysis
• Graphs
• +9 more

Dr. Palina Salanevich is an Assistant Professor at Utrecht University's Mathematical Institute, specializing in Mathematical Modeling. Her work bridges harmonic and time-frequency analysis, geometric functional analysis, signal and image processing, and high dimensional probability theory. Her research focuses on: Geometric properties of random frames and matrices, particularly Gabor frames Phase retrieval problems and their applications in ptychography and speech recognition Machine learning with randomization, especially Random Vector Functional Link networks Data processing using non-negative matrix factorization for audio and EEG signals Harmonic and time-frequency analysis on graphs Dr. Salanevich's recent publications show a strong focus on phase retrieval with Gabor frames, random matrix theory applications in signal processing, and randomized approaches to machine learning. Her work consistently bridges theoretical mathematics with practical signal processing applications, particularly in developing stable and efficient algorithms for complex inverse problems. Her notable scientific achievements include: NWO Veni grant (2021) for "Time-frequency structured measurements in phase retrieval: stability and reconstruction" Board member of the NDNS+ research cluster (2024) Dr. Salanevich actively supervises students, including PhD student Olov Schavemaker (since December 2021), and offers various projects for Bachelor and Master students in areas related to mathematical modeling and signal processing. She has been involved in several collaborative research projects with institutions like CWI and researchers including Deanna Needell, Goetz E. Pfander, and others. She is organizing the "Phase retrieval in mathematics and applications" workshop at the Lorentz Center in August 2024, demonstrating her leadership in her research community.

Remco van der HofstadView profile

Professor

• Probability Theory
• Statistics
• Complex Networks
• +6 more

Remco van der Hofstad is a Full Professor and chair of probability at the Department of Mathematics and Computer Science, Eindhoven University of Technology (TU/e). He also serves as scientific director of EURANDOM and co-applicant of the Gravitation program NETWORKS. His research spans probability theory, statistics, and their applications in complex networks, statistical physics, and medical imaging. PhD from University of Utrecht (1997) Positions at McMaster University, Delft TU/e, and TU/e His research focuses on probability questions related to complex networks, including percolation, random graphs, self-interacting processes, and interdisciplinary applications in electrical engineering, computer science, and theoretical physics. Recent work includes medical imaging for glioma patients and theoretical advancements in PageRank analysis, sparse matrices, and dynamic intersection graphs. Key awards include the Prix Henri Poincaré (2003), Rollo Davidson Prize (2007), and Netherlands Organisation for Scientific Research (NWO) VIDI (2003) and VICI (2008) grants. He is editor-in-chief of the Network Pages and acts as spokesman for Platform Wiskunde Nederland. Teaches courses: Probability theory, Probability and Modeling, Probability and Statistics Ancillary activities: Chair of Applied Probability Trust, member of Dutch Chamber of NetSci, KNAW councils