جست‌وجوی استاد و پژوهشگر

Michael Bankمشاهده پروفایل

دانشیار مدعو

• Environmental Toxicology
• Contaminant Biology
• Bayesian Mathematical Modeling
• +۵ مورد دیگر

Michael Bank is an Adjunct Associate Professor in the Department of Environmental Conservation at the University of Massachusetts Amherst. His work bridges environmental science, statistical modeling, and policy translation, with a strong focus on contaminant impacts and governance. Research Interests: Dr. Bank's research is highly interdisciplinary, grounded in Bayesian mathematical modeling, environmental toxicology, and contaminant biology. He investigates how pollutants affect organisms—including humans—how these contaminants can be modeled across large spatial and temporal scales using Bayesian statistics and information theory, and how such scientific insights can inform environmental governance and policy decisions. His work emphasizes real-world, large-scale datasets and scientific translation for global impact. Leadership and Affiliations: Since 2010, he has served as the North American Chairperson for the Society of Environmental Toxicology and Chemistry (SETAC) Global Mercury Working Group, an official member of the United Nations Environment Programme’s Global Mercury Partnership. This role underscores his international engagement and leadership in mercury research and policy. Awards and Recognition: No specific awards mentioned in the provided text. Advising and Grants: While no current advisees or grant funding details are listed, his leadership position and interdisciplinary research suggest active collaboration and potential involvement in funded scientific initiatives related to environmental contamination and governance. Labs and Research Teams: Dr. Bank is affiliated with the SETAC Global Mercury Working Group, where he leads the North American branch. This team operates under the UNEP Global Mercury Partnership, indicating a high-level, policy-relevant research network focused on monitoring, modeling, and mitigating mercury pollution worldwide.

• Applied Mathematics
• Mathematical Modeling
• Multiphase Flow
• +۸ مورد دیگر

Steinar Evje is a Professor of Applied Mathematics at the University of Stavanger, affiliated with the Faculty of Science and Technology and the Department of Energy and Petroleum Technology. His research bridges mathematical modeling with applications in petroleum engineering and cancer biology. His research interests include: Mathematical modeling of multiphase flow in porous media Conservation laws and PDEs Tumor cell migration and biomechanics Fluid-structure and cell-fluid interactions Data-driven discovery of PDEs Numerical analysis and scientific computing His recent publications (2023–2024) show a strong trend in combining machine learning with physical modeling, particularly in learning flux and diffusion functions from data, solving conservation laws using graph neural networks, and modeling tumor progression under fluid pressure. These works span disciplines from petroleum engineering to computational oncology, reflecting interdisciplinary impact. Scientific awards are not mentioned in the provided text. He actively collaborates with researchers in both engineering and biomedical fields. While specific students are not listed, his extensive publication record suggests mentorship of graduate students and postdocs. He has contributed to modeling in oil recovery, well control, and tumor microenvironment dynamics, with no mention of external grants but evident sustained research output. He is involved in modeling efforts related to: Two-phase and three-phase flow in porous media Cell migration under interstitial fluid flow Spontaneous imbibition and wettability alteration Gas migration in leaking wells Low salinity flooding in oil recovery

Ana Alejandra Cabaña Nigroمشاهده پروفایل

استاد

• Empirical Processes
• Goodness-of-Fit Testing
• Time Series Analysis
• +۳ مورد دیگر

Ana Alejandra Cabaña Nigro is a Professor in the Department of Mathematics at Universitat Autònoma de Barcelona, specializing in Advanced Stochastic Modelling. She holds a Ph.D. in Mathematical Sciences from the Central University of Venezuela (1993), a Master's in Mathematics from Universidad Simón Bolívar (1986), and a Bachelor's degree in Mathematics from the same institution (1985). Her research focuses on empirical processes, goodness-of-fit testing, time series analysis, and statistical modeling with applications to epidemiology and public health. She has contributed to methodologies for analyzing under-reported data, particularly during the COVID-19 pandemic, and has developed techniques for transforming empirical processes (TEPs) to enhance statistical testing power. Current interests include integrating neural networks with empirical processes to study variable importance. She has collaborated on projects such as stochastic modeling for biosciences (2022–2025) and statistical analysis of health risks (2016–2022), addressing topics like epidemiological data under-reporting and environmental health risks. She has supervised 47 academic works and actively participates in interdisciplinary initiatives like the Ecodep project. Key contributions include publications on ARCH models for under-reported time series, Bayesian analysis of pandemic burdens, and methods to quantify data misreporting in public health contexts. Her work contributes to UN Sustainable Development Goals related to health and well-being.

• Computer Science
• Mathematics
• Bioinformatics
• +۴ مورد دیگر

Pieter Audenaert is an Associate Professor at Ghent University's Faculty of Engineering and Architecture, affiliated with the Department of Information Technology and the Internet Technology and Data Science Lab. He simultaneously holds a postdoctoral researcher position at IMEC. His interdisciplinary research bridges computer science, mathematics, and engineering. Primary research domains include: Algorithm design for network optimization (Steiner trees, fiber networks) Computational biology (de Bruijn graphs, genome assembly) Transportation logistics (container drayage, port operations) Telecommunication systems (VLC, network flows) Discrete mathematics applied to network science Recent publications (2019-2025) demonstrate strong focus on optimization algorithms for both biological networks and transportation systems, with increasing applications of probabilistic modeling and simulation techniques. Bioinformatics work frequently employs graph-theoretic approaches to genome analysis. Awards and honors: Knuth Reward Check (2003) Laureaat Vlaamse Wiskunde Olympiade (1996) Leads research at the Internet Technology and Data Science Lab, collaborating extensively with industry partners like Port of Antwerp and Belgian retailers for transportation optimization projects.

Bruce Tidorمشاهده پروفایل

استاد

• Computational Biology
• Systems Biology
• Molecular Biophysics
• +۳ مورد دیگر

Bruce Tidor is a Professor of Biological Engineering and Computer Science at the Massachusetts Institute of Technology (MIT), leading the Tidor Lab. His research focuses on computational and systems biology, integrating principles from biophysics, artificial intelligence, and applied mathematics to study complex biological systems at molecular and systems levels. Research Interests: Protein and nucleic acid structure-function analysis Computational design of proteins and ligands Gene expression networks and systems biology modeling Drug resistance mechanisms and therapeutic design Lab Activities: The Tidor Lab develops methods for analyzing biomolecular interactions, parameterizing biological networks, and optimizing drug resistance strategies. Notable projects include studying calcium sensor design, oscillatory biological systems, and mechanotransduction in proteins such as FAT domains. Collaborations and Grants: Ongoing work includes MIT-Skoltech collaborations, computational re-engineering of enzymes, and modeling cytokine interactions. The lab has advised numerous PhD and master’s students in biological engineering and related fields.

• Bayesian Optimization
• Reinforcement Learning
• Artificial Intelligence Ethics
• +۱۲ مورد دیگر

Eduardo César Garrido Merchán is an Assistant Professor in the Intelligent Systems area at the Technological Research Institute of the Comillas Pontifical University. He is affiliated with both the Faculty of Economics and Business Administration, where he teaches Machine Learning, Statistics, and Quantitative Models, and with the Higher Technical School of Engineering - ICAI. His research bridges artificial intelligence, ethics, and societal impact. Education: PhD cum laude in Computer Science and Telecommunications Engineering, Autonomous University of Madrid Master's in Artificial Intelligence, Polytechnic University of Madrid (José Cuena Prize) Bachelor's in Computer Engineering (Honors), Pontifical University of Comillas His research interests lie at the intersection of Bayesian Optimization , Deep Reinforcement Learning , and AI Ethics . He investigates methods to make AI systems more inclusive, fair, and socially responsible, particularly in applications related to ESG finance, social inclusion, and human-AI interaction. He critically examines philosophical aspects of AI, such as machine consciousness and the limits of computational models of mind. His recent publications demonstrate a strong trend in applying advanced machine learning techniques—especially Bayesian optimization and deep reinforcement learning—to complex, real-world problems in finance and ethics. He integrates explainability and multi-objective optimization to address environmental, social, and governance challenges, while also publishing critical perspectives on the philosophical foundations of AI. Scientific Awards: José Cuena Prize for Master’s in Artificial Intelligence He supervises researchers interested in AI, ethics, and society, guiding them toward developing AI agents and policies that promote justice and equality. Though no grants are explicitly mentioned, his active publication record in high-impact journals suggests ongoing research support. He has participated in thesis tribunals and delivered invited lectures, including at the CIUTI Conference 2025 on AI in language mediation. He is actively involved in training and dissemination, having taught courses on Gaussian Processes and Bayesian Optimization and contributed opinion pieces on AI and humanism. His work reflects a commitment to both technical rigor and ethical responsibility in AI development.

Suzanne van der Veldtمشاهده پروفایل

استادیار

• Neuroscience
• Behavioural Neuroscience
• Anxiety Disorders
• +۵ مورد دیگر

Suzanne van der Veldt serves as an Assistant Professor at the University of Groningen's Faculty of Science and Engineering within the Groningen Institute for Evolutionary Life Sciences (GELIFES), specializing in the behavioural neuroscience cluster. Her research focuses on how the brain distinguishes between safe and threatening contexts, particularly examining failures in this process that lead to anxiety disorders, PTSD, and other fear-related conditions. PhD in Neuroscience from McGill University (2016-2021) as a Vanier Canada Graduate Scholar Postdoctoral fellowship at Université de Montréal and CHU Sainte-Justine (2021-2025) supported by CIHR and FRQS fellowships MSc in European Master in Neuroscience (Neurasmus program) (2013-2015) Her research integrates traditional laboratory approaches with innovative studies of free-roaming mice in semi-naturalistic environments to bridge the gap between controlled experiments and real-world complexity. She specializes in the septohippocampal system, employing cutting-edge techniques including multi-region neural recordings, cell-type specific manipulations, and miniaturized calcium imaging in freely behaving animals. Her work examines how neural circuits assign emotional value to places and experiences, with particular attention to sex differences in emotional processing and vulnerability to anxiety disorders. Analysis of her publication record reveals a consistent focus on hippocampal-septal circuitry, with significant contributions to understanding spatial memory coding, neural oscillations (particularly theta and gamma rhythms), and the neural basis of fear conditioning. Her research increasingly incorporates sex as a biological variable and examines circuit dysfunctions in psychiatric conditions. Vanier Canada Graduate Scholarship (prestigious Canadian doctoral award) CIHR Fellowship (Canadian Institutes of Health Research) FRQS Fellowship (Fonds de recherche du Québec - Santé) Dr. van der Veldt leads the Van der Veldt Lab for Behavioural Neuroscience, which investigates emotional processing through innovative approaches combining circuit neuroscience with ethologically relevant behavioral paradigms. Her lab receives funding through tenure-track mechanisms at the University of Groningen and previously secured competitive fellowships during her postdoctoral training. The lab actively collaborates with international neuroscience groups, particularly with researchers at McGill University and Université de Montréal.

James Danckertمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Boredom
• Mental Model Updating
• +۳ مورد دیگر

James Danckert is a Professor in the Department of Psychology at the University of Waterloo, where he also serves as the Cognitive Neuroscience Area Head. He is cross-appointed to the Research Institute in Aging and leads the Danckert Attention and Action Group (Danckert Lab). His research spans cognitive neuroscience with particular focus on understanding the mechanisms and brain states that give rise to boredom and mental model updating. Dr. Danckert earned his BA from Melbourne University (Australia), followed by his MA and PhD from La Trobe University (Australia). His academic journey has positioned him as a leading expert in cognitive neuroscience with a specific emphasis on boredom psychology and attention mechanisms. His primary research interests include the cognitive and neural mechanisms of boredom, particularly in individuals with traumatic brain injuries, and mental model updating in relation to neglect syndrome. Danckert's work explores boredom through behavioral tasks like foraging, sustained attention tasks, and executive control tasks, utilizing neuroimaging techniques such as fMRI and tDCS. His mental model updating research involves working with stroke patients (with access to a database of over 800 patients), fMRI, and computational modeling. Analysis of Dr. Danckert's most recent publications reveals a continued focus on boredom and mental model updating, with increasing interdisciplinary approaches incorporating computational modeling, AI, and genetic perspectives. His work bridges cognitive psychology, neuroscience, and clinical applications, with particular emphasis on how boredom relates to attention, agency, and decision-making processes. Dr. Danckert has received significant recognition for his work, including: Former Canada Research Chair (Tier II) in Cognitive Neuroscience Recipient of the 40 Under 40 Award from the Region of Waterloo As a mentor, Dr. Danckert supervises graduate students in the Psychology Department at Waterloo, teaching advanced courses such as Psych 783: Neuroimaging and Cognition. His research is supported by prestigious funding sources including the Natural Sciences and Engineering Research Council (NSERC) and the Canada Foundation for Innovation (CFI). The Danckert Lab maintains a comprehensive research program with two main streams: boredom research and mental model updating. The lab utilizes diverse methodologies including behavioral testing, neuroimaging, computational modeling, and collaborations with experts in evolutionary genetics. The lab's work has significant implications for understanding cognitive processes in both healthy individuals and those with neurological conditions.

Michele Costolaمشاهده پروفایل

دانشیار

• Financial Economics
• Economic Policy
• ESG Investing
• +۴ مورد دیگر

Michele Costola is an Associate Professor in the Department of Economics at Ca' Foscari University of Venice, with additional affiliation at the Interdepartmental School of Economics, Languages and Entrepreneurship for International Exchanges based in Treviso. His research focuses on financial economics, economic policy, and sustainable finance. His research interests include: Financial sectors - models and methods Financial sectors - resources and tools Economic system - models and methods ESG Investing and Sustainable Finance Climate Risk Analysis Financial Networks and Systemic Risk Dr. Costola's recent work shows a strong focus on the intersection of climate change, ESG factors, and financial markets. His research examines how environmental, social, and governance factors influence credit analysis, risk pricing, and investment decisions. He has developed sophisticated models for analyzing spillovers across financial markets and assessing systemic risks related to climate transition, with particular attention to energy efficiency metrics in financial products. His notable scientific awards include: Young Investigator Training Program Research Prize (YITP) at the Econometric Models of Climate Change Conference 2019 Best poster award (ex aequo) at the IFABS Conference 2013 Dr. Costola has secured funding from prestigious organizations including the European Commission (LIFE and H2020 programs), European Investment Bank Institute, and MIUR for projects focusing on ESG ratings, sustainable finance, and energy efficiency in financial markets. He has presented his research at numerous international conferences including the Financial Risks International Forum and Computational and Financial Econometrics conferences across Europe. He maintains active research collaborations with institutions including the Leibniz Institute for Financial Research SAFE in Frankfurt, Germany, and has held visiting positions at the European Central Bank and Aarhus University.

• Statistical Relational Artificial Intelligence
• Human-aware AI
• Explainable AI
• +۱۲ مورد دیگر

Tanya Braun is a Junior Professor in the Institute of Computer Science at the University of Münster, Department of Mathematics and Computer Science. She leads the Data Science research group, focusing on statistical-relational AI, human-aware AI, and text understanding. Her work bridges formal AI methods with real-world applications in healthcare, digital humanities, and public sector systems. Education: Bachelor's and Master's in Computational Informatics, Hamburg University of Technology Doctorate in Computer Science, University of Lübeck (2020), thesis: 'Rescued from a Sea of Queries - Exact Inference in Probabilistic Relational Models' Her research centers on probabilistic inference in relational domains , with a focus on lifted inference techniques that exploit symmetries to scale reasoning. She investigates human-aware AI , particularly how AI systems can reconcile learned models with human expectations to improve explainability and trust. Her work on text understanding addresses challenges in data-scarce settings such as digital humanities, where traditional large language models fail. She has developed methods for identifying and enriching subjective content descriptions, topic modeling in specialized domains, and feedback-driven model improvement. The 15 most recent publications highlight a strong trajectory in lifted inference, model compression, privacy-preserving AI, and explainability . Her work integrates formal AI foundations with practical concerns in high-stakes domains like healthcare. She frequently publishes in top venues such as AAAI, IJCAI, ECAI, and Artificial Intelligence, often in collaboration with Ralf Möller, Marcel Gehrke, and Jan Speller. Scientific Awards: No specific awards listed in the provided text. Tanya Braun actively advises students and leads the HAPPI project, which focuses on human-AI model reconciliation using lifted probabilistic inference. She has supervised multiple theses and mentored researchers including Jan Speller (PostDoc), Nazlı Nur Karabulut, and Sagad Hamid. She has secured funding from the Ministry of Culture and Science of North Rhine-Westphalia for her research. She is deeply involved in academic service: serving as program co-chair for KI 2025, guest-editing special issues in journals like Künstliche Intelligenz and Annals of Mathematics and Artificial Intelligence , and organizing major conferences including ICCS and KR. Labs and Teams: She leads the Data Science Group at the University of Münster, which conducts research in AI, probabilistic modeling, and data science. The group is actively involved in teaching and mentoring students in advanced AI topics.

Valérie de Lapparentمشاهده پروفایل

پژوهشگر

• Galaxy Evolution
• Large-Scale Structure of the Universe
• Galaxy Morphology
• +۴ مورد دیگر

Valérie de Lapparent is a senior researcher at the Paris Institute of Astrophysics (IAP) , a joint research unit of Sorbonne University and CNRS. Her work focuses on galaxy evolution, large-scale cosmic structure, and data analysis techniques. Education Baccalauréat, Série C (1979) Preparatory Classes, Lycée Louis le Grand (1979-1981) École Normale Supérieure, Physics Section (1981-1985) Licence in Physics, Univ. Paris VI (1982) Master's in Physics, Univ. Paris VI (1982) DEA in Astronomy & Space Techniques, Univ. Paris VII (1983) PhD, Univ. Paris VII (1986) Research Expertise Valérie de Lapparent investigates galaxy evolution, large-scale distribution, morphometry, spectroscopy, and collective properties like luminosity functions. Her methods include optical/infrared observations, redshift measurements, Bayesian inference, and neural networks. Scientific Contributions 1988 CNRS Bronze Medal for discovering the cosmic 'honeycomb' structure Leadership in ESO and CFHT surveys Creation of IAP's 'Origin and Evolution of Galaxies' research group Editorial direction of IAP's website Outreach through public lectures and the 'Harmonia Celestis' educational platform

Daisaku Yokoyamaمشاهده پروفایل

استادیار

• Parallel and Distributed Processing
• Combinatorial Search
• Game Tree Search
• +۴ مورد دیگر

Daisaku Yokoyama is an Assistant Professor at the Institute of Industrial Science, University of Tokyo, where he works in Department 3 of the Kitsuregawa-Toyoda Laboratory. His research focuses on parallel and distributed processing, combinatorial search, game tree search, and other search processes. He is also involved in the development of "Gekisashi," a computer shogi (Japanese chess) player. His academic background includes: March 1998: Graduated from the Department of Electronic and Information Engineering, Faculty of Engineering, The University of Tokyo March 2000: Completed Master's course in Information Engineering at the University of Tokyo 2002.3: Graduated from the Doctoral Program in Information Engineering, Graduate School of Engineering, The University of Tokyo September 2006: Obtained a PhD in Science from the Graduate School of Frontier Sciences, University of Tokyo Daisaku Yokoyama's research interests primarily center around parallel and distributed computing systems, with a particular focus on combinatorial search algorithms and game tree search techniques. His work bridges theoretical computer science with practical applications, especially in the domain of computer shogi where he has developed "Gekisashi." Beyond game AI, his research has expanded into big data analytics, particularly in transportation systems where he analyzes passenger flows in metro networks and driver behavior using vehicle recorder data. His work demonstrates a consistent thread of applying parallel processing techniques to solve computationally intensive problems across various domains. Yokoyama's publication record shows a clear evolution from foundational work in parallel combinatorial optimization (PopKern library) to more applied research in computer shogi and eventually to big data applications in transportation systems. His early work established frameworks for parallel search algorithms, while more recent publications demonstrate applications of these techniques to real-world problems involving massive datasets from metro systems and vehicle recorders. His research consistently emphasizes the importance of domain-specific knowledge in optimizing parallel algorithms. His notable scientific achievements include: DBSJ Best Paper Award 2014 for "Application and Evaluation of a Bayesian-Based Monte Carlo Tree Search Algorithm to Shogi" Game Programming Workshop Excellent Paper Award (awarded twice) Throughout his career, Yokoyama has been actively involved in academic service, serving on editorial boards, program committees, and as an organizer for numerous conferences and workshops related to programming, parallel computing, and game AI. His work on the Gekisashi shogi engine represents a long-term research project that has evolved from basic search algorithms to sophisticated AI systems, demonstrating both theoretical rigor and practical implementation skills. He is part of the Kitsuregawa-Toyoda Laboratory at the Institute of Industrial Science, University of Tokyo, which focuses on advanced computing systems, database technologies, and large-scale data processing. The laboratory provides a collaborative environment for research spanning theoretical computer science to real-world applications in transportation analytics and game AI.

Felipe De Brigardمشاهده پروفایل

عضو هیئت علمی

• Memory and Imagination Interaction
• Episodic Counterfactual Thinking
• Neural Correlates of Memory
• +۵ مورد دیگر

Felipe De Brigard is a Professor of Philosophy and Associate Professor of Psychology and Neuroscience at Duke University. He is an Associate of the Duke Initiative for Science & Society and Member of the Center for Cognitive Neuroscience . His research at the intersection of philosophy, psychology, and neuroscience focuses on memory-imagination interactions, counterfactual thinking, and their implications for moral cognition and identity. Harvard University Postdoctoral Fellow in Philosophy (2011-2013) Ph.D. and M.A. from University of North Carolina, Chapel Hill M.A. from Tufts University Undergraduate degree from Universidad Nacional de Colombia His work challenges traditional memory theories, proposing memory as a system for hypothetical thinking rather than mere recall. Current projects examine how episodic and semantic memory contribute to counterfactual simulations, with recent 2025 publications on memory fidelity in plausible counterfactuals and neural connectivity analysis. He uses fMRI and eye-tracking to explore autobiographical memory modification, moral cognition, and anxiety-related neural patterns. Major grants include: $1.8M Templeton Foundation grant for Summer Seminars in Neuroscience and Philosophy (2020-2025) National Institutes of Health funding for aging and memory research (2018-2025) In the Imagination and Modal Cognition Lab , he leads interdisciplinary research on memory's role in future planning, identity formation, and therapeutic applications for anxiety. His empirical approach to philosophical questions has sparked debates about neuroscience-philosophy integration, particularly regarding consciousness and moral responsibility theories.

• Bayesian Statistics
• Artificial Intelligence
• Natural Language Processing
• +۷ مورد دیگر

Rens van de Schoot is a full professor of Collaborative Methods in AI and Data Science at Utrecht University in the Netherlands and serves as an extra-ordinary professor at North-West University in South Africa. He directs the AI-LAB on AI-aided Knowledge Discovery and coordinates the open-source ASReview project (Active learning for systematic text reviewing), which has become a significant tool for researchers conducting systematic reviews across various disciplines. His research spans multiple domains with a strong focus on Bayesian statistics, artificial intelligence applications in systematic reviews, and psychological methodology. Van de Schoot's work bridges technical AI development with practical applications in social sciences and healthcare research, particularly in PTSD and mental health studies. His methodological contributions include significant work on measurement invariance, small sample size solutions, and the development of the WAMBS checklist for Bayesian statistics. The analysis of his recent publications reveals a clear trajectory toward increasingly sophisticated AI applications in systematic reviewing, with a strong emphasis on Bayesian methodology. His work demonstrates how machine learning can enhance traditional systematic review processes while maintaining methodological rigor. The interdisciplinary nature of his research connects statistics, computer science, psychology, and healthcare research in innovative ways. APA award for best dissertation of division 5 Recipient of prestigious VENI and VIDI research grants from the Netherlands Organization for Scientific Research Member of the Young Academy (Jonge Akademie) of the Royal Netherlands Academy of Arts and Sciences (KNAW) Elected member of the Society of Multivariate Experimental Psychology (SMEP) Van de Schoot has supervised numerous PhD candidates, as evidenced by his research on PhD delays and academic careers. His research program has been supported by multiple competitive grants, including his VENI project on integrating trauma-related background knowledge into statistical models and his VIDI project on expert knowledge and limited data. His work bridges theoretical methodological advances with practical applications across multiple domains. As director of the AI-LAB, he leads a team developing cutting-edge AI tools for knowledge discovery. His coordination of the ASReview project has created an international community of researchers applying active learning to systematic reviewing. He's also actively involved with the Utrecht Platform for Applied Data Science, fostering interdisciplinary collaboration on data-intensive research projects.

• Applied Data Science
• Network Analysis
• Longitudinal Data Analysis
• +۶ مورد دیگر

Dr. Mahdi Shafiee Kamalabad is an Assistant Professor in the Department of Methodology and Statistics at Utrecht University's Faculty of Social and Behavioural Sciences. His research focuses on developing advanced statistical and machine learning methods for complex data analysis, particularly in social and behavioral sciences, life sciences, and bioinformatics. Applied Data Science Network Analysis Bayesian Statistics Longitudinal Data Analysis He specializes in Dynamic Bayesian Network Models, Relational Event Models, and Change Point Detection algorithms. His work spans interdisciplinary collaborations, combining educational psychology, applied linguistics, and data science to improve understanding of multilingual classroom interactions and epidemic prediction models. He has contributed to R software packages like remify, remstats, and remstimate for relational event history data analysis. Notable projects include "Better Together: A Social Network Analysis of Multilingual Interactions in the Classroom" (2022) and methodological developments for malaria dynamics analysis in Cameroon. His teaching includes Data Wrangling and Data Analysis courses. Funding sources include Utrecht University's Faculty of Social and Behavioural Sciences.