Dr. Remco Renken is a neuroimaging researcher at the University of Groningen's Faculty of Medical Sciences , specializing in advanced MRI and PET scan analysis. His work bridges clinical neuroscience and computational methods , with a focus on movement disorders, visual processing, and neurodegenerative diseases. Department of Radiology and Nuclear Medicine (UMCG) Clinical Cognitive Neuropsychiatry Research Program (CCNP) Perceptual and Cognitive Neuroscience (PCN) group Research Interests : Using fMRI , PET , and machine learning to study brain connectivity , visual cortex plasticity , and neurodegenerative patterns in Parkinson’s, multiple sclerosis, and glaucoma. His innovations include algorithms for 3D motion perception tracking and neuroimaging harmonization . Notable Contributions : Co-developed IRMA (Machine learning harmonization for multicenter PET scans) Co-inventor of a 3D motion perception evaluation system Co-supervisor for PhD research on auditory cortex responses and schizophrenia hallucinations Collaborations span neurology, psychiatry, and biomedical engineering, with datasets shared via Mendeley and DataverseNL.
Dennis R. Schaart is a Professor and head of the Medical Physics & Technology section at the Department of Radiation Science & Technology, Faculty of Applied Sciences, Delft University of Technology (TU Delft). He is also a member of the R&D Program Board of the Holland Proton Therapy Centre (HollandPTC), highlighting his significant role in advancing clinical radiation technologies. His work bridges fundamental physics with medical applications, particularly in imaging and therapy. His primary research interests include Medical Physics, Radiation Oncology, Medical Imaging, Radiation Detection, Dosimetry, and Biomedical Engineering . He specializes in positron emission tomography (PET), time-of-flight methods, proton therapy, and scintillation detector development. His expertise in Monte Carlo simulation and experimental physics enables rigorous evaluation and innovation in detector systems and imaging protocols. The recent publications (2025–2021) reveal a strong trend toward photon-counting X-ray and PET detectors , proton therapy optimization , and novel scintillator materials . There is a clear emphasis on improving spatial, temporal, and energy resolution in imaging, with applications in both diagnostics and treatment planning. The integration of machine learning and Monte Carlo simulations further enhances the predictive and analytical power of his research. Scientific Awards: SNMMI 2015 International Best Abstracts Award Awarded for the highest number of citations for an article published over 2004–2008 Most cited paper in preceding five years (GATE V6 paper) Recognition at Trace 'n Treat conference for radionuclide state determination Dennis Schaart has (co-)authored over 100 journal papers and is a frequently invited speaker, indicating strong leadership and influence in the medical physics community. While no direct mention of students is found, his leadership role and extensive publication record suggest active supervision and mentorship. He is involved in national advisory roles, including serving on committees for the Ministry of Economic Affairs, reflecting broader impact beyond academia. His work is supported by collaborations with institutions like Philips, CERN, and various medical centers. Laboratories and Research Groups: He leads the Medical Physics & Technology research group within the Radiation Science & Technology department at TU Delft. The group focuses on developing and evaluating novel detector systems for medical imaging and therapy, using both experimental and computational approaches. The lab is equipped for scintillator characterization, detector prototyping, and advanced simulations, particularly using the GATE platform.
Rinke van Tatenhove-Pel is an Assistant Professor in the Department of Biotechnology at the Faculty of Applied Sciences, Delft University of Technology (TU Delft), where she leads research in Industrial Microbiology. She is based in room B58.B1.140 and can be contacted at R.J.vanTatenhove-Pel@tudelft.nl. Her work bridges predictive modeling and experimental biology to advance biotechnological applications. Her research focuses on understanding interactions between microbial cells, strains, and species through synthetic consortia and high-throughput screening platforms. She employs microdroplet-based cultivation systems and integrates modeling approaches such as reaction-diffusion and probabilistic models to predict microbial fitness and optimize selection processes. Her goal is to improve industrial bioprocesses including biofuel production and sustainable fermentation technologies. Rinke is actively involved in teaching within the Bachelor's program Life Science and Technology, contributing to courses such as Microbial Physiology and Metabolic Engineering. These courses emphasize quantitative and model-driven analysis of biological systems, preparing students for research and innovation in biotechnology. She supervises a dynamic research group comprising four PhD candidates and one postdoctoral researcher, fostering interdisciplinary collaboration within TU Delft and beyond. Her team explores topics ranging from yeast metabolic engineering to the evolution of microbial consortia for industrial applications. PhD Students: Sagarika Bangalore Govindaraju – Developing high-throughput screening platforms using microdroplets. Tobias Fecker – Engineering yeast metabolism and microbial communities for low-emission substrates. Tom de Kanter – Improving Ogataea parapolymorpha via laboratory evolution for enhanced biomass yield. Thomas Visser – Creating novel methodologies to evolve microbial consortia for food, fuel, and sustainability applications. Postdoc: Michelle Rossouw – Selecting hemicellulose-utilizing strains using microdroplet screening for bioethanol production. Rinke values collaborative and multidisciplinary research, believing that complex biological challenges require diverse perspectives. Her group works closely with other PIs, students, and technicians within the Industrial Microbiology section, and collaborates with scientists across the Biotechnology Department and external institutions.
Wilker Ferreira Aziz is an Assistant Professor at the Institute for Logic, Language and Computation (ILLC) within the Faculty of Science at the University of Amsterdam, where he leads the Probabilistic Language Learning group. His primary affiliation is with the Natural Language Processing & Digital Humanities research unit. His research focuses on the intersection of machine learning, natural language processing, and probabilistic modeling. Key areas of interest include language modeling, machine translation, syntactic parsing, text classification, and question answering. He develops techniques for probabilistic inference, gradient estimation, and uncertainty quantification in neural language models. Dr. Aziz's recent publications demonstrate a strong focus on uncertainty in natural language generation, with multiple papers at top-tier conferences like EACL, EMNLP, and ICLR. His work examines how language models represent uncertainty compared to humans, calibration issues when humans disagree on labels, and methods for more robust decision-making in text generation. Best Paper Award at Coling 2020 He actively supervises both PhD and MSc students, with several ongoing PhD projects focusing on uncertainty in language models and neural text generation. Dr. Aziz serves on program committees for major ML and NLP conferences including ACL, EMNLP, NeurIPS, and ICLR, and has acted as area chair for several of these venues. His research has been supported through positions at the Mercury Machine Learning Lab, a collaboration between Booking.com, TU Delft, and the University of Amsterdam.
Dr Raquel Garrido Alhama is an Assistant Professor of Artificial Intelligence for text and human interaction at the Institute for Logic, Language and Computation (ILLC) , University of Amsterdam. She also co-directs the /k/omputation and Language Lab and previously served as Assistant Professor at the Cognitive Science and Artificial Intelligence department of Tilburg University. She earned her PhD from the ILLC under the supervision of Jelle Zuidema, Remko Scha and Carel Ten Cate, with a dissertation entitled Computational Modelling of Artificial Language Learning . Prior post-doctoral positions include the Max Planck Institute for Psycholinguistics and the Basque Center on Cognition, Brain and Language. Research Interests: Computational modelling of language learning and evolution Language acquisition, word learning and sentence processing Neural-symbolic integration and connectionist architectures Visual and auditory word recognition, syntactic parsing and part-of-speech tagging In recent years her work has converged on validating developmental milestones—such as the onset of productive determiner–noun combinations—using large-scale child corpora and neural models. She also investigates how multi-agent referential games can lead to emergent compositional languages, moving from image to richer graph representations. Scientific Awards: Best Article Award (2019) from Psychonomic Bulletin & Review for the review on computational models of rule learning Best Poster Award (2015) at the International Conference on Cognitive Modeling Teaching & Supervision: Dr Alhama has designed and lectured courses on advanced programming for cognitive science, computational linguistics, linear algebra and language acquisition at Tilburg University, University of Amsterdam, Radboud University and the University of the Basque Country. She currently supervises eleven MSc and BSc students in AI, Logic and Cognitive Science. Labs & Teams: She is the founding co-director of the /k/omputation and Language Lab together with Phong Le, where they use Language Emergence frameworks to explore the cognitive prerequisites for displacement and compositionality in human language.
Mireille Boutin is a Professor at the Eindhoven University of Technology , affiliated with the Department of Mathematics and Computer Science within the Discrete Algebra and Geometry group. Her research spans applied mathematics, signal processing, and machine learning, with a focus on discrete inverse problems and the integration of geometric tools in probabilistic frameworks. Academic Background : She earned a Ph.D. in Mathematics from the University of Minnesota under Peter Olver, followed by postdoctoral positions at Brown University and the Max Planck Institute for Mathematics in the Sciences . Before joining TU/e, she served on the faculty at Purdue University . Research Trends : Recent publications demonstrate her interdisciplinary approach, including solutions to global positioning uniqueness problems via algebraic geometry, computer-aided qualitative data analysis in education, symmetry-breaking in acoustic path tracking, and novel clustering models for high-dimensional data. Her work often bridges theoretical mathematics with practical applications in engineering and education. Educational Contributions : She actively teaches Computational Algebra at TU/e and has collaborated on studies applying machine learning to qualitative education research, such as analyzing student explanations in programming and faculty teaching conceptions.
Dr. Mojtaba Rostami Kandroodi is a University Lecturer at Tilburg University's Tilburg School of Humanities and Digital Sciences (TSHD) within the Department of Intelligent Systems. His academic profile demonstrates active engagement in both teaching and research, with courses including Machine Learning, Mathematics for Premasters DSS, and supervision of Master's theses through the Data Science in Action program. Dr. Rostami Kandroodi's research centers on the intersection of cognitive neuroscience, computational modeling, and psychiatric disorders. His work investigates how motivational biases influence decision-making processes, with particular focus on reinforcement learning mechanisms in both healthy individuals and those with psychiatric conditions. He examines how neurochemical factors like dopamine and substances such as LSD modulate learning processes, seeking to understand the neurocognitive basis of conditions including depression, anxiety, and addiction. His research employs sophisticated computational models to analyze behavioral data from probabilistic reversal learning tasks, revealing nuanced insights into reward-punishment asymmetries and cognitive control mechanisms. Analysis of his publication record shows a consistent focus on reinforcement learning dynamics across multiple contexts. His work spans from theoretical computational models of asymmetric updating in volatile environments to clinical investigations of learning biases in psychiatric populations and pharmacological studies examining how substances alter learning processes. This research trajectory demonstrates both methodological sophistication and clinical relevance, bridging computational neuroscience with practical applications for understanding and potentially treating psychiatric disorders. Dr. Rostami Kandroodi maintains active collaborations with researchers across institutions, as evidenced by his co-authorship with prominent figures like Hanneke den Ouden. His teaching responsibilities in Machine Learning and data science methodologies complement his research focus, creating a cohesive academic profile centered on quantitative approaches to understanding cognition and behavior.
