جست‌وجوی استاد و پژوهشگر

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).

Daniel Kasperمشاهده پروفایل

پژوهشگر

• Evaluation of Education Systems
• Primary School Research
• Educational Disparities
• +۴ مورد دیگر

Daniel Kasper is a researcher at the University of Hamburg's Faculty of Education, specializing in Educational Science with a focus on International Educational Monitoring and Reporting. He currently serves as the National Project Manager for TIMSS 2027, continuing his leadership role from previous TIMSS cycles (2023, 2019). His work centers on large-scale educational assessments, statistical methodology, and primary education research. Dr. Kasper completed his Habilitation in Educational Science with special consideration of empirical educational research at TU Dortmund (2012-2020), followed by his PhD in Educational Science at the same institution (2010-2012). He earned his Diploma in Educational Science from the University of Münster (2001-2007). His research interests span evaluation of education systems, primary school research, educational disparities, and advanced statistical methods in education. He has developed significant expertise in TIMSS methodology, multilevel modeling, and analysis of large-scale assessment data, with numerous publications addressing methodological challenges in international comparative studies. Analysis of his 15 most recent publications reveals a strong focus on TIMSS methodology and results, particularly regarding mathematics and science competencies in primary education. His work demonstrates expertise in statistical methodology for educational assessment, with several publications developing and refining analytical techniques for large-scale data. A recurring theme is examining educational disparities related to student composition, socioeconomic factors, and gender differences. Dr. Kasper has served as National Project Manager for multiple TIMSS cycles (2019, 2023, 2027) and has been involved in PIRLS studies, demonstrating sustained leadership in major international educational assessments. His work bridges methodological innovation with practical application in educational monitoring. He teaches courses across all academic levels, including 'Introduction to Empirical Research Methods' for Bachelor students, 'Methods of Empirical Educational Research' for Master students, and advanced workshops on longitudinal scaling and the Rasch model for doctoral candidates. His teaching reflects his dual expertise in educational research methodology and statistical analysis.

Uwe Walzمشاهده پروفایل

استاد

• Private Equity
• Startup Finance
• Innovation Economics
• +۵ مورد دیگر

Prof. Dr. Uwe Walz is a full Professor of Economics at Goethe University Frankfurt , holding the Chair of Industrial Organization since 2002. He serves as Dean of Studies for the Faculty of Economics and Business Administration and Deputy Scientific Director of the Leibniz Institute SAFE (Sustainable Architecture of Finance in Europe). His academic career includes professorships at the University of Bochum (1995-1997) and University of Tübingen (1997-2002), with visiting appointments at LSE and UC Berkeley. Doctorate (1991) and Habilitation (1995) in Economics Current roles: Professor, Dean of Studies, Deputy Director of SAFE His research focuses on private equity , startup finance , and innovation economics , with recent publications analyzing buyer group dynamics (2025), PE spillovers (2024), and venture capital networks (2023). Articles span corporate governance, financial regulation, and technology transfer. The 15 most recent articles (2020-2025) demonstrate consistent focus on private equity (5 papers), venture capital (4), corporate governance (3), and innovation economics (3). Key methodological themes include empirical analysis of financial data and institutional economics. Current advisees include doctoral candidate Ella-Maria Schirra . His team at Goethe University Frankfurt comprises research assistants Leo Leitzinger (since 2020) and Olaf Kehne . The Chair of Industrial Organization collaborates with the Leibniz Institute SAFE on sustainable financial architecture research.

• Biophysics
• Cellular Biophysics
• Dynamic Self-Organization in Living Matter
• +۵ مورد دیگر

Matthias Weiss is a Professor of Experimental Physics at the University of Bayreuth since 2010. Previously, he held roles including BIOMS Junior Group Leader at the German Cancer Research Center (2004–2010) and postdoc positions at EMBL Heidelberg and the MEMPHYS-Center in Denmark. He studied physics at Frankfurt and Heidelberg, earning a Diploma (1997) and PhD in quantum chaos (2000). His research bridges physics and biology, focusing on dynamic self-organization in living systems. Key areas include transport processes, organelle formation, embryogenesis, and parasite-host interactions. Techniques include advanced light microscopy and computational modeling. His work spans projects like the physics of Giardia adhesion and Trypanosoma motility, funded under SPP 2332 PoP. Notable publications explore anomalous diffusion in nuclei, ER network dynamics, and microtubule roles in Trypanosoma. Collaborations involve institutions like DKFZ, EMBL, and the University of Würzburg. He leads the Experimental Physics I department at Bayreuth and advises on biophysical methodologies.

• Physics beyond the Standard Model
• Z' Physics
• Axions and Axion-like Particles
• +۵ مورد دیگر

Prof. Felix Yu leads the Theoretical Elementary Particle Physics Group (THEP) at Johannes Gutenberg University Mainz. His research focuses on physics beyond the Standard Model, including Z' physics, axions, dark matter, CP violation, Higgs physics, and solutions to the hierarchy problem. He collaborates with experiments like ATLAS, CMS at the LHC, and the MESA accelerator in Mainz. His work bridges theoretical models with experimental validation, emphasizing both particle phenomenology and collider physics. Research Interests: Z' Physics: Explores U(1) gauge symmetries, kinetic mixing, and collider signatures at the LHC. Axions/ALPs: Studies non-minimal axion models and their couplings in effective field theories. Dark Matter: Investigates weakly interacting particles and upcoming terrestrial experiments. CP Violation & Flavor Physics: Links low-energy flavor observables to high-energy collider data. Higgs Physics: Examines Higgs couplings and future collider potential for precision measurements. Advising & Collaborations: Supervises PhD/Master students (Luca Bau, Greta Blank, Lucas Heger, Alexey Kivel) and collaborates with international teams at CERN and MESA. Research emphasizes interdisciplinary approaches to unresolved questions in particle physics. Labs/Teams: Core member of THEP and affiliated with LHC collaborations (ATLAS/CMS) and MESA experiments.

• Databases
• Spatial Data Management
• Geospatial Systems
• +۳ مورد دیگر

Mohamed Sarwat is an Associate Professor at Arizona State University specializing in databases , spatial data management , and recommender systems . His research focuses on GeoSpark —a cluster computing framework for spatial data—and its extensions like GeoSparkViz for visualization and GeoSparkSim for traffic simulation. Key Contributions: LARS* (Location-Aware Recommender System), Horton* (Graph Reachability), Sindbad (GeoSocial Platform), and Riso-Tree (Graph Database Indexing) Research Themes: Integration of spatial/temporal data with machine learning, efficient indexing for big geospatial datasets, and scalable frameworks for mobility data science His work spans collaborations with 23+ co-authors across institutions like University of Minnesota, University of Melbourne, and University of Salzburg. Current projects emphasize GeoTorchAI —a spatiotemporal deep learning system—and mobility data science infrastructure.

• Artificial Intelligence
• Probabilistic Graphical Models
• Deep Generative Models
• +۴ مورد دیگر

Ewa Szczurek is a Professor at the University of Warsaw's Faculty of Mathematics, Informatics and Mechanics and Co-director of the Institute for AI for Health, leading joint labs at Helmholtz Munich and the University of Warsaw. Education: Master degree in Computer Science from Uppsala University, Sweden (2005) Master degree in Computer Science from University of Warsaw, Poland (2006) Doctoral degree from Max Planck Institute for Molecular Genetics, Berlin (2011) Postdoctoral fellowship at ETH Zurich, Switzerland (2011) Habilitation at University of Warsaw, Poland (2020) Visiting associate professor at Northwestern University, USA (2023) Research Focus: Her work centers on probabilistic graphical models and deep generative models applied to computational medicine, with emphasis on oncology (tumor microenvironment modeling and evolution), pulmonology, and AI-driven antimicrobial peptide design to combat antibiotic resistance. She develops specialized deep learning frameworks for spatial transcriptomics analysis, tumor evolution reconstruction, and synthetic antimicrobial compound generation. Publication Trends: Recent publications (2022-2023) demonstrate consistent innovation in AI-driven biomedical solutions, bridging machine learning with oncology and infectious disease research. Her work shows increasing focus on generative models for drug design and spatial data analysis, with strong industry-academia collaborations (e.g., Merck, IMMUcan consortium). Awards: ERC Consolidator Grant (2023) for DOG-AMP project Scientific and didactic award from Rector of University of Warsaw ETH Zurich and IMPRS fellowship awards Grants & Leadership: Leads the ERC Consolidator Grant project DOG-AMP for antimicrobial peptide design. Serves as Associate Editor for Genome Biology and participates in program committees for ISMB and RECOMB-CCB conferences. Collaborates with international consortia including IMMUcan and Merck's Oncology Bioinformatics department. Labs & Networks: Co-directs AI for Health Institute with joint labs at Helmholtz Munich and University of Warsaw. Active in ELLIS (pan-European AI network) and Polish Bioinformatics Society, driving interdisciplinary research at the AI-medicine interface.

Marios Georgakisمشاهده پروفایل

پژوهشگر

• Cardiovascular Disease
• Stroke
• Atherosclerosis
• +۸ مورد دیگر

Marios Georgakis is a clinician-scientist and Junior Group Leader at the Institute for Stroke and Dementia Research (ISD) at LMU Munich. He also holds a Visiting Scientist position at the Broad Institute of MIT and Harvard. His research focuses on leveraging multi-omics data and causal inference methods (e.g., Mendelian randomization) to discover drug targets for atherosclerosis, develop personalized risk stratification tools for cerebrovascular disease, and identify in vivo biomarkers of disease activity. His work bridges human genetics, molecular biology, and clinical translation. Education : MD and PhD (Epidemiology) from the National and Kapodistrian University of Athens; doctoral studies in Systemic Neurosciences at LMU Munich. Honors : Emmy Noether Award (DFG), CHARGE Consortium Early Career Achievement Award, Hertie Network Fellowship, and multiple scholarships/fellowships. Research Themes : Drug target discovery for cardiovascular disease via multiomics integration Molecular phenotyping of atherosclerosis using single-cell RNA-seq and spatial transcriptomics Development of AI-driven tools for vascular imaging and aging Genetic studies of inflammation, cytokines, and stroke subtypes Causal inference in vascular risk prediction and post-stroke outcomes Recent Article Trends : His team's publications (2024-2020) emphasize: Proteogenomic and genetic studies of atherosclerosis Cytokine signaling pathways (e.g., IL-6, CCL2/CCR2) Polygenic and genomic risk scores for stroke Multi-omics biomarkers in cerebrovascular disease Clinical translation of Mendelian randomization findings Meta-analyses of population-based data Scientific Awards : Emmy Noether Group Leader Award (DFG, 2023) CHARGE Consortium Early Career Achievement (2023) Hertie Network Fellowship (2023) Walter-Benjamin Postdoctoral Fellowship (2021-2022) Team Leadership : Georgakis mentors multiple PhD students and postdocs in his lab. His group collaborates with vascular surgeons, neurologists, and computational biologists. Current projects include the AtherOMICS biobank and AI-driven vascular phenotyping tools.

• Transportation Systems
• Smart Cities
• Traffic Simulation
• +۳ مورد دیگر

Maria Paz Linares Herreros is a Lecturer at the Universitat Politècnica de Catalunya (UPC), affiliated with the School of Mathematics and Statistics (FME) and the Department of Statistics and Operations Research. She is a member of the IMP (Information Modeling and Processing) research group and collaborates with inLab FIB on intelligent transportation systems. Research interests: Transportation systems, smart cities, traffic simulation, data-driven modeling, environmental impact assessment Specializes in applying machine learning and simulation to urban mobility challenges Her recent publications focus on: Parking availability prediction using deep learning Traffic emission modeling linked to urban policies Dynamic ride-sharing system optimization Integration of IoT data in transportation planning Scientific recognition: Recipient of the IV International Award on Transport Infrastructure Management Research (2018) Active contributor to projects like CitScale and Virtual Mobility Lab Collaborator in European initiatives like KIC Urban Mobility

Emtiyaz Khanمشاهده پروفایل

پژوهشگر

• Machine Learning
• Bayesian Inference
• Deep Learning
• +۲ مورد دیگر

Emtiyaz Khan is a Researcher at the RIKEN Center for AI Project in Tokyo, Japan. His work focuses on Bayesian deep learning, optimization, and variational inference methods. He leads research on the Bayesian Learning Rule framework, which bridges deep learning optimization with Bayesian principles. His research interests include developing scalable Bayesian methods for large neural networks, uncertainty quantification in deep learning, optimization algorithms (natural gradients, variational inference), and applications to foundation models. Key areas are efficient adaptation methods, model sensitivity analysis, and Bayesian principles for deep learning. Khan's publications demonstrate strong focus on Bayesian deep learning, optimization techniques, and uncertainty estimation, with applications ranging from large-scale models (GPT-2, ImageNet) to theoretical foundations of variational inference. He leads the Team Approx-Bayes research group focused on approximate Bayesian inference methods and maintains collaborations through JST CREST-ANR and Kakenhi grants.

Melanie Krügerمشاهده پروفایل

استاد

• Embodiment Research
• Cognitive Aging
• Motor Decision Making
• +۷ مورد دیگر

Prof. Dr. Melanie Krüger is a faculty member at Leibniz University Hannover , where she serves as Head of the Research Unit Sports and Cognition within the Institute of Sports Science. Her academic career spans institutions including the Technical University of Munich, University of Tasmania, and Ludwig Maximilian University. She leads research initiatives in embodiment, cognitive aging, motor decision-making, and the impact of physical activity on neurocognitive and motor functioning. Current Position: Professor, Institute of Sports Science, Leibniz University Hannover Prior Appointments: Post-doctoral roles at Technical University of Munich, University of Tasmania, and Pennsylvania State University Education: PhD in Systemic Neurosciences, Ludwig Maximilian University; Diploma in Sports Science, University of Leipzig Krüger's research focuses on: Embodiment Research: Systemic interactions between cognitive and motor development Cognitive Aging: Age-related changes in motor decision-making and coordination Human Locomotion: Collision avoidance, mixed reality interactions, and real-world movement studies Open Science: Advocacy for research data management in sports science Her recent publications explore mixed reality applications ( 2024 ), stress effects on motor function ( 2023 ), and methodological innovations in locomotion studies ( 2021-2025 ). Current projects include Moving Forward (2021-2022) and two Innovation Plus initiatives (2022-2024) developing experimental frameworks for human movement analysis. Krüger actively participates in academic governance: Spokesperson, German Society of Sport Science's Ad Hoc Committee on Research Data Management (since 2022) Board member, DVS Section 'Sports Motor Skills' (since 2022) Doctoral Committee, Faculty of Humanities Examination Board, MSc Sustainable Health Promotion in Sports Science

Johannes Soedingمشاهده پروفایل

پژوهشگر

• Computational Biology
• Bioinformatics
• Protein Structure Prediction
• +۹ مورد دیگر

Johannes Soeding is a Research Group Leader in the Computational Biology department at the Max Planck Institute for Multidisciplinary Sciences in Göttingen, Germany. His work bridges physics, bioinformatics, and molecular biology, focusing on computational methods for biological data analysis. His research interests include computational biology, protein structure and function prediction, metagenomics, transcriptional regulation, and statistical genomics. He develops widely used software tools such as HH-suite, HHpred, MMseqs2, and Foldseek for protein sequence and structure analysis. The recent publications demonstrate a strong focus on high-throughput biological data, particularly in protein structure search (e.g., Foldseek), metagenomic gene discovery (e.g., MetaEuk), and regulatory genomics. His work combines algorithm development with deep biological insights, often published in top-tier journals like Nature Biotechnology , Science , and Nature Methods . He has been involved in significant methodological advances in sequence clustering, contact prediction, and eQTL analysis, showing a consistent trend toward scalable, data-driven approaches in genomics and proteomics. Soeding has contributed to major projects in gene regulatory networks and RNA biology, often in collaboration with experimental groups. His leadership in developing open, efficient bioinformatics tools has had a broad impact on the scientific community. He is affiliated with several graduate programs including IMPRS Physics of Biological and Complex Systems, Biomolecules: Structure - Function - Dynamics, and Genome Science, indicating active participation in training the next generation of scientists.

Petra Ahrweilerمشاهده پروفایل

استاد

• Technology and Society
• Innovation Networks
• Policy Modeling
• +۳ مورد دیگر

Prof. Petra Ahrweiler is a full professor of Sociology of Technology and Innovation at the Johannes Gutenberg University Mainz (since 2013), with a focus on social simulation and innovation policy. She previously served as Director of the EA European Academy for Technology Assessment (2013–2017) and held a professorship at University College Dublin. Her research explores technology-society interactions, innovation networks, and AI ethics, emphasizing participatory approaches and policy modeling. She leads initiatives like the TISSS LAB and Sensorithm Digital Transformation Lab, focusing on AI, climate action, and societal impact. A recipient of fellowships from acatech and AcademiaNet, she also served as ESSA President (2020–2022). Education: PhD in Social Sciences from the Free University of Berlin (on AI), Habilitation from the University of Bielefeld (simulation in science studies). Former roles include research at MIT (USA) and leadership at the Innovation Research Unit (IRU), UCD. Research Interests: Her work bridges sociology and computational modeling, addressing topics like participatory AI, innovation dynamics, and policy simulation. Recent foci include culture-sensitive AI, climate action catalysts, and pandemic response modeling. She co-leads major projects such as AI FORA, CECAN, and PEERE, advancing interdisciplinary collaboration. Awards & Leadership: Acatech and AcademiaNet fellowships, ESSA presidency, and leadership in international research consortia. Her work emphasizes ethical AI, societal engagement, and systemic innovation. Labs & Teams: Core leader of TISSS LAB (Technology Innovation & Simulation Science) and the Sensorithm Lab, which develop adaptive sensor systems and participatory AI tools. Collaborates globally on digital transformation and social simulation.

Kristin Litteckمشاهده پروفایل

پژوهشگر

• Mathematics Education
• Acquisition of Mathematical Concepts
• Prior Knowledge in Learning
• +۲ مورد دیگر

Kristin Litteck is a postdoctoral researcher at the Leibniz Institute for Science and Mathematics Education (IPN) in Kiel, Germany. She earned her PhD in 2024 and holds degrees in Mathematics and English education from Kiel University. Focuses on acquisition of mathematical concepts (derivative/integral) Investigates prior knowledge effects at upper secondary transition Explores intercultural differences in mathematics instruction Develops AI applications for mathematics education Active in the LPA – Learning Progression Analytics and MiO – Mathematik in der Oberstufe projects, her work bridges cognitive theory and educational practice. She serves on the IPN ethics committee since 2025 and has contributed to PME and GDM conference proceedings.

Ralf Wilkeمشاهده پروفایل

استاد

• Applied Econometrics
• Microeconometrics
• Labour Economics
• +۳ مورد دیگر

Ralf Wilke is a Professor in Applied Econometrics and Microeconometrics at the Copenhagen Business School , with affiliations to ZEW as a research associate. His career spans multiple institutions including the University of Leicester, University of Nottingham, and University of York. Education: Diplom-Volkswirt (Bonn University), DEA (University of Toulouse I), Dr. rer. pol. (Dortmund University) Current Roles: Professor at CBS, Research Associate at ZEW Prior Roles: Lecturer/Associate Professor at Leicester/Nottingham, Readership at York Research Focus: Specializing in econometric methods for analyzing duration data, competing risks, panel models with group structures, and applications in industrial life modeling. His work addresses complex censoring mechanisms and spatial diffusion patterns in historical economic contexts. Recent Publication Trends: Recent articles emphasize duration modeling with dependent censoring, copula-based competing risks frameworks, and structural econometric approaches to panel data. Key disciplines include computational statistics, quality engineering, and cliometrics.