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 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-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.
Sebastian Murgueitio Ramirez is an Associate Professor in the Department of Philosophy at Purdue University, specializing in the philosophy of physics and metaphysics of natural laws. His academic journey includes a PhD in the history and philosophy of science and a master’s in physics from the University of Notre Dame, along with dual bachelor’s degrees in philosophy and physics from Universidad de los Andes (Colombia). His research focuses on symmetries in quantum mechanics, the metaphysics of physical laws, and the historical foundations of quantum theory. He has contributed to journals like The British Journal for the Philosophy of Science and Studies in the History and Philosophy of Modern Physics . His work spans empirical significance of symmetries, relativistic principles, and causation in general relativity. His recent publications examine non-relational empirical significance, shape dynamics, and the intersection of local symmetries with measurement theory. He also led the digitization of Epistemological Letters , a pivotal 1970s journal for Bell inequality discussions, and created studia.app , a free physics pedagogy resource. Postdoctoral Fellowship, University of Oxford (2021-2022) Collaborator, Event Horizon Telescope project (2023)
Sandro Sozzo is an Associate Professor of Logic and Philosophy of Science at the University of Udine, where he has been a faculty member since 2022. He previously served as a lecturer (2013) and associate professor (2015) at the University of Leicester, UK. He holds a PhD in Physics from the University of Salento (2006) and completed postdoctoral training at the Free University of Brussels (VUB). His academic affiliations include the Centre for Quantum Social and Cognitive Science (CQSCS), which he founded and directs, and leadership roles in the International Quantum Structures Association (IQSA) and the journal Foundations of Science. His research centers on the logical and epistemological foundations of natural and cognitive sciences, employing analytic philosophy to develop novel theoretical frameworks. He has pioneered a quantum-theoretic approach to modeling human cognition, explaining cognitive fallacies and probabilistic judgment under uncertainty. This work extends to applications in information retrieval and natural language processing, forming the basis of interdisciplinary projects like QUARTZ. The most recent articles reflect a consistent trajectory in quantum cognition, decision theory, and foundational physics. They demonstrate a deep integration of quantum structures into cognitive modeling, with recurring themes in Hilbert space representations, non-classical probability, and conceptual dynamics. His publications span high-impact journals in physics, cognitive science, and philosophy, indicating a strong interdisciplinary footprint. Awards for teaching quality (2016, 2018, 2020, 2022) Member of the Higher Education Academy (HEA) since 2015 Sandro Sozzo has supervised numerous research initiatives and students through the Centre for Quantum Social and Cognitive Science (CQSCS). He was the scientific leader of the Marie Curie Innovative Training Network 'QUARTZ', securing significant international funding. He has also held leadership roles as director for teaching, research, and internationalization. His editorial work as managing editor of Foundations of Science and organizational role in five international conferences further underscore his academic influence. He founded and currently directs the Centre for Quantum Social and Cognitive Science (CQSCS), an interdisciplinary research center focused on the epistemological foundations of natural and cognitive sciences. The center fosters collaborations across physics, philosophy, cognitive science, and computer science, supporting projects like QUARTZ and promoting quantum-inspired approaches to information systems and human reasoning.
Yulia Alexandr is a Hedrick Assistant Adjunct Professor (Adjunct Assistant Professor) in the Department of Mathematics at the University of California, Los Angeles (UCLA) and a Postdoctoral Fellow in Applied Mathematics at Harvard University. Her research centers on algebraic statistics, applied algebraic geometry, and mathematical machine learning, with current focus on geometric structures in statistical models and neural networks. She is actively on the academic job market for the 2025-26 cycle. Education: Ph.D. in Mathematics, University of California, Berkeley (2023). Advisors: Bernd Sturmfels and Serkan Hoşten. Thesis: From Voronoi Cells to Algebraic Statistics . B.A. in Mathematics (high honors), Wesleyan University (2019). Advisor: Karen Collins. Thesis: Combinatorial Nullstellensatz: Various Proofs, Extensions and Applications . Dr. Alexandr's research develops algebraic and geometric frameworks for statistical modeling and machine learning. She pioneers methods for analyzing logarithmic Voronoi cells in Gaussian and discrete models, investigates structural properties of graphical models and mixture distributions, and establishes algebraic constraints in neural network architectures. Her work bridges abstract algebra with practical machine learning applications, particularly in understanding geometric constraints of ReLU networks and information divergence in statistical models. Analysis of her 15 most recent publications (2018-2025) reveals three dominant research thrusts: (1) geometric foundations of statistical models through Voronoi structures and moment varieties, (2) algebraic analysis of graphical models including decomposable and context-specific variants, and (3) mathematical theory of neural networks with emphasis on ReLU constraints. Her publications demonstrate consistent progression from combinatorial foundations to advanced applications in machine learning, with increasing focus on computational implementations using tools like HomotopyContinuation.jl. Scientific Awards: No scientific awards were documented in the provided materials. Dr. Alexandr has mentored undergraduate researchers through Berkeley's Directed Reading Program (Spring 2020), focusing on algebraic combinatorics and graph theory. Her teaching portfolio includes instructing programming courses (PIC 10A/B, PIC 16A) at UCLA and serving as Graduate Student Instructor for mathematics courses at UC Berkeley. While no specific grants are listed, her participation in workshops at IMSI, AIM, and MPI MIS indicates collaborative research funding support. She co-organizes the Berkeley Nonlinear Algebra Seminar and maintains active research collaborations with Guido Montúfar (UCLA), Anna Seigal (Harvard), and her doctoral advisors. Her work is regularly presented at premier venues including SIAM AG, ISSAC, and JMM. Additionally, Dr. Alexandr is a published Russian-language poet with a 2017 collection Лирическое Наступление and contributions to The Birch journal, reflecting her interdisciplinary engagement beyond mathematics.
Professor Jingyun Fan is a Chair Professor at the Department of Physics, Southern University of Science and Technology (SUSTech) , Shenzhen. Previously, he served as a Professor at SUSTech (2020-2024) and Visiting Professor at the Shanghai Research Institute of the University of Science and Technology of China (2015-2019). B.Sc. in Physics (1992), University of Science and Technology of China M.S. in Physics (1994), University of Science and Technology of China Ph.D. in Physics (2002), University of Maryland, College Park His research focuses on quantum optics , quantum measurement , and foundations of quantum physics . Key areas include experimental validation of quantum theories, entanglement studies, and space-based quantum experiments. His work spans optical quantum networks, topological photonics, and optomechanical systems. Recent publications highlight advancements in multipartite nonlocality , gravitationally induced decoherence , and device-independent randomness . These contributions have been recognized as Top Ten Advances of the American Physical Society Top Ten Advances in Chinese Science He leads the Institute of Quantum Science and Engineering at SUSTech, offering positions for doctoral students, postdoctoral fellows, and research professors. Collaborations and recruitment inquiries can be directed to fanjy@sustech.edu.cn .
Mariana Belgiu is an Associate Professor at the Department of Earth Observation Science (EOS) within the Faculty of Geo-Information Science and Earth Observation (ITC) at the University of Twente. Her work bridges Earth Observation (EO), data-centric artificial intelligence (AI), and food security, with a focus on addressing environmental and societal challenges through innovative geospatial solutions. PhD in Remote Sensing, University of Salzburg MSc in Applied Geoinformatics, University of Salzburg Her research develops AI methods for analyzing multi-temporal EO data, particularly in hidden hunger (micronutrient deficiencies) and slum mapping. Key themes include: Data-centric AI in scarce-label environments Transferability of EO-driven models Imaging spectroscopy for crop nutrient estimation Citizen science integration for climate vulnerability assessments The 69 research outputs span EO applications for: Global crop nutrient prediction Urban poverty mapping Climate resilience in Sub-Saharan Africa AI fairness and explainability in geospatial contexts Earth observation education frameworks Scientific Recognition Copernicus Masters 2015, T-Systems Big Data Challenge Esri Young Scholar Award (2013) Best Master Thesis in Geoinformatics (2010) As a supervisor of 7 PhD students , she mentors work on deep learning for cloud removal, global crop monitoring, and transferable slum mapping. She also leads the EO4all working group, promoting gender equity in EO science, and serves as Associate Editor for the ISPRS Journal (Impact Factor 12.7). Major grants include the SPACE4ALL project (NWO, 2023–2027) and EO4Nutri (ESA, 2023–2025), alongside contributions to Horizon Europe's ASTRAIOS initiative.
Claude Warnick is a Professor of Mathematical Physics at the University of Cambridge, holding a joint appointment between the Department of Pure Mathematics and Mathematical Statistics (DPMMS) and the Department of Applied Mathematics and Theoretical Physics (DAMTP). His research focuses on partial differential equations (PDEs), particularly hyperbolic PDEs with applications in classical general relativity. Notably, he studies gravitational waves, black hole dynamics, and the mathematical properties of spacetime geometries such as Anti-de Sitter (AdS) space and extremal black holes. His work has contributed to understanding quasinormal modes, stability analyses, and boundary conditions in curved spacetimes. Affiliations: DPMMS & DAMTP, Faculty of Mathematics, University of Cambridge Key Research Areas: PDE Analysis, General Relativity, Mathematical Physics His research interests include the rigorous analysis of wave equations in curved spacetime, with applications to gravitational waves and the LIGO observations, as well as the study of black hole stability and asymptotic properties of field equations in AdS. He has contributed to the understanding of quasinormal modes, which describe the ringdown phase of black holes, and their role in characterizing black hole parameters. Warnick’s publications span over 20 years, addressing topics such as the stability of AdS spacetimes, the behavior of fields near black hole horizons, and the interplay between geometry and field equations in general relativity. His work combines analytical techniques from PDE theory with geometric insights from general relativity. His academic homepage is https://www.dpmms.cam.ac.uk/~cmw50 .
Allison L. Steiner serves as Chair and Professor in the Climate and Space Sciences and Engineering (CLaSP) department at the University of Michigan's College of Engineering. Her leadership extends to national scientific organizations including serving as President of the Atmospheric Sciences section of the American Geophysical Union (2023-present) and membership on the National Academy of Sciences Board on Atmospheric Sciences and Climate (2016-2022). She directs a vibrant research group focused on biosphere-atmosphere interactions and maintains an active role in professional service through editorial positions and scientific steering committees. Education: Ph.D., Atmospheric Science, Georgia Institute of Technology B.S., Chemical Engineering, Johns Hopkins University Dr. Steiner's research examines the complex interactions between vegetation and atmospheric processes, with particular emphasis on how biological emissions affect air quality and climate. Her group investigates pollen emissions and their impacts on cloud formation, studies biogenic trace gas emissions from forests, and models regional climate responses to land surface changes. Recent work has expanded to include microplastics in agricultural dust, lake spray aerosols in the Great Lakes region, and the public health implications of pollen concentrations. Her interdisciplinary approach integrates field observations, satellite data, and advanced modeling techniques to address critical environmental questions. Analysis of recent publications reveals a strong trend toward interdisciplinary research connecting atmospheric science with public health, energy systems, and environmental policy. Her group has increasingly focused on translating climate research into actionable insights for stakeholders, evidenced by publications on power grid resilience, pollen-related respiratory health, and Great Lakes ecosystem management. The research spans multiple spatial scales from canopy-level processes to continental climate modeling, with growing emphasis on integrating social dimensions into environmental science. Notable Awards: American Meteorological Society Walter Orr Roberts Lecturer in Interdisciplinary Sciences (2022) Sarah Goddard Power Award, University of Michigan (2020) Harold R. Johnson Diversity Service Award, University of Michigan (2018) American Geophysical Union Atmospheric Sciences Ascent Award (2016) Henry Russel Award, University of Michigan (2013) NSF CAREER Award (2010-2015) Dr. Steiner has successfully mentored numerous graduate students to completion of their PhDs, with many securing prestigious postdoctoral positions at institutions like NCAR, NOAA, and major research universities. Her research program is supported by multiple grants from NSF, NASA, DOE, and NOAA, including leadership roles in the NSF CoastalSEES project on harmful algal blooms and participation in NASA's ECOSTRESS mission. She has established productive collaborations with public health researchers, particularly on pollen-related health impacts. The Steiner research group operates the Frontiers in Atmospheric Chemistry Seminar Series (FACSS) and maintains active field sites at the University of Michigan Biological Station and in the Great Lakes region. They collaborate extensively with national laboratories including NCAR, NOAA, and DOE facilities, and participate in major field campaigns such as TRACER in Houston. The group's modeling work leverages advanced tools including WRF-Chem, RegCM, and custom canopy chemistry models like FORCAsT.
Dr. Mauricio Garnier Villarreal is an Assistant Professor at the Faculty of Social Sciences, Department of Sociology, and a member of the Social Inequality and the Life Course (SILC) research group at Vrije Universiteit Amsterdam. His work focuses on developing and testing data analysis methods, particularly latent variable models and Bayesian inference, with applications in sociology, education, nursing, and psychology. He emphasizes open science practices, contributing to R packages and sharing materials on GitHub and OSF. Research interests include methodological advancements in latent variable models (SEM, IRT, mixture models), Bayesian statistics, and their application to social inequality, especially intersectionality theory. Collaborations span aging, special education, and health sciences. He leads or participates in projects like DYNANSE (ERC grant) and COMBINE, addressing non-standard employment dynamics and latent variable methodologies. Recent articles explore measurement error impacts on clustering, hidden Markov models for employment mobility, and Bayesian SEM model evaluation. His work bridges methodological innovation with applied research to address societal issues.
Ricard Gavaldà is a Professor in the Department of Computer Science at Universitat Politècnica de Catalunya (UPC), affiliated with the LARCA research group. Since February 2020, he has been on academic leave to work full-time at Amalfi Analytics, focusing on healthcare data analytics. His research interests include Machine Learning, Data Mining, and their applications to healthcare and social good. He has supervised numerous PhD students and contributed to impactful projects in clinical decision support, traffic prediction, and energy-efficient systems. Education details are not explicitly mentioned in the provided texts. His work spans data stream mining, algorithmic frameworks (e.g., MOA), and healthcare informatics. He has co-founded Amalfi Analytics to develop AI-driven solutions for healthcare management. Notable recent activities include co-chairing the Nectar Track at ECML PKDD 2025 and organizing workshops on Data Science for Social Good. Key technical contributions include methods for adaptive learning, probabilistic modeling, and interpretable AI systems. His research bridges theoretical foundations and real-world applications, with a focus on improving healthcare outcomes and sustainable practices.
Prof. David Borchers is a Professor in the Department of Statistics at the University of St Andrews, UK, affiliated with the School of Mathematics and Statistics. He specializes in developing statistical methods for ecological applications, particularly in estimating wildlife population abundance, distribution, and dynamics using advanced survey techniques. His research integrates spatial capture-recapture, distance sampling, and hidden Markov models to address challenges in conservation and wildlife management. Key research areas include spatial capture-recapture methods, acoustic and digital survey technologies, and ecological modeling of species such as snow leopards, gibbons, and marine mammals. He collaborates with global organizations like the Snow Leopard Trust and the Global Snow Leopard & Ecosystem Protection Program. His work emphasizes methodological innovation, such as improving detection probability estimates and addressing biases in survey data. Borchers has supervised numerous PhD students and led projects funded by EPSRC and other grants. His contributions span theoretical statistics and applied ecology, with a focus on bridging gaps between data collection and ecological inference. Current projects include automated animal density estimation using AI and acoustic data, and the PAWS initiative for global snow leopard assessment. He holds leadership roles in research units and has published extensively in journals like Biometrics , Methods in Ecology and Evolution , and Frontiers in Marine Science . His methodologies are widely adopted in conservation policy and wildlife management worldwide.
Gudmund Horn Hermansen is an Associate Professor at the University of Oslo, affiliated with the Department of Mathematics within the Faculty of Mathematics and Natural Sciences. His research focuses on advanced statistical methodologies, including Bayesian analysis, time series modeling, and applications in fields such as conflict dynamics, neuroscience, and environmental science. He is a member of the Statistics and Data Science research group and collaborates with interdisciplinary teams on projects involving uncertainty quantification and statistical inference. Key research interests include change-point analysis, hidden Markov models, astrocytic calcium signaling in Alzheimer’s research, and probabilistic forecasting. His work bridges theoretical statistics with practical applications, such as analyzing democratization processes and reservoir parameter interactions. Hermansen has contributed to over 30 peer-reviewed publications, emphasizing methodological innovations and interdisciplinary collaborations. Notable publications include studies on Bayesian hidden Markov models in conflict research, astrocytic signaling mechanisms in sleep regulation, and statistical frameworks for temporal heterogeneity analysis. He maintains an active role in academic service, including editorial contributions and conference participation.
Dr. Almut Beige is an Associate Professor for Quantum Photonics at the School of Physics and Astronomy , University of Leeds, where she has led the Theoretical Physics Group since 2014. Her research spans quantum optics, quantum information processing, and cavity-mediated laser cooling. Research Interests : Quantum Optics, Open Quantum Systems, Quantum Electrodynamics, Quantum Metrology, and Sonoluminescence Students : Huda Alshemmari, Abeer Al Ghamdi, Arwa Bukhari, Thomas Hartwell, Basil Altaie, Daniel Hodgson, and numerous former PhD students/postdocs Funding : Supported by Innovate UK and EPSRC through the Oxford Quantum Technology Hub NQIT Activities : Editor-in-Chief for EPJ D, co-founder of NIQS Tech Limited, and instructor for undergraduate and postgraduate courses
