جست‌وجوی استاد و پژوهشگر

Thomas Wöhlingمشاهده پروفایل

پژوهشگر

• Integrated environmental systems modeling
• Surface water-groundwater interactions
• Braided river systems
• +۹ مورد دیگر

PD Dr. habil. Thomas Wöhling serves as a Senior Research Scientist and Team Leader for Stochastic Modelling of Hydrosystems at the Chair of Hydrology, Dresden University of Technology's Faculty of Environmental Sciences. His research spans integrated environmental systems modeling with particular expertise in surface water-groundwater interactions, braided river systems, and vadose zone processes. Previously, he held research positions at Water and Earth System Sciences Competence Cluster in Tübingen (2010-2015) and Lincoln Environmental Research in New Zealand (2006-2010). Dr. Wöhling completed his Dipl.-Hydrol. (1999) and PhD in Hydrology (2005) at Dresden University of Technology, followed by habilitation in Stochastic Hydrology (2021). His educational background includes extensive research at the Institute of Hydrology and Meteorology at TU Dresden (1999-2005) where he developed foundational expertise in hydrological modeling. Wöhling's research focuses on integrated modeling of coupled environmental systems , particularly flow and contaminant transport in surface water-groundwater systems, nutrient and energy fluxes in soil-plant-atmosphere systems, and distributed hydrological modeling. His work emphasizes stochastic modeling and uncertainty analysis , with significant contributions to inverse modeling, model calibration, multiobjective optimization, and Bayesian model averaging techniques. He has pioneered methods for evaluating monitoring network worth and data utility for environmental models. His publication record demonstrates consistent contributions to hydrological science, with recent work (2023-2025) focusing on machine learning applications in hydrology, advanced statistical inversion techniques, and complex karst system modeling. Key trends include integration of physics-based and data-driven approaches, improved uncertainty quantification methods, and applications to climate change impacts on water resources. His work bridges theoretical advances with practical applications in New Zealand's braided rivers and European hydrological systems. STAHY Best Paper Award (2018) ASCE Journal of Irrigation and Drainage Engineering Best Reviewer Awards (2008, 2010, 2011, 2015, 2018) ASCE Journal of Irrigation and Drainage Engineering Best Paper Awards (2008, 2009) Dr. Wöhling leads the Stochastic Modelling of Hydrosystems team and has secured funding for numerous projects including Klimakonform, ISOSIM, VAMOS II, and the International Research Training Group 'Integrated Hydrosystem Modelling.' His work combines novel monitoring techniques with modeling and optimal sensor placement to improve prediction reliability for river-groundwater exchange fluxes. He collaborates extensively with international partners, particularly in New Zealand through the Lincoln Agritech's Braided Rivers program. His laboratory work focuses on combining traditional hydrological measurements with advanced computational techniques, including deep learning applications for soil surface hydrology and time-windowed Bayesian analysis for predictive modeling. The team maintains strong connections with field sites in Germany's Saxon region and New Zealand's Canterbury Plains, facilitating integrated theoretical and empirical research approaches.

• Robotics
• Human-Robot Interaction (HRI)
• Artificial Intelligence
• +۳ مورد دیگر

Marc Hanheide is a Professor of Intelligent Robotics and Interactive Systems at the University of Lincoln 's School of Computer Science. With a career spanning EU projects like VAMPIRE, COGNIRON, CogX, and STRANDS, his work focuses on long-term robotic behavior, human-robot spatial interaction, and cognitive system architectures. He has secured over 12 major grants from organizations including EPSRC, BBSRC, and the European Commission. Key Research Areas : Autonomous robotics, HRI, AI, cognitive systems, agricultural robotics Current Projects : STRANDS (long-term behavior), AgriFoRwArdS (robotics training), NCNR (nuclear robotics) Major Contributions : Human-aware navigation modules, topology optimization for robot fleets, causal analysis frameworks Scientific Awards: While no specific awards are listed, his numerous EPSRC grants and leadership in multi-institutional projects highlight his impact. He has over 172 publications and collaborates with institutions like CoR-Lab and CITEC.

Andreas Huthمشاهده پروفایل

پژوهشگر

• Ecological Modeling
• Vegetation Dynamics
• Biodiversity Modeling
• +۴ مورد دیگر

Andreas Huth is a prominent researcher at the Helmholtz Centre for Environmental Research (UFZ) in Leipzig, Germany, where he works within the Department of Ecological Systems Analysis. His research focuses on developing and applying computational models to understand complex ecological systems, with particular emphasis on vegetation dynamics, biodiversity patterns, and environmental interactions. Huth is actively involved in several major research initiatives including the FORMIND project, which simulates forest growth using individual-based vegetation models, and contributes to the UFZ's research program on Smart Models and Monitoring. Huth's research interests span ecological modeling, vegetation dynamics, biodiversity assessment, and environmental system analysis. He specializes in developing sophisticated computational frameworks that integrate atmospheric, ecological, and spatial data to model complex environmental processes. His work often involves coupling different modeling approaches, such as connecting atmosphere and vegetation radiative transfer models to study cloud-vegetation interactions. Huth is particularly interested in understanding spatial patterns in ecological systems, species-habitat relationships, and the impacts of environmental change on biodiversity. His methodological expertise includes agent-based modeling, individual-based modeling, and developing digital twin technologies for environmental systems. Analysis of Huth's recent publications reveals a strong focus on integrating different modeling approaches to address complex environmental questions. His work demonstrates a progression from basic ecological modeling toward more sophisticated integrative frameworks that connect atmospheric processes with vegetation dynamics, and social systems with ecological outcomes. A notable trend is his increasing involvement in digital twin technology for environmental systems, particularly for biodiversity monitoring and prediction. His research spans multiple scales - from microbial ecosystems to continental-scale species distributions - while maintaining a consistent methodological thread of spatially explicit modeling and causal analysis in complex systems. Huth collaborates extensively with researchers across the UFZ and internationally, as evidenced by his co-authorship on numerous interdisciplinary publications. While specific awards are not mentioned in the available information, his consistent publication record in high-impact journals and leadership in major modeling initiatives like FORMIND indicate significant recognition within the ecological modeling community. His work has practical applications for environmental policy, conservation planning, and climate change adaptation strategies. Huth is an active member of the Ecological Systems Analysis research group at UFZ, which develops and applies computational models to investigate key mechanisms in ecological systems. He contributes to several major research platforms including FORMIND (an individual-based vegetation model for species-rich forests) and participates in the UFZ's broader research initiatives on environmental monitoring and sustainable technologies. His work is integrated within the UFZ's research program on Smart Models and Monitoring, which aims to develop innovative approaches for understanding and predicting environmental change.

Chase Broederszمشاهده پروفایل

استاد

• Statistical Physics
• Biological Physics
• Soft Matter Physics
• +۴ مورد دیگر

Prof. Dr. Chase Broedersz is a tenured W2 Professor of Theoretical Physics – Statistical and Biological Physics at Ludwig-Maximilians-Universität München (LMU Munich) since 2015. He also holds a secondary affiliation as Associate Professor at VU Amsterdam since 2020. He is a member of the Young College of the Bavarian Academy of Science since 2017. Education Ph.D. in Theoretical Physics , VU Amsterdam (2011) MSc. in Physics , VU Amsterdam (2007) BSc. in Physics and Astronomy , VU Amsterdam (2005) Research Interests Prof. Broedersz leads a research program at the intersection of statistical physics and biological physics , with a focus on: Soft Matter Physics : mechanics of biopolymer networks, semiflexible filaments, and composite materials Active Matter : non-equilibrium dynamics in living systems, motor-driven networks, and active gels Cell Mechanics : confined cell migration, mechanosensing, and force transmission in tissues Chromosome Organization : bacterial chromosome structure, protein-DNA interactions, and SMC condensins His work combines theoretical modeling , computational simulations , and experimental collaborations to uncover the physical principles governing complex biological systems. Publication Trends Prof. Broedersz has authored over 50 peer-reviewed publications since 2010. His recent work (2019–2025) emphasizes: Cell migration in confined and soft environments, with studies in Nature Physics and PNAS Non-equilibrium dynamics in active biological systems, including broken detailed balance and emergent contractility Machine learning approaches to infer stochastic dynamics in biological data, published in Nature Communications and Physical Review Letters Chromosomal organization in bacteria, combining polymer physics with genomic data Labs and Teams He leads the Statistical and Biological Physics Group at LMU Munich, affiliated with the Arnold Sommerfeld Center for Theoretical Physics (ASC) . The group collaborates closely with experimental teams at LMU, Princeton, and other institutions, focusing on interdisciplinary biophysics research. Teaching Prof. Broedersz has taught advanced courses including: Advanced Statistical Physics Soft Matter Physics Stochastic Processes in Physics and Biology

Volkert Paulsenمشاهده پروفایل

مدرس ارشد

• Financial Mathematics
• Stochastic Analysis
• Optimal Stopping
• +۵ مورد دیگر

Volkert Paulsen is a Senior Lecturer at the Institute of Mathematical Stochastics at the University of Münster. His career spans institutions including the University of Kiel, where he completed his Habilitation (2000), Dissertation (1994), and Diplomarbeit (1989). He has taught extensively in Financial Mathematics , Stochastic Analysis , and Mathematical Statistics , supervising over 50 Bachelor, Master, and Diploma theses on topics such as risk modeling, portfolio optimization, and derivative valuation. Research Interests: Paulsen's work focuses on Financial Mathematics (continuous-time models, American options, unit-linked insurance), Stochastic Analysis (optimal stopping, martingale methods), and Risk Modeling (credit risk, extreme value statistics). His publications include foundational studies on nonlinear observation costs in optimal stopping problems and stochastic approaches to portfolio management. Scientific Contributions: His research spans journal articles in Stochastic Processes and their Applications and Journal of Applied Probability , with recent seminar topics covering Lévy Processes , Copula Modeling , and Stochastic Volatility . He employs R for statistical applications and integrates mathematical theory with practical finance and insurance contexts. Contact: Email: Volkert.Paulsen@uni-muenster.de Room: 130.010, Orléans-Ring 10, 48149 Münster Phone: +49 251 83-33771

Karl R. Gegenfurtnerمشاهده پروفایل

استاد

• Visual Perception
• Color Vision
• Cognitive Neuroscience
• +۷ مورد دیگر

Prof. Dr. Karl R. Gegenfurtner is a full Professor for General Psychology at the Department of Psychology, Faculty of Psychology and Sports Science, Justus-Liebig-University Giessen. He has held this position since 2001 and leads a prominent research group on visual perception. His work bridges low-level sensory processing with higher cognitive functions and motor control. Education: Psychology Student at the University of Regensburg, Diploma in Psychology, 1986 Ph.D. in Experimental Psychology, New York University, 1990 Postdoc at Howard Hughes Medical Institute and Center for Neural Science, NYU, 1990–1993 Research Scientist at Max Planck Institute for Biological Cybernetics, Tübingen, 1993–2000 Habilitation in Medical Psychology and Behavioral Neurobiology, 1998 Professor for Biological Psychology, Otto-von-Guericke University Magdeburg, 2000–2001 His research focuses on the neural and cognitive mechanisms of visual perception, particularly color vision, object recognition, eye movements, and sensorimotor integration. He investigates how humans perceive complex scenes and objects in natural environments, how these are represented in the brain, and how visual information guides motor actions. His recent work explores topics such as color categorization in neural networks, lightness perception, dynamic size recalibration, and the role of eye movements in perceptual decisions. The 15 most recent articles reflect a strong trend toward understanding perception in real-world contexts, integrating computational modeling, psychophysics, and neuroscientific methods. Key themes include color and shape perception, attention, eye movement control, and the interplay between perception and action. Scientific Awards: Member, German National Academy of Sciences Leopoldina (2015) Wilhelm Wundt Medal, German Society for Psychology (2016) Palmer Lecture, Colour Group (UK) (2019) Turrell Lecture Berlin (2019) Russell Devalois Memorial Lecture, UC Berkeley (2024) ICVS Verriest Medal (2024) Pineapple Science Award (2024) Prof. Gegenfurtner has supervised numerous research projects and training networks, including the DFG Collaborative Research Center TRR 135 on 'Cardinal mechanisms of perception' and the International Research Training Group BrainAct. He has received major funding, including an ERC Advanced Grant (2020) for 'Color 3.0'. He has served on editorial boards of top journals such as Journal of Vision , Vision Research , and Psychological Review , and was President of the Vision Science Society (2012–2013). He is actively involved in academic service, including the Alexander von Humboldt Foundation’s fellowship selection committee. He leads the Visual Perception research group at Giessen, which investigates cortical mechanisms of vision, perception of natural scenes, and the integration of sensory and motor information. The lab employs psychophysical experiments, eye tracking, computational modeling, and neuroimaging to study perception in ecologically valid settings.

Tim Kacprowskiمشاهده پروفایل

استاد

• Medical Informatics
• Data Science
• Network Medicine
• +۹ مورد دیگر

Summary Prof. Tim Kacprowski is a Professor and Head of Data Science in Biomedicine at the Peter L. Reichertz Institute for Medical Informatics (PLRI), jointly affiliated with TU Braunschweig and Hannover Medical School. His research focuses on integrating computational methods with biomedical challenges, particularly in network medicine, federated learning, and alternative splicing analysis. Key projects include the development of the NeDRex platform for drug repurposing and the FeatureCloud framework for privacy-preserving federated learning in healthcare. Research Interests His work spans multiple domains: Network Medicine: Leveraging molecular networks for disease module identification and drug discovery. Data Science: Advanced analytics for biomedical data, including ECG monitoring, microbiome studies, and flow cytometry. Federated Learning: Developing decentralized AI systems to protect patient data while enabling collaborative research. Alternative Splicing: Investigating splicing patterns in diseases like cancer and kidney disorders. Publications Trends Recent publications emphasize tools for drug repurposing (NeDRex-Web), ethical AI in clinical decision-making, and microbiome dynamics in chronic diseases. His work bridges computational methods with clinical applications, addressing challenges in precision medicine and healthcare technology. Labs & Collaborations As head of the Data Science group at PLRI, he leads interdisciplinary teams advancing biomedical informatics. Collaborations span institutions in Germany and internationally, focusing on translational research and AI-driven healthcare solutions.

• Numerical Analysis
• Uncertainty Quantification
• Inverse Problems
• +۳ مورد دیگر

Oliver G. Ernst is a Professor of Numerical Analysis at Technische Universität Chemnitz . His research focuses on Numerical Analysis , Uncertainty Quantification , and Inverse Problems , with applications in Thermo-Hydro-Mechanical (THM) processes , Electromagnetics , and Stochastic Partial Differential Equations . He is associated with the Numerical Analysis group at TU Chemnitz. Key Research Areas : Efficient numerical methods for PDEs Krylov subspace techniques Stochastic finite element methods Multi-physics modeling Geoscientific applications Recent Publications (2025-2010): THM simulations under uncertainty Neural network PDE solvers Bayesian inversion frameworks Rational Krylov algorithms Deflated restarting strategies Collaborations : TU Bergakademie Freiberg University of Manchester Technical University of Munich University of Maryland University of Geneva Software Development : Contributor to OpenGeoSys platform Developer of FEMALY MATLAB library Academic Recognition : h-index 32, i10-index 66, with over 4423 citations since 2020.

Claudia Klüppelbergمشاهده پروفایل

استاد

• Applied Probability
• Mathematical Statistics
• Financial Risk Management
• +۳ مورد دیگر

Claudia Klüppelberg is a Professor and Chair of Mathematical Statistics at the Center for Mathematical Sciences, Technische Universität München (TUM). Her academic journey includes positions at ETH Zurich, University of Mainz, and TUM since 1997. She holds a Carl von Linde Senior Fellowship at TUM-IAS, focusing on Risk Analysis and Stochastic Modeling. Her research bridges applied probability, statistics, and their applications in finance and insurance, emphasizing extreme value theory, risk processes, and stochastic networks. She has received prestigious awards such as the New Frontiers in Risk Management Award (2007) and Cross of Merit (2001). Her work addresses real-world challenges in financial risk management, including systemic risk in networks and operational risk modeling. Her recent publications explore causal analysis of extreme risks in networks, max-linear models, and Bayesian networks for extreme events. She contributes to academic leadership as an editorial board member and advisor, promoting interdisciplinary stochastic sciences.

Sven Scheweمشاهده پروفایل

استاد

• Automata Theory
• Game Theory
• Formal Verification
• +۱۲ مورد دیگر

Sven Schewe is a Professor in the Department of Computer Science at the University of Liverpool, affiliated with the School of Electrical Engineering, Electronics and Computer Science. He leads the AI Section and is a founding member and former leader of the Verification Group. He also has secondary affiliations with the Algorithms, Complexity Theory and Optimisation Group and the Institute for Risk and Uncertainty. Research Interests: His research centers on automata theory and game theory, particularly their applications in the verification and synthesis of reactive and safety-critical systems. He investigates infinite-duration games, automata over infinite words and trees, and develops algorithms and tools for automated verification, synthesis, and learning of optimal control strategies. His work extends to reinforcement learning with formal guarantees, cyber-physical systems, and AI safety. Recent Research Trends: His recent publications demonstrate a strong integration of formal methods with machine learning, particularly in adversarial training, neural network robustness, and model-free reinforcement learning under omega-regular objectives. He also applies formal reasoning to interdisciplinary domains such as chemical space exploration and materials science. Scientific Awards: Finalist for the ERCIM Cor Baayen Award 2010 Dr. Eduard Martin Preis 2009 GI Dissertation Award 2008 Advising and Grants: He actively supervises numerous PhD students and postdoctoral researchers. He is Principal Investigator (PI) or Co-Investigator (CI) on multiple major grants, including EPSRC Programme Grants, Royal Society Fellowships, and Horizon Europe projects. His funded research spans topics such as game theory, verification, synthesis, reinforcement learning, and risk analysis. He has hosted visiting researchers and collaborated internationally with institutions in Germany, France, India, Taiwan, and the US. Labs and Teams: He co-founded and led the Verification Group and previously led the AI Section at the University of Liverpool. These groups focus on formal methods, automata, games, and their applications in AI and safety-critical systems.

Fabian Schmidtمشاهده پروفایل

استاد

• Cosmology
• Dark Matter
• Dark Energy
• +۳ مورد دیگر

Fabian Schmidt is a Professor (Forschungsgruppenleiter) at the Max Planck Institute for Astrophysics (MPA) in Garching, Germany. He specializes in cosmology, focusing on dark matter, dark energy, and the evolution of cosmic structures. His research explores the universe's composition, history, and the mechanisms behind primordial density fluctuations. Education: Diplom (equivalent to MSc) in cosmic gamma rays from Humboldt University Berlin, PhD (2005-2009) at the University of Chicago under advisors Scott Dodelson and Wayne Hu. Held fellowships at Caltech (Moore Fellow) and Princeton (NASA CXC Einstein Fellow) before joining MPA in 2013. Research interests include cosmological observables in general relativity, baryon acoustic oscillations (BAO) reconstruction, and field-level inference techniques. His group is funded by the Excellence Cluster Origins and MPA resources, with ongoing work on large-scale structure, gravitational lensing, and dark matter physics. Notable contributions span theoretical cosmology, computational methods for galaxy clustering analysis, and Bayesian forward modeling. His team advises PhD students and postdocs in areas like EFT-based BAO reconstruction, simulation-based inference, and dark matter dynamics. Outreach: Active in public talks (Lange Nacht der Wissenschaften) and collaborations with science journalists. Co-author of Modern Cosmology textbook (2nd ed. 2020, 3rd ed. 2025), addressing topics like inflation and quantum vacuum cosmology.

Jilles Vreekenمشاهده پروفایل

استاد

• Causal Inference
• Machine Learning
• Data Mining
• +۵ مورد دیگر

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.

Thomas A. Runklerمشاهده پروفایل

استاد مدعو

• Machine Learning
• Data Analytics
• Pattern Recognition
• +۳ مورد دیگر

Thomas A. Runkler is an Adjunct Professor at the Technical University of Munich (TUM), holding a Chair in the Foundations of Software Reliability and Theoretical Computer Science within the TUM School of Computation, Information and Technology. Since 1999, he has taught computer science at TUM while concurrently serving in various expert and managerial roles at Siemens AG, where he currently holds the position of Senior Principal Research Scientist. His academic journey includes a Master’s and PhD in electrical engineering from TU Darmstadt (1992/1995), followed by postdoctoral research at the University of West Florida (1996–1997). Runkler’s research spans machine learning , data analytics , fuzzy systems , and optimization . He has authored/co-authored over 200 publications, focusing on topics like clustering algorithms, neural networks, and decision-making frameworks. Professional contributions include leadership roles in organizations such as the IEEE CIS committees and the German Association for Computer Science’s Fuzzy Systems group. His teaching includes courses on Data Mining and Knowledge Discovery . Key publications include foundational works on fuzzy preference structures, Bayesian decomposition of dynamical systems, and interpretable reinforcement learning policies. Runkler’s work bridges academia and industry, emphasizing practical applications in automation, recommendation systems, and industrial procurement forecasting. His research often addresses challenges in uncertainty handling, algorithm design, and scalable data analysis.

• Neuroscience
• Physics of Complex Systems
• Computational Neuroscience
• +۵ مورد دیگر

Prof. Viola Priesemann is a physicist and neuroscientist at the Max Planck Institute for Dynamics and Self-organization and University of Göttingen (W3 Professorship, 20% position). Her research spans self-organization in adaptive networks across neural and societal scales, with applications to neural criticality , information processing , and COVID-19 pandemic dynamics . She leads an independent Max Planck Research Group since 2017 and has held affiliations with Caltech , MPI Brain Research , and Bernstein Center Göttingen . Her research combines statistical physics , information theory , and non-equilibrium systems to study: Self-organized criticality in brain networks Subsampling theory for large-scale systems Neural basis of curiosity and learning Test-trace-isolation strategies for epidemics Information spread in social networks Design of energy-efficient AI architectures Key scientific awards include: Communitas Award (Max Planck Society, 2021) Science award of Lower-Saxony (2021) Medaille für naturwissenschaftliche Publizistik (DPG, 2021) Young Scientist Award (DPG, 2024) Her work on cross-scale information flow has influenced both neuroscience and public health policy , including advisory roles in the German government's COVID-19 expert panel .

• computational neuroscience
• theoretical neuroscience
• sensory systems
• +۳ مورد دیگر

Wiktor Młynarski is a Professor and Research Group Leader at the Faculty of Biology, Ludwig-Maximilians-University Munich, where he directs computational neuroscience research focused on adaptive neural computations in sensory systems. His work bridges theoretical frameworks with experimental validation to uncover fundamental principles of biological information processing. His research centers on computational and theoretical neuroscience, leveraging information theory, statistics, and probabilistic machine learning to model how neural systems efficiently represent dynamic natural environments. Key interests include sensory coding adaptation across timescales, statistical structure of natural stimuli, and the development of normative frameworks applicable from synaptic to behavioral levels. This inherently collaborative approach integrates theoretical predictions with experimental neuroscience to identify universal processing rules. Analysis of Młynarski's publication trajectory (2014-2025) reveals consistent exploration of efficient coding principles across auditory and visual domains, with recent work emphasizing anticipatory processing during locomotion, time-constrained decision-making, and panoramic visual statistics. His research demonstrates how environmental dynamics shape neural representations, moving beyond static models to capture the brain's adaptive capabilities in real-world contexts. Professor Młynarski leads the Computational Neurobiology Research Group (https://compneurobio.org), fostering interdisciplinary collaborations to confront theoretical models with experimental data. The group's work aims to establish general rules for biological information processing by examining how sensory systems dynamically optimize computations in response to environmental regularities and changes.