Anton Feenstra is an Associate Professor at the Vrije Universiteit Amsterdam, affiliated with the Faculty of Science's Bioinformatics department, as well as AIMMS and Integrative Bioinformatics. He holds a PhD (dr.) and an engineering degree (ir.). His research focuses on structural bioinformatics, protein structure prediction, computational biology, and bioinformatics algorithms. Key interests include protein-protein interactions, molecular dynamics, and knowledge graph applications in health and microbiota studies. Feenstra leads projects such as ELIXIR-NL (Digital Research Infrastructure) and has contributed to initiatives like BIOEXE and ENFIN. He teaches courses including Algorithms in Sequence Analysis and Fundamentals of Bioinformatics. His work spans over 86 publications, with recent contributions on protein interface prediction (PIPENN-EMB), microbiota-gut-brain axis analysis, and structural bioinformatics tools. Notable achievements include developing the PRALINE alignment toolkit and advancing machine learning methods for protein function prediction. Collaborations include work on Mycobacterium tuberculosis and SARS-CoV-2 protein analysis. His research aligns with UN Sustainable Development Goals, particularly in health and innovation.
Fahimeh Mokhtari is an Assistant Professor in the Department of Mathematics at Vrije Universiteit Amsterdam, affiliated with the Amsterdam Center for Dynamics and Computation. Her research focuses on nonlinear differential equations, normal forms of dynamical systems, bifurcation theory, and network dynamics. She holds a Ph.D. in Applied Mathematics from Isfahan University of Technology, with a thesis on 'Normal Form of Some Three-Dimensional Singular Vector Fields'. Education includes a B.Sc. in Pure Mathematics (University of Isfahan), M.Sc. and Ph.D. in Applied Mathematics (Isfahan University of Technology). She has received multiple awards, including the Khwarizmi Youth Award (2013) and grants from Iran's National Elites Foundation. Research interests span dynamical systems, representation theory, Hamiltonian systems, and symbolic computations. She has co-organized the VU Mathematics Colloquium and currently chairs the OLC Committee for the Medical Natural Science program. Teaching includes courses on Dynamical Systems, Calculus, and Mathematical Modelling. Recent work emphasizes network dynamical systems, triple-zero bifurcations, and Lie algebraic structures in feedforward networks. Supervised research projects include network dynamics, bifurcation analysis, and mathematical modelling at BSc/MSc levels.
M.K. (Kanat) Camlibel is a Full Professor at the University of Groningen, affiliated with the Bernoulli Institute within the Faculty of Science and Engineering. His primary research focuses on systems and control theory, applied mathematics, and interdisciplinary applications. Camlibel's work emphasizes data-driven control methods, model reduction techniques, and the analysis of complex dynamical systems. His recent contributions include advancements in system identification, optimal control design, and the theoretical foundations of convex processes. He also explores applications in energy systems and networked multi-agent systems. Publications highlight innovations in data-driven simulation for continuous-time systems, robust feedback control under noisy conditions, and the development of reduced-order models using moment matching and balanced truncation. His research frequently bridges theoretical insights with practical engineering challenges, such as market optimization in energy systems. Camlibel holds positions in the Bernoulli Institute's Systems, Control and Applied Analysis department and collaborates on projects involving structural controllability of networks and topology reconstruction of dynamical systems. His work contributes to the UN's Sustainable Development Goals through advancements in energy-efficient systems and networked control frameworks.
M.M. (Matthew) Cook, PhD is a Full Professor at the University of Groningen's Faculty of Science and Engineering, affiliated with the Artificial Intelligence group at the Bernoulli Institute. His research focuses on theoretical computer science, neural networks, and biologically inspired computation. He holds a PhD and has extensive experience in neuromorphic engineering, cellular automata, and computational neuroscience. His work spans interdisciplinary areas including spiking neural networks for biosignal processing, self-assembly algorithms, and paradox resolution in causal inference. Key contributions include foundational studies on universality in cellular automata (e.g., Rule 110), neuromorphic hardware implementations, and neuroimaging analysis techniques for synaptic partner detection in Drosophila brain data. Recent publications highlight advancements in event-driven neural networks for epilepsy monitoring, low-power neuromorphic systems, and algorithmic exploration strategies. His research bridges theoretical computer science with practical applications in biomedical engineering and cognitive modeling.
Frank Chan is an Associate Professor in the Faculty of Science and Engineering at the University of Groningen, leading the Chan lab - Quantitative Genetics and Genomics. He holds a PhD in Developmental Biology from Stanford University School of Medicine (2009) and a BA in Molecular Biology & Biochemistry from Wesleyan University (2003). His research focuses on evolutionary genomics, particularly in sticklebacks, mice, and butterfly species, exploring topics like genomic basis of adaptation, haplotype analysis, and hybridization. Key research interests include evolutionary genetics, quantitative biology, and the application of genomic tools to study parallel evolution and hybridization. He has received prestigious grants such as the ERC Starting Grant (2014) and ERC Proof of Concept (2022), and fellowships including the Alexander von Humboldt and EMBO awards. Recent work includes studies on haplotype tagging in butterflies (PNAS 2021), genomic analysis of mouse limb evolution (eLife 2019), and structural variation in stickleback pelvic reduction (Science 2010). His lab also investigates T cell receptor diversity in mice and population genomics of Faroe Island mice. Awards: ERC Grants, Humboldt Fellowship, VolkswagenStiftung Grant Lab: Chan Lab - Quantitative Genetics & Genomics Positions: Board of Reviewing Editor at eLife (2023–2024), Former Max Planck Research Group Leader (2012–2023)
Dr. Pim Heijnen is an Associate Professor at the University of Groningen, Faculty of Economics and Business, specializing in Economics, Econometrics & Finance. He holds a Ph.D. in Economics (2007) and M.Sc. in Econometrics (2002), both from the University of Groningen. His research focuses on environmental economics, industrial organization, and applied game theory, with recent work addressing rent-seeking dynamics, consumer search behavior, and environmental policy design. He has contributed to understanding optimal taxation strategies, corporate financing policies, and nonlinear dynamics in environmental systems. Key research themes include: Rent-seeking mechanisms and their macroeconomic impacts Consumer search and information asymmetry in markets Game-theoretic models of environmental regulation and policy Price competition and spatial market structures His articles analyze diverse topics like reversible environmental catastrophes, corporate leverage effects, and collusion detection. He has held roles at the University of Amsterdam (Center for Nonlinear Dynamics in Economics and Finance) and contributed to policy-relevant studies on waste disposal taxes and retail gasoline market competition.
Alessio Arleo is an Assistant Professor at the Eindhoven University of Technology (TU/e) within the Mathematics and Computer Science department, specializing in the Visualization group and Visual Analytics for Data Science . He holds a PhD in distributed computing and graph drawing from the University of Perugia (2018), followed by postdoctoral research at Vienna University of Technology's Visual Analytics unit. His work focuses on temporal network visualization, information diffusion modeling, and distributed graph algorithms. Research interests include temporal networks , interactive visual analytics , and explainable graph drawing . Notable projects include TimeLighting (space-time cube visualization) and XGD (explainable graph drawing frameworks). Recent publications address guidance strategies in visual analytics, healthcare data visualization, and AI-driven graph layout transparency. He teaches the Seminar Visualization course and has co-authored over 30 peer-reviewed articles since 2014. No ancillary activities are listed, and his work is supported by grants from WWTF, FFG, FWF, and SANE.
Chaobo Zhang is a postdoctoral researcher at the Eindhoven University of Technology, specializing in fast building performance simulation and smart building energy management. He belongs to the Building Performance Group within the Department of the Built Environment. Education: Bachelor in Heating, Ventilation and Air Conditioning, Harbin Institute of Technology (2017) PhD in Refrigeration and Cryogenic Engineering, Zhejiang University (2022) His research integrates artificial intelligence technologies into building systems to enhance energy efficiency and fault detection. Key areas include generative pre-trained transformers (GPT) for energy load prediction, convolutional neural networks for computational fluid dynamics, and domain-specific large language models for HVAC fault diagnosis. The 15 most recent publications highlight trends in AI-driven building energy management, focusing on technologies like graph convolutional networks for uncertainty quantification, hybrid data-driven physics-based cooling demand models, and self-attention variational autoencoders for digital twin parameter imputation. His work addresses challenges such as data imbalance, federated learning for data silos, and real-time anomaly detection. Scientific Awards: 2020 Best Paper Award, Energy and Built Environment Chaobo collaborates internationally and contributes to UN Sustainable Development Goals through energy-efficient building technologies. His expertise spans fault detection, machine learning, and computational optimization in building systems.
Pim van der Hoorn is an Assistant Professor in Probability at the Department of Mathematics and Computer Science at Eindhoven University of Technology. He is also a member of the Eindhoven Young Academy of Engineers and serves as a board member of the Dutch Network Science Society. His primary research affiliation is with the Institute for Complex Molecular Systems (ICMS), where he is part of the ICMS Core research group. Dr. van der Hoorn's research focuses on developing mathematical foundations of network science using probability theory, statistics, random graphs and graph limits. His work centers around two main pillars: the design of mathematical frameworks for statistical analysis of networks and understanding emerging geometry in networks. He also applies network science to solve problems in computational biology and materials science, particularly in studying cell-cell interactions in cancer and molecular charge transport in organic semiconductors. His recent publications demonstrate a strong trend toward unifying theoretical frameworks for network analysis while simultaneously applying these frameworks to solve complex problems across disciplines. His work bridges pure mathematical theory with practical applications, showing how network science can provide insights into diverse fields from cancer biology to materials engineering. Dr. van der Hoorn is actively involved in teaching with courses including Extreme values and other catastrophes, Network Statistics for Data Science, and Measure, Integration and Probability Theory. His educational activities reflect his research interests, providing students with both theoretical foundations and practical applications of probability and network science. He collaborates extensively across disciplinary boundaries, working with researchers in computational biology, cancer research, and materials science. His research has received support from organizations including the Netherlands Organization for Scientific Research (NWO) and the Immunoengineering Program of the Institute for Complex Molecular System.
G.J.T. Leus is a Professor at Delft University of Technology's Faculty of Electrical Engineering, Mathematics and Computer Science, where he leads research in the Signal Processing Systems department. His work spans foundational algorithms and applied systems across wireless communications, medical imaging, and network analysis. With over 498 research outputs including journal articles, conference papers, and patents, he maintains an active research program in cutting-edge signal processing methodologies. Research interests focus on: Signal Processing Systems : Advanced algorithms for spectral estimation, array design, and sensor optimization Computational Imaging : Ultrasound techniques for medical diagnostics including carotid artery visualization Graph-Based Methods : Graph neural networks for localization and topology identification Wireless Technologies : OTFS modulation, MIMO systems, and phased array design Recent publications (2023-2025) demonstrate strong emphasis on: Integration of deep learning with tensor decompositions for channel estimation Fundamental limits in estimation theory using Cramér-Rao bound frameworks Topological signal processing extensions to simplicial complexes Computational imaging innovations for medical ultrasound Academic leadership includes editorial roles for the Eurasip Journal on Advances in Signal Processing , keynote presentations at major conferences (e.g., Asilomar 2023), and supervision of graduate researchers. Current projects involve multi-sensor systems for network localization, ultrasound imaging with limited transceivers, and robust graph neural architectures.
Professor Ineke Maas serves as Associate Professor in the Department of Sociology at Utrecht University and holds a professorship at Vrije Universiteit Amsterdam since 2012, where she chairs research on 'Long term trends in social openness and exclusion.' Her scholarly work focuses on social mobility, inequality, and integration within the Institutions for Open Societies (IOS) framework. She has been instrumental in the Interuniversity Centre for Social Science Theory and Methodology (ICS) and has published extensively on historical and contemporary social stratification across European contexts. Professor Maas earned her PhD in Sociology from Utrecht University in 1990 with her dissertation 'Participating in Cultural Activities: Substitution and Learning-effects.' She completed her MA in Sociology with distinction from Utrecht University in 1986 and undertook postgraduate training at the Interuniversity Centre for Social Science Theory and Methodology. She also holds Senior Qualifications in both University Teaching and Research from Utrecht University (2006). PhD Sociology, Utrecht University (1990) MA Sociology (with distinction), Utrecht University (1986) Postgraduate Courses 'Researcher in the Social Sciences', ICS Utrecht (1989) Senior Qualification University Teaching, Utrecht University (2006) Senior Qualification Research, Utrecht University (2006) Professor Maas's research program examines trends and country differences in intergenerational mobility, career mobility, and marital mobility, with significant contributions to understanding immigrant integration, educational inequality, and gender inequality. Her work employs historical and comparative perspectives to analyze social mobility patterns across different European countries during industrialization and modernization periods. She is internationally recognized for developing the HISCO (Historical International Standard Classification of Occupations) and HISCLASS (Historical Social Class Scheme) frameworks with Marco H.D. van Leeuwen. Her recent publications (2022-2024) reveal a sophisticated methodological approach combining historical data with contemporary analysis, increasingly incorporating intersectional perspectives examining how education, gender, age, and migration background interact to shape social outcomes. Her work frequently utilizes advanced techniques including multilevel modeling, gene-environment interaction analysis, and historical comparative methods to investigate educational inequality, social mobility, and benefit receipt patterns. Professor Maas has supervised numerous PhD students and research projects focused on social stratification. She has been actively involved in major research initiatives including the Historical Sample of the Netherlands and the Historical International Social Mobility Analyses. Her collaborative work spans multiple European institutions and has received support from various research councils and foundations. She maintains strong affiliations with the Research Center ICS Utrecht (Interuniversity Centre for Social Science Theory and Methodology) and contributes to the Dutch Sociological Association. Her scholarly influence extends through editorial board memberships on prominent sociology journals and regular presentations at international conferences on social stratification and mobility.
Dr. Michael Behrisch is an Associate Professor for Visual Analytics in the Visualization and Graphics Group at Utrecht University's Department of Information and Computing Sciences. With a PhD from University of Konstanz, his career includes postdoctoral work at Harvard and Tufts Universities, and over six years as a research associate at Konstanz. Specializes in matrix-based representations for relational data Focuses on cognitive load reduction in visual analytics Develops interactive systems for pattern discovery Research Highlights: Combines algorithmic approaches with user-centric visualization techniques to address challenges in large-scale, multivariate, and dynamic datasets. Research themes include: Automated pattern quantification Matrix reordering algorithms Explainable AI integration Game research applications Scientific Contributions: Recognized through 61 publications and multiple awards, including the EuroVA 2022 Best Paper and IEEE VAST 2018 Honorable Mention. His work bridges theoretical research with practical applications across life sciences, network analysis, and big data domains.
Dr. Mitzy Kennis is a Lecturer at Utrecht University's School of Social and Behavioural Sciences, Department of Clinical Psychology. Her academic work focuses on Posttraumatic Stress Disorder (PTSD) , resilience , and depression , with a strong emphasis on neuroimaging and psychosocial assistance . Teaches Psychopathology courses Expertise in quantitative and qualitative research design Collaborates with the ENIGMA-PGC PTSD Consortium Her research explores PTSD treatment response using neuroimaging, DNA methylation changes, and emotional processing patterns in veterans. Articles highlight computational psychiatry , brain connectivity , and biomarker discovery for stress-related disorders. Key trends in her publications include multisite big data analysis , neuroimaging-based classification , and cross-cohort validation of PTSD and depression mechanisms.
Dr. Shihan Wang is an Assistant Professor at Utrecht University's Faculty of Science, Department of Information & Computing Sciences, specializing in Intelligent Systems . Their research balances theoretical development and practical application, focusing on human-centered artificial intelligence and reinforcement learning. Wang's expertise spans Artificial Intelligence, Machine Learning, Data Mining, Computational Social Sciences, and Social Networks , with a particular emphasis on applying these fields to healthcare (e.g., diabetes and dementia care), behavioral science (e.g., physical activity promotion), and social network analysis . 2025 Articles: Efficient dialogue policies with evolutionary techniques • Factorized communication in multi-agent systems • Sparse communication frameworks 2024 Highlights: Goal-shaping for dialogue systems • Reward structure learning • Taxonomy induction with multi-critic RL Scientific contributions are recognized through a University Teaching Qualification (2020) and a Teaching Prize (2021-2022) . Currently, they coordinate the Applied HAI special interest group and participate in The Hybrid Intelligence Center and Benelux Association for Artificial Intelligence . Teaching activities include Advanced Machine Learning (lecturer 2020-2023), Applications of Machine Learning (2020-2022), and program coordination for the Artificial Intelligence MSc since 2022.
Vladimir Filipović is a Full Professor at the Department for Computer Science, Faculty of Mathematics, University of Belgrade. He is also a member of the Modelling and optimization group within the Department for Computer Science. His academic career spans over 30 years at the University of Belgrade, where he has held various positions including teaching assistant, assistant professor, associate professor, and since December 2019, Full Professor. He has also served in administrative roles such as Head of the Software Examination and Certification Laboratory (2007-2016), Vice Dean for Academic Affairs (2008-2011), and Head of the Department for Computer Science (2017). Additionally, he was a Visiting Fellow at University Milano-Bicocca (2017-2018) and a visiting professor at University of Banja Luka (2007-2020). Dr. Filipović earned his BSc in Computer Science (1993), MSc degree (1998), and PhD in Computer Science (2006), all from the Faculty of Mathematics, University of Belgrade. His doctoral thesis was titled 'Selection and Migration Operators and Web Services in Evolutionary Applications'. His research interests span across Operational research, Computational intelligence, Big data, Soft-computing, Metaheuristics, Evolutionary algorithms, Bioinformatics, and Graph theory. His work demonstrates a strong interdisciplinary approach, bridging theoretical computer science with practical applications in bioinformatics, network optimization, and biomedical data analysis. His research has resulted in numerous publications in high-impact journals and conferences, with a recent focus on topological variable neighborhood search methods and cancer evolution inference. Analysis of his recent publications (2020-2024) reveals a strong trend toward applying advanced metaheuristics to complex problems in bioinformatics and graph theory. His work increasingly focuses on topological approaches to variable neighborhood search, cancer phylogeny inference, and complex network analysis in biological systems. This represents a maturation of his research from foundational work in evolutionary algorithms to sophisticated applications in computational biology and network science. Best Practice in the area of the 'Introduction of the IT in Service Provision Process' awarded by Union of Municipalities of Montenegro (December 2010) Best Paper Prize award for 'Two Hybrid Genetic Algorithms for Solving the Super-Peer Selection Problem' presented at Online World Conference on Soft Computing in Industrial Applications, WSC 2008 Dr. Filipović has supervised numerous Master's students at both University of Belgrade and University of Banja Luka, with over 25 successful thesis completions. His professional activities extend beyond academia to include leadership in significant IT projects such as the 'eMunicipality' system for city administration, the 'Polyclinic' information system, and digitalization projects for cultural heritage institutions like the Bar County Museum. He has also been involved in educational initiatives including developing new study programs and translating computer science textbooks. He is actively involved with several professional organizations including IEEE Systems, Men and Cybernetics Society - Technical Committee for Soft Computing, IEEE Computational Intelligence Society, IEEE Big Data Community, Mathematical Society of Serbia, and The Heritage Forum of Serbia. His work with the Modelling and optimization group at University of Belgrade represents a significant research hub for computational optimization methods.
