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Raquel FernándezView profile

Professor

• Computational Linguistics
• Dialogue Systems
• Cognitive Science
• +3 more

Raquel Fernández is Full Professor of Computational Linguistics and Dialogue Systems at the University of Amsterdam, where she leads the Dialogue Modelling Group at the Institute for Logic, Language & Computation (ILLC). As Vice-Director for Research at ILLC and a Fellow of the ELLIS Society, she bridges computational linguistics, cognitive science, and artificial intelligence through her research on language use in multimodal and conversational contexts. PhD in Computational Linguistics from King's College London Prior research positions at University of Potsdam and Stanford University's CSLI Her work explores how cognitive constraints, social interaction, and perception shape language use, with a focus on: Visually-grounded language processing Multimodal dialogue modeling Model uncertainty and calibration Language grounding in multimodal data Language learning and semantic change Dialogue reference resolution Recent publications analyze multimodal reasoning limitations, cross-lingual knowledge consistency, and uncertainty modeling in dialogue systems. She has received multiple accolades including an ERC Consolidator Grant , NWO VENI/VIDI/Aspasia fellowships , and EMNLP/GenBench awards . Outstanding Paper Award (EMNLP 2023) Best Data Award (GenBench Workshop 2023) ELLIS Society Fellow ERC Consolidator Grant #819455 recipient NWO VENI/VIDI/Aspasia awardee As a leader in academic service, she serves on the SIGDAT Executive Committee and chairs multiple conference committees. Her lab develops models for multimodal dialogue, visual storytelling, and grounded language understanding.

Vedran DunjkoView profile

Professor

• Quantum Computing
• Quantum Machine Learning
• Quantum Artificial Intelligence
• +7 more

Professor Vedran Dunjko is a faculty member at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University, with affiliations to the Leiden Institute of Physics (LION). He leads the Applied Quantum Algorithms group and co-founded the Quantum@LIACS initiative, focusing on the intersection of quantum computing, machine learning, and artificial intelligence. His research interests include quantum machine learning, quantum-enhanced reinforcement learning, quantum heuristics, and the application of AI to quantum computing challenges. Dunjko's work bridges theoretical foundations with experimental implementations on near-term quantum devices, exploring both quantum advantages in learning and the use of classical AI for quantum system design. The recent publications show a strong trend toward proving quantum advantages in learning tasks, optimization, and topological data analysis, with publications in Nature , Nature Communications , and NeurIPS . Key themes include quantum policy gradients, quantum TDA, and reinforcement learning for quantum circuit optimization. ERC Consolidator Grant (2024) PNAS Cozzarelli Prize (2018) Editor’s Suggestion in Physical Review Letters (2014, 2018) Featured in Physics (American Physical Society) (2014, 2018) Dunjko advises several PhD candidates and postdocs, including Rahul Bandyopadhyay, Sofiene Jerbi, and Lea Trenkwalder. He has received competitive grants, most notably the ERC Consolidator Grant in 2024. His group fosters international collaborations with institutions across Europe and industry partners. The Applied Quantum Algorithms group and the Quantum@LIACS team combine theoretical investigations with practical implementations on quantum hardware, focusing on scalable quantum algorithms and AI-driven quantum discovery.

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.

Nina SchwarzView profile

Assistant Professor

• Urban land-use change
• Urban ecosystem services
• Agent-based modelling
• +7 more

Nina Schwarz is Assistant Professor of human-environment interactions in cities at the Department of Urban and Regional Planning and Geo-Information Management, ITC—University of Twente. Holding a Diploma in Environmental Sciences (University of Lüneburg, 2003) and a PhD in Social and Economic Sciences (University of Kassel, 2007), she spent a decade as senior scientist at the Helmholtz Centre for Environmental Research – UFZ before joining ITC. Her interdisciplinary research integrates urban land-use science, ecosystem-service evaluation and advanced modelling techniques—especially agent-based models—to explore sustainable urban development under global change. Research interests revolve around three interconnected themes: (i) urban land-use change —understanding how residential, commercial and green-space dynamics co-evolve; (ii) urban ecosystem services —quantifying both supply and demand of services such as local climate regulation, recreation and biodiversity; and (iii) behavioural modelling —formalising human decision-making to simulate policy scenarios ranging from slum-upgrading to vineyard management. She actively links these themes to UN Sustainable Development Goals, notably SDG 11 (Sustainable Cities) and SDG 15 (Life on Land). Recent publications (2022-2025) reveal a methodological breadth spanning citizen-science impact assessment in Suriname, cooling effects of urban water bodies in Chinese mega-cities, European wine-growers’ adaptive behaviour, and methodological advances in upscaling and validating agent-based land-use models. Across these studies, Schwarz consistently combines empirical field data, geospatial analytics and participatory approaches to produce policy-relevant insights for cities in both the Global North and South. She has (co-)authored >80 peer-reviewed works, accumulating c. 5 900 citations and an h-index of 28 (Scopus). While specific honours are not itemised in the supplied text, her sustained citation impact and invited contributions to major conferences (e.g., iEMSs 2020, IAHR 2025) underscore scientific recognition. Schwarz frequently engages with societal stakeholders: her projects have generated open datasets on urban green-space monitoring in Paramaribo, e-learning modules for Latin-American universities, and interactive dashboards for sustainable water management. Within ITC she contributes to capacity-building programmes for emerging economies, supervises graduate researchers and maintains active collaborations across Europe, Latin America, Africa and Asia. No explicit lists of PhD students or personal grants are provided in the current corpus, but her leadership of externally funded projects (e.g., citizen-science evaluation, vineyard decision-making database) indicates ongoing acquisition of research funding.

Joaquin VanschorenView profile

Associate Professor

• Machine Learning
• Automated Machine Learning
• Algorithm Selection
• +4 more

Joaquin Vanschoren is an Associate Professor of Machine Learning at Eindhoven University of Technology (TU/e), affiliated with the Faculty of Mathematics and Computer Science. He leads the Automated Machine Learning group and serves as Education Director for the Data Science program. His research focuses on democratizing AI, algorithm selection, and open science platforms like OpenML. He has received awards including the Dutch Data Prize and Amazon Research Award. Education: PhD in Engineering (KU Leuven, Belgium), MSc in Computer Science (KU Leuven). Research visits included IBM, Amazon Research, and universities globally. Research Interests: Machine Learning, Automated ML, Meta-learning, AI Safety, Data-centric AI. He co-founded OpenML and chairs MLCommons' AI Safety working group. Key Projects: NeurIPS Datasets and Benchmarks track, MLCommons initiatives, OpenML platform. Supervised 78 research works and authored 200+ papers. Awards: Dutch Data Prize (2016), Amazon Research Award (2019), Microsoft Azure Research Awards (2016–2017). Labs/Teams: OpenML open source team, MLCommons collaborations, Automated Machine Learning group at TU/e.

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.

Aaqib SaeedView profile

Assistant Professor

• Human-Centric AI
• Federated Learning
• Self-Supervised Learning
• +3 more

Aaqib Saeed is an Assistant Professor in the Department of Industrial Design at Eindhoven University of Technology. His research focuses on Human-Centric AI, Federated Learning, Self-Supervised Learning, and Audio Understanding, with applications in Personal Health. He holds a PhD (cum laude) from TU/e and an MSc (cum laude) from the University of Twente. Education: PhD in Computer Science (cum laude), TU/e (2021) MSc in Computer Science (cum laude), University of Twente (2018) Research Interests: Development of robust federated learning frameworks for decentralized data Self-supervised learning for audio and physiological signal analysis AI-driven solutions for healthcare monitoring Key Contributions: DeltaMask: Reducing communication overhead in federated fine-tuning FedNS: Mitigating noisy decentralized data in federated learning Labeling Chaos to Learning Harmony: Handling label noise in FL Professional Experience: Visiting Industrial Fellow, University of Cambridge (2023) Research Scientist, Philips Research (2019–2023) Research Internships: Google Research, TNO/EIT Digital Awards: UT Scholarship (MSc) Cum Laude awards for both PhD and MSc Labs/Teams: EAISI Health, EAISI Foundational, Computational Design Systems.

Anna WilbikView profile

Professor

• Data Fusion
• Intelligent Interaction
• Fuzzy Logic
• +4 more

Anna Wilbik is a Professor in Data Fusion and Intelligent Interaction at the Department of Advanced Computing Sciences, Faculty of Science and Engineering, Maastricht University (The Netherlands). Her research bridges data understanding and human-machine synergy in complex systems, focusing on multi-criteria decision making, explainable AI, and data fusion techniques. PhD in Computer Science (with honors), Systems Research Institute, Polish Academy of Science (2010) Postdoctoral Fellow, University of Missouri (2011) Stanford University TOP500 Innovators Program Alumnus Research Pillars: Intelligent human-machine interaction for joint decision making Data fusion methods for heterogeneous data integration Contextualized multi-criteria decision frameworks Fuzzy logic and linguistic summaries for explainability Federated learning systems Article Trends: Recent work focuses on intuitionistic fuzzy sets for knowledge-intensive processes, federated learning with uncertainty handling, and linguistic summarization techniques for interpretable AI. She actively explores explainability , collaborative business models , and driver behavior analysis through attention-based models. Professional Leadership: Vice-chair of IEEE Fuzzy Systems Technical Committee Organizer of IEEE World Congress on Computational Intelligence (2024)

• Multimedia Modeling
• Machine Learning
• Computer Vision
• +7 more

Dr. Stevan Rudinac is a Researcher at the University of Amsterdam's Faculty of Economics and Business , Section Business Analytics . His work focuses on interactive learning systems and multimodal data analysis, particularly in urban contexts and multimedia modeling. Education: PhD in Multimedia and Information Retrieval from Delft University of Technology (2013). Research Interests: Stevan specializes in multimedia modeling , hypergraph learning , and interactive video search . He develops frameworks for scalable analysis of social networks, urban imagery, and large multimodal datasets, bridging machine learning with practical applications in city planning and financial social media. Recent Trends: His 2024-2025 publications highlight large language model optimization , diffusion model evaluation , and dynamic graph embedding for meme stocks. Collaborative projects include the CASTLE 2024 dataset and Exquisitor , a system for 100 million image exploration. Labs & Teams: He contributes to the Business Analytics group at UvA, collaborating with Prof. Marcel Worring and Dr. Björn Þór Jónsson. He co-organized the UrbanMM'21 workshop and participates in ACM Multimedia and MMM conferences.

Alfred SteinView profile

Professor

• Spatial Statistics
• Image Analysis
• Remote Sensing
• +7 more

Prof. Alfred Stein is a Full Professor in Spatial Statistics and Image Analysis at the Department of Earth Observation Science, Faculty ITC, University of Twente. He earned his MSc in Mathematics and Information Science from Eindhoven University of Technology and a PhD in Spatial Statistics from Wageningen University. His career spans roles at Wageningen University (1988–2002), ITC (2002–present), including leadership positions as department head, vice-rector research, and portfolio holder for education. Education: MSc (Eindhoven University of Technology), PhD (Wageningen University) Leadership: Department Head (Earth Observation Science), Vice-Rector Research (2008–2012), Portfolio Holder Education (2012–) His research focuses on Spatial and Spatio-Temporal Statistics , emphasizing Bayesian inference , data quality , image analysis , and fuzzy techniques . Key application domains include agriculture, health, urban land use, coastal systems, hazards, and wildlife. He has mentored over 30 PhD students since 1998, with 11 currently under supervision. Recent research trends highlight AI-driven remote sensing for glacier mapping, urban livability, and disease modeling. Publications span Deep Learning for SAR tomography, Bayesian hierarchical models for health data, and multitemporal SAR analysis for environmental monitoring. Awards include the Best Paper Award (2019) and ISARA Founder's Award (2020) . Scientific Awards Best Paper Award (2019) ISARA Founder's Award (2020) As Editor-in-Chief of Spatial Statistics and associate editor for multiple journals, he leads academic discourse. Collaborations include the University of Cape Town and University of Pretoria as Honorary Professor. His work contributes to UN Sustainable Development Goals, particularly in climate action and sustainable cities.

Laura ToniView profile

Associate Professor

• Machine Learning
• Signal Processing
• Immersive Communications
• +5 more

Laura Toni is an Associate Professor in the Department of Electronic & Electrical Engineering at University College London (UCL). She serves as Director of the MSc in Telecommunications and Internet Engineering and the MRes in Telecommunications. Additionally, she is a Turing Fellow at the Alan Turing Institute and a member of ELLIS (European Lab for Learning and Intelligent Systems). Her research focuses on coding, streaming technologies, machine learning for immersive communications, decision-making under uncertainty, and large-scale signal processing. She leads the LASP (Learning And Signal Processing) group at UCL. Education: MSc (2005) and PhD (2009) from the University of Bologna, followed by postdoctoral research at UC San Diego and EPFL under Professors L. Milstein, P. Cosman, and P. Frossard. Key roles include Technical Program Chair at ACM MM 2022, Keynote Co-Chair at ACM MMSys 2022, and leadership in organizing workshops on graph-based machine learning and emerging technologies in performing arts. She is a Senior IEEE Member and holds editorial roles in IEEE Multimedia Magazine and EURASIP Journal on Signal Processing. Her work bridges communication systems and machine learning, with contributions to adaptive streaming, network optimization, and graph signal processing. She actively promotes diversity and inclusion in technical conferences, including roles as Diversity Chair at MMSys 2021 and PIMRC 2020.

Carlo CavicchiaView profile

Assistant Professor

• Statistics
• Data Science
• Machine Learning
• +6 more

Dr. Carlo Cavicchia is an Assistant Professor of Statistics at the Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam. He holds a PhD in Methodological Statistics from La Sapienza University of Rome and has held roles such as Research Fellow at UnitelmaSapienza University and Consultant for NGOs in Zanzibar. His research focuses on latent variable models, composite indicators, and unsupervised classification, with applications in environmental policy, sports analytics, and teacher job satisfaction. Cavicchia teaches statistics and data science courses at undergraduate and graduate levels and actively contributes to academic communities through journal reviewing, conference organizing, and editorial roles. Education: PhD in Methodological Statistics (La Sapienza University of Rome, 2020) MSc in Statistics and Decision Sciences (La Sapienza University of Rome, 2016) BSc in Statistics (La Sapienza University of Rome, 2013) Dutch University Teaching Qualification (BKO, 2022) Research Interests: Cavicchia’s work emphasizes hierarchical models, non-parametric statistics, and data science applications. He develops methodologies for composite indicators, including ultrametric Gaussian mixture models and disjoint principal component analysis. His research bridges theoretical advancements with real-world problems, such as waste management in Italian municipalities and ranking European football teams using composite metrics. Grants & Awards: 2024: IFCS Chikio Hayashi Award 2023: ESE Starter Grant (€300,000) 2017: Research Grant for Junior Researchers (€1,270) 2016: PhD Scholarship, La Sapienza University Academic Engagement: Cavicchia serves as IASC Data Analysis Competition Officer (2023–2025), co-edits the ISI Magazine , and organizes conferences like DSSV 2020 and DSSV-ECDA 2021. He is an elected member of the International Statistical Institute and contributes to SVQS’s Sustainability initiatives. Labs & Teams: He co-organizes the Econometrics internal seminars at Erasmus University and collaborates with researchers at University of Naples Federico II on hierarchical models and convex clustering.

Arianna BisazzaView profile

Associate Professor

• Natural Language Processing
• Multilingualism
• Low-resource Language Modeling
• +7 more

Arianna Bisazza is an Associate Professor in the Computational Linguistics Group at the University of Groningen, where she leads the InClow research group focused on Interpretable, Cognitively inspired, Low-resource language models. Her work bridges computational linguistics, cognitive science, and language acquisition to develop more robust and interpretable language processing algorithms that can adapt to diverse linguistic phenomena worldwide. Dr. Bisazza's research interests span statistical modeling of human languages in multilingual contexts, with particular focus on improving language model performance for "challenging" or low-resource languages. Her work explores how insights from human language acquisition can inform better language modeling techniques, and she investigates methods to make state-of-the-art NLP systems more interpretable and transparent. As a cross-disciplinary researcher, she actively seeks to enhance our understanding of human language processing and evolution through computational modeling tools. Her recent publications reveal a strong emphasis on multilingual evaluation frameworks (like TurBLiMP and MultiBLiMP), interpretability of language models, and connections between human language acquisition and neural network learning. Her work consistently addresses the challenge of making language technology more robust across diverse linguistic structures and typological features. Outstanding Paper Award at the BabyLM Challenge (CoNLL'24 Shared Task) for "BabyLM Challenge: Exploring the Effect of Variation Sets on Language Model Training Efficiency" Dr. Bisazza currently leads a Vidi project funded by the Dutch Research Council (NWO) on improving low-resource language modeling through child language acquisition insights. She is also part of two national consortium projects funded by NWA-ORC initiatives: InDeep (Interpreting deep learning models for language, speech & music) and LESSEN (Low Resource Chat-based Conversational Intelligence). She supervises multiple PhD students, including two China Scholarship Council (CSC)-funded researchers working on simulating human patterns of language learning and change. Her earlier research was supported by a Veni grant (2017-2021) focused on understanding and improving the encoding of linguistic structure in Neural Machine Translation models. As head of the InClow research group, Dr. Bisazza oversees a team investigating interpretable, cognitively inspired approaches to low-resource language modeling. The group's work combines insights from cognitive science and linguistics with cutting-edge NLP techniques to develop language models that better reflect human language processing capabilities, particularly in resource-constrained settings.

• Computational Neuroscience
• Neural Networks
• Deep Learning
• +4 more

Prof. Sander M. Bohte holds a part-time appointment as a Professor of Computational Neuroscience at the Swammerdam Institute for Life Sciences (SILS), University of Amsterdam, and is a researcher at the CWI Machine Learning group. His research focuses on computational models of neural information processing, emphasizing spiking neural networks, predictive coding, and reinforcement learning. He bridges computational neuroscience and machine learning, exploring how biological insights can improve neural network designs and vice versa. Key collaborations include work with Cyriel Pennartz (UvA), Pieter Roelfsema (NIN), and Steven Scholte (B&C). His applied research spans scientific machine learning applications in finance and genomics. He actively supervises MSc thesis students, prioritizing those from UvA, with projects ranging from biologically inspired neural architectures to efficient spiking network simulations. Research highlights include developing biologically plausible learning rules for deep networks, predictive coding models for sensory data, and spiking network models for working memory tasks. His work also addresses challenges in temporal dynamics and scalable neural computation, leveraging both theoretical and applied perspectives.

• Digital Social Science
• Artificial Intelligence
• Natural Language Processing
• +4 more

Krista H. Lagus serves as Professor of Digital Social Science at the University of Helsinki's Faculty of Social Science, where she co-founded and directed the Center for Social Data Science (CSDS) starting in 2019. Her interdisciplinary work bridges computational methods with social science inquiry to analyze complex societal behaviors through digital footprints. Her educational foundation includes an M.Sc. in Computer Science (1996) and Ph.D. in Computer and Information Sciences (2000), both earned at Helsinki University of Technology (now Aalto University). This technical background enabled her transition into pioneering computational social science methodologies. Lagus's research integrates artificial intelligence and social science through signature projects: WEBSOM for visualizing large text corpora via self-organizing maps, Citizen Mindscapes for gauging public opinion through digital communication analysis, and Morfessor , a groundbreaking unsupervised morphological segmentation algorithm widely adopted in natural language processing. Her work demonstrates how machine learning can decode societal patterns from digital traces. Her scientific recognition includes: Academy Research Fellow, Finnish Academy of Sciences (2006-2012) Lagus actively supervises graduate students in AI and digital social science while contributing to academic governance as an Editorial Board Member for PeerJ Computer Science. Her research leadership is anchored in the Center for Social Data Science, which she established to foster cross-disciplinary collaboration between computational and social scientists. The Center for Social Data Science (CSDS) operates as her primary research ecosystem, driving initiatives that apply artificial intelligence to contemporary societal challenges. Through CSDS, Lagus cultivates methodological innovations that transform how social phenomena are observed and interpreted in the digital age.