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Sam PowerView profile

Lecturer

• Computational Statistics
• Monte Carlo Methods
• Markov Chain Monte Carlo (MCMC)
• +4 more

Sam Power is a Lecturer in the School of Mathematics at the University of Bristol . He holds a PhD in Mathematics from the University of Cambridge (awarded January 2021) and an MMath. His research focuses on computational statistics, Monte Carlo methods, and probabilistic modeling. Education PhD, University of Cambridge (30 Aug 2016 – 30 Jan 2021) MMath, University of Cambridge Research Interests Dr Power’s work lies at the intersection of probability theory , statistics , and machine learning . He investigates advanced Monte Carlo techniques including Markov Chain Monte Carlo (MCMC), particle methods, and piecewise-deterministic Markov processes. His recent projects explore convergence guarantees via functional inequalities such as Poincaré and log-Sobolev inequalities, state-space models for online learning, and uncertainty quantification. Publication Trends Across 20+ publications (2019–2025), Power has consistently advanced theoretical understanding and practical performance of sampling algorithms. Key themes include error bounds for particle and gradient-based methods, weak Poincaré inequalities, and applications in machine-learning systems such as online skill rating and Bayesian active learning. Scientific Awards No awards explicitly listed in the provided material. Students & Grants No explicit information on supervised students or funded grants is present. Labs & Teams Dr Power is affiliated with the School of Mathematics at Bristol; no specific laboratory or research group name is provided.

• Philosophy of Physics
• Quantum Mechanics
• Relativity Theory
• +3 more

Tomasz Placek is a Professor at the Jagiellonian University within the Institute of Philosophy , specifically the Department of Epistemology . His work bridges philosophy of physics , ontology , and theories of agency , with a focus on quantum mechanics, relativity, and epistemological frameworks. Research Interests : Philosophy of physics (quantum theory, relativity), causal set theory, determinism vs. indeterminism, and modal interpretations of spacetime. Key Contributions : Analysis of Wigner’s Friend paradoxes, causal structures in quantum correlations, and topological issues in indeterministic models. Email : tomasz.placek@uj.edu.pl

Jilles VreekenView profile

Professor

• Causal Inference
• Machine Learning
• Data Mining
• +5 more

Prof. Dr. Jilles Vreeken is tenured faculty at the CISPA Helmholtz Center for Information Security , where he leads the Exploratory Data Analysis group. He also serves as an Honorary Professor at Saarland University . Research focuses on causal inference, machine learning, and data mining Develops unsupervised methods for robust, interpretable models PI on grants like HAICU's Neuro-Explicit Models and Crushing Antimicrobial Resistance His recent work spans causal discovery in non-stationary time series ( SPACETIME ), federated binary matrix factorization, interpretable neural search patterns, and data modification rule mining from event logs. He applies information-theoretic approaches to address hidden confounding, selection bias, and multi-environment causal modeling. Key trends in his publications include: Integrating causal inference with machine learning via algorithmic Markov conditions Advancing federated learning for privacy-preserving causal discovery Creating interpretable pattern mining frameworks for graphs, sequences, and high-dimensional data Developing MDL-based methods for reliable dependency and rule discovery Scientific Recognition: 2018 - IEEE ICDM Tao Li Award 2018 - IEEE ICDM Best Paper 2015 - UdS-CS Busy Beaver Teaching Award 2011 - ACM SIGKDD Best Student Paper 2010 - ACM SIGKDD Doctoral Dissertation Runner-Up 2009 - ECML PKDD Best Student Paper As an educator, he has supervised 15+ PhD/MSc students and taught courses like Topics in Algorithmic Data Analysis and Information-Theoretic Machine Learning . His research group pioneers methods for trustworthy information processing and causal anomaly detection , with applications in materials science, epidemiology, and cybersecurity.

Pierre-Alexandre MatteiView profile

Researcher

• Statistical Machine Learning
• Generative Modeling
• Model Uncertainty
• +7 more

Pierre-Alexandre Mattei is a research scientist at Inria, affiliated with the Maasai team in Sophia Antipolis and the J.A. Dieudonné Laboratory at Université Côte d'Azur. He holds a chair at the 3IA Côte d'Azur institute and has a strong academic background in applied mathematics, having earned his Ph.D. from Université Paris Descartes (now Université Paris Cité) under Charles Bouveyron and Pierre Latouche, followed by a postdoc at the IT University of Copenhagen with Jes Frellsen. Ph.D. in Applied Mathematics, Université Paris Descartes (2017) Postdoctoral Researcher, IT University of Copenhagen His research lies at the intersection of statistical machine learning, generative modeling, and uncertainty quantification, with a focus on hidden variables, missing data, and model interpretability. He has co-organized major workshops such as Artemiss, GenU, SophI.A Summit, and Statlearn, and teaches at the Generative Modeling Summer School (GeMSS). His recent work spans energy-based models, clustering, semi-supervised learning, and medical AI applications. The 15 most recent publications reflect a consistent trend in developing statistically principled methods for generative modeling, with emphasis on likelihood-based inference, missing data, and information-theoretic approaches to clustering and representation learning. His work frequently appears in top venues like NeurIPS, ICML, ICLR, and AISTATS, as well as in journals such as Statistics and Computing and JACC: Advances. Co-organizer, Generative Modeling Summer School (GeMSS) Co-organizer, Workshop on Generative Models and Uncertainty Quantification (GenU) Co-organizer, SophI.A Summit Co-organizer, Statlearn Mattei has advised several PhD students and postdocs, including Raphaël Razafindralambo, Hugo Senetaire, Louis Ohl, Federico Bergamin, and Hugo Schmutz, many of whom have gone on to research positions in Copenhagen, Grenoble, Linköping, and Marseille. He collaborates extensively with researchers across France and Denmark, particularly with Jes Frellsen, Frédéric Precioso, and Charles Bouveyron. He is actively involved in the development of open-source tools and libraries such as the GemClus Python library for discriminative clustering. He is a key member of the Maasai team at Inria Sophia Antipolis, which focuses on models and algorithms for artificial intelligence, and contributes to the broader 3IA Côte d'Azur initiative aimed at advancing AI research through interdisciplinary collaboration.

Paul MillerView profile

Professor

• Computational Neuroscience
• Neuroscience
• Dynamical Systems
• +5 more

Paul Miller is a Professor of Biology at Brandeis University and a member of the Volen National Center for Complex Systems. His research focuses on computational neuroscience, integrating dynamical systems theory with experimental data to understand neural mechanisms underlying decision-making, memory, and sensory processing. He holds a Ph.D. from the University of Bristol (UK) and a B.A. from Cambridge University. Before joining Brandeis in 2000, he was at Georgetown University and conducted postdoctoral work in theoretical physics at Oak Ridge National Laboratory. Education: B.A., Cambridge University (1991) Ph.D., University of Bristol (1994) Research Interests: Computational Neuroscience emphasizes modeling neural circuits for short-term/long-term memory, decision-making processes, and the dynamics of neural ensemble activity. His work bridges theoretical approaches (e.g., hidden Markov modeling, spiking neuron networks) with experimental data from cortical and hippocampal systems. He explores how quasistable attractor states enable robust computation and how homeostatic mechanisms stabilize neural activity. Notable Contributions: Developed models explaining how integral feedback control enables robust sequential decision-making, elucidated synaptic mechanisms for spatiotemporal discrimination, and analyzed the role of calcium/calmodulin-dependent protein kinase II (CaMKII) in long-term memory stability. His textbook An Introductory Course in Computational Neuroscience (MIT Press) is widely used in graduate programs. Labs/Teams: Leads the Computational Neuroscience Lab at Brandeis, collaborating with experimentalists to validate theoretical models. Active in interdisciplinary projects involving neurophysiology, systems biology, and mathematical neuroscience.

Claire ArthurView profile

Assistant Professor

• Music Perception & Cognition
• Computational Musicology
• Music Information Retrieval
• +3 more

Claire Arthur is an Assistant Professor in the School of Music and Adjunct Professor in the School of Psychology at the Georgia Institute of Technology. She leads the Computational and Cognitive Musicology Lab (CCML), co-led with Dr. Nat Condit-Schultz, focusing on computational modeling of music and cognitive processes. Her work bridges music theory, neuroscience, and technology, with software contributions like humdrumR . Education: PhD in Music Theory and Cognition from Ohio State University (under David Huron), MA in Music Theory from the University of British Columbia, and Bachelor’s degree from the University of Toronto. Research interests include music perception, computational musicology, and music information retrieval. Her studies explore how musical features influence memory, emotion, and reward systems. Recent work involves corpus analysis of film music (e.g., Star Wars themes) and experimental studies on earworms and harmonic expectancy. Publications span journals like Music Perception and Music & Science , with conference contributions at ISMIR. Her interdisciplinary approach combines human-subject experiments with computational tools to address questions in music cognition and AI-generated music. No scientific awards listed. Active in labs and software development, with no explicit mentions of grants or advising roles. Collaborates widely on projects like the SWTC corpus and humdrumR .

• Neural Data Science
• Systems Neuroscience
• Computational Neuroscience
• +3 more

Benjamin Adric Dunn is an Associate Professor in (Neural) Data Science at the Department of Mathematical Sciences, NTNU. His research focuses on computational neuroscience, systems neuroscience, and topological data analysis. He holds a PhD and postdoctoral experience from NTNU's Kavli Institute for Systems Neuroscience, an MS in Computational Engineering from Purdue University, and a BS in Applied Mathematics from the University of Connecticut. Previously, he worked as a Computational Methods Engineer at Pratt & Whitney, UTC. His work explores neural mechanisms underlying spatial navigation, grid cell dynamics, and neural coding in rodents. Key contributions include studies on toroidal representations in grid cells and cortical coding of 3D posture. Dunn has published extensively in journals like Nature , Science , and Neuron , with a focus on interdisciplinary methods combining topology, statistics, and neuroscience. He has collaborated on projects involving neural ensemble activity analysis and hidden variable modeling in biological data. Dunn’s research also addresses computational challenges in neuroscience, such as removing experimental variability in functional data.

Andrea RotnitzkyView profile

Professor

• Causal Inference
• Epidemiological Methods
• Statistical Theory
• +3 more

Andrea Rotnitzky is a Professor and Chair of Biostatistics at the University of Washington. Born in Argentina with Jewish heritage, she values both Argentine culture and Jewish traditions. She joined UW in 2022 after a global recruitment, attracted by its collaborative research environment and partnerships with institutions like the Fred Hutchinson Cancer Center. Her work focuses on causal inference, missing data analysis, and semiparametric methods to address public health challenges. Rotnitzky holds a Licenciate in Mathematics from the Universidad de Buenos Aires (1982) and a PhD in Statistics from UC Berkeley (1988). Her research emphasizes developing statistically efficient tools to estimate intervention effects from observational or imperfect experimental data. Key areas include causal graphical models, longitudinal data analysis, and survival analysis. Her recent work highlights include articles on efficient adjustment sets in causal inference, debiased regression methods, and applications to post-COVID-19 symptom studies. Awards include IMS Fellowship (2025) and a $1M Rousseeuw Prize (2023) for statistical contributions. She prioritizes mentoring students and translating statistical innovations into real-world health solutions.

Benedetta CatanzaritiView profile

Research Fellow

• Science and Technology Studies
• Artificial Intelligence Ethics
• Social Dimensions of Data Practices
• +3 more

Benedetta Catanzariti is a British Academy Postdoctoral Fellow at the University of Edinburgh's School of Social and Political Science, with dual affiliation as a PostDoctoral Affiliate at the Centre for Technomoral Futures within the Edinburgh Futures Institute. She actively contributes to the AI Ethics & Society network, focusing on the social, historical, and political dimensions of data-driven technologies through qualitative STS (Science and Technology Studies) methodologies. Her work critically examines machine learning data practices, classification systems in algorithmic decision-making, and engineering cultures across industry, research, and educational contexts. Education: PhD in Science, Technology and Innovation Studies, University of Edinburgh (2023) MScRes in Science and Technology Studies, University of Edinburgh (2019) Master in Philosophy, University of Turin (2016) Her research investigates how data objectivity claims emerge within specific cultural imaginaries, with current emphasis on translating medical uncertainty into diagnostic AI outputs. Recent projects analyze facial expression recognition in healthcare, generative AI threats to parliamentary democracy, and ethical integration in computer science curricula. She develops reflexive tools to address algorithmic harm while documenting global labor practices in AI development and anti-surveillance resistance tactics. Article trends reveal escalating focus on AI's societal crises: 2025 works dissect objectivity construction in data annotation and AI governance metaphors, while 2024 outputs target democratic vulnerabilities (Chamberfakes), CS curriculum politics, and translational ethics teaching. Medical AI and emotion recognition studies (2020-2023) establish foundations for current work on medical imaging uncertainty. All publications consistently apply STS lenses to expose hidden power structures in data systems. Scientific Awards: SPS Outstanding Dissertation Award (2023) for 'Seeing affect: knowledge infrastructures in facial expression recognition systems' AsSIST-UK Andrew Webster PhD Prize (2024) She supervises Oksana Dorofeeva (visiting PhD, Aarhus University) and four CDT project students (Jacqueline Rowe, Amanda Horzyka, Osman Batur Ince, Cyndie Demeocq), previously guiding Sandra Wheeler's MSc in Data Science for Health and Social Care. Funded by a British Academy Postdoctoral Fellowship (2023-2026) for 'Technology in Translation: Investigating Organizational Contexts of AI Development', she also secured DCMS Policy Fellowship support under AHRC's BRAID programme. Current teaching includes Data and AI Ethics as Practice (2025) and Data Ethics in Health and Social Care (2024). Operates within the Centre for Technomoral Futures and AI Ethics & Society network, collaborating with Scottish Centre for Crime & Justice Research on parliamentary democracy threats. Organizes key events like the 2024 'AI as the Broken Machine' conference and 2022 'Ethics of Care and Community in AI Practice' workshop, while developing conceptual tools for medical AI practitioners through her active British Academy project.

Andrew TitmanView profile

Professor

• Survival Analysis
• Multi-State Modeling
• Medical Statistics
• +3 more

Andrew Titman is a Professor in the School of Mathematical Sciences at Lancaster University . His research focuses on survival analysis and multi-state modeling with applications in medical statistics and quantitative methodology. Primary Affiliation: School of Mathematical Sciences, Lancaster University Contact: a.titman@lancaster.ac.uk Research Interests include: Development of non-Markov multi-state models for complex survival data Latent variable modeling for health-related quality of life questionnaires Methodology for informative observation times in clinical trials Health economic decision modeling applications Survival analysis with misclassification and time non-homogeneity Recent Publications demonstrate expertise in: Bayesian state-space models for sports analytics Multi-state model diagnostics and goodness-of-fit testing Medical applications in endocrinology and hematology Simulation methodology with input uncertainty quantification Scientific Contributions include: Development of R packages: nhm, CIFresolve, miscIC Creation of the MarkovTest GitHub repository for multi-state model validation Methodological frameworks for non-homogeneous Markov models

• Environmental Economics
• Health Economics
• Inequality Studies
• +4 more

Dr. Julia Mink is a Tenure Track Assistant Professor at the University of Bonn, specializing in empirical environmental and health economics. Her research focuses on quantifying societal costs of pollution, climate change, and inequality through quasi-experimental methods and large-scale data analysis. Current affiliations: Institute for Applied Microeconomics, University of Bonn Previous affiliations: French National Research Institute for Agriculture, Food and Environment (INRAE) Her research combines environmental economics with health outcomes, emphasizing: Health impacts of air pollution and agricultural pesticides Inequality distribution in pollution exposure Climate resilience via healthcare systems Long-term socioeconomic impacts of disasters Recent publications analyze air pollution healthcare costs, pesticide effects on child development, and climate shocks in sub-Saharan Africa. She collaborates with researchers in Berkeley, Paris, and within the University of Bonn. Teaching roles: Environmental Economics (Graduate), Introductory Econometrics (Undergraduate) Argelander Professorship recipient

Ilya ShpitserView profile

Associate Professor

• Causal Inference
• Missing Data
• Graphical Models
• +5 more

Ilya Shpitser is a John C. Malone Associate Professor in the Department of Computer Science at Johns Hopkins University (JHU), within the Whiting School of Engineering. He is also a member of the Malone Center for Engineering in Healthcare and the Data Science and AI Institute. His research focuses on causal inference, missing data, graphical models, algorithmic fairness, and semi-parametric inference, with applications in healthcare, public health, and criminal justice. Shpitser holds a BA in Computer Science and Mathematics from UC Berkeley (1999), and MS (2004) and PhD (2008) in Computer Science from UCLA. Postdoctoral work included Harvard University and the University of Southampton. He has received awards such as the Causality in Statistics Education Award (2017) and the NSF CAREER Award (2020). His work addresses disparities in algorithmic bias, causal pathway analysis, and high-dimensional observational data. Key contributions include developing causal inference methods for missing data and fairness, as well as software tools like Ananke. He serves as an associate editor for the Journal of Causal Inference and the American Journal of Epidemiology. His research is funded by NSF, NIH, DARPA, and the Office of Naval Research. Shpitser advises numerous PhD students, many of whom have transitioned to academic and industry roles. His interdisciplinary collaborations span biostatistics, computer science, and public health, emphasizing real-world applications of causal methods.

Göran KauermannView profile

Professor

• Nonparametric Models
• Semi- and nonparametric analysis
• Generalized Linear Models
• +2 more

Prof. Dr. Göran Kauermann is a Full Professor of Statistics at the Ludwig-Maximilians-University Munich , holding the Chair of Applied Statistics in Social Sciences, Economics and Business . His research spans nonparametric models, generalized linear models, and network data analysis, with applications in economics, epidemiology, and data science. Education: Diplom in Economic Mathematics (1991, TU Berlin), PhD in Statistics (1994), Habilitation (Venia Legendi) in Statistics (2000) Kauermann’s research interests focus on penalized regression , network analysis , and statistical modeling in economics, social sciences, and public health. Recent work explores label uncertainty in machine learning , spatio-temporal conflict diffusion , and dynamic network models for economic and social data. Scientific trends in his publications include penalized splines for nonlinear modeling, network flow estimation in social and economic contexts, and label variation analysis in machine learning. His collaborations span climate zone classification , Covid-19 mortality modeling , and smart city parking analytics . Scientific Awards: Bruce Russett Award (2020) for political network research Leadership Roles: He served as Dean of the Faculty of Mathematics, Informatics and Statistics (2019–2021), Speaker of the Elite Master Program in Data Science (2016–2026), and Chair of the German Statistical Society (2005–2013). He also held editorial roles in journals like AStA Advances in Statistical Analysis and Statistical Modelling .

Qi TangView profile

Research Fellow

• Hydrogeology
• Data Assimilation
• Earth System Modeling
• +3 more

Qi Tang is a Research Fellow in the Department of Environmental Science at the University of Basel and concurrently serves as Coordinator of the Swiss Water Earth Systems PhD School at the University of Neuchâtel. His expertise spans hydrogeology, data assimilation, and Earth system modeling. He holds a PhD in Hydrogeology from RWTH Aachen University (2017) and has held postdoctoral positions at institutions including the Alfred Wegener Institute (Germany), the University of Basel, and the Chinese Academy of Sciences. Education: PhD in Hydrogeology, RWTH Aachen University, Germany (2012–2017) MSc in Hydrology and Water Resources, Beijing Normal University (2009–2012) BSc in Applied Mathematics, China Agriculture University (2005–2009) Research Interests: Qi Tang focuses on advancing coupled Earth system models through data assimilation techniques. His work integrates hydrological, oceanographic, and climatic processes to improve predictive accuracy. Key areas include river-aquifer interaction dynamics, satellite data integration in ocean-atmosphere models, and cloud computing for real-time water resource management. His research bridges theoretical modeling with practical applications in environmental monitoring and climate prediction. Publications: His articles emphasize data-driven approaches to environmental systems. Recent work highlights coupled model improvements using satellite data (e.g., ocean-atmosphere interactions), ensemble Kalman filtering for flood simulations, and Bayesian networks for precipitation modeling. These studies underscore his expertise in both computational methods and field applications. Advising & Grants: While no formal advisees are listed, his postdoctoral roles suggest involvement in mentoring junior researchers. No specific grants are mentioned in the provided texts. Labs/Teams: Affiliated with the Hydrogeological Processes group at the Center for Hydrogeology and Geothermal Energy (CHYN), University of Neuchâtel. This group specializes in geothermal energy, hydrochemistry, and stochastic hydrogeology.

Gábor Hofer-SzabóView profile

Research Fellow

• Foundations of Quantum Theory
• Probabilistic Causality
• Noncommutative Causality
• +3 more

Gábor Hofer-Szabó is a Senior Research Fellow and Deputy Head at the Institute of Philosophy within the Research Centre for the Humanities at the Hungarian Academy of Sciences in Budapest. As a Friedrich Wilhelm Bessel Research Award Winner of the Alexander von Humboldt Foundation (2021-22), he spent the 2021/22 academic year at the Munich Center for Mathematical Philosophy (MCMP) at Ludwig-Maximilians-Universität München. His work bridges foundational quantum theory, probabilistic causality, and interdisciplinary applications of contextuality. Education: DSc in Philosophy of Science (Hungarian Academy of Sciences, 2020) Habilitation in Philosophy of Science (Eötvös Loránd University Budapest, 2012) PhD in Philosophy of Science (Budapest University of Technology and Economics, 2001) MA in Physics (Eötvös Loránd University Budapest, 1993) Research Interests: Hofer-Szabó's primary focus is on the foundations of quantum theory and probabilistic causality , with specific projects in noncommutative causality, dynamical systems approaches to causation, emergence in statistical physics, and contextuality in physical, social, and psychological sciences. His work integrates formal methods with philosophical analysis to address fundamental questions in quantum mechanics and causality. Publications: His recent articles explore quantum contextuality, noncommutative causality, and Bell-type inequalities, demonstrating a consistent focus on the interplay between quantum foundations, probabilistic models, and causal inference. Key themes include operational equivalence, Kochen-Specker arguments, and macrostates in statistical physics. Awards and Grants: Friedrich Wilhelm Bessel Research Award, Alexander von Humboldt Foundation (2021-22) János Bolyai Research Scholarship (2008-2011, 2003-2006) Teacher-Researcher Award, Hungarian Academy of Sciences (2004) Fulbright Research Grant (2011-2012) Principal Investigator for multiple OTKA grants including 'Rethinking probability, causality, and contextuality' (2020-2024) Collaborations: Hofer-Szabó leads research teams at the Institute of Philosophy and collaborates internationally, including projects at the Edelstein Center (Jerusalem), MCMP (Munich), and partnerships across European institutions. His work involves interdisciplinary applications of quantum contextuality in social and psychological sciences.