George Dimitri Konidaris is a Professor at Brown University in Providence, RI, USA, specializing in artificial intelligence and robotics. His research focuses on developing advanced algorithms for reinforcement learning, robotic planning, and hierarchical skill acquisition. He leads investigations into model-based reinforcement learning, temporal abstraction, and human-robot interaction frameworks. Konidaris's research bridges fundamental machine learning concepts with applied robotics. Key interests include: Hierarchical reinforcement learning for complex decision-making Robotic skill transfer and generalization Language-guided agent learning Continuous control in high-dimensional spaces Uncertainty-aware planning systems His publications demonstrate consistent focus on reinforcement learning theory applied to robotic systems, with recent emphasis on language integration and hierarchical abstraction. Work frequently appears in premier venues including ICML, NeurIPS, ICRA, and IROS.
Dimitrije Marković is a Researcher in the Department of Psychology at Technische Universität Dresden, affiliated with the Faculty of Science. His work bridges cognitive neuroscience, theoretical neuroscience, and machine learning, focusing on computational models of human decision-making and adaptive behavior. He employs concepts from information theory and probability to develop and test experimental predictions from these models. Education: PhD in Physics (2013), Goethe University Frankfurt, advised by Prof. Claudius Gros Diploma in Theoretical and Experimental Physics (2007), Belgrade University Undergraduate studies in Physics (2002–2007), Belgrade University Research Focus: His research emphasizes understanding neurophysiological and computational mechanisms underlying human decision-making, particularly in dynamic environments. He explores topics like adaptive learning, Bayesian inference, and neural modeling of uncertainty and agency perception. Publications Trends: Recent work includes advancements in active inference frameworks, machine learning models for decision-making, and applications to mental health (e.g., depression). His 2020–2025 publications highlight innovations in scalable models (e.g., AXIOM), Bayesian methods, and interdisciplinary approaches linking neuroscience with AI. Professional Experience: Postdoc at TU Dresden (2014–present) Guest Researcher, Max-Planck Institute for Human Cognitive and Brain Sciences (2013–2015) Postdoc, University Clinic Jena (2013–2014) Labs/Teams: Affiliated with TU Dresden’s Chair for Neuroimaging, focusing on neuroimaging techniques and collaborative projects in computational psychiatry and AI-driven neuroscience.
Aishwarya Bhaskaran is a Lecturer in Theoretical Statistics at Macquarie University's School of Mathematical and Physical Sciences. She holds a PhD in Statistics from the University of Technology Sydney (Award Date: 1 August 2023) and completed a postdoctoral position at Macquarie University. Her research focuses on theoretical and methodological advancements in survival analysis, generalised linear mixed models, asymptotic theory, and variational approximation methods. She emphasizes scalability for large datasets and bridging statistical theory with real-world applications. Education: PhD in Statistics, University of Technology Sydney (2023) Research Interests: Her work spans survival analysis, generalised linear mixed models, asymptotic theory, Bayesian inference (particularly variational approximation methods), and scalable statistical methodologies. She explores applications in biostatistics, computational statistics, and mathematical modeling. Teaching: She teaches STAT7111/8111: Generalized Linear Models (S2, 2024) and STAT3191/6191: Statistical Inference for Data Science (S1/S2, 2025) . Publications: Recent work includes advancements in asymptotic theory for mixed models, survival analysis methods, and scalable Bayesian variational techniques. Her 2024 papers highlight improvements in GLMM asymptotics and penalized likelihood approaches for censored data. Collaborations: Engages in collaborations across statistical theory and methodology, with a focus on interdisciplinary applications. Active in Australia’s academic statistical community.
Prof. Dmitry Vetrov is a Professor of Computer Science at Constructor University Bremen, affiliated with the School of Computer Science and Engineering. His research focuses on integrating Bayesian methodologies with deep learning, particularly in diffusion models, generative adversarial networks (GANs), and domain adaptation. He leads the Bayesian Method Research Group and has contributed to advancements in areas such as neural optimal transport, unsupervised voice restoration, and genetic fine-mapping. Key research interests include probabilistic modeling, generative AI, and optimization techniques for neural networks. Notable work includes innovations in diffusion samplers, adaptive learning rate analysis, and encoder-based approaches for image and audio generation. His publications span top venues like NeurIPS, ICLR, and AAAI. Current projects emphasize scalable diffusion models, thermodynamic views of SGD, and Bayesian approaches in genetic analysis. He collaborates on applied areas like speech enhancement (HIFI++), protein sequence generation, and efficient parameterization of GANs. Labs/Teams: Leads the Bayesian Method Research Group, actively involved in Constructor University's AI and machine learning initiatives.
Angela-Maria Chira is a postdoctoral researcher at the Max Planck Institute for Evolutionary Anthropology, specializing in the Department of Linguistic and Cultural Evolution . She is a core member of the Comparative Oceanic Linguistics (CoOL) team , focusing on cross-cultural and linguistic evolution across Oceania. Her interdisciplinary work bridges macroevolutionary biology and cultural evolution , using computational methods to model large-scale patterns. Education: PhD in Evolutionary Biology, University of Sheffield (2018) MBiolSci in Zoology, University of Sheffield (2014) Her research leverages graph algorithms to quantify travel costs in prehistoric societies and applies evolutionary principles to understand cultural and linguistic diversification. She has developed models to test Jared Diamond’s geographic hypotheses and investigate the role of alcohol in societal complexity. Angela-Maria’s publications span journals like Science Advances , Scientific Reports , and Nature , reflecting her expertise in both biological and cultural evolution. Her work on avian trait competition and linguistic disparity highlights her methodological diversity in phylogenetic analysis and ecological modeling . She collaborates with interdisciplinary teams and has presented at conferences including the Cultural Evolution Society and International Conference on Historical Linguistics . Her hobbies include birdwatching and attending cultural events, aligning with her academic interests in nature and human practices.
Prof. Dr. Holger Drees is a Professor of Actuarial Mathematics at the University of Hamburg, affiliated with the Faculty of Mathematics, Computer Science and Natural Sciences. He holds a position in the Department of Mathematics, specializing in the ST – Mathematical Statistics and Stochastic Processes research group. His office is located at Bundesstraße 55, Room T15 in Hamburg. He earned his diploma in mathematics from the University of Dortmund (1990), his PhD from the University of Siegen (1993), and his habilitation from the University of Cologne (1998). His research focuses on extreme value theory, actuarial mathematics, financial time series modeling, and non/semiparametric statistics. He is a member of the Hamburger Zentrum für Versicherungswissenschaft (HZV) and serves as an Associate Editor for *Bernoulli* and *Extremes* journals. His recent research emphasizes statistical inference on extreme value dependence structures, cluster-based methods for time series extremes, and dimension reduction techniques for multivariate extremes. His work bridges theoretical advancements in extreme value analysis with practical applications in finance and insurance. Teaching activities include advanced courses on extreme value theory and actuarial mathematics. Professional contributions include editorial roles and collaborative projects on statistical methodologies for extremes. His research has been supported by grants such as the DFG Heisenberg grant (2000–2002). He maintains an active international research network, collaborating with institutions like the University of Cologne and the University of Heidelberg.
Sebastian Siegloch is Professor of Economics at the University of Cologne and a member of the DFG Excellence Cluster ECONtribute of the Universities of Cologne and Bonn. He serves as Head of the Department of Economics and Director at the FiFo Institute for Public Economics at the University of Cologne. Previously, he was Full Professor of Economics at the University of Mannheim (2018-2022) and Head of the Research Department 'Inequality and Public Policy' at ZEW – Leibniz Centre for European Economic Research. He also held positions as Assistant Professor at the University of Mannheim (2014-2017) and was a Visiting Scholar at UC Berkeley and Stanford University in 2017-2018. Dr. Siegloch earned his PhD in Economics from the University of Cologne in 2013. His academic journey reflects a strong foundation in German economic research institutions before establishing himself as a leading scholar in public economics. Siegloch's research focuses on the intersection of Public, Labor, and Urban Economics, with particular emphasis on how economic policies affect market outcomes and shape inequality. He employs large datasets and exploits quasi-experimental variation to quantify behavioral responses to policy changes. His work spans tax policy, housing markets, social capital, government surveillance effects, and disability insurance systems, consistently addressing questions of policy efficiency and distributional consequences. His publication pattern reveals a strong focus on empirical microeconomics with policy relevance, particularly examining tax incidence, labor market responses, and social policy evaluation. The research demonstrates methodological sophistication with increasing use of quasi-experimental designs and large administrative datasets to address causal questions in public economics. ERC starting grant for the project 'Housing, Inequality and Public Policies (HIPPO)' (2021) Research Fellow at CEPR, CESifo, CReAM, and IZA Research Associate at ZEW Siegloch's work has secured significant research funding, including the prestigious ERC starting grant. His publications in top journals like American Economic Review, Econometrica, and Journal of the European Economic Association demonstrate his influence in the field. He actively engages with policy debates through media contributions in major German outlets like Frankfurter Allgemeine Zeitung and Süddeutsche Zeitung, translating complex economic research for broader audiences. As Head of Department and Director at FiFo Institute, Siegloch leads research teams focusing on public economic policy analysis. His leadership position at the newly founded Department of Economics at University of Cologne indicates his significant institutional role in shaping economic research and education.
Marília R. Nepomuceno serves as a Research Scientist and Training Chair at the Max Planck Institute for Demographic Research (MPIDR) in Rostock, Germany, where she is affiliated with the Laboratory of Population Dynamics and Sustainable Well-Being and the Laboratory of Demographic Data. Her work bridges demographic methodology with empirical analysis of population health trends. Her research centers on mortality dynamics, lifespan variation, and demographic responses to crises like the COVID-19 pandemic. Key interests include: Regional mortality inequalities in Brazil and Europe Methodological innovations in lifespan variation measurement (e.g., CAL† metric) Data quality assessment for extreme age reporting Pandemic impact analysis beyond age-structure effects Her recent publications reveal a strong focus on spatial disparities in life expectancy, cohort mortality reconstruction, and the demographic implications of pandemic responses. Nepomuceno contributes to major demographic resources including COVerAGE-DB, the global database tracking age-structured COVID-19 outcomes. As Training Chair, she oversees academic development programs at MPIDR, though specific student mentorship details are not publicly documented. Her work frequently involves cross-institutional collaborations with researchers across Latin America and Europe. She actively participates in laboratory initiatives focused on population dynamics and demographic data infrastructure, contributing to MPIDR's role as a global leader in demographic science. Current projects examine seasonal mortality patterns and their implications for life expectancy disparities across European regions.
Michael Muehlebach is a Research Group Leader at the Max Planck Institute for Intelligent Systems in Tuebingen, Germany, leading the Learning and Dynamical Systems group. He holds a B.Sc. and M.Sc. in Mechanical Engineering from ETH Zurich (2010, 2013) and a Ph.D. from the Institute for Dynamic Systems and Control (2018), advised by Prof. R. D'Andrea. He later pursued postdoctoral research under Prof. Michael I. Jordan at UC Berkeley. Research Interests: Machine Learning, Dynamical Systems, Control Theory, Optimization, Physics-Informed Learning for Cyber-Physical Systems. Scientific Awards: Outstanding D-MAVT Bachelor Award, Willi-Studer Prize, ETH Medal, HILTI Prize, Branco Weiss Fellowship (2018), Emmy Noether Fellowship (2020), Amazon Fellowship (2024). Students: Supervised Julien Kohler (Master's thesis on Quadrotor Control). Projects: Developed Floaty (wind-energy robot), Flying Platform (ducted fan actuation testbed), One-Wheel Cubli (3D inverted pendulum), and electromagnetic navigation systems.
Alistair Moffat is a prominent faculty member at the University of Melbourne's School of Computing and Information Systems, with an extensive publication record spanning over four decades from 1980 to the present. His career demonstrates sustained contributions to information retrieval, data compression, and evaluation metrics within the field of computer science. Moffat's primary research interests encompass: Information Retrieval and Search Engine Technologies Data Compression Algorithms and Indexing Techniques Development and Analysis of Evaluation Metrics Process Mining and Event Log Analysis User Modeling in Information Seeking Behavior Theoretical Foundations of Search Effectiveness His recent work (2023-2025) reveals a continued focus on refining search evaluation methodologies, with particular attention to user-oriented metrics, rank-biased quality measurement, and the relationship between query variations and experimental consistency. Moffat has also expanded into process mining applications, developing entropy-based metrics like Entropia for measuring log representativeness. His publications consistently appear in top-tier venues including SIGIR, ACM Transactions on Information Systems, and Information Processing & Management. Moffat maintains extensive research collaborations, most notably with Justin Zobel (70 joint publications), J. Shane Culpepper (35), and Matthias Petri (32), demonstrating a strong network within the information retrieval research community. His work bridges theoretical computer science with practical applications in search technology, medical information retrieval, and process analysis. As evidenced by his continuous publication output through 2025, Moffat remains an active and influential researcher in his fields of expertise, contributing both to foundational theories and practical implementations in information access systems.
Jan-Willem van de Meent is an Associate Professor at the University of Amsterdam where he co-directs the AMLab with Max Welling. He also maintains an Assistant Professor position at Northeastern University, though currently on leave while continuing to advise students and collaborate. His research develops AI models by combining probabilistic programming and deep learning, focusing on understanding inductive biases that enable models to generalize from limited data. His research spans multiple domains: Probabilistic programming frameworks and inference methods Inductive biases for generalization from limited data Physical system simulators incorporating domain knowledge Causal structure and symmetries in AI models Applications in robotics, NLP, healthcare, and physical sciences Van de Meent is one of the creators of Anglican, a probabilistic programming language based on Clojure, and currently develops Probabilistic Torch, a library for deep generative models extending PyTorch. He is writing a book on probabilistic programming (draft available on arXiv) and serves as co-chair of the international conference on probabilistic programming (PROBPROG). His recent publications show a strong trend toward developing more efficient inference methods for probabilistic models, exploring disentangled representations across vision and language domains, and applying these techniques to healthcare, robotics, and neuroscience. His work on nested variational inference, energy-based models, and state abstraction in reinforcement learning has been particularly influential. Awards and Recognition NSF CAREER award (2021) Van de Meent actively advises multiple PhD students and postdocs across interdisciplinary projects. His lab maintains strong collaborations with researchers in robotics, healthcare, neuroscience, and other scientific domains, applying advanced probabilistic modeling to challenging real-world problems. He also develops practical tools for the research community, making advanced inference techniques more accessible to practitioners.
Elena A. Erosheva is a Professor of Statistics at the University of Washington with a joint appointment in the School of Social Work. She serves as Associate Director of the Center for Statistics and the Social Sciences (CSSS) and is an Associate Editor for the Journal of the American Statistical Association and the Annals of Applied Statistics. Education PhD in Statistics from Carnegie Mellon University (2002) Elena Erosheva is a statistician whose research focuses on the development and application of modern statistical methods for addressing complex substantive questions in the social, medical and health sciences. Her ongoing methodological research focuses on latent variable and mixed membership models, multivariate and longitudinal data analysis, and text and network analysis. Her work has important applications in understanding disability in elderly populations, criminal desistance patterns, and scientific collaboration networks. Dr. Erosheva's research bridges statistical theory with practical applications, making significant contributions to both methodology and substantive fields. Analysis of Dr. Erosheva's recent publications reveals a strong focus on mixed membership models and their applications across diverse domains. Her work spans from theoretical developments in statistical methodology (such as variational inference techniques and bootstrap methods) to applied research in health sciences (particularly aging and disability) and social sciences (including criminology and scientific collaboration patterns). A notable trend in her recent work is the application of advanced statistical techniques to understand gender dynamics in scholarly publishing and to improve peer review processes. Scientific Awards 2013 Mitchell Prize from the International Society of Bayesian Analysis First Prize from the National Institutes of Health's Peer Review Challenge Dr. Erosheva has successfully advised numerous graduate students across multiple departments including Statistics, Sociology, Health Services, and Educational Psychology. Her research has been supported by grants from the National Institute on Aging (funding her work on operational definition of disability in the National Long Term Care Survey) and NIH (funding the COAP - Cognitive Outcomes with Advanced Psychometrics project). She has collaborated extensively with researchers across disciplines, demonstrating the interdisciplinary nature of her work. Dr. Erosheva is actively involved in the Center for Statistics and the Social Sciences (CSSS) at the University of Washington, where she serves as Associate Director. She leads research projects focused on measuring gender-based homophily in scientific collaborations, modeling life course transitions, developing partial mastery cognitive diagnosis models, and identifying commensuration bias in grant review.
PD Dr. Stefan Merker is Head of the Zoology Department and Curator of Mammalogy at the State Museum of Natural History Stuttgart (Staatliches Museum für Naturkunde Stuttgart) and holds a Habilitation (Venia Legendi) in Zoology at the University of Hohenheim, where he has taught since 2014. He leads an active research program in mammalian biogeography, conservation genetics and primate evolution. Education : Habilitation in Zoology, University of Hohenheim, 2023 PhD (Dr. rer. nat.) in Biology, Georg-August-Universität Göttingen, 2003 Diplom in Biology, Georg-August-Universität Göttingen, 1999 Exchange studies, University of California, Santa Barbara, 1995–1996 Research Interests Merker’s work centers on the evolutionary history and conservation of mammals , especially small primates and island fauna. Using field ecology, molecular genetics and bioacoustics, he investigates how geological events and anthropogenic change shape species distributions and genetic diversity. His flagship projects include: Ecology and evolution of tarsiers – phylogeography, hybrid zones and speciation on Sulawesi Island, Indonesia. Population genetics of the Eurasian beaver – recolonization dynamics and genetic structure in Central Europe. Urban ecology of European rabbits – gene flow and behavioral adaptation along rural–urban gradients. Scientific Awards & Grants Habilitation Scholarship & Venia Legendi, University of Hohenheim (2023) Postdoctoral Fellowships – EU Marie Curie & DFG (2003–2008) PhD Fellowships – Studienstiftung des deutschen Volkes & DAAD (2000–2003) Diplom Fellowship – Studienstiftung & GfP Christian-Vogel-Fonds (1998) Advising & Teaching Merker has supervised 15+ graduate students (3 PhD, 12 MSc/Diplom) and numerous bachelor theses, covering topics from tarsier hybridization to urban beaver genetics. He teaches undergraduate and master modules at the University of Hohenheim and previously at Goethe University Frankfurt and Johannes Gutenberg University Mainz, with courses on vertebrate biology, evolutionary ecology and field methods. Laboratories & Teams He heads the Mammalogy Section at the Stuttgart State Museum of Natural History, curating extensive zoological collections and running molecular laboratories for DNA barcoding, microsatellite genotyping and next-generation sequencing. The group participates in international consortia such as the Primate Genome Project and the European Beaver Genetic Monitoring Network.
Arne Hildenbrand, M.Sc., serves as a Research Associate at the Chair of Vibro-Acoustics of Vehicles and Machines led by Prof. Dr.-Ing. Steffen Marburg at the Technical University of Munich (TUM). He is affiliated with the TUM School of Engineering and Design within the Department of Engineering Physics and Computation. His research focuses on uncertainty quantification and Bayesian/variational inference methods, data-driven deep learning approaches, and Galerkin methods for computational acoustics. His work spans automotive vibroacoustics, particularly in developing variational autoencoder models for dimensionality reduction and admittance response modeling of car bodies. Hildenbrand teaches several courses including Computational Acoustics, Numerical Acoustics in Python, Vibroacoustic Finite Element Simulations in Virtual Prototyping, and Machine Learning in Computational Engineering Mechanics. These courses emphasize practical implementation of numerical methods for acoustic analysis using Python programming. His scientific work demonstrates a strong trend toward integrating machine learning techniques with traditional computational acoustics methods, particularly focusing on variational autoencoders for dimensionality reduction in automotive vibroacoustic applications. The research bridges theoretical acoustics with practical automotive engineering challenges. Hildenbrand collaborates with researchers including Steffen Marburg, Mert Dogu, and Michael Buba on various teaching and research activities within the vibroacoustics laboratory at TUM.
Dr. Monika Korte is the Acting Head of the Geomagnetism Section (Section 2.3) at the GFZ German Research Centre for Geosciences. She leads the working group on the 'Evolution of the Earth's Magnetic Field' and has been instrumental in advancing research on geomagnetic field reversals, secular variation, and space weather effects. Her work integrates paleomagnetic data, satellite observations, and computational modeling to understand Earth's core dynamics and their climatic impacts. Education & Career: PhD in Geophysics, Free University of Berlin (1999) Diploma in Geophysics, Ludwig Maximilian University (1996) Acting Head of Section 2.3 since 2021 Leadership roles in international projects, including the ERC Synergy Grant GERACLE (2024–2030) Research Interests: Geomagnetic field reversals/excursions Global and regional magnetic field modeling Geomagnetic observatory networks Cosmogenic isotope analysis Machine learning applications in geophysics Awards & Grants: ERC Synergy Grant (2024): €10M for GERACLE project GFZ Research Award (2003) Feodor-Lynen Fellowship (2001) Labs & Collaborations: Leading the Niemegk Geomagnetic Observatory Coordinating the GERACLE international consortium Member of IAGA Executive Committee and AGU editorial boards
