Find Academic Supervisors

• Foundations of Probability
• Randomness
• Graph Theory
• +8 more

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 .

Aad van der VaartView profile

Professor

• Mathematical Statistics
• Bayesian Statistics
• Medical Imaging
• +3 more

Aad van der Vaart is a Professor of Stochastics at Leiden University's Mathematical Institute. He was awarded the prestigious NWO Spinoza Prize in 2015 for groundbreaking work in mathematical statistics, particularly Bayesian methods applied to medical imaging, genetic data, and complex models. His research bridges pure mathematical theory with applied domains like neuroscience and astronomy. Research Interests : Van der Vaart focuses on infinite-dimensional Bayesian statistics, nonparametric models, and statistical genetics. His work emphasizes rigorous mathematical analysis of prior distributions and their impact on data-driven conclusions. Applications include gene network modeling and PET scan image reconstruction. Key Contributions : Authored influential books on estimation theory; pioneered modern Bayesian approaches to high-dimensional data. His Spinoza Prize funds will support interdisciplinary research and hiring new talent in statistical methods. Awards : NWO Spinoza Prize (2015), recognized as a global leader in statistical theory. Future Directions : Expanding into astronomical data analysis and medical applications, leveraging Bayesian frameworks for big datasets.

Nezihe Merve GürelView profile

Assistant Professor

• Machine Learning
• Computer Science
• Pattern Recognition
• +4 more

Nezihe Merve Gürel is an Assistant Professor in Computer Science at Delft University of Technology (TU Delft), affiliated with the Pattern Recognition & Bioinformatics Group within the Intelligent Systems Department of the Faculty of Electrical Engineering, Mathematics and Computer Science. Her research focuses on developing robust, reliable, and efficient machine learning methods with enhanced reasoning capabilities, bridging theoretical rigor and practical applications. She emphasizes data-centric approaches to improve ML systems. Education: PhD in Computer Science from ETH Zurich, MSc from EPFL (Switzerland). Research Interests: ML robustness, reliability, reasoning, data-centric ML, federated learning, and explainable AI. Her recent work includes certified robustness for retrieval-augmented models and time-efficient learning algorithms. She has contributed to the Journal of Data-centric Machine Learning Research as an executive editor and served as a reviewer for top ML conferences (NeurIPS, ICML, ICLR). She previously held roles at IBM Research, Stanford University's Human-Centered AI Lab, and Westlake Institute for Advanced Study. Her awards include the Generation Google Scholarship and Cisco Research Funding . Scientific Awards : Generation Google Scholarship (2021) Cisco Research Center University Funding Labs & Teams : She leads research in the Pattern Recognition Laboratory at TU Delft and collaborates with international institutions like Stanford and Westlake Institute for Advanced Study.

Marie-Colette van LieshoutView profile

Professor

• Stochastic Geometry
• Spatial Statistics
• Image Analysis
• +6 more

Marie-Colette van Lieshout is a Professor of Spatial Stochastics at the Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, and a Scientific Staff Member in the Stochastics group at Centrum Wiskunde & Informatica (CWI), Amsterdam. She has been active in research since 1997 and is a leading expert in stochastic geometry, spatial statistics, and image analysis. Her educational and professional background includes positions at the University of Warwick and the Free University Amsterdam. She is currently engaged in advanced research on point processes, random fields, and tessellation models, with applications in seismic hazard, fire risk, and machine learning. Her research interests include: Stochastic Geometry Spatial Statistics Image Analysis Point Process Modeling Seismic Risk Assessment Machine Learning for Spatial Data Her recent publications (2023–2025) focus on spatial intensity estimation, marked point processes, and data-driven risk modeling, showing a strong integration of classical spatial statistics with modern computational and machine learning techniques. Key themes include adaptive kernel smoothing, infill asymptotics, and applications in environmental and public safety domains. She has received significant recognition, including: Elected Fellow, International Statistical Institute (ISI) She has been awarded multiple research grants from NWO and other agencies, including the KLEIN grant for fire risk management and the DeepNL grant for seismicity prediction in Groningen. She has supervised or collaborated with researchers such as C. Lu, Z. Baki, and R. Markwitz. She is also active in academic service, serving on editorial boards (e.g., Methodology and Computing in Applied Probability), advisory boards (InHolland University), and councils of learned societies (Bernoulli Society, KWG). She leads and participates in research clusters such as STAR and contributes to outreach and education through courses and public lectures on earthquake modeling and spatial statistics.

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

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.

Ivana NikoloskaView profile

Assistant Professor

• Machine Learning
• Quantum Computing
• Communication Engineering
• +7 more

Ivana Nikoloska is an Assistant Professor at the Department of Electrical Engineering, Eindhoven University of Technology (TU/e). She is affiliated with the Center for Quantum Materials and Technology Eindhoven and BIASlab (Bayesian Intelligence and Stochastic Agents Lab). Her academic career includes prior roles as a Research Associate at King’s College London and a Visiting Researcher at Aalborg University. PhD: Monash University, Australia (2023) MSc & Dipl.-Ing.: University of Ss. Cyril and Methodius, North Macedonia Research Interests span foundational and applied machine learning, quantum computing, and information/communication engineering. Her work focuses on integrating Bayesian inference, variational methods, and quantum technologies for tasks like signal processing, channel estimation, and power control optimization. Quantum Machine Learning Bayesian Simulation-Based Inference Meta-learning for Wireless Systems Hybrid Quantum-Classical Architectures Stochastic Signal Processing Quantum Sensing & Metrology Notable Trends in Publications include quantum recurrent neural networks with adaptive gating, Bayesian frameworks for quantum sensing, and meta-learning applications in communication systems. She explores variational inference for planning and robust algorithms for channel estimation under non-ideal conditions.

Tom BootView profile

Associate Professor

• Macroeconomic Forecasting
• High-Dimensional Data Analysis
• Econometric Theory
• +4 more

Dr. Tom Boot is an Associate Professor at the Department of Economics, Econometrics & Finance at the University of Groningen. He holds a PhD in Econometrics from Erasmus University Rotterdam (2017) and an MSc in Econometrics from the same institution (2012), along with an MSc in Physics from the University of Groningen (2010). His research focuses on econometric theory applied to macroeconomic forecasting, high-dimensional data analysis, and causal inference. He has been recognized with the Veni grant (2021–2024) for his work on forecasting methodologies. Boot’s research interests include improving forecast accuracy through methods like subspace projections, structural break modeling, and privacy-aware marketing analytics. His recent work explores privacy-utility trade-offs in data-driven marketing and unbiased estimation techniques for clustered errors. He has supervised PhD students including Jhordano Aguilar Loyo and Gilian Ponte, whose theses addressed panel data heterogeneity and differential privacy applications. Boot is also a program director for the MSc Econometrics, Operations Research, and Actuarial Studies (since 2024). His contributions to econometrics span over a dozen peer-reviewed publications, with a focus on advanced statistical techniques for economic forecasting and policy analysis. Collaborations include work with institutions like Harvard/MIT and the organization of workshops on causal inference and machine learning.

Antonis PapapantoleonView profile

Professor

• Mathematical Finance
• Stochastic Processes
• Lévy Processes
• +5 more

Antonis Papapantoleon is a Professor of Mathematical Finance at the Delft Institute of Applied Mathematics (DIAM), part of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at TU Delft. He is also affiliated with the Delft FinTech Lab and serves as an Affiliated Researcher at the Institute of Applied and Computational Mathematics (IACM), FORTH. Previously, he held roles including Assistant Professor at the National Technical University of Athens (NTUA), Juniorprofessor at TU Berlin (2011–2017), and Deputy Professor at the University of Mannheim (2016–2017). His research focuses on stochastic systems, Lévy processes in finance, term structure/LIBOR modeling, systemic risk management, model-free methods, and applications of machine learning in finance. Key contributions include advancements in affine LIBOR models, systemic risk analysis, and copula-based risk allocation. His work has been published in top journals like Annals of Applied Probability , Mathematical Finance , and Finance and Stochastics . His research has been supported by the Hellenic Foundation for Research and Innovation (HFRI), Europlace Institute of Finance, DAAD, MATHEON, and private corporations. He has delivered invited talks globally and co-edited Advanced Modelling in Mathematical Finance (Springer, 2016). Current affiliations include leadership in the Delft FinTech Lab and active participation in international collaborations. His work bridges theoretical stochastic analysis with practical financial applications, emphasizing robust frameworks for risk measurement and derivatives pricing.

Erik QuaeghebeurView profile

Assistant Professor

• Probability Theory
• Statistics
• Uncertainty in Artificial Intelligence
• +4 more

Erik Quaeghebeur is an Assistant Professor at Eindhoven University of Technology's School of Mathematics and Computer Science, focusing on uncertainty modeling in artificial intelligence. His work spans probabilistic circuits, imprecise probability theory, and wind energy applications. PhD in Applied Mathematics (Ghent University, 2002-2009) Master's in Applied Mathematics (Université catholique de Louvain, 2001-2002) Master's in Physics Engineering (Ghent University, 1998-2001) Research interests include probabilistic modeling under uncertainty, with applications in AI and wind energy systems. His recent work explores tensor factorizations, equivariant graph neural networks, and scalable probabilistic circuits. Scientific contributions include 60 research outputs and 2 datasets . Awards encompass the ERCIM Alain Bensoussan Fellowship (2013), BOF Postdoc (2010), and B.A.E.F. Francqui Fellowship (2009). He serves on committees for the Society for Imprecise Probability and acts as editorial board member for related conferences. Foundations of Artificial Intelligence course (since 2020) Uncertainty Representations and Reasoning course (since 2021)

Wouter M. Koolen-WijkstraView profile

Professor

• Machine Learning Theory
• Game Theory
• Information Theory
• +5 more

Wouter M. Koolen-Wijkstra is a Professor of Mathematical Machine Learning at the University of Twente (Statistics group) and a Scientific Staff Member at Centrum Wiskunde & Informatica (CWI), Amsterdam, in the Machine Learning department. His research bridges theoretical machine learning, game theory, and statistics, with active projects on multi-armed bandits, online learning, and safe inference methodologies. He co-leads INRIA-CWI associate teams (6PAC and 4TUNE) and is an ELLIS Scholar. His work emphasizes provable guarantees in learning algorithms, including: Regret minimization under risk-averse scenarios Multi-scale adaptation in online decision-making Game-theoretic equilibria computation Anytime-valid statistical inference via e-processes Recent publications demonstrate a focus on robust learning frameworks , particularly in bandit problems, hypothesis testing, and Nash equilibrium characterization, often leveraging information-theoretic and optimization principles. Awards include: Veni Grant (2015) for 'Learning at the Intrinsic Task Pace' QUT Vice-Chancellor's Fellowship (2013) for multitask learning Rubicon Grant (2010) for game-theoretic online learning ELLIS Scholar recognition He teaches graduate courses on Machine Learning Theory and Graphical Models at CWI. Current grants include collaborations with INRIA (4TUNE and 6PAC teams) and industry partnerships (e.g., PPS Booking.COM).

Andreas Milias ArgeitisView profile

Associate Professor

• Systems Biology
• Biochemistry & Molecular Biology
• Mathematics, Interdisciplinary Applications
• +6 more

Andreas Milias Argeitis is an Associate Professor in the Faculty of Science and Engineering at the University of Groningen. He leads the Milias-Argeitis Lab within the Molecular Systems Biology research unit at the Groningen Biomolecular Sciences & Biotechnology Institute (GBB). His research focuses on integrating experimental and computational approaches to understand cellular processes, particularly the coordination between cell growth, division, and metabolic dynamics in budding yeast. Key research areas include systems biology, TOR signaling pathways, cell cycle regulation, and the application of machine learning and mathematical modeling. His lab develops advanced tools like optogenetic control systems and deep learning algorithms for cell segmentation and tracking. Recent work has revealed metabolic oscillations linked to the cell cycle and explored the role of proteins like Sch9 in TORC1-dependent signaling. Notable achievements include an NWO Vidi Grant (2018) and an ENW Science-M Grant (2023) for studying how growth drives the cell division cycle. He has over 40 peer-reviewed publications, including work in Nature Communications , Nature Metabolism , and Journal of Cell Science . His research bridges fundamental biology with technological innovations in single-cell analysis and synthetic biology. Lab activities include CRISPR/Cas9 genome editing protocols, fluorescent protein maturation studies, and the development of photo-switchable enzymes. Collaborations span biochemistry, mathematics, and engineering, reflecting his interdisciplinary approach to unraveling cellular mechanisms.

• Stochastic Processes
• Operations Research
• Data-Driven Decision Making
• +4 more

Stella Kapodistria is an Associate Professor at Eindhoven University of Technology's Department of Mathematics and Computer Science, specializing in Stochastic Operations Research. She holds roles as EAISI High Tech Systems Associate Professor and editorial board member of journals like MCAP and PEIS. Her research focuses on data-driven decision-making, stochastic systems optimization, and maintenance policies, with applications in renewable energy, critical infrastructure, and cryptocurrency networks. She has secured grants including NWA-ORC, NWO Big Data, and TKI WoZ, and collaborates with industry partners in the Brainport region. Education: BSc (2003), MSc (2006, Hons.), and PhD (2009, summa cum laude) in Mathematics from the University of Athens. Postdoc at TU/e, followed by roles at Groningen University and TU/e's Stochastic Operations Research group. Teaching includes courses on Optimal Decision Making, Stochastic Performance Modeling, and Financial Mathematics. Research interests emphasize real-time learning, system resilience, and scalable algorithms for complex networks. Recent work addresses maintenance logistics, blockchain confirmation times, and wind energy prediction. She has published over 40 peer-reviewed articles and contributed to the 4TU Resilience Engineering Center. Awards include editorial leadership roles and grant funding. Advised 32 academic works and oversees industrial projects bridging theory and practice. Her labs and collaborations focus on adaptive systems, predictive analytics, and sustainable engineering solutions.

Bystrov Alexander AlexandrovichView profile

Associate Professor

• Stochastic integrals
• Statistics of dependent observations
• Limit theorems
• +1 more

Bystrov Alexander Alexandrovich is an Associate Professor at the Department of Probability Theory and Mathematical Statistics, Mechanics and Mathematics Faculty, Novosibirsk State University (NSU). He has been a full-time employee since 2006 and has held his current position since 2008. Education: Graduated with honors from Novosibirsk State University (2001) PhD in Probability Theory and Mathematical Statistics (01.01.05) from NSU (2006), thesis: Stochastic integrals and asymptotics of canonical von Mises statistics constructed from dependent observations Research Interests: His work focuses on theoretical aspects of probability and statistics, including multiple stochastic integrals for non-random functions, statistical methods for dependent observations, limit theorems, and inequalities for U- and V-statistics. These topics bridge stochastic analysis with foundational statistical theory. Publications: His articles (2005–2014) concentrate on stochastic integration theory and asymptotic statistics, particularly involving Gaussian processes, von Mises statistics, and deviation probabilities. Recurring themes include generalizations of stochastic integrals and limit theorems for dependent data. Academic Activities: He supervises undergraduate and graduate students at NSU's Department of Probability Theory and Mathematical Statistics. No grants or awards are documented in the provided text.

Christian SteglichView profile

Associate Professor

• Social Networks
• Stochastic Modelling
• Sociology
• +5 more

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.

Frank van der Duijn SchoutenView profile

Visiting Professor

• Operations Research
• Applied Mathematics
• Statistical Modeling
• +3 more

Frank van der Duijn Schouten is a Visiting Professor at the Department of Econometrics and Operations Research, Vrije Universiteit. He previously served as Rector Magnificus at Vrije Universiteit (2013-2015) and held leadership roles at Tilburg University, including Rector Magnificus and Dean (1987-2013). He obtained his PhD in Operations Research from Leiden University in 1979. His research focuses on stochastic processes, particularly nonhomogeneous Poisson processes, power law models, and applications in reliability engineering and quality control. He has published extensively on topics like statistical modeling in group testing and operational research applications. His work combines theoretical advancements in statistical methodologies with practical applications in quality control, epidemiological modeling, and system optimization. Despite no publicly listed scientific awards, his contributions to academic leadership and operations research are notable. Frank has advised on research projects involving stochastic systems and testing models, though specific student/advisor relationships are not documented here. His research has implications for improving efficiency in healthcare diagnostics, manufacturing quality systems, and public health monitoring frameworks.