جست‌وجوی استاد و پژوهشگر

Michael Engelمشاهده پروفایل

پژوهشگر

• Bayesian Inverse Problems
• Machine Learning
• Optimization Methods
• +۳ مورد دیگر

Michael Engel is a Researcher at the Chair of Remote Sensing Technology, Technical University of Munich since March 2021. He holds a Master's in Materials Science and Engineering (2020) and a Bachelor's in Engineering Science (2018), both from TU Munich, with a focus on Uncertainty Quantification and Bayesian Inverse Problems. His research interests revolve around optimizing objective functions in inverse problems, leveraging methods like Curriculum Learning and Multi-Task Systems via Multiresolution Analysis. He combines classical numerical techniques with machine learning to address challenges in Bayesian problems and regression. Education: Bachelor's in Engineering Science, TU Munich (2018) Master's in Materials Science and Engineering, TU Munich (2020) Research interests include: - Bayesian Inverse Problems - Machine Learning Integration in Numerical Methods - Optimization of Multi-Task Systems - Uncertainty Quantification in Environmental Models Key projects include the Global Earth Monitor (GEM) , aiming to enhance Copernicus data utilization for large-scale environmental monitoring. His publications span remote sensing optimization, wavelet-based sensitivity analysis, and deforestation assessment using multi-sensor data. He contributes to the TU Munich's Engineering Risk Analysis Group and collaborates with the Chair of Numerical Mathematics and Hydrology. His work bridges physical modeling principles with computational efficiency in earth observation systems.

• Macroeconomics
• Monetary Macroeconomics
• Expectation Formation
• +۶ مورد دیگر

Krisztina Molnar is an Associate Professor at the Norwegian School of Economics (NHH), specializing in macroeconomics and monetary policy. Her research focuses on expectation formation, household consumption, saving decisions, and the integration of big microdata into economic models. She collaborates extensively with institutions like Banca d'Italia and the London School of Economics (LSE). Key research areas include identifying preference shocks through natural disasters, analyzing the perils of price stabilization under agent learning, and exploring income shocks' effects on household behavior. Her methodological innovations include textual analysis and survey data integration in econometric models. Notable contributions include work on the Handbook of Monetary Economics and the Journal of Monetary Economics. She has presented groundbreaking research such as the HANKSSON framework at the 2024 SED conference. Despite no explicit awards listed, her work has been incorporated into leading academic curricula, including Woodford's syllabus. Current projects involve interdisciplinary approaches to microdata and preference shocks, with ongoing collaborations on the effects of earthquakes on household savings and the implications of learning dynamics in monetary policy design.

Stephan C. Meylanمشاهده پروفایل

پژوهشگر

• Computational Cognitive Science
• Language Acquisition
• Machine Learning
• +۳ مورد دیگر

Stephan C. Meylan is a computational cognitive scientist and researcher at the Department of Linguistics at UC Berkeley. His work focuses on understanding how children and adults learn language through computational models such as neural networks and Bayesian probabilistic frameworks, with an emphasis on multi-agent and multi-task learning contexts. He previously worked at MIT's Computational Psycholinguistics Lab under Dr. Roger Levy and contributed to projects like the Bergelson Lab. His research addresses Moravec’s Paradox and employs experimental methods like eyetracking to study language processing. Funded by NIH, NSF, and DARPA, his work bridges machine intelligence development and human developmental processes. Meylan also has industry experience in data science and geospatial analysis.

Tobias Alfersمشاهده پروفایل

پژوهشگر

• Item Response Theory
• Psychological Test Development
• Bayesian Statistics
• +۳ مورد دیگر

Tobias Alfers is an affiliate researcher at Freie Universität Berlin's Department of Education and Psychology, specializing in methods and evaluation. He concurrently works as a psychometrician and web developer at Test 4 U GmbH in Vienna. His academic background includes a Master's in Psychology from the University of Vienna (2012-2018) and ongoing PhD research at FU Berlin (since 2020) on test-taking disengagement modeling. Professional roles include adjunct lecturer in mathematics at MODUL University Vienna and teaching assistant in psychological research methods at the University of Vienna. Research focuses on item response theory, test development, and Bayesian statistical applications. Key contributions include presentations at international psychometric conferences and collaborations on RIASEC-based career counseling tools. He maintains active involvement in academic outreach, recently hosting events for prospective students and public lectures on astrobiology-related research. Current affiliations include the Methods and Evaluation division's quality assurance team, where he has mentored student assistants and contributed to departmental initiatives like Boys' Day university visits.

Martin Kolbمشاهده پروفایل

استاد

• Stochastic Processes
• Probability Theory
• Mathematical Physics
• +۳ مورد دیگر

Roles & Affiliations: Martin Kolb is a Professor (W3) at Paderborn University's Institute of Mathematics (Applied Mathematics and Stochastics group leader). He previously held roles including Professor (W2) at Paderborn (2014–2017), Lecturer at University of Reading (2012–2014), and Harrison Early Career Professor at Warwick University (2011–2012). Education: PhD in Mathematics from TU Kaiserslautern (2009), Diploma in Mathematics from Ludwig-Maximilians-University Munich (2005). Studied at University of Copenhagen (2002–2003). Research Interests: Focuses on stochastic processes, probability theory, and mathematical physics. Key topics include quasi-stationary distributions, Fleming-Viot models, spectral asymptotics, and applications in quantum computing (e.g., Photonic Quantum Computing project). His work bridges theoretical mathematics with computational methods and physical systems. Scientific Engagement: Organized major events like the Weierstrass-Lecture series (since 2019) featuring Fields medalists and co-organized workshops such as the Oberwolfach Workshop on Stochastic Processes (2020). Active in conference committees for topics like 'Stochastic Processes under Constraints' (2016). Labs/Teams: Leads research projects including 'PhoQC: Photonic Quantum Computing' and 'Super-Brownian Motion with single point source'.

Jan Eggerمشاهده پروفایل

مدرس ارشد

• Medical Image Analysis
• Artificial Intelligence
• Image-Guided Therapies
• +۳ مورد دیگر

Jan Egger is a Senior Lecturer at the Technical University of Graz and a research group leader at the Institute for Artificial Intelligence in Medicine (IKIM) at Essen University Hospital. With over 10 years of experience in medical image analysis and AI-guided therapies, he focuses on advancing healthcare through open science platforms like StudierFenster and MedShapeNet. His interdisciplinary background combines Computer Science (Ph.D., Habilitation) and Human Biology (Ph.D.). Ph.D. in Computer Science Ph.D. in Human Biology Habilitation in Computer Science His research spans medical imaging, augmented reality surgery, and explainable AI, with a strong emphasis on clinical translation and startup incubation (e.g., Augmentomy). He has secured over €14 million in third-party funding and supervised ~100 student theses. Recent publications analyze transfer learning, synthetic data augmentation, and multimodal AI applications in oncology and radiology. Jan leads the open science client/server-based medical platform StudierFenster and contributes to datasets like MedShapeNet and CytoNuke. His work bridges academic research with industry collaboration, particularly in AI-guided therapies and extended reality systems for healthcare.

Joost-Pieter Katoenمشاهده پروفایل

استاد

• Probabilistic Programming
• Model Checking
• Formal Verification
• +۴ مورد دیگر

Joost-Pieter Katoen is a full professor at RWTH Aachen University in Germany where he leads the Software Modeling and Verification (MOVES) group. Since 2013, he holds a distinguished professorship at RWTH Aachen University and is a member of the Academia Europaea. He also maintains a part-time association with the Formal Methods & Tools group at the University of Twente in the Netherlands. Professor Katoen's research primarily focuses on probabilistic programming, model checking, and formal verification. His work spans theoretical foundations of probabilistic systems, deductive verification techniques for probabilistic programs, and practical applications in software verification. He has made significant contributions to the development of algorithms for analyzing probabilistic models, including Markov chains and probabilistic automata. His research bridges theoretical computer science with practical verification challenges, particularly in the context of probabilistic systems. His recent publications reveal a strong focus on advancing probabilistic programming verification, with particular emphasis on generating functions, expected runtimes, and probabilistic bisimulation. The research trends show increasing sophistication in handling continuous distributions, parameter synthesis, and multi-objective verification problems. His work consistently appears in top-tier conferences including POPL, PLDI, SPLASH, and CAV. Scientific Awards and Recognition: Distinguished Professorship at RWTH Aachen University (since 2013) Member of Academia Europaea Professor Katoen has supervised numerous PhD students whose work spans various aspects of formal methods and probabilistic systems. His MOVES research group develops several verification tools including PROPHESY, PRINSYS, COMPASS, MRMC, and SMYLE. His editorial service includes membership on steering committees for major conferences such as CONCUR and ETAPS (where he serves as chair), and editorial boards for journals including STTT and PeerJ Computer Science.

• Neural Networks
• Information Processing
• Statistical Physics
• +۱۳ مورد دیگر

Viola Priesemann is a Professor of Physics at the University of Göttingen and heads the Max Planck Research Group on the Theory of Neural Systems at the Max Planck Institute for Dynamics and Self-Organization in Göttingen. She also holds affiliations with the Cluster of Excellence 'Multiscale Bioimaging', the Campus Institute for Data Science, and the Max Planck-University of Toronto Centre for Neural Science and Technology. Her research focuses on understanding the fundamental principles of collective information processing in both neural and social networks. She employs approaches from statistical physics and information theory to investigate self-organization and learning processes in living networks. Her work spans several interconnected areas: neural criticality and subsampling effects in network analysis, information processing in neural circuits, and the spread of information (and misinformation) in social networks. During the COVID-19 pandemic, she applied her expertise in spreading dynamics to study viral transmission and the effectiveness of interventions, becoming a member of the German government's expert panel on the pandemic. Her recent publications reveal a strong focus on understanding how neural networks maintain optimal information processing through controlled criticality, how subsampling affects our ability to infer network properties, and the parallels between information spread in neural and social networks. Her work bridges theoretical physics, computational neuroscience, and epidemiology, demonstrating how principles from statistical physics can illuminate collective phenomena across vastly different scales. Medal for Scientific Journalism of the German Physical Society (DPG) Communitas Award of the Max Planck Society Member of the Junge Akademie Dannie-Heineman Award of the Academy of Science and Humanities, Göttingen Science award of Lower-Saxony - Wissenschaftspreis Niedersachsen Young Scientist Award for Socio- and Econophysics from the German Physical Society Professor Priesemann actively mentors numerous PhD students and postdocs in her research group, fostering interdisciplinary collaboration between physics, neuroscience, and data science. Her lab investigates topics ranging from neural plasticity and learning to epidemic modeling and information spread in social networks. She has secured significant research funding, including a German-Israel Foundation Young Investigator Grant and projects within the Cluster of Excellence 'Multiscale Bioimaging' and the Max Planck-University of Toronto Centre. Her research group maintains strong connections with experimentalists and theorists across multiple institutions, creating a vibrant environment for studying the principles of living adaptive networks. She has initiated several interdisciplinary research projects, including a major initiative on curiosity and hierarchical information processing across species.

Thomas Dufhuesمشاهده پروفایل

پژوهشگر

• Rural Development
• Migration Studies
• Decentralization
• +۵ مورد دیگر

Dr. Thomas Dufhues is a Researcher at the Institute of Agricultural Development in Transition Europe (IAMO) since 2005, affiliated with the Department of Agricultural Policy. He holds a PhD in agricultural economics from the University of Hohenheim (Germany) and has held research positions at Philipps-University of Marburg, Technical University of Munich, and Martin Luther University Halle. His work focuses on rural transformation in Southeast Europe and Central/Southeast Asia, integrating microeconomic theory with empirical analysis of migration, decentralization, social capital, and rural finance. He has contributed to projects like TraFFF (Transnational Families, Farms, and Firms) and RuWell (Rural Wellbeing in Transition). Research Interests: Rural institutions, migration dynamics, decentralization, subjective wellbeing, social capital, and rural financial systems. Key Projects: Internal migration in Kazakhstan, rural well-being drivers, social comparisons and inequality, status quo bias in farm work, sustainable practices in Uzbekistan. Publications: Over 15 peer-reviewed articles, book chapters, and policy briefs spanning topics like loan repayment performance in Vietnam, participatory governance in Thailand, and migration modeling in Kazakhstan. Collaborations: Extensive co-authorship with Judith Möllers, Gertrud Buchenrieder, Diana Traikova, and others in multidisciplinary teams. Email: dufhues@iamo.de

Dirk Blömkerمشاهده پروفایل

استاد

• Stochastic partial differential equations
• Surface growth
• Cahn-Hilliard equation
• +۱۱ مورد دیگر

Dirk Blömker is a Professor in the Institute of Mathematics at the University of Augsburg (since 2006). His research focuses on Stochastic Partial Differential Equations (SPDEs) with applications to surface growth models , phase separation (Cahn-Hilliard, Allen-Cahn), stochastic bifurcation , and multiscale analysis (amplitude/modulation equations, fast diffusion). He also works on filter theory (Ensemble Kalman) and numerics of SPDEs . Habilitation : RWTH Aachen (2006) Doctorate : University of Augsburg (2000) Diploma : University of Münster (1997) Intermediate Diploma : University of Münster (1993) Abitur : Gymnasium Lengerich (1989) His work spans rigorous mathematical analysis of SPDEs (existence/uniqueness, blow-up, regularity) and numerical methods (convergence analysis, a-posteriori bounds). Recent articles examine fractional noise, Lévy processes, and ensemble Kalman inversion. Collaborators include Luigi Bianchi, Claudia Schillings, and Philipp Wacker. He has held academic positions at RWTH Aachen (Assistant/Acting Lecturer), University of Bonn (Acting Lecturer), and University of Warwick (DFG Research Fellowship).

• Quantitative Finance
• Financial Mathematics
• Structured Products
• +۷ مورد دیگر

Holger Graf is a Professor at the Faculty of Business Administration and International Finance, Nürtingen-Geislingen University of Applied Sciences. His research focuses on financial mathematics, quantitative methods, and risk management, with prior roles at Munich University of Applied Sciences and Goldman Sachs. He earned his dissertation at the Technical University of Munich and conducted research at the Oxford-Man Institute of Quantitative Finance. Research Interests His work spans Arbitrage-free financial modeling using fractional processes Structured product transparency and risk evaluation Portfolio optimization under expected utility measures Implied volatility and correlation dynamics Regime-switching models for global equity indices Applications of long-memory processes in credit risk Selected Publications His recent publications incorporate trends in quantitative finance, including fractional Lévy-driven models, volatility forecasting, and regime-switching copulas. Notably, he explores intersections between mathematical statistics and financial engineering, though one outlier article addresses veterinary parasitology.

Katharina Loiblمشاهده پروفایل

استاد

• Instructional Design
• Learning from Errors
• Problem-Solving Prior to Instruction (PS-I)
• +۲ مورد دیگر

Prof. Dr. Katharina Loibl is a full Professor (W3) for Interdisciplinary Teaching and Learning Research at the Institute of Psychology, University of Education Freiburg. She serves as the Executive Director of the Freiburg Advanced Center of Education (FACE) and co-founder of the Center of Interdisciplinary Research on Digital Education (CIRDE). Her research focuses on instructional design, learning from errors, problem-solving prior to instruction (PS-I), diagnostic reasoning in teachers, and Bayesian cognition in educational settings. University of Education Freiburg – Department of Interdisciplinary Teaching and Learning Research Co-leader of DFG-funded projects on adaptive instruction and digital education Dr. Loibl’s research explores how pre-instructional problem-solving phases (PS-I) enhance conceptual understanding and transfer. She investigates cognitive processes in teacher diagnostics, error processing in mathematics and physical education, and adaptive learning environments using digital tools. Her work bridges cognitive science and educational practice, particularly in mathematics and motor skills development. Recent publications emphasize PS-I frameworks (CID model), Bayesian reasoning in teacher diagnostics, and digital education mechanisms. Awards include the ISLS Early Career Award (2020/21) and multiple DFG grants. She supervises theses on PS-I applications in physical education and digital cognition, though specific student names are not listed. Early Career Award (ISLS) 2020/21 Wilhelm-Hollenberg Dissertationspreis 2014 Best Student Paper Award (CSCL) 2013 Nachwuchswissenschaftlerpreis (PAEPS) 2011 Dr. Loibl leads collaborative projects like the DFG network on PS-I mechanisms and the Baden-Württemberg-funded Di.ge.LL Kolleg for digital didactics. She co-ordinates the Freiburg Advanced Center of Education (FACE) and integrates data literacy through initiatives like the Future Teaching Lab.

Markus Casperمشاهده پروفایل

استاد

• Hydrology
• Physical Geography
• Water Balance Modeling
• +۵ مورد دیگر

Markus Casper is a full professor at the University of Trier , holding a position in Physical Geography within Faculty VI . His career spans from being a Junior Professor (2003-2009) to a Universitätsprofessor (W2) since 2009. He has been involved in significant roles such as member of the University of Trier Senate, the DFG-funded Modelbildung und Simulation working group, and leadership in the German Hydrological Society. Education: Diplom in Geoökologie (University of Karlsruhe, 1995), Dr.-Ing. (University of Karlsruhe, 2002). Research Interests: Dr. Casper’s work focuses on hydrological modeling , catchment classification , and climate change impacts on water balance . Key projects include: DFG-funded mesoscale hydrological modeling with remote sensing data assimilation (2019-2022) SOFI : Smart soil information for farmers (BMEL-funded, 2019-2022) Catchment regionalization with Self-Organizing Maps (SOM) (DFG, 2012-2017) His research integrates groundwater-surface water interactions , soil erosion , and remote sensing for process identification. Scientific Trends: His recent articles emphasize multi-criteria model evaluation , machine learning (SOM) in hydrology , and uncertainty quantification in water balance simulations. Themes include climate nonstationarity , pedotransfer functions , and soil-vegetation-atmosphere modeling . Advising: He has supervised numerous doctoral students, including Gayane Grigoryan (climate impacts on water balance), Philipp Reiter (hydrological modeling), and Hadis Mohajerani (hydrological processes). Completed works address flood prediction , SVAT modeling , and land use sustainability . Labs & Teams: He leads the Modelbildung und Simulation working group at the University of Trier and collaborates with institutions like the University of Leipzig, FH Trier, and LUWG Mainz. His team utilizes GIS pools , hydrological test basins , and field laboratories for runoff and erosion studies.

Hadis Mohajeraniمشاهده پروفایل

پژوهشگر

• Hydrology
• Water Resources Management
• Hydrological Modeling
• +۲ مورد دیگر

Dr. Hadis Mohajerani is a Researcher at the Department of Physical Geography, Faculty VI, University of Trier. Her work bridges hydrology, environmental modeling, and resource management, with a focus on developing analytical frameworks for water systems under uncertainty. Research Focus: Her expertise spans: Hydrological process modeling using pedotransfer functions and water balance approaches Bayesian decision networks for groundwater management in data-scarce regions Multi-criteria evaluation methods for environmental parameterization Climate-water interactions and sustainability challenges Publication Trends: Recent work (2017-2021) demonstrates consistent focus on uncertainty quantification in hydrology, with methodological contributions to model parameterization, decision-making under data constraints, and integrated water cycle analysis. Awards & Recognition: No scientific awards are documented in available materials. Academic Contributions: She collaborates within the Physical Geography research team but no specific advising roles, grants, or lab leadership details are provided.

Christian Möllmannمشاهده پروفایل

استاد

• Marine Ecosystem Dynamics
• Climate Change Impact on Fisheries
• Ecosystem-based Management
• +۳ مورد دیگر

Christian Möllmann is a Professor at the University of Hamburg within the Faculty of Mathematics, Computer Science and Natural Sciences , specifically in the Department of Biology and its Institute of Marine Ecosystem and Fisheries Sciences . His research focuses on climate and fisheries impacts on marine ecosystems, with a transdisciplinary approach integrating scientific and non-scientific stakeholders. 1989-1996: University of Giessen/Kiel (Biology, Fisheries Biology, Marine Zoology) 1996: M.Sc. thesis on zooplankton consumption by herring/sprat 2002: Ph.D. on mesozooplankton dynamics in the Baltic Sea Research areas include: Climate change effects on marine ecosystems Fisheries management and trophic dynamics Gill microbiota responses to environmental stress Adaptive capacity of social-ecological systems Ecosystem-based marine policy frameworks Recent publications analyze: Estuarine food web restructuring under climate change Trophic plasticity in eutrophic environments Bayesian modeling of fisheries adaptive capacity Multi-omics integration for stressor detection Current projects funded by: BMBF : SpaCeParti, balt_ADAPT, SeaUseTip, marEEshift City of Hamburg : AuTagBeoFisch (autonomous underwater monitoring) EU Horizon 2020 : COMFORT, MISSION ATLANTIC DFG : RTG 2530 on biota-mediated carbon cycling His work emphasizes transdisciplinary collaboration, combining natural sciences with socio-economic factors to develop sustainable marine management strategies.