جستجوی استاد راهنمای دانشگاهی

Alexander Van Werdeمشاهده پروفایل

پژوهشگر

• Random Matrix Theory
• Spectral Algorithms
• Stochastic Processes
• +۵ مورد دیگر

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.

Burcu Kulahcioglu Ozkanمشاهده پروفایل

استادیار

• Formal Methods
• Software Testing
• Concurrent Systems
• +۷ مورد دیگر

Burcu Kulahcioglu Ozkan is an Assistant Professor and Delft Technology Fellow at the Delft University of Technology (TU Delft) Software Engineering Research Group (SERG). Her research bridges formal methods with software engineering to enhance the reliability of concurrent and distributed systems , with applications in blockchain systems and graph databases . Recipient of Amazon Research Award (2022) and Stellar Academic Research Grant (2023) Founder of the FORSE Lab , focusing on lightweight formal methods for software engineering Co-PI of Ripple’s UBRI program at TU Delft, targeting blockchain testing Co-leader of the TU Delft-JetBrains AI4SE collaboration track Her research spans model checking , debugging , and fuzz testing , addressing challenges in event interleavings and network faults in distributed systems. She has received multiple best paper awards , including OOPSLA’23 Distinguished Paper and ICGT’25 Best Software Science Paper . She serves on program committees for major conferences like ICSE’26 , CAV’25 , and ECOOP’25 , and has delivered invited talks at VLDB Summer School (2025), Sabanci University (2024), and Dagstuhl Seminars (2023).

• Probability Theory
• Random Graphs
• Stochastic Processes
• +۲ مورد دیگر

Dr. S.A. Donderwinkel is a researcher at the Bernoulli Institute within the Faculty of Science and Engineering at the University of Groningen. Their work focuses on probability theory, random graph models, and stochastic processes, with recent publications exploring graphic sequences, critical tree structures, and directed configuration models. They actively contribute to academic research through collaborations and peer-reviewed publications. University: University of Groningen School: Faculty of Science and Engineering Department: Bernoulli Institute Role: Researcher Email: s.a.donderwinkel@rug.nl Research interests include: Integrated random walks and graphic sequences Critical tree analysis and Cauchy distributions Height bounds in random trees Universality in directed configuration models Asymptotic behavior of random structures Their recent publications (2024-2025) demonstrate expertise in probabilistic combinatorics and graph theory, with applications in theoretical computer science and mathematical physics. All articles exhibit rigorous peer-reviewed academic contributions.

Wouter De Baeneمشاهده پروفایل

دانشیار

• Cognitive Neuropsychology
• Neuroimaging
• Medical Imaging Analysis
• +۳ مورد دیگر

Wouter De Baene is an Associate Professor in the Department of Cognitive Neuropsychology at Tilburg University’s Tilburg School of Social and Behavioral Sciences. He holds a PhD from KU Leuven and a PostDoc from Ghent University, specializing in Experimental Psychology and Neuroimaging. His research focuses on brain anatomy, function, and cognitive functioning in both healthy and clinical populations, employing neuroimaging techniques like diffusion MRI, resting-state fMRI, and task-based fMRI, alongside advanced analyses such as machine learning and graph theory. Affiliations: Tilburg University, Department of Cognitive Neuropsychology; Academic Collaborative Center for Digital Health & Mental Wellbeing. Education: Master’s and PostDoc in Experimental Psychology (Ghent University); PhD in Neuroimaging (KU Leuven). Research Interests: Neuroimaging applications in clinical populations (e.g., glioma, meningioma, brain metastases). Machine learning for automated tumor segmentation and predictive modeling. Functional and structural connectivity in brain tumors and neurocognitive disorders. Effects of treatments (e.g., surgery, radiotherapy) on cognitive outcomes. Publications: Recent work emphasizes automated tumor segmentation, cognitive function prediction in glioma patients, and neuroimaging-based analyses of brain networks. His articles span topics like meningioma segmentation using nnUNet, executive function impairments linked to tumor location, and machine learning applications in oncology. Grants & Labs: Involved in multidisciplinary collaborations, including the Academic Collaborative Center for Digital Health & Mental Wellbeing. Active in editorial roles (e.g., BMC Neuroscience).

• Magnetoencephalography
• Functional Connectivity
• Neuroimaging Analysis
• +۳ مورد دیگر

Rikkert Hindriks is an Assistant Professor at the Faculty of Science in the Department of Mathematics at Vrije Universiteit Amsterdam. He also holds an affiliation with Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep . His research focuses on advanced neuroimaging techniques, particularly magnetoencephalography (MEG) and electroencephalography (EEG), with emphasis on functional connectivity, neural signal processing, and brain network dynamics. Key research areas include phase-lag analysis, non-reversible brain state characterization, and spatiotemporal modeling of neural activity. He teaches courses such as Probability and Statistics and oversees Bachelor Project: Business Case . Hindriks has published extensively in journals like NeuroImage and PLoS Computational Biology , with recent work addressing MEG data reconstruction and neural field theory applications. No ancillary activities or scientific awards are listed in the provided profile. His research collaborations span institutions globally, reflecting his interdisciplinary approach to neuroscience and computational modeling.

Wouter Kagerمشاهده پروفایل

استادیار

• Probability Theory
• Discrete Systems
• Random Spatial Behavior
• +۶ مورد دیگر

Wouter Kager is an Assistant Professor of Probability in the Department of Mathematics at Vrije Universiteit Amsterdam. His research focuses on discrete systems with random spatial behavior, including lattice aggregation models, random fields, and self-interacting random walks. He is particularly interested in the limiting behavior and scaling limits of these models, contributing to Probability Theory and related disciplines. Research Interests: Kager's work explores the intersection of probability theory and statistical physics. His research includes studies on stochastic processes, lattice models (e.g., Ising model), aggregation phenomena, and network stability. He has developed tools for analyzing stochastic systems, such as rotor-router aggregation and diamond aggregation models. His recent work also addresses applications in coordination games and queueing theory. Tools & Contributions: Kager has created open-source tools for converting EPS/FIG files to PDF/PNG, optimizing n-up printing, and LaTeX presentation themes. These tools are widely used in academic and technical contexts. His contributions to computational methods reflect his interdisciplinary approach to research. Publications: His articles span topics like stochastic domination, critical phenomena in random graphs, and stability analysis of stochastic networks. Recent works include advancements in likelihood ratio analysis, convex cone theory, and coordination game models.

Franco Ruzzenentiمشاهده پروفایل

استادیار

• Energy & Fuels
• Environmental Studies
• Complex Systems
• +۷ مورد دیگر

Franco Ruzzenenti is an Assistant Professor (tenure track) at the University of Groningen, affiliated with the Faculty of Science and Engineering and the Integrated Research on Energy, Environment & Society (IREES). He holds additional roles as Programme Director of the MSc 'Energy & Environmental Sciences' and Director of Education at the Energy & Sustainability Institute Groningen (ESRIG). His research focuses on complex systems, network theory, and energy sustainability, with particular emphasis on topics like energy efficiency, rebound effects, and carbon implications of global systems. Affiliations: University of Groningen, Faculty of Science and Engineering, IREES, ESRIG Expertise: Energy Modelling, Industrial Ecology, Climate Change, Sustainable Development Goals Ruzzenenti’s work bridges environmental and socio-economic systems, addressing challenges such as fossil carbon accumulation in the technosphere, sovereign wealth funds’ carbon footprints, and the dynamics of grid-scale battery storage in the EU. He employs interdisciplinary methods, including agent-based modeling, econometrics, and network theory, to analyze energy transitions and policy impacts. His research outputs span high-impact journals like Cell Reports Sustainability , Nature Climate Change , and Energy Policy , reflecting contributions to global sustainability science. Collaborations include projects funded by the EU and international partnerships on energy systems and environmental policy.

Johan van Leeuwaardenمشاهده پروفایل

استاد

• Queueing Theory
• Stochastic Processes
• Load Balancing
• +۱ مورد دیگر

Johan van Leeuwaarden is a Full Professor specializing in stochastic operations research, with expertise in queueing theory, load balancing, and random graph analysis. His work develops mathematical models for large-scale systems like cloud networks and service industries. He analyzes heavy-traffic regimes and distributionally robust optimization for systems with uncertain parameters. Current research includes appointment scheduling under service time uncertainty and learning algorithms for dynamic resource allocation.

• Network Science
• Graph Theory
• Markov Processes
• +۷ مورد دیگر

P.F.A. Van Mieghem is a Professor at the College of Electrical Engineering, Mathematics and Computer Science, specializing in Network Architectures and Services. His research focuses on mathematical modeling of complex networks, including reliability analysis, epidemic dynamics, and temporal graph generation. Network Reliability Polynomials Continuous-Time Markov Processes Temporal Contact Graphs Community Detection Algorithms He received the James P.G. Sterbenz Best Paper Award (2019) for his contributions to topological network analysis. Recent work explores fractional calculus applications in epidemiological network models and random walker-based graph reproduction techniques.

Floske Spieksmaمشاهده پروفایل

دانشیار

• Operations Research
• Stochastic Processes
• Decision Theory
• +۴ مورد دیگر

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.

Guido Schäferمشاهده پروفایل

استاد

• Algorithms
• Combinatorial Optimization
• Algorithmic Game Theory
• +۴ مورد دیگر

Guido Schäfer is a Professor of Algorithms, Optimization, and Game Theory at the Institute for Logic, Language and Computation (ILLC) within the Faculty of Science at the University of Amsterdam (UvA). He is also a Senior Researcher at CWI (Centrum Wiskunde & Informatica) in Amsterdam, where he has led the Networks and Optimization Group (N&O) and serves on the Management Team. His academic journey includes a PhD from the Max-Planck-Institute for Informatics and postdoctoral research at Sapienza University of Rome and a junior professorship at TU Berlin, where he earned his Habilitation in 2009. His research focuses on algorithms and complexity, combinatorial optimization, and algorithmic game theory. Key themes include optimization algorithm design, equilibrium analysis, and mechanism design. Applications span routing, logistics, auctions, and algorithms with predictions. He has organized major conferences like SAGT 2024 and WINE 2015, and leads the Dutch Network on the Mathematics of Operations Research (LNMB). Teaching includes advanced courses on Algorithmic Game Theory, Algorithmic Mechanism Design, and Behavioral Operations Research. He supervises PhD and Master's students, particularly in the LNMB and UvA programs. Industry collaborations involve applying algorithmic techniques to real-world problems. His work bridges theoretical foundations with practical applications, emphasizing efficiency and societal impact.

Julia Komjathyمشاهده پروفایل

دانشیار

• Random Graph Models
• Spatial Random Graphs
• Hyperbolic Random Graphs
• +۷ مورد دیگر

Julia Komjathy is an Associate Professor at Delft University of Technology's Faculty of Electrical Engineering, Mathematics and Computer Science (EWI), within the Delft Institute of Applied Mathematics (DIAM). She leads the Applied Probability Group and focuses on probabilistic network models, including random graphs, spatial networks, and epidemic processes. Her research explores structural properties like 'explosion' in weighted graphs and phase transitions in contact processes. Education: Ph.D. in Mathematics from TU Budapest under Professors Márton Balázs and Károly Simon (2012). Thesis: Topics in Markov chains: Mixing and escape rate . Research Interests: Random graph models of complex networks Spatial random graphs and hyperbolic geometry Weighted random graphs and distance evolution Epidemic spread dynamics on networks Branching processes and percolation theory Phase transitions in scale-free systems Professional Activities: Organizer of TU Delft's Probability & Statistics Seminar Keynote speaker at WAW 2024 (Warsaw) and Discrete Probability Days 2023 (Barcelona) Supervised 4 PhD students, including Enrico Baroni (2017), Viktoria Vadon (2020), and Joost Jorritsma (2023) Awards: None explicitly listed, but her work has been published in top journals like PNAS, Annals of Applied Probability, and Random Structures & Algorithms. Current Projects: Investigating degree-dependent contact processes, first-passage percolation growth regimes, and cluster-size decay in spatial networks.

• Causal Inference
• Epidemiology
• Mendelian Randomization
• +۹ مورد دیگر

Jeremy Labrecque is an Assistant Professor in the Department of Epidemiology at Erasmus MC, Faculty of Medicine, Netherlands. His research focuses on causal inference methods applied to epidemiological and clinical questions in public health, mental health, and neurodegenerative diseases. He is actively involved in major cohort studies such as the Generation R Study and the Rotterdam Study. Research Interests: His work centers on advanced statistical methodologies, particularly instrumental variable analysis, Mendelian randomization, and directed acyclic graphs, to infer causality from observational data. Key topics include ADHD, prenatal exposures, internalizing symptoms in children, dementia, and health care access. He applies these methods to understand causal pathways in complex biological and social systems. The recent publications highlight a strong trend in methodological innovation in causal inference, particularly in medical decision making and perinatal epidemiology. His work integrates biostatistics, neuroscience, and public health, often using large longitudinal datasets to evaluate hypothetical interventions and mediation pathways. The use of causal diagrams and natural experiments underscores a commitment to robust evidence generation. Scientific Awards: No awards explicitly mentioned in the provided text. Advising and Grants: While specific students or grants are not listed, his role as an assistant professor and involvement in high-impact publications suggest active supervision of junior researchers and participation in funded research projects. The mention of 'Supervised Work (1)' on his profile indicates at least one mentored project. Labs and Teams: Jeremy Labrecque is embedded within the epidemiological research infrastructure at Erasmus MC, collaborating extensively on the Generation R Study and the Rotterdam Study. These are large, interdisciplinary cohorts focused on population health across the lifespan, indicating integration into collaborative, data-rich research environments.

Alessandro Zoccaمشاهده پروفایل

استادیار

• Applied Probability
• Stochastic Dynamics on Networks
• Power Systems Resilience
• +۳ مورد دیگر

Alessandro Zocca is an Assistant Professor in the Department of Mathematics at the Vrije Universiteit Amsterdam , where he has been since 2019. His research sits at the intersection of applied probability , reinforcement learning , and network optimization , with primary applications in power systems resilience against climate-driven uncertainties. Education: PhD in Mathematics (2015, Eindhoven University of Technology), Postdoctoral work at Caltech (2017-2019) and CWI Amsterdam (2016-2017) Key Research Themes: Stochastic dynamics on networks, climate crisis impact on infrastructure, optimization under uncertainty Awards: Applied Probability Trust award (2015), NWO Rubicon grant (2017) His recent work focuses on reinforcement learning for grid topology control, two-stage stochastic programming for exponential constraint handling, and Markov Chain Monte Carlo methods for rare-event analysis. He actively supervises five PhD students on topics ranging from weather-driven grid failures to stochastic frequency reserve optimization . Co-author of the textbook Hands-On Mathematical Optimization with Python (Cambridge University Press, 2025), he also organizes the IFIP Performance 2025 conference in Amsterdam.

Palina Salanevichمشاهده پروفایل

استادیار

• Signal Processing
• Data Analysis
• Graphs
• +۹ مورد دیگر

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.