جست‌وجوی استاد و پژوهشگر

Nikos Doltsinisمشاهده پروفایل

استاد

• Theoretical Chemistry
• Computational Chemistry
• Materials Science
• +۴ مورد دیگر

Prof. Dr. Nikos Doltsinis is a faculty member at the Institute of Solid State Theory, Westfälische Wilhelms-Universität Münster, holding a W2 Professorship since 2011. His research focuses on computational chemistry and molecular dynamics simulations of quantum mechanical systems in condensed phases, bridging theoretical chemistry , materials science , and nanostructure analysis . He has led major DFG-funded projects on topics like "A very strong ligand field" (2018–present) and "Multidimensional photoresponsive architectures for solar cells" (2016–present). 2011–present: Professor (W2), Institute of Solid State Theory, WWU Münster 2009–2011: Senior Lecturer, Department of Physics, King's College London 2006: Habilitation in Theoretical Chemistry, Ruhr University Bochum 1995–1998: PhD in Theoretical Chemistry, University of Birmingham His work integrates high-precision quantum methods and classical force fields to model light-induced processes , metal-organic interfaces , and molecular electronics . Recent publications emphasize multiscale simulations for organometallic complexes in OLEDs and solar cells. Notable collaborations include teams at the Center for Multiscale Theory and Computation (CMTC) and Collaborative Research Centres 1459 ("Intelligent Matter") . Scientific awards include the Lehrpreis der Fachschaft Physik (2016). He has mentored PhD students like Christian Schwermann , who received the Infineon Promotionspreis (2022). His group contributes to DFG grants and participates in TRR 61 and SFB 858 consortia.

Wolfgang Domckeمشاهده پروفایل

استاد

• Theoretical Chemistry
• Photochemistry
• Quantum Dynamics
• +۴ مورد دیگر

Wolfgang Domcke is a Professor of Theoretical Chemistry at the Technical University of Munich (TUM), specializing in photoinduced chemical dynamics of polyatomic molecules. His research employs ab initio quantum mechanics to study photochemical processes, focusing on photostability in biomolecules and solar water splitting. With over 420 publications, his work integrates computational chemistry with ultrafast spectroscopy to map reaction pathways. Education includes a doctorate in theoretical physics from TUM and a habilitation from the University of Freiburg. Previous academic roles include professorships at Heidelberg University and Heinrich Heine University Düsseldorf. Research interests span quantum wave-packet dynamics, molecular spectra, and nonadiabatic transitions. Recent articles emphasize photocatalytic water splitting, excited-state dynamics, and spectroscopic simulations. Awards include the 2008 Copernicus Award and an honorary doctorate from Charles University (2012). He is a member of the International Academy of Quantum Molecular Science and a Fellow of the Royal Society of Chemistry. Current projects explore organic photocatalysts for renewable energy and quantum dynamics of biological chromophores. Future work targets mechanistic insights for sustainable energy technologies.

• Numerical Analysis
• Computational Fluid Dynamics
• Partial Differential Equations
• +۳ مورد دیگر

Aleksey Sikstel is a part-time faculty member at RWTH Aachen University , holding the title of Vertr.-Prof. (Part-Time Lecturer). His research focuses on advanced numerical methods for hyperbolic conservation laws, including Discontinuous Galerkin schemes, stochastic Galerkin formulations, and error estimation techniques. Key research areas: Numerical Analysis, Computational Fluid Dynamics, and Applied Mathematics Primary affiliation: Faculty 10 Institutions at RWTH Aachen University Contact: aleksey.sikstel@rwth-aachen.de His work emphasizes coupling hyperbolic systems, entropy-stable discretizations, and adaptive grid methods. Recent publications analyze Baer-Nunziato-type models, multiresolution strategies, and boundary control for hyperbolic equations.

Frank Ziegeleمشاهده پروفایل

استاد

• Higher Education Management
• Science Management
• Strategic Management in Academic Institutions
• +۳ مورد دیگر

Prof. Dr. Frank Ziegele is a Professor of Higher Education and Science Management at Osnabrück University of Applied Sciences, part of the Faculty of Economics and Social Sciences. He holds a Dr. rer. oec. from Ruhr-Universität Bochum and has led the MBA program in Higher Education and Science Management since 2004. His research focuses on higher education management, strategic budgeting, governance, and societal responsibility of universities. He has authored influential works on university rankings, funding models, and organizational change. Key roles include serving as CEO of the CHE Centrum für Hochschulentwicklung since 2008 and leading the Erasmus Mundus program 'Research and Innovation in Higher Education' since 2012. He co-edits the journal 'Wissenschaftsmanagement' and contributes to policy debates on higher education reforms. His work bridges academic research and practical management, emphasizing data-driven decision-making and institutional effectiveness. Publications span topics like multidimensional university rankings, financial strategies, and governance structures. He advises national and international institutions, including the EU Commission and World Bank, on higher education policies and organizational development.

• Data Stream Management
• Process Mining
• Energy Informatics
• +۳ مورد دیگر

Hans-Jürgen Appelrath is a Professor in the Department of Information Systems at the University of Oldenburg. His research focuses on data stream management systems, process mining, energy informatics, and database systems. He has made significant contributions to the development of modular frameworks for real-time data processing and multidimensional process analysis. His work integrates ICT solutions for energy systems, healthcare, and logistics, emphasizing practical applications in smart grids and distributed computing. Key research areas include: Data stream management and real-time systems Process mining and business intelligence Energy informatics and smart grid technologies Database systems and data integration Recent publications highlight advancements in crash recovery for streaming systems, interactive process mining tools like PMCube, and energy-efficient logistics solutions. His work combines theoretical contributions with practical implementations, such as the Odysseus framework for data stream management and CARESS for healthcare data analysis. Publications reflect collaborations across multiple domains, including energy markets, healthcare analytics, and distributed systems. Projects often address interdisciplinary challenges, such as integrating cloud-based dashboards for electricity trading and optimizing resource allocation in container terminals.

Ann-Katrin van den Hamمشاهده پروفایل

استادیار

• Educational Methods
• Mathematics Education
• Early Childhood Development
• +۳ مورد دیگر

Prof. Dr. Ann-Katrin van den Ham is an Assistant Professor in Educational Science at the University of Hamburg, specializing in empirical methods in education and evaluation of educational systems. She holds a PhD from Leuphana University Lüneburg and has extensive research experience in mathematics education, textbook quality analysis, and early childhood development. Her work focuses on the role of educational resources, socio-economic impacts on learning, and validation of assessment tools. Education: Bachelor of Science in Pedagogical Science, University of Groningen, Netherlands (2007–2010) Master of Science in Educational Science, University of Groningen (2010–2011) PhD in Education, Leuphana University Lüneburg (2012–2016) Research Interests: Educational methods and empirical research design Impact of mathematics textbooks on student learning Early childhood mathematical competence and development Socio-economic influences on educational outcomes Validation of large-scale assessments (e.g., PISA, NEPS) Publications: Her recent work explores AI in education, kindergarten mathematical competence structures, and textbook quality effects. Key themes include bridging educational theory and practice, addressing equity through resource analysis, and improving assessment methodologies. Projects: MAMBO: Textbook effects on curriculum implementation Mathe macht stark: Mathematics support programs International collaborations on digital media in teacher education Labs/Teams: Active in the General, Intercultural and International Comparative Education department at Hamburg, collaborating with institutions like the Leibniz Institute for Science and Mathematics Education.

Ullrich Kötheمشاهده پروفایل

دانشیار

• Machine Learning
• Neural Networks
• Explainable AI
• +۶ مورد دیگر

Prof. Dr. Ullrich Köthe is an Associate Professor and group leader in the Visual Learning Lab Heidelberg at the University of Heidelberg . He focuses on Explainable Machine Learning , leveraging Invertible Neural Networks to enhance transparency and utility in image analysis and medical applications . He also maintains the widely used VIGRA image analysis library. Education: PhD in Informatics, University of Hamburg, 2000 Habilitation in Informatics, University of Hamburg, 2008 His research interests center around machine learning , image analysis , and scientific computing , particularly the development of robust algorithms for medical imaging , computer vision , and life sciences . His work on invertible neural networks and parameter-free segmentation has led to significant advancements in the field. Recent publications highlight his contributions to Bayesian inference , neural network interpretability , and stochastic modeling , with applications ranging from disease outbreak dynamics to connectomics . Key trends include generative models , parameter-free segmentation , and likelihood-free inference . Scientific Awards: DAGM 2003 Main Prize DAGM Best Paper Award 2008 He has supervised numerous Master and Bachelor theses in machine learning and image analysis , with teaching roles in Advanced Machine Learning and Explainable AI . His collaborative research grants include funding from HARMAN International (2024). He leads the Explainable Machine Learning subgroup and has contributed to open-source software projects like ilastik and VIGRA , which are critical tools in bioimage analysis .

Pascal Kilianمشاهده پروفایل

پژوهشگر

• Machine Learning
• Psychometrics
• Educational Research
• +۷ مورد دیگر

Pascal Kilian is a postdoctoral researcher at the Methods Center within the Faculty of Economics and Social Sciences at the University of Tübingen. His work lies at the intersection of machine learning, psychometrics, and social sciences, with applications in education and sports analytics. PhD in Mathematics, University of Tübingen (2018) BSc/MSc in Mathematics and Physics, University of Tübingen (2007–2012) His research focuses on modeling human behavior through advanced statistical and machine learning techniques. Key areas include latent variable models , variational autoencoders , mixed-effects machine learning , and longitudinal data analysis . He applies these methods to problems such as predicting student dropout in mathematics and identifying talent in youth soccer. The recent publications reflect a strong trend toward integrating deep learning with psychometric theory , particularly in modeling multidimensional performance data. His work increasingly bridges artificial intelligence and behavioral science , with emphasis on non-linear dynamic models and individualized forecasting . He has received recognition for his doctoral work: Doctoral Award for Excellent PhD Thesis, University of Tübingen (Tübingen School of Education) Kilian has been involved in externally funded research projects, including those supported by the Federal Ministry of Education and Research (BMBF) and the Deutsche Telekom Stiftung . While no formal advisees are listed, he collaborates closely with senior researchers such as Prof. Augustin Kelava and Dr. Judith Glaesser. He contributes to the development of the mixedML framework and supports the DFB's Talent Identification and Development (TID) program through scientific analytics. He is affiliated with the Methods Center at the University of Tübingen, a hub for interdisciplinary methodological innovation in the social sciences, where he contributes to both applied research and method development in machine learning and psychometrics.

Ulrich Kohlerمشاهده پروفایل

استاد

• Social Inequality
• Empirical Social Research Methods
• Political Participation
• +۷ مورد دیگر

Ulrich Kohler is a Professor for Methods of Empirical Social Research at the Faculty of Economics and Social Sciences, University of Potsdam, a position he has held since 2012. His work bridges sociology, political science, and quantitative methodology, with a strong focus on social inequality and democratic participation. He leads a research team involved in multiple funded projects and contributes actively to open science through software development and teaching materials. His research interests center on the causes and consequences of social inequality, particularly in education, political participation, and life risks. He employs advanced quantitative methods, including longitudinal analysis, decomposition techniques, and survey methodology. His work often compares Germany and the USA, examining how institutional contexts shape inequality. He has developed influential tools like the KHB method and PSID data access packages, widely used in social sciences. The recent publications and projects reflect a consistent focus on inequality across domains: educational choices (Latin/Greek), political non-participation, health disparities, and environmental decision-making. His methodological contributions include web scraping, survey coding, and multilevel modeling tools, showing a strong commitment to reproducible and transparent research. He also emphasizes teaching, offering courses and online materials in empirical methods. Lorenz von Stein Prize 2002 Kohler supervises bachelor’s and master’s theses and has led major research projects funded by the German Research Foundation (DFG), including studies on life risks, educational transitions (EDUCHANGE), and citizen participation. His team includes research assistants and project staff, and he collaborates with institutions like the WZB Berlin Social Science Center. He does not list specific PhD students, but his mentorship is evident through thesis supervision and project leadership. He leads the Chair of Methods of Empirical Social Research, which conducts ongoing research on social inequality, voting behavior, and educational transitions. The team develops software tools (e.g., Stata packages khb, twostep, psidtools) and maintains online teaching resources. The chair is involved in both national and international collaborations, particularly in longitudinal data analysis and comparative social research.

Stephanie Thevesمشاهده پروفایل

پژوهشگر

• Structure-learning and neural concept representations
• Neural mechanisms of relational reasoning
• Neuro-cognitive processing mechanisms underlying intelligence

Dr. Stephanie Theves is a Research Group Leader at the Max Planck Institute for Empirical Aesthetics. Her work focuses on understanding the neural mechanisms underlying intelligence, relational reasoning, and concept representation through cognitive neuroscience approaches. Department of Cognitive Neuropsychology Her research explores how the hippocampus and prefrontal cortex encode hierarchical concepts, construct cognitive maps, and support relational memory systems. Key findings demonstrate that neural representations prioritize conceptual relationships over raw feature distances, and that place-cell-like mechanisms in the brain modulate memory organization. Theves' recent publications examine category abstraction, spatial cognition, and beta-band activity modulation through sensory training. Her methodological approach combines neuroimaging with computational modeling to investigate neural processes. As part of the Max Planck Society, her work contributes to foundational knowledge in cognitive neuroscience while intersecting with interdisciplinary research themes in music, language, and computational auditory perception.

Rudolf Schererمشاهده پروفایل

استاد

• Numerical Analysis
• Applied Mathematics
• Ordinary Differential Equations (ODEs)
• +۵ مورد دیگر

Prof. Dr. Rudolf Scherer is a faculty member at the Department of Mathematics, Karlsruhe Institute of Technology (KIT). He specializes in Numerical Analysis , Applied Mathematics , and Mathematical Modeling , with a focus on Ordinary and Partial Differential Equations and Fractional Differential Equations . His research spans geometric integration, symplectic methods, and computational techniques for physical systems. Affiliation: Institute for Applied and Numerical Mathematics, KIT Research Interests: Numerical Analysis, ODE/PDEs, Fractional Calculus, Symplectic Integration His work includes collaborations with institutions such as the Chinese Academy of Sciences, Tsinghua University, and Kuwait University, with extended stays across multiple years. Publications highlight applications in stochastic Hamiltonian systems , Maxwell’s equations , and Protter-Morawetz problems for mixed-type PDEs.

• Latent Variable Models
• Dynamic Latent Variable Models
• Latent Class Methods
• +۸ مورد دیگر

Augustin Kelava is a Professor at the Department of Quantitative Methods, Eberhard Karls University of Tübingen. He has held this position since 2018 and leads the Methods Center as Managing Director. Previously, he was Professor at the Hector Institute for Empirical Educational Research (2013-2018) and Junior Professor at Technical University of Darmstadt (2011-2013). PhD in Psychology (Goethe University Frankfurt, 2009) Diploma in Psychology (Goethe University Frankfurt, 2004) Kelava specializes in latent variable modeling, machine learning in social sciences, and educational research. His work spans dynamic latent class models, Bayesian regularization techniques, and prediction of human behavior using intensive longitudinal data. He contributes to psychometric theory (e.g., item response theory extensions) and applies these methods to diverse fields including sports science and emotion regulation. Editor of "Testtheorie und Fragebogenkonstruktion" (3rd ed., Springer, 2020) Key researcher in the Cluster of Excellence "Machine Learning in Science" Active in methodological conferences (FGME 2017, SEM 2019) Review activities for 20+ journals and foundations including Psychometrika, DFG, and SNSF His recent publications focus on integrating machine learning with psychometrics, addressing identifiability in complex models, and evaluating personality assessment validity for large language models. He collaborates with researchers across psychology, education, and computational fields.

Shaul Mukamelمشاهده پروفایل

استاد

• Ultrafast Spectroscopy
• Nonlinear Optical Spectroscopy
• Biophysical Chemistry
• +۷ مورد دیگر

Shaul Mukamel is the Chancellor Professor of Chemistry at the University of California, Irvine (UCI), with affiliations at the Freiburg Institute for Advanced Studies (FRIAS) in Germany. He holds a Ph.D. from Tel Aviv University (1976) and has held faculty positions at Rice University, the Weizmann Institute, and the University of Rochester. His research focuses on developing computational techniques for ultrafast laser spectroscopy, probing electronic and vibrational dynamics in molecules, and applying these to biophysical systems like protein folding, photosynthetic complexes, and semiconductor nanostructures. He is a recipient of prestigious awards including the Sloan, Dreyfus, Guggenheim, and Alexander von Humboldt Fellowships, and is a Fellow of the American Physical Society and Optical Society of America. Research Interests: His work spans nonlinear optical spectroscopy, attosecond X-ray techniques, quantum coherence in photosynthetic systems, and entangled photon-based spectroscopy. He has pioneered methods for analyzing multidimensional optical signals and simulating energy transfer pathways in biological complexes. Grants & Funding: Supported by NIH, NSF, DOE, and Petroleum Research Fund grants for studies on molecular relaxation, nonlinear optical phenomena, and single-molecule spectroscopy. Awards: OSA Lippincott Award (2015), APS Plyler Prize (2008), and numerous fellowships. His textbook Principles of Nonlinear Optical Spectroscopy (Oxford, 1995) is a seminal reference in the field.

Cedric du Mouzaمشاهده پروفایل

استاد

• Database Systems
• Data Mining
• Digital Humanities
• +۶ مورد دیگر

Cédric du Mouza is a full Professor within the CEDRIC Laboratory at the Conservatoire National des Arts et Métiers (CNAM) in Paris, France. His research activities lie at the intersection of database systems, data mining, and digital humanities, with a strong emphasis on social network analysis, knowledge base population, and spatial/temporal data management. Education details are not explicitly provided in the source material. His research interests can be summarized as follows: Database Technologies: scalable indexing, distributed repositories, spatial and temporal data structures. Data Mining & Machine Learning: community detection, influence maximization, recommender systems. Digital Humanities: prosopographic databases, historical social networks, credibility assessment of historical sources. Social Media Analytics: Twitter user profiling, early detection of popular accounts, reducing filter bubbles. Across more than two decades, Prof. du Mouza has produced a rich scholarly record that blends theoretical advances with practical applications. Recent publications (2020-2024) highlight a methodological shift toward end-to-end evaluation frameworks for knowledge base population, uncertainty modeling in historical corpora, and real-time influence maximization in advertising ecosystems. These works are published in premier venues such as SIGIR , VLDB Journal , WISE , CIKM , and leading French conferences (EGC, BDA). No specific scientific awards, funded projects, or doctoral student names are mentioned in the supplied text. Prof. du Mouza collaborates extensively within interdisciplinary teams involving historians, computer scientists, and statisticians, reflecting CEDRIC’s integrative research culture. While no dedicated laboratory or team name is provided, his continuous affiliation with CEDRIC/CNAM confirms an active and ongoing role in both research and graduate-level education.

Ayala Aradمشاهده پروفایل

استاد

• Psychology and Economics
• Experimental Economics
• Microeconomic Theory
• +۲ مورد دیگر

Ayala Arad is a Full Professor at the Coller School of Management, Tel Aviv University. She serves as the Head of the Solomon Lew Center for Consumer Behavior and Director of the Interactive Decision Making Lab. She earned her PhD in Economics from Tel Aviv University in 2011 and completed postdoctoral research at the University of California, Berkeley (2011-2013). Her research investigates bounded rationality in individual and strategic decision-making through experimental methods. Key domains include: Psychology and economics Behavioral game theory Experimental microeconomics Cognitive aspects of choice under uncertainty Design of incentive systems Her publications demonstrate a consistent focus on developing behavioral models of decision-making, with recent work examining team-based reasoning, communication effects, and multidimensional strategic interactions. Research frequently employs controlled experiments to test theoretical predictions. She leads the Interactive Decision Making Lab, specializing in experimental studies of strategic behavior and consumer choice.