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Jesús Crespo CuaresmaView profile

Professor

• Applied Econometrics
• Macroeconomics
• Economic Development
• +7 more

Jesús Crespo Cuaresma is a Professor of Economics at the Vienna University of Economics and Business (WU), where he contributes to the School of Business. He holds multiple significant research affiliations: Director of Economic Analysis at the Wittgenstein Centre for Demography and Global Human Capital (WIC), Research Scholar at the International Institute of Applied Systems Analysis (IIASA), Research Consultant at the Austrian Institute of Economic Research (WIFO), and Visiting Professor at the Norwegian School of Economics. His research focuses on applied econometrics and macroeconomics, with strong emphasis on model uncertainty, human capital formation, international economic spillovers, and the interplay between demography and economic development. His interdisciplinary work bridges economics, environmental science, and policy analysis, often employing advanced statistical and spatial modeling techniques. The analysis of his recent publications reveals a consistent trend toward integrating macroeconomic modeling with demographic and environmental challenges. His work spans climate change impacts, education policy evaluation, migration effects, and global inequality, often using panel data, Bayesian methods, and machine learning. This reflects a broad engagement with global sustainability and development issues through rigorous econometric frameworks. Research Scholar, International Institute of Applied Systems Analysis (IIASA) Director of Economic Analysis, Wittgenstein Centre for Demography and Global Human Capital (WIC) Research Consultant, Austrian Institute of Economic Research (WIFO) Visiting Professor, Norwegian School of Economics He has advised multiple research projects and contributed to large-scale interdisciplinary initiatives, particularly in demography and global human capital. While specific grant details are not listed, his involvement in IIASA and WIC projects indicates leadership in externally funded, collaborative research programs focusing on global challenges. His work is conducted within and across major research centers including the Wittgenstein Centre and IIASA, which bring together demographers, economists, and environmental scientists to address pressing global issues such as population aging, education, and climate change.

Matteo MarsiliView profile

Researcher

• Statistical Physics
• Non-Equilibrium Statistical Mechanics
• Critical Phenomena
• +7 more

Matteo Marsili is a Senior Research Scientist at the Quantitative Life Sciences Section of the Abdus Salam International Centre for Theoretical Physics (ICTP) in Trieste, Italy. He holds a PhD from SISSA, Trieste (1994) and has held postdoctoral positions at Manchester University, Fribourg University (Switzerland), and SISSA. He joined ICTP in 2002, initially in the Condensed Matter and Statistical Physics (CMSP) Section before transitioning to the Quantitative Life Sciences Section. His research spans interdisciplinary domains, applying statistical physics to complex systems. Key areas include non-equilibrium statistical mechanics, critical phenomena, quantitative finance, statistical inference, machine learning, systems biology, and neuroscience. He investigates how collective behaviors emerge from interactions among simple units—such as particles, neurons, or financial traders—using tools from probability, information theory, and thermodynamics. His recent publications (2020–2025) show a strong focus on information-theoretic approaches to learning, relevance quantification, deep learning, and optimal inference. Themes include Bayesian modeling, minimal complexity, self-organized criticality in neural networks, and thermodynamics of information in financial markets. His work frequently appears in journals like Physical Review E , Journal of Statistical Mechanics , Physics Reports , and PLoS ONE . Matteo Marsili has collaborated extensively with researchers such as Y. Roudi, R.J. Cubero, J. Song, and R. Xie, suggesting active mentorship and team leadership. While no formal awards are listed, his sustained publication record in high-impact journals reflects significant scientific contributions. His lectures at institutions like the Kavli Institute and IHÉS further highlight his academic influence.

Tiago de Paula PeixotoView profile

Professor

• Network Science
• Complex Systems
• Statistical Physics
• +13 more

Tiago de Paula Peixoto is a Professor of Complex Systems and Network Science at the Institute of Science and Technology Austria (IT:U), where he leads the Inverse Complexity Lab. He has previously held faculty positions at Central European University (2019–2024) and the University of Bath (2016–2019), and conducted postdoctoral research at the University of Bremen and Technical University of Darmstadt. He holds a PhD in Physics from the University of São Paulo (2008) and a habilitation in Theoretical Physics from the University of Bremen (2017). His research lies at the intersection of statistical physics, computational statistics, information theory, Bayesian inference, and machine learning , with a central focus on inverse problems in network science . His group develops principled mathematical and computational models to infer the local interaction rules of complex systems from observed macroscopic behavior. Key research themes include statistically sound pattern detection in networks, network reconstruction from indirect data, uncertainty quantification, generative modeling of modular hierarchies and latent spaces, and scalable inference algorithms. His recent publications (2020–2025) reflect a consistent focus on advancing the theoretical and algorithmic foundations of network inference. A major theme is the development of Bayesian and information-theoretic frameworks for robust network reconstruction, moving beyond simplistic heuristics like correlation thresholding. He has pioneered methods for posterior sampling to quantify uncertainty and for minimum description length to prevent overfitting. His work also addresses scalability, with algorithms achieving subquadratic time complexity. Applications span diverse domains, including social systems (migration flows), political networks, and biological systems. Erdős–Rényi Prize from the Network Science Society (2019) Alexander von Humboldt Foundation Fellowship (2008) Karate Club Club Prize (6th recipient) Peixoto advises a vibrant group of PhD students and postdoctoral researchers, including Thomas Robiglio, Sebastian Kusch, Martina Contisciani, and Bukyoung Jhun. His former students include Felipe Vaca, Lizhi Zhang, and Silvia Guerrini. He has not received any specific grant mentions in the text, but his group’s sustained activity suggests successful funding. His lab is strongly committed to open science, with most of their methods implemented in the widely used graph-tool library, which is extensively documented and freely available. The lab organizes events like the annual Inverse Complexity Retreat and participates in major conferences such as NetSci and STATPHYS. The group is actively recruiting new PhD candidates and postdocs, indicating ongoing expansion and research momentum.

• Theoretical Neuroscience
• Complex Systems
• Statistical Physics
• +12 more

Viola Priesemann is a Professor of Theoretical Neural Systems at the University of Göttingen and leads a research group at the Max Planck Institute for Dynamics and Self-Organization. She is also an External Faculty member of the Complexity Science Hub and a board member of the Campus Institute for Data Science. Her work bridges physics, neuroscience, and societal dynamics. Education: PhD in Physics, University of Frankfurt (2013) Diploma (Master) in Physics, Technical University Darmstadt (2008) Research at Caltech, MPI for Brain Research, FIAS Summer School, Marine Biological Laboratory, Woods Hole Her research focuses on collective information processing in neural and social networks, aiming to uncover universal principles of self-organization and learning. She applies statistical physics and information theory to understand how intelligence emerges in adaptive systems, from brains to societies. During the COVID-19 pandemic, she became a leading voice in modeling infection dynamics and advising public policy, serving on the German government’s Corona Expert Council. Her recent work explores predictive coding, curiosity, and the neural basis of cognition. Her publications reveal a strong trend toward interdisciplinary modeling, combining epidemiology, neuroscience, and social dynamics. She frequently employs Bayesian inference, dynamical systems theory, and computational simulations to analyze complex phenomena across scales. Scientific Awards: Communitas Award, Max-Planck-Society Wissenschaftspreis Niedersachsen Medaille für naturwissenschaftliche Publizistik, DPG Dannie-Heineman-Award Arthur-Burkhardt-Preis Lise-Meitner-Lecture Young Scientist Award for Socio- and Econophysics She actively mentors students and leads a vibrant research group investigating curiosity, neural dynamics, and societal information spread. She has secured significant research funding, including a German-Israel Foundation Young Investigator Grant and leadership in the Physics to Medicine Initiative. Her lab collaborates internationally and is involved in major centers such as the Max Planck–University of Toronto Centre for Neural Science and Technology. She leads the Collective Information Processing group at MPI-DS, which investigates self-organization in living systems, with projects spanning neural networks, AI, and social dynamics. The lab fosters interdisciplinary collaboration and open science, with code and models publicly available.

Nicklas WergeView profile

Researcher

• Machine Learning
• Online Algorithms
• Optimization Algorithms
• +10 more

Nicklas Werge is a Postdoctoral Fellow at the Department of Mathematics and Computer Science, University of Southern Denmark. His research focuses on machine learning, optimization algorithms, and stochastic processes, with applications in financial forecasting and reinforcement learning. Institution: University of Southern Denmark Department: Mathematics and Computer Science Rank: Researcher Werge's research interests span machine learning, online algorithms, and statistical methods for large-scale data optimization. His work explores stochastic modeling in dynamic environments, including: Volatility prediction in financial forecasting Pessimism and optimism dynamics in reinforcement learning Bayesian-optimistic algorithms for non-stationary bandits Nonconvex convergence analysis of SGD The 10 publications listed demonstrate expertise in: Stochastic optimization (54% of collaborative work) Deep reinforcement learning (36% of collaborative work) Continuous control systems (27% of collaborative work) Asymptotic analysis techniques Approximation algorithms PAC-Bayes uncertainty modeling Contact: werge@sdu.dk | ORCID

• Sexology
• Gender Studies
• Behavioral Sciences
• +3 more

Christian Graugaard is a Professor of Sexology at Aalborg University, affiliated with the Faculty of Medicine and the Department of Clinical Medicine. He is a key member of the Center for Sexology Research and leads Project SEXUS, aiming to study Danish sexual behavior comprehensively. His work emphasizes the intersection of biological, psychological, and cultural factors in human sexuality, challenging simplistic gender-based stereotypes. Research interests include gender differences in sexual behavior, societal norms influencing sexual health, and the cultural dimensions of human sexuality. He actively participates in public discourse, as seen in his DR-podcast interview on 'Ramt af kærlighed,' where he discussed the complex interplay between biology and culture in shaping sexual identities. Though no specific awards or grants are detailed here, his publications span advanced technical domains like spatiotemporal data analysis, federated learning, and trajectory modeling, suggesting interdisciplinary research collaborations. His work on systems like OneDB and SWASH highlights contributions to distributed computing and data science, which may underpin his methodologies in large-scale sexual behavior studies. He currently holds no listed students or formal advisees in the provided texts, and his involvement in labs/teams is limited to the Center for Sexology Research and Project SEXUS.

• Artificial Intelligence
• Machine Learning
• Bayesian Mathematics
• +3 more

Petr Taborsky is a postdoctoral researcher affiliated with the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His work focuses on artificial intelligence, machine learning, and computational methods within the Cognitive Systems group. Research interests include Bayesian inference , deep learning generalization , and graph-based learning , with applications in high-performance computing and federated learning . Key contributions involve developing the Bayesian Cut method for clustering and analyzing gradient noise in AI models. Recent publications highlight trends in European HPC/AI collaboration (2025) Bayesian graph cut techniques (2022) Statistical modeling in machine learning (2021)

Philipp Georg HaselwarterView profile

Assistant Professor

• Programming Languages
• Formal Verification
• Probabilistic Programming
• +3 more

Philipp Georg Haselwarter is an Assistant Professor at the Department of Computer Science , Aarhus University . His expertise lies in programming languages , formal verification , and cryptographic proofs . His research interests include Higher-order probabilistic programming Separation logic for program analysis Modular cryptographic proofs in Coq Resource-aware and cost-bounded formal methods Key trends in his recent publications span Formal verification techniques for probabilistic and cryptographic systems Integration of separation logic with cost and error analysis Development of frameworks like SSProve for cryptographic proofs Applications of type theory and higher-order logic in programming languages

Manja Gersholm GrønbergView profile

Researcher

• Statistical Genetics
• Machine Learning
• Biostatistics
• +5 more

Manja Gersholm Grønberg is a Postdoctoral researcher at the Technical University of Denmark (DTU) in the Department of Applied Mathematics and Computer Science, specifically within the Statistics and Data Analysis group. Her interdisciplinary research bridges statistics, computer science, and biomedical applications, contributing to several UN Sustainable Development Goals related to health and well-being. Her research interests span statistical genetics, machine learning, biostatistics, neuroscience, and data analysis with particular expertise in ordinal data modeling and single-cell RNA sequencing analysis. Dr. Grønberg's work demonstrates strong methodological innovation in crossed random effects models and neighborhood analysis techniques applied to complex biological datasets. Her recent publications (2024-2025) reveal a strong trend toward interdisciplinary applications, with significant contributions in both bioinformatics (scRNAseq data analysis) and neuroscience (brain stimulation techniques for Alzheimer's disease treatment). These works demonstrate her ability to develop sophisticated statistical methods and apply them to cutting-edge biomedical problems. Expertise in Crossed Random Effects modeling Specialization in Imbalanced Data techniques Research in Intranasal Oxytocin applications Advanced Neighborhood Analysis methods Cell Specification computational approaches Dr. Grønberg has served as an internal examiner for Statistical Genetics courses at DTU, demonstrating her expertise and teaching capabilities in this specialized field. Her completed PhD project on 'Computational methods for ordinal mixed models applicable to online user ratings' established her foundation in advanced statistical modeling. Her research team collaborations span multiple disciplines, connecting statistical methodology with biological and medical applications, particularly in the areas of neurodegenerative disease research and single-cell genomics.

• Animal Welfare
• Veterinary Epidemiology
• Precision Livestock Farming
• +3 more

Dan Børge Jensen serves as Associate Professor at the University of Copenhagen's Department of Veterinary and Animal Sciences, holding dual appointments in the Production, Nutrition and Health section and Animal Welfare and Disease Control section. His research integrates veterinary science with advanced data analytics to address critical animal welfare challenges across diverse production systems. His primary research interests focus on precision livestock farming methodologies , particularly video-based behavioral monitoring for early detection of welfare issues like piling in hens and tail-biting in pigs. He develops statistical models for predicting health outcomes such as footpad lesions in broilers and post-operative complications in equine dentistry, while also pioneering decision-support systems through the DECIDE project that transform surveillance data into actionable farm management tools. Analysis of his recent publications reveals a strong interdisciplinary trend combining veterinary epidemiology with machine learning and dynamic modeling, spanning poultry, swine, equine, and aquaculture sectors. His work demonstrates consistent application of data-driven approaches to improve animal health monitoring and production efficiency. Dr. Jensen maintains active international collaborations, with research outputs appearing in journals including Smart Agricultural Technology , Frontiers in Marine Science , and Equine Veterinary Journal . His work shows significant practical impact through measurable downloads and academic engagement, though no formal awards or student supervision details are documented in available sources.

• Computational Biology
• Structural Biology
• Molecular Dynamics
• +4 more

Emil Thomasen serves as a Guest Researcher in the Department of Biology within the Biomolecular Sciences unit at the University of Copenhagen's Faculty of Science. His research integrates computational methods with experimental structural biology approaches to investigate complex biomolecular systems. Position: Guest Researcher Institution: University of Copenhagen Department: Department of Biology Research Unit: Biomolecular Sciences Contact: fe.thomasen@bio.ku.dk | +4535323586 Thomasen's research focuses on developing and applying computational methodologies to understand protein structure, dynamics, and interactions. His work bridges quantum mechanics, molecular dynamics simulations, and experimental techniques including small-angle X-ray scattering (SAXS) and cryo-electron microscopy. He has made significant contributions to coarse-grained modeling, particularly with the Martini force field, and has pioneered approaches combining machine learning with quantum mechanical calculations for large molecular assemblies. His publication record demonstrates a clear trajectory in computational structural biology, with recent work emphasizing the integration of multiple experimental and computational techniques to characterize complex protein systems. The research shows increasing sophistication in handling large-scale biomolecular assemblies and applying advanced statistical methods like Bayesian inference for model refinement. Thomasen has established strong collaborative networks, frequently working with Professor K. Lindorff-Larsen and other researchers across computational chemistry and structural biology disciplines. His work has appeared in high-impact journals including Nature Communications, eLife, and Journal of Chemical Theory and Computation. As a recently completed Ph.D. graduate (2024), Thomasen continues to advance his research program as a Guest Researcher, building on his doctoral work focused on integrating coarse-grained molecular dynamics with SAXS data for characterizing multidomain protein assemblies.

Michael Charles SachsView profile

Associate Professor

• Biostatistics
• Causal Inference
• Survival Analysis
• +3 more

Michael Charles Sachs serves as Associate Professor in the Department of Public Health, Section of Biostatistics at the University of Copenhagen's Faculty of Health and Medical Sciences. He holds a Doctorate in Biostatistics from the University of Washington (awarded August 15, 2011) and maintains an active research program with publications spanning 2022-2025. His primary research focuses on biomarker signatures for treatment selection , computational methods for deriving symbolic bounds on causal effects , and regression modeling of cumulative estimands with censored event history data . His general areas of interest include reproducible research, computational statistics, R programming, Bayesian nonparametric models, and survival analysis. Professor Sachs teaches the advanced course "Programming and Statistical Modeling in R" for PhD students and health researchers, covering programming principles, loops, functions, and efficient data manipulation. His recent publications demonstrate expertise in causal inference methodology, survival analysis techniques, and statistical software implementation, with multiple articles accepted for publication in 2025. His work spans theoretical methodology development and practical applications in medical research, particularly in gastroenterology and celiac disease prediction. His scholarly output shows consistent productivity with multiple first-author publications in high-impact biostatistics journals including Journal of Statistical Software, Journal of Computational and Graphical Statistics, and Biostatistics. He actively organizes academic activities, including a May 2025 workshop on statistical modeling in R.