جست‌وجوی استاد و پژوهشگر

Hannes Zacherمشاهده پروفایل

استاد

• Work and Organizational Psychology
• Occupational Health and Well-being
• Proactive Employee Behavior
• +۳ مورد دیگر

Hannes Zacher is a Professor of Work and Organizational Psychology at Leipzig University's Wilhelm Wundt Institute of Psychology. He previously held academic positions at Queensland University of Technology (Australia) and the University of Groningen (Netherlands). His research focuses on occupational health, proactive employee behavior, aging in the workplace, and sustainable organizational practices. He has been awarded grants from institutions like the German Science Foundation (DFG) and the Volkswagen Foundation. Zacher earned his Ph.D. in Psychology from Justus-Liebig-University Gießen in 2009, following a Diploma (equivalent to B.Sc./M.Sc.) in Psychology from the Technical University of Braunschweig (2000–2006). His research interests include occupational health and well-being, proactive and adaptive employee behavior, aging at work and career development, and environmentally sustainable behavior in organizations. He employs methodologies such as longitudinal surveys, experience sampling, and experiments. Key themes in his work address the interplay between work environments and employee well-being, with a focus on fostering sustainable practices and mitigating age-related challenges in the workplace. Zacher leads projects such as the Centre for Digital Work and studies like 'The Role of Work in the Development of Civilization Diseases.' His publications span topics from green workplace behavior to leadership dynamics during crises. He has no listed scientific awards but has secured significant grant funding for his research. Advising and grant details reflect his commitment to impactful research, though specific student names are not provided. His lab and team efforts emphasize translating psychological insights into organizational practices for sustainability and employee well-being.

• Educational Science
• Empirical Research in Education
• Student Motivation
• +۵ مورد دیگر

Rainer Watermann is a Full Professor for Empirical Research in Education at the Free University of Berlin since 2011, previously serving as Full Professor for Education and Empirical Research in Schools at Georg-August University of Göttingen (2005-2011). His academic career includes significant research positions at the Max Planck Institute for Human Development in Berlin (1997-2005). Watermann earned his Diploma in Educational Science from the University of Münster (1996), followed by a Dr. phil (2002) and Habilitation (2005), both from Freie Universität Berlin. His educational background established the foundation for his extensive research in educational transitions and disparities. His research focuses on educational transitions, particularly from primary to secondary school and into higher education, examining motivational factors, social background influences, and achievement goal development. Watermann's work consistently addresses how social disparities affect educational opportunities and outcomes, with particular attention to gender differences and longitudinal developmental patterns. His methodological expertise includes latent class analysis, structural equation modeling, and large-scale assessment design. Watermann's publication portfolio reveals consistent research trajectories examining motivational frameworks (particularly expectancy-value theory), educational transitions, social disparities in education, and political socialization. His work spans both theoretical development and practical applications for educational policy and practice, with increasing focus on intervention effectiveness in recent years. As an active member of the academic community, Watermann serves on multiple editorial boards including the Swiss Journal for Educational Sciences and Empirical Educational Science , and regularly reviews for major educational and psychological journals. He has also contributed to significant research centers, including serving as spokesman for the Center for Empirical Research on Teaching and Learning in Schools (ZeUS) at the University of Göttingen (2008-2010). Watermann maintains an active research program with numerous collaborations across Germany and internationally, evidenced by his consistent publication record through 2025. His work bridges educational psychology, sociology of education, and policy-relevant research, maintaining strong connections between theoretical frameworks and practical educational contexts.

• Self-organized criticality
• Neuronal network dynamics
• Excitation/Inhibition balance
• +۵ مورد دیگر

Anna Levina is an Assistant Professor for Computational Neuroscience at the University of Tübingen , affiliated with the Department of Computer Science under the Faculty of Science. Her research focuses on the self-organization of neuronal activity, critical dynamics in neural networks, and the excitation/inhibition balance in cortical circuits. Current positions: Assistant Professor (since 2018), Group Leader (2017-2018), Equality Officer (Computer Science) Previous roles: IST Fellow (2015-2017), Associated Researcher (2011-2015), Postdoc/PI (2011-2015), Postdoc (2008-2011) Her research integrates mathematical modeling , statistical physics , and computational neuroscience to study criticality phenomena, neural avalanches, and adaptive network dynamics. Key interests include: Self-organized criticality in neural systems Excitation/Inhibition balance mechanisms Network topology and dynamics Timescale analysis in neural processing Stochastic modeling of neural activity Recent publications reveal trends in understanding critical dynamics across biological and artificial networks, with applications to memory systems, sensorimotor integration, and disease modeling. She has received recognition as an IST Fellow .

Jay Pujaraمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Machine Learning
• Knowledge Graph Construction
• +۵ مورد دیگر

Dr. Jay Pujara is a Research Associate Professor of Computer Science at the University of Southern California (USC) and Director of the Center on Knowledge Graphs. He is also a Principal Scientist at the Information Sciences Institute (ISI) and leads research teams in data science and AI. Ph.D., University of Maryland, College Park (2016) M.S. and B.S. in Computer Science, Carnegie Mellon University Research Interests include artificial intelligence, probabilistic models, knowledge graph construction, statistical relational learning, NLP, and streaming inference. His work focuses on scalable algorithms for big data and uncertainty modeling in dynamic environments. Recent Publications highlight advancements in knowledge graphs, LLM reasoning, and table understanding. Notable topics include non-verbal abstract reasoning , faithful conversational datasets , and KGQA re-ranking . Scientific Awards : SWSA Ten-Year Award (2023), Outstanding Paper (IUI 2019), Top Reviewer (NeurIPS 2018), Best Paper (SRL Workshop 2016) Advising & Grants : Mentored 12+ graduate students, including Ph.D. advisees on topics like causal modeling and neuro-symbolic tasks. Secured NSF funding for table understanding in paleoclimate studies.

Daniel Frischemeierمشاهده پروفایل

استاد

• Mathematics Education
• Statistics Education
• Data Science Education
• +۵ مورد دیگر

Daniel Frischemeier is a Professor of Mathematics Didactics with a focus on Primary Education at the University of Münster's Faculty of Mathematics and Computer Science. He has established himself as a leading researcher in statistics and data science education for primary school students, with extensive contributions to educational methodology and teacher training. University of Münster (2021-present) TU Dortmund (2020-2021) University of Paderborn (2009-2020) Ludwig-Maximilians-Universität München (2017-2018) Dr. Frischemeier completed his doctoral studies at the University of Paderborn with a dissertation on statistical thinking and research using TinkerPlots software. His educational background includes graduate studies in Mathematics and undergraduate studies in Mathematics and Physics for teaching at various school levels. His research focuses on the design and testing of teaching-learning environments for primary mathematics education, particularly in the areas of data analysis, probability, and statistics. He conducts qualitative analysis of learners' cognitive processes related to the guiding principle of 'data and chance' in primary education. His work also includes the design and evaluation of teaching materials in data science and civil statistics, the use of learning videos to promote process-related skills, and the implementation of Fermi tasks and computer science education within primary mathematics lessons. Analysis of Dr. Frischemeier's recent publications reveals a strong emphasis on data literacy development in primary education, with increasing focus on the integration of digital tools and the conceptual understanding of data as models. His work bridges mathematics education with emerging fields of data science, addressing both theoretical frameworks and practical classroom applications. The research demonstrates a progression from basic statistical concepts toward more complex data modeling approaches suitable for young learners. Elected member of the International Statistical Institute (ISI) Chair of the Local Organizing Committees for IASE Satellite 2025 Conference Council-Member of the International Statistical Institute Special Edition Editor of the Statistics Education Research Journal Member of International Program Committees for major statistics education conferences Co-Leader of CERME Thematic Working Group 5 on Probability and Statistics Education Dr. Frischemeier serves in numerous editorial capacities and review roles for prominent journals in mathematics and statistics education. He leads significant research projects including 'Promoting Data Science Education for Teacher Education at the University level (DataSETUP)' and 'Data Science Education in STEAM for Civic Engagement and Social Justice from the Early Years (DataScEd4CiEn)'. His work has substantial impact on teacher education programs and curriculum development in statistics and data science for primary schools. He is actively involved in the development and leadership of the Math Center Münster (MaZ), which promotes mathematical potential for all students. His team includes numerous research assistants and doctoral candidates working on various aspects of mathematics education research, particularly focusing on data literacy and statistical reasoning in primary education contexts.

• Multisensory Perception
• Predictive Processing
• Speech Perception
• +۳ مورد دیگر

Prof. Helen Blank is a Professor leading the Multisensory Perception Group and the Prediction in Communication Lab at the Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf. Her work focuses on understanding how sensory information is integrated and predicted in contexts like speech perception and face recognition. She holds a Marie Curie Fellowship for her research on prior information's role in human communication. Fluent in German, English, and French, she contributes to experimental medicine and systems neuroscience. Her research spans predictive coding, neuroimaging, and clinical applications in Parkinson’s and developmental disorders. Education: Not explicitly stated in text, inferred as advanced degrees in neuroscience or related fields. Her research interests emphasize multisensory integration, predictive processing in speech and vision, and the neural bases of perception. Recent articles explore topics such as pupil responses to auditory surprise, face expectation hierarchies, and audio-visual speech processing. Awards include the Marie Curie Fellowship supporting her predictive communication work. She leads interdisciplinary teams within the Center for Experimental Medicine, advancing knowledge on perceptual mechanisms and their clinical implications.

Julian Bergerمشاهده پروفایل

پژوهشگر ارشد

• Human-AI Interaction
• Collective Intelligence
• Machine Learning
• +۳ مورد دیگر

Julian Berger is a postdoctoral researcher at the Max Planck Institute for Human Development in the Center for Adaptive Rationality , where he explores how to enhance decision-making through hybrid human-AI systems. He is also a fellow of the Joachim Herz Foundation and has received funding from the Foundation of German Business and the Danish Data Science Academy. Education: M.A. Psychology in Business and Economics, Universidade Catolica Portuguesa (2021) B.A. Politics, Administration and International Relations, Zeppelin Universität (2018) His research spans human-AI collaboration , collective intelligence , and interpretable machine learning . A recurring theme in his work is developing methods to combine human expertise with AI capabilities for accuracy in domains like medical diagnostics , credit scoring , and football analytics . He has authored publications in high-impact venues such as PNAS , Nature Human Behavior , and Science and Medicine in Football . Scientific awards and funding include: Fellowship for interdisciplinary economics, Joachim Herz Foundation (2024) PhD funding from the Foundation of German Business (Stiftung der deutschen Wirtschaft) Research grant from the Danish Data Science Academy His recent article trends emphasize ensembling techniques that leverage complementary human and AI errors, algorithmic fairness, and practical heuristics like Hybrid Confirmation Trees. These works demonstrate significant improvements in diagnostic accuracy and decision cost-efficiency. Beyond academia, Berger works as a consultant and ML engineer with Simply Rational , focusing on interpretable models for financial and sports analytics. His work bridges theoretical research with real-world applications, prioritizing fairness, transparency, and human accountability in AI systems.

Slava Jankinمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Slava Jankin is a Professor of Data Science and Government at the University of Birmingham’s School of Government, where he also serves as Deputy Director of the Institute for Data and AI and Founding Director of the Centre for Artificial Intelligence in Government. He is concurrently a Fellow and Founding Director of the Data Science Lab at the Hertie School in Berlin. Previously, he held a Professorship at the University of Essex and has worked at University College London (UCL) and the London School of Economics (LSE). His research bridges computational methods, governance, and climate policy, with a focus on AI applications in public institutions, climate-health surveillance, and misinformation resilience. Jankin earned a PhD in Political Science from Trinity College Dublin (2009), a Postgraduate Diploma in Statistics (2006), and a BSc from Belarus State Economic University (2002). **Education**: • PhD in Political Science, Trinity College Dublin (2009) • Postgraduate Diploma in Statistics, Trinity College Dublin (2006) • BSc Econ with Distinction, Belarus State Economic University (2002) **Research Interests**: Jankin’s work integrates AI and computational methods with governance challenges, including climate policy, health surveillance, and institutional effectiveness. He leads initiatives like the Lancet Countdown’s climate-health monitoring and the CATALYSE project on climate impacts. His research also explores digital twins for governance systems and the role of cultural diversity in societal resilience against misinformation. **Grants & Collaborations**: He advises the UN and EU on AI and data science, co-leads the Lancet Countdown, and collaborates with institutions like the Alan Turing Institute. His applied work includes developing AI tools for public service optimization and policy simulations. **Labs & Teams**: Directs the Centre for AI in Government (University of Birmingham) and the Hertie School’s Data Science Lab, fostering interdisciplinary teams to advance computational methods in public policy.

Melanie Schienleمشاهده پروفایل

استاد

• Econometrics
• Statistical Methods
• Financial Risk Analysis
• +۴ مورد دیگر

Prof. Melanie Schienle is a Professor and Chair of Statistical Methods and Econometrics at the Department of Economics and Management, Karlsruhe Institute of Technology (KIT). She also holds a professorship in the Department of Mathematics at KIT since 2021. Her expertise spans statistical methods, econometrics, financial risk analysis, and forecasting. She leads the HKMetrics Network and the RespiNow Hub for respiratory disease forecasting. She serves as a Senior Fellow at the Rimini Center for Economic Analysis (RCEA), a steering committee member of the German Economic Association, and a member of the University Research Council at KIT. Education: Ph.D. (Dr. rer. pol.) in Economics from Mannheim University (2008), summa cum laude; Diploma in Mathematics (University of Karlsruhe, 2003) with a minor in theoretical physics. She has held academic positions at Leibniz University Hannover (2012–2015) and Humboldt University of Berlin (2008–2012). Research interests focus on financial networks, systemic risk, time series analysis, and machine learning applications in economics. She co-leads projects on nowcasting and forecasting, including collaborative efforts during the pandemic to predict hospitalizations. Her work integrates advanced statistical techniques with real-world policy implications. Prof. Schienle is an Associate Editor for the International Journal of Forecasting and Journal of Time Series Analysis . She has authored over 50 peer-reviewed publications and contributed to high-impact journals like Nature Communications and Journal of Business & Economic Statistics . She leads the Institute of Statistics at KIT and chairs the MathSEE initiative for interdisciplinary mathematical applications.

• Business Process Management
• Process Mining
• Complex Event Processing
• +۵ مورد دیگر

Prof. Dr. Matthias Weidlich is a faculty member at Humboldt University of Berlin within the Institute of Computer Science under the Faculty of Mathematics and Natural Sciences . His research focuses on Process Mining , Complex Event Processing , and Data Privacy with applications in Business Process Management and Scientific Workflows . Research Interests: Business Process Management and Process Mining Complex Event Processing and Stream Data Analysis Data Privacy and Security in Process Systems Scientific Workflow Systems and User Behavior Heterogeneous Network Embeddings Algorithm Design and Optimization Recent Publications (2023-2025) demonstrate expertise in: Efficient stream processing techniques Privacy-preserving process mining frameworks Scientific workflow analysis tools Graph neural network applications Multi-modal data integration Adaptive querying systems Contact: Office: Unter den Linden 6, 10099 Berlin Phone: 030 2093-41277 Email: matthias.weidlich@hu-berlin.de Web: hu.berlin/data

Krisztina Kis-Katosمشاهده پروفایل

استاد

• Applied Development Economics
• Political Economy
• Effects of (De-)Globalization
• +۱۱ مورد دیگر

Krisztina Kis-Katos serves as Professor for International Economic Policy at the University of Göttingen's Department of Economics, a position she assumed in 2016. She holds prominent leadership roles including Chairwoman of the Standing Field Committee of Development Economics of the German Economic Association (Verein für Socialpolitik) and Chairwoman of the Scientific Advisory Board of the RWI Leibniz Institute for Economic Research. Her institutional affiliations extend to research fellowships at IZA and RWI, along with editorial positions at the Journal of Labour Market Research, European Journal of Political Economy, Bulletin of Indonesian Economic Studies, and Journal of Development Studies. Professor Kis-Katos earned her Economics education in Szeged and Konstanz, attended the Swiss Doctoral Program at the Study Center Gerzensee, and received her doctoral degree from the University of Freiburg in 2010. Her scholarly work spans applied development economics and political economy with particular focus on how (de-)globalization and macroeconomic processes affect social and economic outcomes including labor markets, firm performance, land use change, deforestation, and conflict. Her research portfolio reveals consistent thematic threads across recent publications: the intersection of environmental concerns with economic development (particularly deforestation and palm oil in Indonesia), the gendered impacts of trade liberalization, the socioeconomic effects of pandemics like COVID-19, and the complex relationship between governance, corruption, and economic outcomes. Methodologically, her work combines rigorous econometric approaches with innovative data sources including satellite imagery and high-frequency power usage data. Teaching prize for the best doctoral course in RTG 1723, University of Göttingen (2019) Teaching prize of the Student Union of Economics of the University of Freiburg (2014) BMZ/GIZ Public Policy Award (2013) Friedrich-August-von-Hayek-award (2011) Excellence award of the KfW Development Bank (2011) Professor Kis-Katos leads multiple significant research initiatives including the BMZ-DEval funded evaluation of Madagascar's forest restoration program, the DFG-funded Thailand-Vietnam Socioeconomic Panel, and the DFG Research Training Group on Sustainable Food Systems. Her advisory role extends to supervising doctoral candidates through these projects and previously serving as spokesperson for the Research Training Group 1723 on Globalization and Development. Her substantial grant portfolio demonstrates strong research leadership across international collaborations involving institutions in Germany, Indonesia, Thailand, Vietnam, and the United States. Her work connects closely with the Collaborative Research Centre 990 on Ecological and Socioeconomic Functions of Tropical Lowland Rainforest Transformation Systems in Sumatra, Indonesia, reflecting her deep engagement with environmental-economic research questions in Southeast Asia. She also contributes to interdisciplinary teams through projects like PlanetHealth examining global land-use impacts of the COVID-19 pandemic.

Stefano Noventaمشاهده پروفایل

پژوهشگر ارشد

• Mathematical Psychology
• Psychometrics
• Psychophysics
• +۷ مورد دیگر

Stefano Noventa is a Research Fellow at the Methods Center, Department of Social Sciences, Faculty of Economics and Social Sciences, University of Tübingen. He has held multiple postdoctoral positions at the University of Tübingen and previously at the University of Verona and the University of Padova. Education: Ph.D. in Cognitive Psychology, University of Padova (2011) M.Sc. in Physics, University of Padova (2006) Studies in Physics, University of Padova (1999–2006) International Visiting Graduate Student, University of Toronto (2009, 2010) Dr. Noventa's research lies at the intersection of mathematical psychology, psychometrics, and psychophysics, with a focus on developing and unifying quantitative models of human cognition and assessment. His work integrates Item Response Theory (IRT) and Knowledge Space Theory (KST) to create more robust frameworks for educational and psychological measurement. He investigates latent variable models, probabilistic knowledge structures, and the identifiability of complex psychometric models, often applying these to domains such as education, organizational psychology, and entrepreneurship. His recent publications (2020–2024) demonstrate a strong trend toward theoretical integration, particularly in bridging cognitive diagnosis models with traditional psychometric frameworks. The articles emphasize mathematical rigor, model generalization, and empirical validation, with applications in both cognitive science and applied psychology. Topics include the unification of assessment models, parameter estimation under local dependence, and the modeling of intuitive physical reasoning. Scientific Awards: No awards or honors listed in the provided text. Dr. Noventa has not been explicitly mentioned as an advisor to students, but he has served as a corresponding author and collaborator on multiple research projects, indicating a leadership role in research teams. He has been involved in a DFG-funded project (GLI NON-NORM) since 2019, suggesting active grant participation. His work is highly collaborative, involving researchers from Germany, Italy, Austria, and Canada. Labs and Research Groups: Methods Center, University of Tübingen Hector Institute of Education Science and Psychology, University of Tübingen Center of Assessment, University of Verona Department of General Psychology, University of Padova

Hansjörg Kuttererمشاهده پروفایل

استاد

• Geodesy
• Geophysics
• Environmental Science
• +۳ مورد دیگر

Prof. Hansjörg Kutterer is a Professor and Dean at the KIT-Department of Civil Engineering, Geo and Environmental Sciences at Karlsruhe Institute of Technology (KIT). His primary affiliation is with KIT's Department of Civil Engineering, Geo and Environmental Sciences. He leads geodetic research initiatives focusing on Earth observation systems, atmospheric modeling, and geophysical data analysis. His research emphasizes advanced applications of GNSS, InSAR, and satellite gravimetry for monitoring climate-related phenomena such as water vapor dynamics, terrestrial water storage changes, and ground motion patterns. Key projects include developing machine learning-enhanced models for tropospheric delay corrections and integrated water vapor estimation in the Upper Rhine Graben region. Prof. Kutterer actively contributes to international geodetic frameworks like the Global Geodetic Observing System (GGOS), particularly through DA-CH regional collaborations. His work bridges geodetic methodologies with interdisciplinary challenges in climate science and environmental engineering. He oversees departmental operations as Dean, fostering innovation in geospatial education and infrastructure. His technical expertise spans geodetic deformation analysis, statistical robust estimation, and the integration of geophysical models with observational data.

Andreas Seifertمشاهده پروفایل

عضو هیئت علمی

• Educational Psychology
• Teacher Education
• Developmental Psychology
• +۴ مورد دیگر

Dr. Andreas Seifert serves as a Research Associate at the University of Paderborn's Department of Educational Psychology and Developmental Psychology, affiliated with the Professional School of Education (PLAZ). His work focuses on impact research in teacher education, fostering reflective practices among educators, and advancing media pedagogy. He holds a dual office address at Warburger Str. 100 (H4.143) and Mersinweg 3 (W4.240) in Paderborn. His research interests span educational psychology, teacher training methodologies, media competency development, and empirical research methods. He has contributed to publications on psychology didactics, reflective workshop efficacy, and media-related attitudes in pre-service teachers. Seifert teaches courses including Quantitative Research Methods, Survey Design, and Media Education Research at Paderborn University, Leuphana University Lüneburg, and Bielefeld University. He has organized workshops on statistical methods, test theory, and qualitative research techniques since 2005. He serves on editorial boards for journals like Lehrerbildung auf dem Prüfstand and participates in professional groups such as the Competence Center for University Didactics in Mathematics (KHDM). His teaching portfolio includes innovative projects addressing intercultural education and refugee integration through language programs.

Atreyee Banerjeeمشاهده پروفایل

پژوهشگر

• Polymer Physics
• Condensed Matter Physics
• Materials Science
• +۳ مورد دیگر

Atreyee Banerjee is a Researcher and Early Career Fellow at Albert-Ludwigs-Universität (Oct 2024–Jun 2025) and a Postdoctoral Researcher at the Max Planck Institute for Polymer Research (MPIP), Mainz, Germany, under Prof. Kurt Kremer. She completed her PhD in Chemical Science at CSIR-National Chemical Laboratory (2017) and postdocs at Cambridge University (2017–2019) and MPIP (2019–present). Education PhD (2017): CSIR-NCL, Pune, India (Supervisor: Dr. Sarika Maitra Bhattacharyya) MSc (2011): Visva Bharati, Santiniketan (Physical Chemistry) BSc (2009): Visva Bharati, Santiniketan Research Interests Focused on data-driven analysis of complex systems, including supercooled liquids, polymers, and organic crystals. Specializes in combining theory, simulations, and machine learning to study structural/dynamical properties. Key areas include glass transition, free energy landscapes, and polymer dynamics. Publications Overview Recent work includes machine learning approaches to glass transition in acrylic polymers (J. Chem. Phys., 2023), data-driven analysis of polymer dynamics (ACS Macro. Lett., 2023), and thermodynamic studies of supercooled liquids (Soft Matter, 2022). Research emphasizes methodological innovations like PCA clustering and basin-hopping optimization. Awards Recipient of DST-India Travel Award (2017), Best Research Scholar Award (CSIR-NCL, 2017), Shell-India Computational Talent Prize Bronze (2015), and multiple best poster awards (2014–2015). Grants & Labs Collaborates with Dr. Oleksandra Kukharenko in the Polymer Theory Group at MPIP. Active in computational initiatives like the ENGAGE Summer School (2023). Research involves datasets from GROMACS trajectories and open-source tools (e.g., scikit-learn).