Find Academic Supervisors

Matthias SchlottbomView profile

Associate Professor

• Numerical Analysis
• Computational Mathematics
• Radiative Transfer
• +4 more

Matthias Schlottbom is an Associate Professor specializing in Mathematics of Computational Science, with a focus on numerical methods and their applications in physics, biology, and engineering. His research integrates advanced computational techniques with interdisciplinary problems, including radiative transfer, photonic crystals, and chemotaxis modeling. Research Interests: Schlottbom’s work spans numerical analysis, finite element methods, and machine learning. He develops high-order discretization schemes, iterative solvers for anisotropic transport, and mathematical frameworks for biological network formation. Publications: Recent articles highlight his contributions to accelerating radiative transfer simulations, extending component mode synthesis for Helmholtz equations, and analyzing diffusion limits in kinetic models. His work often bridges computational mathematics with practical applications in photonics and multiscale systems. Collaborations: He actively collaborates on datasets for optical simulations, radiative transfer algorithms, and photonic crystal modeling, contributing to open-access repositories like 4TU.Centre and Zenodo. Activities: Schlottbom has organized workshops such as the Kinetic Theory Workshop in the Netherlands and delivered keynotes on residual minimization and data-driven methods for transport equations. Scientific Awards: No specific awards or fellowships are mentioned in the provided materials. Advising & Grants: Details about students, advising roles, or grant funding are not included in the available data.

Sara MagliacaneView profile

Assistant Professor

• Causality
• Causal Representation Learning
• Causality-inspired Machine Learning
• +4 more

Sara Magliacane is an Assistant Professor at the University of Amsterdam , affiliated with the Amsterdam Machine Learning Lab (AMLab) and the Informatics Institute . She also holds a Research Scientist position at the MIT-IBM Watson AI Lab and has been an ELLIS Scholar since 2022. Education PhD in Artificial Intelligence (2017), VU Amsterdam MSc in Computer Engineering (2011), Politecnico di Milano BSc in Computer Engineering (2008), Università degli Studi di Trieste Research Focus : At the intersection of Causality and Machine Learning , her work addresses Causal Representation Learning from high-dimensional data (images, sequences) Causal Discovery in latent confounder scenarios Causality-inspired Reinforcement Learning for robustness and adaptability Neurosymbolic AI for theoretical guarantees Publication Trends : Her recent work explores Factored adaptation in non-stationary environments (NeurIPS 2022) Temporal causal identifiability (ICML 2022) Binary interaction-based causal discovery (UAI 2023) Safe exploration in visual RL (HSCC 2021) Structure learning lower bounds (NeurIPS 2020) Scientific Recognition : ELLIS Scholar (2022–present) Spotlight presentations at ICML 2022 and ICLR 2022 Advising & Collaborations : Currently supervising 6 PhD students at the University of Amsterdam and AUMC, with 12 alumni advisees. Collaborates with researchers at MIT-IBM Watson AI Lab, Simons Institute, and TUM.

Frank WillemsView profile

Professor

• Control Systems
• Automotive Engineering
• Powertrain Control
• +5 more

Frank Willems is a Full Professor of Systems and Control Technology and Chair of Integrated Powertrain Control at Eindhoven University of Technology (TU/e), holding a part-time position realized with support from TNO. He is affiliated with the Control Systems Technology group within the Department of Mechanical Engineering, and also contributes to EIRES and EAISI research initiatives. Dr. Willems obtained his MSc (1995) and PhD (2000) in Mechanical Engineering from Eindhoven University of Technology (TU/e). His academic journey continued with a position at TNO Automotive, where he currently serves as a principal scientist in powertrain control. Professor Willems' research focuses on developing optimal and robust control methods for automotive powertrain systems. His work addresses the critical challenge of integrating energy and emission management strategies at the powertrain system level, which is essential as traditional methods become infeasible due to increasingly strict environmental regulations. Key research areas include control-oriented modeling of internal combustion engines, cylinder pressure-based combustion control, and integrated energy and emission management. His research aims to minimize development time and costs through model-based control methods, with the ultimate goal of achieving auto-calibration where powertrain energy efficiency is optimized online using smart sensors and route information. Dr. Willems serves as an Associate Editor for Control Engineering Practice and is an active member of the IFAC Technical Committee Automotive Control. He has participated in numerous international program committees for conferences including the IFAC Conference on 'Engine and Powertrain Control, Simulation and Modeling (E-CoSM)', IFAC Symposium 'Advances in Automotive Control (AAC)', and 'Symposium for Combustion Control (SCC)'. His research has been supported by organizations including the Dutch Technology Foundation (STW) and DENSO Japan. At TU/e, Professor Willems teaches courses on 'Optimal control and reinforcement learning' and 'Advanced control for future heavy-duty powertrains.' His research group, part of the Control Systems Technology group, focuses on developing self-learning powertrain control systems to address the complexity and diversity of future ultra-clean and efficient vehicles.

Rodrigo GonzálezView profile

Assistant Professor

• Data-driven modeling
• System identification
• Control systems engineering
• +4 more

Rodrigo González is an Assistant Professor at the Department of Mechanical Engineering, Eindhoven University of Technology, since 2022. His research focuses on data-driven modeling, estimation, and control methods for high-tech precision systems, with applications in motion control and continuous-time system identification. Education: Ph.D. in Electrical Engineering (KTH Royal Institute of Technology, 2022) M.Sc. in Electronic Engineering (Universidad Técnica Federico Santa María, 2016) His work emphasizes continuous-time system identification, state-space modeling, and Bayesian estimation techniques. Key research themes include motion control tuning, multivariable systems, and noise/disturbance modeling in precision engineering applications. Rodrigo has received the Best Electronic Engineering Student Award (2016) and Best Thesis Award from Universidad Técnica Federico Santa María. He has active collaborations with institutions like Universidad Técnica Federico Santa María through visiting researcher appointments. Scientific awards include: Best Electronic Engineering Student Award (2016) Best Thesis Award (Universidad Técnica Federico Santa María)

Ralf PeetersView profile

Professor

• Signal and Image Processing
• Systems and Control Theory
• Mathematical Modelling
• +7 more

Ralf Peeters is a Full Professor in Mathematics of Knowledge Engineering at Maastricht University's Faculty of Science and Engineering , Department of Advanced Computing Sciences. He serves as Vice-Dean of Research and Director of the STEM Graduate School, while leading the university's team at the inter-university research school DISC and co-chairing the Mathematics Centre Maastricht. Education: PhD in Mathematics (Free University, Amsterdam, 1994) Technical Mathematics (Delft University of Technology, 1988) Research Interests span applied mathematics, systems and control theory, signal/image processing, artificial intelligence, and biomedical engineering applications. His work bridges mathematical techniques with real-world challenges in healthcare and industrial systems. Recent Publications highlight advancements in deep learning for cardiac signal reconstruction, tensor-based signal decomposition, and recurrence plot analysis. These works integrate machine learning with clinical diagnostics, particularly in electrocardiographic imaging and arrhythmia characterization. Key Collaborations: Mathematics Centre Maastricht Dutch Mathematics Platform Dutch Institute of Systems and Control Leadership Roles: Vice-Dean of Research (FSE), Director of STEM Graduate School, Head of DISC-affiliated team, and Co-Chair of Mathematics Centre Maastricht. He has supervised over 25 PhD projects, emphasizing applied research across health and industrial domains.

• Electromagnetism
• Antenna Design
• Microwave Engineering
• +5 more

Angelo Freni is an Associate Professor of Electromagnetism at the University of Florence with a primary focus on antenna design, microwave engineering, and terahertz imaging. His work spans theoretical and applied electromagnetism, including numerical methods for electromagnetic simulation, beamforming, and microwave component optimization. His most recent research includes: 2017: Simultaneous generation of pseudo-Bessel vortex modes with RLSA antennas 2017: THz imaging using uncooled wideband direct detection focal plane arrays 2017: Near-field focusing by non-diffracting Bessel beams Earlier work emphasizes microwave propagation modeling, reflectarray design, and tropospheric interference analysis. Key collaborations include researchers such as M. Albani, P. De Vita, and G. Vecchi.

• Evolutionary Computation
• Genetic Algorithms
• Optimization
• +5 more

Dirk Thierens is an Associate Professor in the Department of Computer Science at Utrecht University's Faculty of Science, specializing in Intelligent Systems within AI & Data Science. His academic career spans over 25 years, with continuous publications from 1996 through 2025, demonstrating sustained research activity and leadership in his field. He maintains an active research program with numerous collaborations, most notably with Peter A.N. Bosman, indicating a long-standing productive research partnership. Thierens' research focuses on evolutionary computation, particularly model-based evolutionary algorithms, genetic algorithms, and optimization techniques. His work has evolved from foundational genetic algorithm research in the late 1990s and early 2000s to more specialized model-based approaches in recent years, including significant contributions to Gene-pool Optimal Mixing Evolutionary Algorithms (GOMEA). His expertise spans single-objective and multi-objective optimization, permutation problems, mixed-integer problems, and real-valued optimization. In recent years, his research has expanded into applications in machine learning, particularly semi-supervised learning and neural network optimization. His publication record shows a consistent output of high-quality research, with numerous papers in top conferences like GECCO and journals in evolutionary computation. His most recent work (2023-2025) demonstrates continued innovation in synthetic data generation, neural network combination techniques, and parameterless evolutionary algorithms. The breadth of his work spans theoretical algorithm development, benchmarking methodologies, and practical applications in healthcare and other domains. While no specific scientific awards are mentioned in the available information, his extensive publication record, tutorial contributions at major conferences, and sustained research productivity over multiple decades indicate recognition within the evolutionary computation community. His tutorial work at GECCO conferences suggests he is considered an authority on model-based evolutionary algorithms. Thierens maintains an active research laboratory focused on evolutionary algorithms and their applications, with recent work exploring the intersection of evolutionary computation and deep learning. His research continues to advance both theoretical understanding and practical applications of optimization techniques in complex problem domains.

Victorita Dolean-MainiView profile

Professor

• Domain Decomposition Methods
• High Performance Computing
• Wave Propagation
• +2 more

Victorita Dolean-Maini is Professor at University of Strathclyde and Visiting Professor at University of Strathclyde (2023-2026). She specializes in domain decomposition methods for PDEs, high-performance computing, and scientific machine learning. Her work develops fast algorithms for wave propagation problems and complex systems. She co-authored the reference work 'Domain Decomposition Methods' (SIAM 2015) and received the Bull-Joseph Fourier Prize for stroke simulation using HPC. Current research explores physics-informed neural networks with domain decomposition architectures and their application to medical digital twins. She serves on the editorial board of SIAM Journal on Scientific Computing and collaborates with industrial partners on scientific machine learning projects. Recent publications focus on neural network architectures combining domain decomposition with physics-informed learning, multilevel methods, and efficient coarse spaces for partial differential equations.

Alberto RavagnaniView profile

Associate Professor

• Coding Theory
• Network Communication
• Combinatorics
• +4 more

Alberto Ravagnani is an Associate Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e), the Netherlands. He specializes in the mathematical foundations of communication theory, with a focus on coding theory, network communication, and combinatorics. His research addresses challenges in error-correcting codes, network coding, quantum error correction, and applications in data storage and cryptography. He holds a PhD from the University of Neuchâtel, Switzerland, and has held academic positions at institutions including University College Dublin, Ireland, and the University of Zurich. His work has been recognized with awards such as the KNAW Early Career Award (2021), Ambizione Research Grant (2019), and the Nexans Prize (2017). Ravagnani’s research spans theoretical and applied domains, including rank-metric codes, network decoding, and combinatorial structures. His recent publications explore eigenvalue bounds for codes, densities in metric spaces, and evasive subspaces. He is actively involved in editorial roles for journals like IEEE Transactions on Information Theory and Designs, Codes and Cryptography. His teaching includes courses on coding theory and combinatorics, and he has mentored multiple PhD students in areas such as DNA storage and quantum error correction. Key grants include a NWO Gravitation Grant (EUR 10M) and an EuroTech PhD Program Grant (EUR 235K).

Sorin PopView profile

Professor

• Applied Mathematics
• Numerical Analysis
• Porous Media Flow
• +3 more

Prof. Sorin Pop is a Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e). His research focuses on numerical analysis and computational methods for multiphase flow in porous media, with emphasis on two-phase flow dynamics, dynamic capillarity, and hysteresis effects. He has published extensively in journals like Computer Methods in Applied Mechanics and Engineering and Studies in Applied Mathematics . His work involves developing and analyzing numerical schemes for complex flow models, including domain decomposition methods and iterative solvers for nonlinear systems. Pop collaborates internationally on projects involving porous media applications in environmental and industrial contexts. He has supervised 21 academic works and contributed to over 117 research outputs since 2000.

M.H.M. WinandsView profile

Professor

• Heuristic Search
• Monte-Carlo Tree Search
• Artificial Intelligence
• +3 more

M.H.M. Winands is a Professor in Machine Reasoning and Chair of the Department of Advanced Computing Sciences at Maastricht University's Faculty of Science and Engineering. He received his PhD in Artificial Intelligence from Maastricht University in 2004. His research focuses on heuristic search algorithms, Monte-Carlo Tree Search (MCTS), and artificial intelligence in games, with significant contributions to General Game Playing and adaptive search methods. Winands leads research on: Monte-Carlo Tree Search variants and hybridization Online learning of search-control knowledge Game-solving algorithms (Proof-Number Search) Self-adaptive AI systems for games His work has been applied to diverse domains including board games (Lines of Action, Clobber), video games (StarCraft, Ms. Pac-Man), and structural engineering. Analysis of his publications shows strong emphasis on: Enhancing MCTS robustness through parameter randomization Developing hybrid algorithms combining MCTS with classical search Creating adaptive systems for general game playing Hardware acceleration of game AI components Awards include: ChessBase Best-Publication Award (2004, 2008) Multiple Computer Olympiad wins for game-playing programs He has supervised 3 PhD students and 22 MSc students, with research supported by NWO grants (GoGeneral, Go4Nature). Leads the DKE Games and AI group and serves as Editor-in-Chief of the ICGA Journal.

Liangliang ChengView profile

Assistant Professor

• Structural Health Monitoring
• Non-Destructive Testing
• Vibration Analysis
• +2 more

Dr. Liangliang Cheng is a Tenure Track Assistant Professor in Dynamics and Vibration at the University of Groningen. His research develops physically interpretable machine learning methods for structural health monitoring and non-destructive testing applications. Research innovations include: Novel computer vision techniques for vibration measurement Advanced signal processing for damage detection Machine learning frameworks for structural diagnostics

Jelle ZuidemaView profile

Associate Professor

• Natural Language Processing
• Explainable AI
• Cognitive Modelling
• +6 more

Dr. Jelle Zuidema is Associate Professor in Natural Language Processing, Explainable AI and Cognitive Modelling at the Institute for Logic, Language and Computation (ILLC) of the University of Amsterdam, with primary affiliation in the Natural Language Processing research group and secondary affiliation in Language & Music Cognition. He directs the Cognition, Language and Computation lab (CLC-lab) within the Faculty of Science. His research bridges artificial intelligence, cognitive science and linguistics with focus on interpretable deep learning models for text and sound. He pioneered techniques including diagnostic classification (probing), attention rollout, TreeLSTM, and masked language modeling. His work addresses fundamental questions about hierarchical compositionality in language and how neural networks can represent linguistic structure. Analysis of his publication record reveals consistent innovation in neural network interpretability methods, with recent work focusing on bias detection in language models and attention flow quantification. His research trajectory shows evolution from foundational neural parsing work to current emphasis on explainable AI and cognitive plausibility of deep learning models. Dr. Zuidema actively supervises PhD and MSc students while teaching courses including Evolution of Language and Music, Foundations of Neural and Cognitive Modelling, and Interpretability & Explainability in AI. He coordinates the Cognitive Science track in the Brain & Cognitive Sciences master's program. He directs the CLC-lab which pursues both hypothesis-driven methods (diagnostic classifiers, representational similarity analysis) and data-driven methods (layer-wise relevance propagation, contextual decomposition) for neural network interpretation. The lab's InDeep project focuses on interpreting deep learning models for text and sound processing.

Jan Bouwe van den BergView profile

Professor

• Nonlinear Partial Differential Equations
• Dynamical Systems
• Pattern Formation
• +5 more

Jan Bouwe van den Berg is Professor of Mathematics and Department Chair at the Department of Mathematics, Faculty of Science, Vrije Universiteit Amsterdam. His research focuses on nonlinear partial differential equations and dynamical systems, particularly pattern formation phenomena. He develops rigorous computational methods combined with topological and variational techniques to analyze complex dynamics. His educational background includes a PhD in Mathematics from Leiden University (1996-2000) under advisor Bert Peletier, preceded by undergraduate studies in Mathematics and Physics at Leiden University (1991-1996). Prior academic positions include EPSRC Research Fellow at Nottingham University (2001-2002) and Assistant Professor at VU Amsterdam (2003-2006). Research interests span: Nonlinear PDEs and dynamical systems with emphasis on pattern formation Topological methods including Conley index and Floer homology Computational dynamics and rigorous numerics Geometric flows (harmonic map heat flow, Willmore flow) Braid theory applications to differential equations Variational methods for elliptic and parabolic problems His publication record shows consistent output in top mathematics journals with recent focus on validated numerics for PDEs and dynamical systems. Key trends include computer-assisted proofs for connecting orbits, rigorous bifurcation analysis, and topological validation of complex patterns. His collaborative work spans institutions including CWI, Leiden University, and international partners. Teaching responsibilities include Single Variable Calculus, Numerical Methods, and specialized courses like Variational Methods for PDEs. He served as Course Director for Mathematics & Business Analytics (2008-2014) and has taught at Amsterdam University College since 2003. He leads the Amsterdam Center for Dynamics and Computation, focusing on computational approaches to dynamical systems. Current projects involve validated integration of PDEs, computational Conley-Floer homology, and applications to geometric flows and pattern formation.

• Decision Making under Uncertainty
• Experimental Economics
• Risk Preferences
• +3 more

Prof. Gijs van de Kuilen is a Full Professor in the Department of Economics at Tilburg University's Tilburg School of Economics and Management (TiSEM). His research focuses on decision making under uncertainty, particularly using experimental methods to explore higher-order risk attitudes, social risk preferences, and ambiguity models. He teaches courses such as Behavioral Economics and Experimental Economics. His work investigates topics like non-expected utility theory, prudence in risk apportionment, and social welfare implications of health and wealth risks. Notable projects include analyzing higher-order risk preferences in non-expected utility frameworks and testing ambiguity attitude models. Key contributions include studies on social risk aversion in health domains and decomposition of risk preferences across health/wealth contexts. His 2010–2012 project explored the behavioral economics of the disposition effect. He leads the Research Group: Economics at Tilburg University.