جست‌وجوی استاد و پژوهشگر

• Biomedical Informatics
• Machine Learning
• Graph Neural Networks
• +۴ مورد دیگر

Yi-Jia Zhang is a Professor at the School of Computer Science and Technology, Dalian University of Technology. They hold affiliations with multiple institutions, including Zhejiang Sci-Tech University and Jilin University. Their research focuses on biomedical informatics, machine learning, and healthcare applications, with significant contributions to medical knowledge fusion, drug recommendation systems, and multimodal analysis. Over 140 publications since 2011 reflect expertise in areas like graph neural networks, natural language processing, and medical image analysis. Key collaborations include work with Hongfei Lin, Mingyu Lu, and Jian Wang on projects such as drug-repositioning models and radiology report generation frameworks. A notable emphasis is placed on applying AI techniques to solve biomedical challenges, including ICD code classification and sentiment analysis in healthcare contexts. No formal awards are listed, but their extensive publication record underscores academic impact.

Thomas A. DiPreteمشاهده پروفایل

استاد

• Social Stratification and Mobility
• Work and Labor Markets
• Organizations
• +۴ مورد دیگر

Thomas A. DiPrete is a Professor of Sociology at Duke University, affiliated with the School of Arts and Sciences. He holds a Ph.D. from Columbia University (1978) and has held academic positions at the University of Chicago and Duke since 1988. His research focuses on social stratification, labor markets, organizational dynamics, and quantitative methodology. Education: B.S. in Sociology from MIT (1972), M.A. and Ph.D. in Sociology from Columbia University (1975, 1978). Professional roles include Secretary/Treasurer of Research Committee 28 (Social Stratification) of the International Sociological Association and editorial board memberships in top journals like Sociological Methodology and Demography . Research emphasizes institutional impacts on labor markets, gender disparities in education, and causal analysis methods. Key publications explore cross-national comparisons of welfare systems, earnings mobility, and family dynamics. Grants include NIH-funded projects on fertility and family policy. Professional activities span international collaborations, including research fellowships at the Max Planck Institute and German Institute for Economic Research. Teaching includes courses on social inequality, advanced statistics, and organizational sociology.

Benedikt Holtmannمشاهده پروفایل

پژوهشگر ارشد

• Behavioral Ecology
• Animal Personality
• Evolutionary Biology
• +۲ مورد دیگر

Dr. Benedikt Holtmann is a Research Fellow at the Faculty of Biology, Ludwig Maximilian University of Munich. His research focuses on behavioral ecology and evolutionary biology, particularly studying how social environments influence behavior-related settlement decisions in wild passerine birds. Funded by the German Research Foundation (DFG), his work examines the non-random assortment of behavioral phenotypes and its implications for individual fitness and population dynamics. Research interests center on animal personality, behavioral syndromes, and evolutionary ecology in avian systems. His investigations utilize model species including great tits ( Parus major ) and dunnocks ( Prunella modularis ) to understand: Social environment effects on settlement patterns Fitness consequences of behavioral phenotypes Personality-matching habitat selection Genetic basis of animal behavior Reproductive strategies and sexual selection Recent publications demonstrate strong emphasis on: Integrative studies of animal personality traits Social structure dynamics in corvids and passerines Reproductive success determinants Long-term ecological monitoring approaches Physiological and genetic mechanisms underlying behavior with research conducted across European and New Zealand ecosystems. Current research infrastructure includes access to the Biozentrum Martinsried facilities and field sites across Germany supporting avian population studies through capture-mark-recapture methodologies and behavioral observation systems.

Alexander Dilgerمشاهده پروفایل

استاد

• Corporate Governance
• Euro Crisis
• University Management
• +۷ مورد دیگر

Prof. Dr. Alexander Dilger is a prominent academic and researcher in the field of business administration, holding the position of Spokesperson of the Center for Management (CfM) at the University of Münster since April 2024. He has been the Director of the Institute for Organizational Economics since October 2010 and is a founding member of the CfM since 2005. His academic journey includes roles such as Professor of Business Administration at the University of Münster (2003–2010) and Visiting Professor at the University of Vienna (2002–2003). He earned his Ph.D. in economics from Humboldt University of Berlin (1997) and completed his habilitation in 2001 at the University of Greifswald. His research focuses on Corporate Governance, Euro Crisis dynamics, University Management, Insolvency Economics, Personnel Economics, Sports Economics, and Business Ethics. He has contributed extensively to academic literature, with publications in journals like Journal of Sports Economics , Economic Inquiry , and European Journal of Law and Economics . He is a member of several prestigious academic associations, including the American Economic Association and the German Economic Association of Business Administration. Prof. Dilger’s work often intersects economic theory with practical applications, such as analyzing the impact of rule changes in sports leagues and the ethical dimensions of corporate governance. His research on sports economics explores topics like doping behavior and the economic implications of sporting success. He also engages in policy-relevant studies, such as evaluating the Euro’s business effects and the governance of universities. His academic leadership roles include chairing the VHB’s academic commission for higher education management (2006–2012) and serving on the editorial boards of journals like Hochschulmanagement . He actively reviews for numerous journals and foundations, contributing to the rigor of academic discourse in business and economics.

Nicole Altvater-Mackensenمشاهده پروفایل

استاد

• Psycholinguistics
• Developmental Psychology
• Speech Perception
• +۵ مورد دیگر

Prof. Nicole Altvater-Mackensen holds the Chair of Psycholinguistics at the University of Mannheim's School of Humanities, within the Department of English (Anglistik I). Her research focuses on developmental psycholinguistics, cognitive aging, and multimodal speech perception. She explores topics such as infant speech processing, statistical learning mechanisms across modalities, and the role of social robots in language education. Her work spans experimental psychology, neuroscience, and computational methods. Key areas include emotion processing in late adulthood, action anticipation in children, and cross-modal language activation in deaf populations. She has published extensively in journals like Developmental Science and NeuroImage , with a strong emphasis on interdisciplinary approaches. Core Research: Infant cognition, aging cognition, statistical learning, language acquisition Methodologies: ERP studies, behavioral experiments, neuroimaging Recent projects investigate how social robots can enhance language learning in children, and the neural correlates of speech perception in infants. She collaborates internationally on topics such as vocal emotion recognition and cross-linguistic phonetic contrasts.

Ulrich Bauerمشاهده پروفایل

استاد

• Applied Topology
• Computational Topology
• Topological Data Analysis
• +۳ مورد دیگر

Prof. Dr. Ulrich Bauer is a Professor at the Technische Universität München (TUM) , leading the Applied and Computational Topology research group within the TUM School of Computation, Information and Technology . His academic career includes positions at Freie Universität Berlin, Georg-August-Universität Göttingen (where he earned a doctoral degree in Mathematics), and the Institute of Science and Technology Austria. Bauer specializes in applied and computational topology, focusing on multi-scale data connectivity and developing computational methods for large datasets. He is a key member of the Collaborative Research Center Discretization in Geometry and Dynamics and the Centre for Topological Data Analysis . Research Interests: Bauer’s work bridges theoretical foundations and practical applications in topology. He explores methods like persistent homology and discrete Morse theory to uncover global data structures. His contributions include advancing algorithms for topological data analysis, with applications in medical imaging, computational biology, and geometric modeling. Bauer’s software tool Ripser is widely recognized for efficient computation of persistence barcodes. Awards: ATMCS Best New Software Award (2016) Best Paper Award TopoInVis (2013) Apple Design Award (2003) O’Reilly Mac OS X Innovators Award (2003) Grants & Leadership: Bauer’s leadership roles include the executive board of the CRC Discretization in Geometry and Dynamics. His research has been supported by grants focusing on topological methods in data science and geometry. He actively contributes to advancing interdisciplinary collaborations between mathematics, computer science, and applied fields. Labs & Teams: As founder of the Applied and Computational Topology group at TUM, Bauer fosters innovation in computational topology, mentoring researchers and students in developing cutting-edge methodologies. His work intersects with the TUM School’s broader mission in computational and data-driven science.

Klaus Ostermannمشاهده پروفایل

استاد

• Functional Programming
• Programming Language Design and Implementation
• Type Systems
• +۷ مورد دیگر

Klaus Ostermann is a Professor of Programming Languages and Software Technology at the Department of Computer Science, Faculty of Science, University of Tübingen, Germany. He leads a research group focused on programming language design, functional programming, and software modularity. Research Interests: His work spans functional programming, type systems, effect handlers, software architecture, and domain-specific languages. He investigates the duality between data and codata, the safe integration of effects in typed languages, and the modularity of software systems. His research often bridges theoretical foundations with practical implementations in languages like Scala and Haskell. The recent publications highlight a strong trend in effect systems, continuation-passing, and the symmetry between data and codata. His group develops the Effekt language and explores advanced compilation techniques for effect handlers, contributing to both theoretical understanding and practical language design. Scientific Awards: Most Influential Paper Award at GPCE 2018 for 'Polymorphic Embedding of DSLs' (2008 paper) He actively supervises PhD and master’s students, including Philipp Schuster, Marius Müller, and Jonathan Brachthäuser. His group has received funding for research in programming language foundations, effect systems, and software modularity, though specific grants are not listed. He serves on the program committees of top conferences such as POPL, ICFP, PLDI, and OOPSLA, reflecting his active role in the programming languages community. The research is conducted within the Uroboro project, which explores duality in programming language constructs, and involves collaboration with both national and international researchers. The team also engages in educational projects, including thesis supervision on topics ranging from probabilistic programming to music notation visualization.

Robert Lehmannمشاهده پروفایل

استاد

• Digital Social Work
• Online consultation
• AI in social work
• +۴ مورد دیگر

Prof. Dr. Robert Lehmann is a faculty member at the Faculty of Social Sciences of the Nuremberg Institute of Technology , specializing in digital social work and AI applications in counseling. He leads the Institut für e-Beratung and collaborates with the Zentrum für Künstliche Intelligenz (KIZ) . His research spans online consultation , empirical social work science , and blended learning models. Research Themes : Digital transformation in social work, AI ethics, pandemic-era counseling adaptations. Projects : HeLB (funded by BMFSFJ), DIA (Staedtler-Stiftung), Casotex, and VirCo. His recent publications highlight generative AI in counselor training, machine learning for text analysis, and digital service accessibility . He emphasizes data privacy and interdisciplinary collaboration in all projects.

• Cloud Computing
• Data Center Optimization
• Edge Computing
• +۵ مورد دیگر

V. Dinesh Reddy is affiliated with SRM University Andhra Pradesh, Department of Computer Science and Engineering in Amaravati, India. He maintains an active research career with publications spanning from 2017 to 2025 across multiple prestigious venues including IEEE Access, Energy Informatics, Quantum Information Processing, and Sensors. Dr. Reddy's research interests span cloud computing infrastructure optimization, edge computing, quantum computing applications, image processing, and cybersecurity. His work demonstrates expertise in developing evolutionary algorithms, machine learning approaches, and optimization techniques to solve complex computing problems with practical applications in IoT security, vehicular networks, and medical diagnostics. His publication record shows consistent output with increasing collaboration and expanding research scope over time. The research demonstrates strong interdisciplinary connections between traditional computer science domains and emerging technologies like quantum computing, addressing real-world challenges in computing infrastructure efficiency and security. Dr. Reddy has collaborated extensively with researchers including G. R. Gangadharan, G. Subrahmanya V. R. K. Rao, Marco Aiello, Md. Muzakkir Hussain, and Ashu Abdul. His research appears well-funded given the scope and diversity of projects, with applications spanning sustainable data centers, edge computing for vehicular networks, and quantum computing implementations. His research spans multiple laboratory contexts, particularly in cloud computing infrastructure, quantum computing applications, and image processing. Dr. Reddy's future research directions appear to be expanding into more specialized quantum computing applications and advanced edge computing scenarios for vehicular networks, as evidenced by his most recent publications from 2024-2025.

Eduard Brüllمشاهده پروفایل

استاد

• Labour Economics
• Health Economics
• Regional Economics
• +۳ مورد دیگر

Eduard Brüll is a substitute professor (W2 Vertretungsprofessur) for Accounting at the University of Mannheim (2025) and a researcher at ZEW’s Research Department Labour Markets and Social Insurance. His work spans Labour Economics , Health Economics , and Regional Economics , focusing on urban amenities, digitalisation, remote work, wage inequality, and healthcare competition. University of Mannheim : Substitute Professor, Accounting Department ZEW : Researcher, Labour Markets and Social Insurance Research interests center on: Urban amenities and skill-based migration patterns Digital capital’s role in pandemic-era labour resilience Competition effects in primary care systems Long-term wage inequality in East/West Germany His publications since 2020 examine urban amenity preferences , temporary work regulations , and digitalisation’s economic impacts . Current work in progress investigates job mobility constraints and expanded labour market boundaries.

• Pancreatic Cancer
• O-Glycosylation
• Chemotherapy
• +۳ مورد دیگر

Dr. Cenap Güngör is a researcher at the University Medical Center Hamburg-Eppendorf (UKE), affiliated with the Faculty of Medicine and the Department of General, Visceral and Thoracic Surgery . His work focuses on pancreatic cancer , O-glycosylation , tumor microenvironment , and chemotherapy efficacy , utilizing transgenic mouse models and SEER database analysis . Key research areas include: Glycan-mediated signaling in cancer progression Microbiome-drug interactions in chemotherapy Metastasis and drug resistance mechanisms Development of prognostic nomograms for cancer patients His publications (2025-2010) span journals like Nature , Cancer Research , and Molecular Cancer , addressing topics such as Tn antigen expression , midkine signaling , and CRC gene polymorphisms in gastrointestinal cancers. No formal awards or student advising details are provided in the text.

• 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.

Malte Prießمشاهده پروفایل

استاد

• Cloud Computing
• Artificial Intelligence
• Software Engineering
• +۵ مورد دیگر

Prof. Dr. rer. nat. Malte Prieß is a Professor for Cloud Technologies at Kiel University of Applied Sciences since October 20224, following eight years as Dean and Professor of Applied Computer Science at Schleswig-Holstein Cooperative State University (DHSH) . He teaches modules including Cloud Computing , Web Applications , and Advanced Cloud Computing , with additional involvement in Agile Development Methods and Software Engineering . Academic Background: Diploma in Physics (with distinction) from Leibniz University Hanover and Max Planck Institute for Gravitational Physics (2002-2007) Dr. rer. nat. (magna cum laude) from Kiel University in Algorithmic Optimal Control (2008-2012) Research Interests focus on the intersection of cloud computing, artificial intelligence, and modern software engineering . His work includes surrogate-based optimization for climate models, AI-driven document capture systems , and ethical considerations in AI deployment within project work. Recent Publications demonstrate expertise in AI vulnerability assessment , deep learning training optimization , and document search algorithms for governmental agencies. Scientific Recognition: Best Paper Award at CLOUD COMPUTING 2025 for "Graph of Effort" vulnerability assessment Accepted fellowship at AI Campus (Stifterverband) for "Teaching AI, learning AI at DHSH" (2022) Research Projects: Central Innovation Programme for SMEs (ZIM): "AI MODULES for the skilled trades" (2024/25) HR dashboard for DRK Schwesternschaft, Lübeck Scalable Data Analytics project under BMBF FHprofUnt program (2018)

• Ecological Modeling
• Statistical Ecology
• Biodiversity
• +۵ مورد دیگر

William J. Castillo is a researcher in the Department of Biometry and Environmental System Analysis at the Albert Ludwig University of Freiburg, Germany. He is actively contributing to the REASSEMBLY research unit, focusing on theoretical and statistical modeling of ecological networks in the context of tropical forest recovery in the Chocó region of Ecuador. His research interests lie at the intersection of ecological theory and applied statistics, particularly in understanding how species trait-matching and environmental filtering shape community assembly and network dynamics. This work combines simulation models with empirical data analysis to unravel patterns of biodiversity restoration. The recent articles extracted show a consistent focus on ecological modeling, biodiversity dynamics, and statistical methods in environmental systems, indicating a strong methodological and theoretical orientation in his research. Scientific Awards: None listed. William J. Castillo is involved in advising opportunities for Bachelor and Master projects, as indicated by departmental announcements, though no specific students are named. There is no mention of grants, but his participation in the REASSEMBLY research unit suggests involvement in funded collaborative research. He is part of the research team within the Department of Biometry and Environmental System Analysis, which specializes in quantitative ecology, statistical modeling, and environmental system analysis, working under the leadership of Prof. Dr. Carsten Dormann.

• Machine Learning
• Computational Modeling
• Biomedical Engineering
• +۳ مورد دیگر

Stephen Baek is a faculty member at the University of Iowa, Department of Industrial and System Engineering, with a PhD from Seoul National University's School of Mechanical and Aerospace Engineering. His research spans interdisciplinary applications of machine learning in computational modeling, biomedical engineering, and geometric data processing. Current Affiliation: University of Iowa, Department of Industrial and System Engineering PhD Institution: Seoul National University Stephen's work focuses on physics-informed machine learning , multiscale modeling , and geometric deep learning . He develops algorithms that integrate physical principles with neural networks for applications in energetic materials , human pose estimation , and medical imaging . His research also includes federated learning and interpretable AI for constraint-based synthesis and text classification. Recent publications highlight a physics-aware deep learning framework (PARCv2) for spatiotemporal dynamics, graph convolutional networks for airway mesh smoothing, and prototype trajectory methods for explainable AI. This work bridges computer science with applied physics and healthcare domains. Stephen actively collaborates across disciplines, evidenced by co-authors from institutions like Iowa, Seoul National University, and Samsung Electronics, with publications in venues such as CoRR , J. Mach. Learn. Res. , and NeurIPS . His methodological innovations address challenges in model heterogeneity , 3D surface processing , and constraint satisfaction .