Lourens Waldorp is an Associate Professor at the University of Amsterdam within the Faculty of Social and Behavioural Sciences , specifically the Department of Psychological Methods . His research focuses on network theory, causal inference, and statistical modeling in psychology and neuroscience. University of Amsterdam IAS Fellow (2024) His research interests include: Network psychometrics Causal inference in psychological models High-dimensional statistical methods Dynamical systems in psychopathology Graph theory applications Signal processing for biophysical data The trends in his recent publications center on causal modeling, network analysis of psychopathology, and statistical techniques for time-series data. He has developed methods for perturbation graphs, moderated network models, and dynamic intervention frameworks. Notable scientific awards : IAS Fellowship for 6 months (2024) He advises PhD students like Kyra Evers and collaborates with researchers across disciplines, including J. Haslbeck , D. Borsboom , and O. Ryan . His work intersects with network theory and clinical psychology .
Prof. Tobias Müller is a Professor at the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence at the University of Groningen. His academic journey includes previous positions at Utrecht University, CWI (Centrum Wiskunde & Informatica), Tel Aviv University, and Eindhoven University of Technology, with a doctorate from the University of Oxford under Colin McDiarmid. His research focuses on combinatorics, probability theory, random graphs, percolation, discrete and stochastic geometry, and combinatorial game theory. He has contributed extensively to understanding complex networks, hyperbolic models, and geometric random structures. Research Interests: Random Graphs and Percolation Theory Discrete and Stochastic Geometry Hyperbolic Network Models Probabilistic Combinatorics Geometric Probability Graph Algorithms and Connectivity Notable Contributions: Analysis of Voronoi and Poisson-Voronoi percolation in hyperbolic planes. Studies on Mallows random permutations and their cycle structures. Research on component games and logical limit laws in graph theory. Investigations into the geometry and properties of random geometric graphs. Grants & Collaborations: Active in organizing workshops and conferences on random graphs and geometric networks, including the BIRS Workshop on Random Geometric Graphs and the STAR Workshops series. Labs/Teams: Member of the Bernoulli Institute’s research groups, focusing on stochastic studies, combinatorics, and algorithmic methods.
Nelly V. Litvak is a Full Professor in Algorithms for Complex Networks at Eindhoven University of Technology (Mathematics and Computer Science). She works on mathematical methods and algorithms for complex networks (social networks, WWW) using random graph models. She joined TU/e as a part-time professor in 2017 after being an Associate Professor at the University of Twente since 2012. Affiliations: 4TU Applied Mathematics Institute, Data Science Center Eindhoven, CTIT Industry Partners: ABN-AMRO Bank, Philips Lighting, Thales Editorial Role: Managing Editor of Internet Mathematics Her research focuses on extracting value from network data across three areas: (1) Information extraction and prediction, (2) Mathematical analysis of network characteristics, and (3) Efficient algorithms for incomplete network data. Key topics include PageRank, HITS algorithm, random graphs, homophilic networks, and network epidemiology. Recent work (2022-2025) spans network growth mechanisms, fairness in ranking algorithms, educational pedagogy, and pandemic forecasting dashboards. She contributes to SDGs through data-driven approaches to societal challenges. Teaching activities include course development at TU/e and earlier institutions, with innovative methods for computer engineering students' statistical understanding.
Michel Mandjes is a Professor at the University of Amsterdam's Faculty of Science and holds a Visiting Professor position at the Faculty of Economics and Business (FEB). His research focuses on stochastic processes, queueing theory, and probability theory, with applications in risk modeling, network analysis, and operations research. Recent publications highlight his contributions to multivariate Hawkes processes , Lévy-driven systems , and dynamic random graphs , emphasizing large deviations, rare event simulation, and statistical inference. His work bridges theoretical probability with practical challenges in traffic flow, financial risk, and social network modeling. The trends in his research include the development of stochastic models for network stability, appointment scheduling optimization, and inference techniques for non-stationary processes. His methodological innovations often leverage advanced probability theory and queueing frameworks to address real-world problems in transportation, healthcare, and finance.
Fiona Sloothaak is an Assistant Professor at Eindhoven University of Technology (TU/e), affiliated with the Department of Mathematics and Computer Science, specializing in Stochastic Operations Research. She holds the Eurandom Assistant Professor title and contributes to the Stochastic Operations Research group. Her research focuses on cascading failures in complex systems, power grids, battery swapping networks, and stochastic processes. Education: Fiona earned a PhD in Stochastic Operations Research from TU/e in 2020 and completed a Master's in Mathematics (2015). Her doctoral thesis, Criticality in power networks: a probabilistic approach , explores cascading failures in power systems. Research Interests: Fiona’s work addresses systemic risk in infrastructure systems, including power grids and transportation networks. She investigates cascading failure dynamics, load balancing in battery swapping stations, and resource pooling strategies. Her studies often employ probabilistic modeling and queueing theory. Key Projects: She co-authored the MARCONI project (2019–2022), focusing on integrated planning for maritime logistics and service systems. This involved optimizing supply chain resilience and remote control tower operations. Scientific Award: Applied Probability Trust Prize (2020) Teaching: Courses include Stochastic Networks, Insurance and Credit Risk, and Probability and Stochastics 1. Grants: MARCONI project funding for maritime logistics optimization. Labs/Teams: Active in TU/e’s Stochastic Operations Research group and Eurandom institute. Collaborations span network topology analysis and interdisciplinary systems research.
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.
Réka Szabó is an Assistant Professor at the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Netherlands, since October 2022. Her academic trajectory includes a postdoctoral fellowship at Université Paris-Dauphine (2019–2022) and a Ph.D. in Mathematics (2015–2019) at the University of Groningen under Daniel Valesin. Education: B.Sc. in Mathematics, Budapest University of Technology and Economics (2008–2011) M.Sc. in Applied Mathematics, Budapest University of Technology and Economics (2011–2015) M.Sc. in Computer Science, University of Trento and Eötvös Lóránd University (2012–2014) Ph.D. in Mathematics, University of Groningen (2015–2019) Réka’s research focuses on Interacting Particle Systems and Percolation Theory , with significant contributions to understanding phase transitions, critical behavior, and stochastic dynamics in models like the Contact Process, Oriented Percolation, and Bootstrap Percolation. Her work combines rigorous mathematical analysis with applications to statistical physics and complex networks. She is actively involved in academic service, including organizing the Workshop On Randomness and Discrete Structures (WORDS 2025) and co-organizing the Probability and Statistics Seminar in Groningen. Réka teaches advanced courses such as Stochastic Processes, Interacting Particle Systems, and Mathematics and its Environment.
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.
National Research Institute for Mathematics and Computer ScienceNetherlands
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.
Ross J. Kang is a Canadian mathematician currently serving as an Associate Professor at the Korteweg–de Vries Institute for Mathematics within the Faculty of Science at the University of Amsterdam since 2022. He is an active member of the Discrete Mathematics and Quantum Information group and the NETWORKS consortium. Previously, he held positions as Assistant/Associate Professor at Radboud University Nijmegen (2014-2022), Assistant Professor at Utrecht University (2013), and Researcher at Centrum Wiskunde & Informatica (2012-2013). His academic journey includes postdoctoral positions at Durham University (2010-2012) and McGill University (2008-2010), where he was advised by Bruce Reed and Louigi Addario-Berry. DPhil in Mathematics, University of Oxford (2008) - Thesis: 'Improper colourings of graphs', advised by Colin McDiarmid BSc (Hons) in Mathematics and Computer Science, University of Victoria (2003) - Governor General's Silver Academic Medal recipient Ross J. Kang's research focuses on probabilistic and extremal combinatorics, random discrete structures, graph coloring, geometric graphs, and algorithms. His work bridges theoretical mathematics with practical applications, exploring fundamental questions in discrete mathematics. He has made significant contributions to understanding graph coloring problems, particularly in the contexts of list coloring, distance coloring, and strong coloring. His research often employs probabilistic methods to establish bounds and structural properties in graph theory. Kang's work on the hard-core model, local occupancy method, and triangle-free graphs has advanced our understanding of the interplay between local constraints and global structure in discrete systems. Analysis of his recent publications reveals a strong emphasis on graph coloring problems, particularly list coloring variants and their extensions. His work frequently explores the relationship between graph structure (such as degree constraints, girth, or forbidden subgraphs) and coloring properties. A notable trend is his development and application of the local occupancy method to establish improved bounds for chromatic numbers in various graph classes. His research also demonstrates a consistent interest in extremal problems, seeking optimal configurations under specific constraints, particularly in the context of triangle-free graphs and geometric representations. NWO Open Competition M-1 grant entitled 'Asymptotic triangle-free structure (3Free)', 2022-2026 NWO Vidi grant entitled 'On the edge: theory and techniques at the frontiers of edge-colouring', 2017-2023 NWO Veni grant entitled 'Generalised colouring for random graph models', 2012-2015 Van Gogh travel grants (2020-2021 with Marthe Bonamy; 2016-2017 with Louis Esperet) Governor General's Silver Academic Medal (2003) Ross J. Kang has successfully supervised multiple PhD students including Eoin Hurley (defending May 2025), Stijn Cambie (defended April 2022), and François Pirot (winner of 2020 prix Charles Delorme). His research is supported by significant grants from the Netherlands Organisation for Scientific Research (NWO), including the prestigious Open Competition M-1 grant. Kang is actively involved in the academic community through his editorial role at Combinatorial Theory, co-organization of conferences like the Dutch Days of Combinatorics, and leadership in initiatives such as Innovations in Graph Theory, a diamond open access journal he helped launch in August 2023. As a member of the Discrete Mathematics and Quantum Information group at the University of Amsterdam and the NETWORKS consortium, Kang collaborates with researchers across various institutions. He has established strong international connections through his Van Gogh travel grants and participation in collaborative projects like the Sparse (Graphs) Coalition sessions. His research group focuses on theoretical aspects of discrete mathematics with connections to quantum information science, and he maintains active collaborations with researchers across Europe and North America.
Gilles Bonnet is an Assistant Professor at the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence within the University of Groningen , Netherlands. He is also affiliated with the Groningen Cognitive Systems and Materials Center (CogniGron) . His academic journey includes a PhD from University of Osnabrück (2016) under Prof. Matthias Reitzner, followed by a postdoc at Ruhr University Bochum (2016-2021) . Research Interests: His work bridges Probability Theory and Convex Geometry , focusing on high-dimensional stochastic structures. Key areas include random polytopes , Poisson hyperplane tessellations , and geometric inequalities . He has explored phase transitions in random polytopes and combinatorial diameter bounds. Scientific Contributions: Co-organized the Workshop On Randomness and Discrete Structures (2025) and the Spring School and Workshop on Polytopes (2019). His 2016 paper on Poisson tessellation earned a best poster award at the 18th Stochastic Geometry workshop. Awards: Best poster award (2016) Teaching: Delivers courses on Probability and Measure , Random Geometry , and Stochastic Processes at the University of Groningen and Ruhr University Bochum.
Nikhil Bansal holds the prestigious Patrick C. Fischer Professorship of Theoretical Computer Science in the Department of Computer Science & Engineering at the University of Michigan's College of Engineering. His research program has established him as a leading figure in theoretical computer science, with significant contributions to algorithm design and analysis, particularly in discrete optimization problems. Bansal's research focuses on theoretical computer science with emphasis on design and analysis of algorithms for discrete optimization problems. His work spans multiple areas including discrepancy theory, approximation algorithms, randomized algorithms, combinatorial optimization, complexity theory, machine learning theory, and probability. He has made significant contributions to understanding the limits of approximation algorithms and developing novel techniques for combinatorial optimization problems. Analysis of Bansal's recent publications reveals a strong focus on discrepancy theory, online algorithms, and combinatorial optimization. His work often bridges theoretical computer science with discrete mathematics and probability theory. A recurring theme across his publications is the development of novel algorithmic techniques for solving NP-hard problems with provable guarantees. His research has evolved from foundational work in approximation algorithms to more recent contributions in quantum computing complexity and stochastic optimization. Patrick C. Fischer Professor of Theoretical Computer Science Bansal has advised numerous PhD students including Marek Elias, Shashwat Garg, and Greg Koumoutsous, as well as mentoring several postdoctoral researchers. He has served on editorial boards for top journals including Journal of the ACM, Theory of Computing, and Stochastic Models, and has been active on program committees for major conferences such as STOC, FOCS, SODA, and ICALP, including serving as chair for ICALP 2021. Bansal has organized multiple academic workshops including the STOC 2020 Workshop on Recent Advances in Discrepancy and Applications, several SDP Days at CWI Amsterdam, and the Semester on Bridging Continuous and Discrete Optimization at UC Berkeley in Fall 2017.
Hans Vernooij is a Lecturer in Farm Animal Health at the Faculty of Veterinary Medicine, Utrecht University. He specializes in statistical methods and data science applications in veterinary epidemiology and animal health. His areas of expertise include: Statistical methods for veterinary research Applied Data Science in Life Sciences Epidemiological modeling Machine learning applications in animal health Vernooij has extensive experience in developing statistical models for animal health applications. His research focuses on applying advanced statistical techniques and data science methods to solve problems in veterinary epidemiology and farm animal health. He has particular expertise in Random Forest models, as demonstrated during his sabbatical at the Human Sciences Research Council in Pretoria where he developed a model for HIV status prediction based on demographic information and knowledge of HIV prevention from large-scale survey data. His publication record shows consistent contributions across veterinary epidemiology, with recent work emphasizing machine learning applications and big data analytics in animal health surveillance. The research demonstrates a clear trajectory from traditional statistical methods toward more advanced data science approaches. Vernooij is actively involved in teaching and mentoring: Teaches statistics to Bachelor students at the veterinary faculty Supports PhD candidates and Master students during data analysis phases of their research Provides statistics education for the Master of Epidemiology program at the Julius Centre of University Medical Centre
Laurens Bliek is an Assistant Professor at the Department of Industrial Engineering & Innovation Sciences, Eindhoven University of Technology (TU/e). He specializes in combining artificial intelligence (AI) with optimization techniques for computationally intensive problems, focusing on sustainable applications such as public transport, electric vehicles, and CO 2 reduction. Education: MSc in Applied Mathematics (2014), PhD in Systems & Control (2019) from Delft University of Technology. Prior Role: Postdoctoral researcher at the Algorithmics group, Delft University of Technology. Research Interests: His work addresses AI-driven optimization of expensive cost functions, particularly in logistics, communications, and healthcare. He develops methods to handle computationally intensive simulators and digital twins, emphasizing real-time decision-making and sustainability. Recent Publications: His research spans predictive maintenance using Fourier graph neural networks, real-time container yard allocation, and 5G network optimization. Articles appear in journals like IEEE Transactions on Neural Networks and Learning Systems and Computer Networks . Collaborations: Laurens collaborates with industry partners (LioniX, Dutch Railways) and organizations (European Supply Chain Forum, Logistics Community Brabant). He co-leads the 12-PhD program AI Planner of the Future and participates in AI sustainability working groups. Supervision: Co-promotor of PhD students Ya Song and Abdo Abouelrous, focusing on AI applications in routing and maintenance logistics.
Dr. Christian Steglich is an Associate Professor in the Department of Sociology at the University of Groningen's Faculty of Behavioural and Social Sciences. His research focuses on stochastic network modeling, social networks, and adolescent behavior, with particular emphasis on peer influence, friendship dynamics, and network co-evolution. He is part of the SIENA software development team for dynamic network analysis. Research Interests: Mathematical sociology and network dynamics Adolescent social networks and behavior Stochastic modeling and statistical inference Interorganizational networks and gender dynamics Missing data methodologies and network imputation Recent Articles: Focus on Chinese adolescents' friendship dynamics, networked solidarity economies, and longitudinal network behavior interactions. His work bridges theoretical modeling with empirical applications in education, health, and organizational contexts. Scientific Awards: William D. Richards Jr. Software Award (2017) 2013 Awards for prosocial relationships and child social development research Grants/Advising: Extensive ERC panel involvement and collaborations on youth behavior and network interventions. Supervised 7 research projects focusing on peer networks and academic outcomes. Labs/Teams: Active in interdisciplinary research teams at the University of Groningen, focusing on social network analysis methodologies and their applications in diverse fields.
