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Raffaella MulasView profile

Assistant Professor

• Spectral Theory of Graphs and Hypergraphs
• Discrete Laplacians
• Non-backtracking Operators
• +7 more

Raffaella Mulas is an Assistant Professor in the Department of Mathematics at the Faculty of Science, Vrije Universiteit Amsterdam. She previously served as a Group Leader and Minerva Fast Track Fellow at the Max Planck Institute for Mathematics in the Sciences, where she maintains an ongoing affiliation. Her research lies at the intersection of spectral graph theory, discrete mathematics, and network science. Research Interests: Her work focuses on the spectral theory of graphs and hypergraphs, particularly the properties of discrete Laplacians and non-backtracking operators. She investigates extremal combinatorics problems such as graph coloring and the Turán problem, often applying spectral methods to derive sharp bounds. Her research has strong applications in modeling and analyzing complex networks. Recent Research Trends: Analysis of the 15 most recent publications reveals a consistent focus on spectral characterizations of graphs and hypergraphs, including signed and complex unit hypergraphs. She frequently studies the normalized Laplacian and its extremal eigenvalues, develops non-backtracking operators, and explores measure-theoretic and geometric representations of networks. A strong thread connects spectral bounds to combinatorial invariants like chromatic number. VU Startpremie Grant Elected Member, European Mathematical Society Young Academy (EMYA) Elected Member, Elisabeth-Schiemann-Kolleg, Max Planck Society Minerva Fast Track Fellow, Max Planck Institute Advising and Grants: While no formal students are listed, she is an active researcher with significant grant funding, notably the VU Startpremie Grant. She collaborates internationally and supervises research projects in spectral graph theory and network analysis. Her affiliation with both VU Amsterdam and MPI-MiS enables broad academic mentorship and collaborative supervision. Labs and Research Groups: Raffaella Mulas leads research within the Mathematics Department at VU Amsterdam and is affiliated with the research group at the Max Planck Institute for Mathematics in the Sciences. Her work contributes to advancing theoretical foundations in discrete mathematics with applications in data science and network modeling.

Herke van HoofView profile

Associate Professor

• Reinforcement Learning
• Modular Reinforcement Learning
• Hierarchical Structures
• +5 more

Herke van Hoof serves as Associate Professor at the University of Amsterdam within the Informatics Institute, where he leads research in the AMLab at Science Park (Lab 42, L4.05). His academic journey spans from AI degrees at Groningen University through doctoral studies at TU Darmstadt under Professor Jan Peters to postdoctoral research at McGill University with Professors Joelle Pineau, Dave Meger, and Gregory Dudek. PhD: TU Darmstadt (2016), supervised by Prof. Jan Peters Postdoc: McGill University, Montreal AI Bachelor & Master: University of Groningen Van Hoof's research focuses on overcoming data inefficiency in reinforcement learning through modular and hierarchical approaches. His work explores how structured representations can enable knowledge transfer between tasks, improve learning efficiency, and facilitate applications in domains with complex state and action spaces. The lab investigates symmetry exploitation in multi-agent systems, gradient estimation techniques for discrete variables, and applications to combinatorial problem solving. His publication record shows consistent contributions to top AI venues including ICML, ICLR, NeurIPS, and JMLR over the past seven years. The research trajectory demonstrates evolution from foundational policy search methods toward increasingly sophisticated modular architectures, with recent emphasis on knowledge-assisted AI for critical infrastructure applications through the AI4REALNET project. Third place in ARC prize (Paper award) Van Hoof actively supervises PhD candidates and postdoctoral researchers, with current projects including human-robot collaboration using brain-computer interfaces (with Maryam Alimardani at VU) and interactive robot learning with flexible human input (through The Hybrid Intelligence Centre). His research receives support from multiple funding initiatives including the AI4REALNET project which focuses on applying AI to critical infrastructure systems. As co-organizer of the BeNeRL 2024 workshop and participant in the AI4REALNET consortium, van Hoof maintains strong connections with the broader European AI research community while directing the AMLab's research on modular reinforcement learning approaches.

Jasper GoselingView profile

Associate Professor

• Differential Privacy
• Network Coding
• Optimization Problem
• +4 more

Jasper Goseling is an Associate Professor at the Digital Society Institute and affiliated with the Mathematics of Operations Research department. His research spans differential privacy, network coding, optimization, and wireless systems, often bridging theoretical and applied domains. Key research areas: Differential Privacy, Network Coding, Optimization, Wireless Sensor Networks, Machine Learning His recent work focuses on robust optimization techniques for local differential privacy, addressing trade-offs between data utility and privacy preservation. Earlier contributions include studies on energy-efficient data collection in sensor networks, caching strategies in wireless environments, and entropy-based analysis of hydrothermal systems. Article trends reveal a strong emphasis on privacy-preserving algorithms (2022-2024) and historical expertise in network coding, queueing theory, and thermodynamic entropy. His research integrates mathematical rigor with practical applications in wireless communication and data management. Activities include organizing the 45th Symposium on Information Theory and Signal Processing (2025) and leadership roles in the IEEE Benelux Chapter on Information Theory (Chair, 2017; Member, 2012-2017). He also contributed to the 2015 European School of Information Theory.

Nelly LitvakView profile

Professor

• Complex Networks
• Applied Probability
• Stochastic Operations Research
• +5 more

Nelly Litvak is a Full Professor in Algorithms for Complex Networks at Eindhoven University of Technology (TU/e), affiliated with the Department of Mathematics and Computer Science and the EAISI Health group. Her research focuses on mathematical methods and algorithms for analyzing complex networks, including social networks, the World Wide Web, and healthcare logistics systems. PhD from TU/e/EURANDOM (2002) Associate Professor at University of Twente (2012) Part-time Professor at TU/e (2017–present) She investigates: Information extraction and predictions from network data Mathematical analysis of network characteristics Efficient randomized algorithms for incomplete network data Applications to real-world systems like power grids and social networks Her 2013 paper on disassortativity in scale-free networks introduced rank correlation methods (e.g., Spearman's ρ) as superior metrics for network mixing patterns, particularly in large graphs like the web. This work revealed stronger negative degree-degree dependencies in web graphs than previously recognized.

Marc Jochen UetzView profile

Professor

• Operations Research
• Computer Science
• Artificial Intelligence
• +11 more

Marc Jochen Uetz is a Full Professor at the Mathematics of Operations Research department and affiliated with the Digital Society Institute. His research spans operations research and computer science, focusing on scheduling, game theory, and optimization problems. PhD in Mathematics from Technische Universität Berlin Research Interests: Active in algorithmic game theory and stochastic scheduling, he investigates equilibrium models, price of anarchy, and efficient resource allocation in transportation and network systems. His work contributes to UN Sustainable Development Goals related to education and infrastructure. Publication Trends: Recent work combines game theory with two-stage facility location, network routing, and stochastic scheduling of Bernoulli-type jobs. Key keywords include Nash equilibrium, approximation algorithms, and dynamic programming. Scientific Recognition: Excellent Reviewer Award (2017) Teaching Award (2018) Academic Activities: Currently chairs Platform Wiskunde Nederland, contributes to editorial work, and delivers invited talks at international conferences like IJCAI 2024.

Yannick HillView profile

Assistant Professor

• Sports Psychology
• Human Performance
• Resilience
• +3 more

Yannick Hill is an Assistant Professor at the Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, with affiliations in the Learning and Performance department. He holds concurrent roles as Assistant Professor at IBBA and AMS-Sports. His research focuses on resilience, stress adaptation, and human performance in sports and virtual environments. He is a member of institutions like the Lyda Hill Institute for Human Resilience (Colorado), Instituut voor Sport- en Prestatiepsychologie (Groningen), and Amsterdam Institute of Sport Science (AISS). His work bridges psychology, sports science, and technology, emphasizing dynamic systems and personalized approaches to resilience. Research interests include stressors' impact on motor performance, home advantage analysis in sports, and immersive VR applications. He has published extensively on topics such as pressure training, network analysis in education, and resilience frameworks. Teaching includes courses like 'Sportpsychologie' and 'Gespreksvoering in de Sportpsychologie'. Recent articles highlight themes like stress protocols in gymnastics, gender differences in field hockey performance, and VR's role in medical training. His work emphasizes practical recommendations for technology integration and ethical considerations in resilience research.

• Machine Learning
• Econometrics
• Financial Econometrics
• +7 more

Anastasija Tetereva is an Assistant Professor in the Department of Econometrics at the Erasmus School of Economics, Erasmus University Rotterdam. Her research lies at the intersection of machine learning and econometrics, with applications in financial markets, real estate pricing, and risk modeling. Her research interests include Machine Learning , Econometrics , Volatility Forecasting , Hedonic Pricing , Financial Sentiment Analysis , and Granger Causality . She applies advanced computational methods to economic and financial data to improve forecasting and decision-making. The recent articles reflect a strong trend in combining machine learning techniques—particularly random forests and hybrid models—with traditional econometric frameworks to analyze volatility, real estate valuations, and financial news causality. Her work demonstrates a consistent focus on improving predictive accuracy in financial and housing markets through data-driven modeling. A Forest Full of Risk Forecasts for Managing Volatility (2022) How much is the view from the window worth? Machine learning-driven hedonic pricing model of the real estate market (2022) Forecasting realized correlations: a MIDAS approach (2019) Sentiment spillover effects for US and European companies (2019) Application of Multivariate Hawkes Graphs to Uncover Granger Causality of Financial News (2018) Anastasija Tetereva has actively contributed to the academic community, including co-organizing the event Financial Econometrics meets Machine Learning (FinEML) in 2023, indicating strong engagement in interdisciplinary research networks. While no direct mention of student advising or grant funding is made in the text, her publication record and collaborative work suggest involvement in research teams and potential supervision of junior researchers. She is affiliated with the research network in financial econometrics and machine learning, contributing to a growing interdisciplinary community that bridges computer science and economic modeling. Her work is disseminated through working papers, peer-reviewed journals, and conference presentations, reflecting a robust and current research agenda.

• Applied Probability
• Stochastic Processes
• Queueing Theory
• +7 more

Bert Zwart is a Professor and Scientific Staff Member at Centrum Wiskunde & Informatica (CWI) in Amsterdam, where he leads the Stochastics group. He also holds a professorship by special appointment at Technische Universiteit Eindhoven (TU/e). His research spans applied probability, stochastic processes, queueing theory, and operations research, with applications in power systems, supply chains, and risk modeling. His research interests focus on extreme value theory , large deviations , asymptotic analysis of stochastic systems , and random graphs . He develops theoretical tools to analyze rare events and performance limits in complex systems such as fork-join queues, power grids, and stochastic networks. His work combines deep probabilistic insights with practical applications in engineering and operations management. Recent publications (2023–2025) reveal a strong focus on tail behavior in queueing systems, large deviations in heavy-tailed processes, and optimization in power networks. The articles span high-impact journals like Mathematics of Operations Research , Queueing Systems , and Random Structures & Algorithms , indicating sustained leadership in applied probability and stochastic modeling. His scientific awards include: Erlang Prize (2008) Dantzig Prize (2015) Best publication award, INFORMS Revenue Management & Pricing (2016) IBM Faculty Award (2008) ASML Prize (2002) Gijs de Leve Prize (2002) Applied Probability Trust Award (2001) Best paper, Performance conferentie, Namen, Belgium (2010) Zwart has secured significant research funding, including a Vici grant from NWO on rare events, multiple NWO TOP and STAR cluster grants, and funding for PhD positions and postdocs. He has advised numerous students (though not explicitly listed here) and collaborates widely with researchers such as Maria Vlasiou, Remco van der Hofstad, and Onno Boxma. He is actively involved in teaching, offering advanced courses like Stochastic Decision Theory and Asymptotic Methods in Queueing Theory . He leads the Stochastics group at CWI, a leading research unit in probability and operations research.

• Probability Theory
• Mathematical Physics
• Statistical Mechanics
• +9 more

Dr. Wioletta Ruszel is an Associate Professor at the Mathematical Institute of Utrecht University, Faculty of Science. As Programme Director for Science Mathematics, she bridges probability theory and mathematical physics through rigorous research and teaching. Academic Rank: Associate Professor Institution: Utrecht University Department: Mathematical Institute Research Interests span Statistical Mechanics , Gibbs Measures , Sandpile Models , and Random Fields . Her work explores critical phenomena, self-organized criticality, and stochastic approximation methods. Publications highlight trends in Nonlocal Models (e.g., isoperimetric inequalities), Reinforced Processes (Pólya urns), and Scaling Limits (divisible sandpiles, Gaussian fields). These intersect with Network Theory and Phase Transitions . Students advised include: Tess van Leeuwen (2024-) Alan Rapoport (2020-2024) Leandro Chiarini (2017-2021) Bruno Kimura (2015-2019) Current Postdocs : Vanessa Jacquier and Fabio Coppini.

Clara StegehuisView profile

Associate Professor

• Random Graphs
• Network Science
• Theoretical Computer Science
• +4 more

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

• Stochastic Processes
• Queueing Theory
• Large Deviations
• +6 more

Bert Zwart is a Professor at Technische Universiteit Eindhoven and holds a Scientific Staff Member position at Centrum Wiskunde & Informatica (CWI), Amsterdam. He leads research groups in stochastic systems and optimization, with expertise in queueing theory, large deviations, and stochastic networks. His work bridges theoretical probability with applications in power systems, operations research, and telecommunications. Zwart has been awarded prestigious honors including the Dantzig Prize (2015), Erlang Prize (2008), and IBM Faculty Award (2008/2009). His research focuses on extreme-value analysis, heavy-tailed processes, and stochastic modeling of complex systems. Notable contributions include studies on fork-join queues, power flow dynamics, and random graph structures. Grants: Led or co-led multiple NWO-funded projects, including a €1.5M VICI grant (2015) for rare event research and a €420K TOP program grant for two-dimensional queueing models (2014–2018). Teaching: Recently taught courses such as Stochastic Decision Theory and Asymptotic Methods in Queueing Theory at TU Eindhoven. Labs/Teams: Active in CWI’s Stochastics group and collaborates with academic/industry partners like Shell and VU University. His recent articles analyze tail behaviors in fork-join systems, power grid optimization under stochastic loads, and dynamic random graph structures. Research emphasizes bridging theoretical insights with practical applications in high-reliability systems.

• Computational Social Science
• Data Science
• Social Network Analysis
• +4 more

Termeh Shafie is a Professor of Computational Social Science and Data Science at the University of Konstanz, Department of Politics and Public Administration. She specializes in statistical network analysis, multigraph modeling, and interdisciplinary applications across social sciences and digital humanities. Her research includes network entropy, data privacy, and archaeological network reconstruction.

Daniel ValesinView profile

Associate Professor

• Interacting Particle Systems
• Percolation Theory
• Random Graphs

Daniel Valesin is an Associate Professor of Mathematics at the Johann Bernoulli Institute, University of Groningen, specializing in Interacting Particle Systems, Percolation Theory, and Random Graphs. His work bridges probability theory and statistical mechanics with applications in network science and epidemic modeling. His academic journey includes: B.Sc. from Fundação Getulio Vargas, Brazil (2000-2004) M.Sc. from Instituto de Matemática Pura e Aplicada, Brazil (2005-2007) Ph.D. from Ecole Polytechnique Fédérale de Lausanne, Switzerland (2007-2011) Post-doctoral fellowship at University of British Columbia, Canada (2011-2014) Valesin's research centers on stochastic processes on graphs , with particular emphasis on phase transitions in contact processes, percolation models, and voter dynamics. His theoretical frameworks address metastability, extinction times, and spatial correlations in complex networks, contributing to fundamental understanding of epidemic spread and critical phenomena. Analysis of his 15 most recent publications reveals consistent focus on contact process dynamics across diverse network structures (random graphs, trees, lattices) and percolation models with long-range interactions. Key trends include rigorous asymptotic analysis, metastability characterization, and exploration of how network topology influences phase transitions. His notable recognitions include: NWO Top Module 2 Grant for epidemic modeling on dynamic networks University of Groningen's Mathematics Teacher of the Year (2014-2015) Canadian Government Post-doctoral Fellowship (2011-2014) Valesin actively mentors doctoral researchers including Réka Szabó, Pedro Luis Barrios Pantoja (co-supervised with Milton Jara), Eric Ossami Endo (completed 2018), and Gabriel Leite Baptista da Silva. His NWO-funded project exemplifies his commitment to translating theoretical advances into network epidemic insights. Collaborations span an international network including Aernout van Enter, Thomas Mountford, and Jean-Christophe Mourrat.

Jorn van der PolView profile

Lecturer

• Matroid Theory
• Combinatorics
• Discrete Mathematics
• +3 more

Jorn van der Pol is a Lecturer in the Department of Mathematics of Operations Research. His research focuses on matroid theory, combinatorics, and discrete mathematics, with notable contributions to the study of matroid enumeration, properties of matroids, and their applications in graph theory and optimization. His work often intersects with extremal combinatorics, hypergraph structures, and algorithmic enumeration techniques. Key research interests include Turán densities in hypergraphs, binary geometries, and the analysis of matroid minors. Recent collaborations explore the interplay between matroid theory and stochastic simulation methods. His publications span prestigious journals such as SIAM Journal on Discrete Mathematics and the Electronic Journal of Combinatorics, with an emphasis on open-access research. Van der Pol’s research trends highlight advancements in enumerative combinatorics, symmetry analysis in matroids, and probabilistic approaches to matroid structures. His work contributes to foundational questions in discrete mathematics and operations research, with applications in graph theory and algorithm design. No scientific awards are explicitly mentioned. His advising and grant activities remain unspecified in the provided texts. Research collaborations include studies on Earth and Planetary Sciences, though the primary focus remains matroid theory and combinatorial structures.

Tom SnijdersView profile

Professor

• Social Networks
• Multilevel Analysis
• Stochastic Models in Social and Behavioral Sciences
• +3 more

Prof. Tom Snijders is a Full Professor at the University of Groningen's Faculty of Behavioural and Social Sciences, affiliated with the Sociology/ICS department. He holds an Emeritus Fellow position at Nuffield College, University of Oxford. His research focuses on social networks, multilevel analysis, stochastic models, and statistical methodologies in social sciences. He has authored 260+ publications and supervised 50+ students. Key research interests include network dynamics, computational modeling, and quantitative methods. Recent work explores opinion dynamics, peer relationships in education, and co-evolution in corporate networks. He has received prestigious awards like the William D. Richards Jr. Software Award (2017) and a 2013 award for network evolution studies. Snijders has contributed to academic governance, serving on editorial boards (e.g., Edward Elgar Publishing) and as Chair of the Scientific Council at the Institute for Advanced Study in Toulouse. His work aligns with UN SDGs through contributions to education quality and sustainable communities.