جست‌وجوی استاد و پژوهشگر

• Cultural Algorithms
• Evolutionary Computation
• Artificial Intelligence
• +۵ مورد دیگر

Robert G. Reynolds is a Professor of Computer Science at Wayne State University's College of Engineering, where he leads pioneering research in Cultural Algorithms and evolutionary computation. With over 197 publications spanning from 1982 to 2024, he has established himself as a leading authority in computational intelligence and socially-inspired optimization techniques. His work bridges computer science, social modeling, and practical applications across diverse domains including education, healthcare, and archaeology. Reynolds' research interests center around Cultural Algorithms, which he developed as a framework for modeling how cultures solve problems through social learning and knowledge transmission. His work extends to evolutionary computation, swarm intelligence, and agent-based modeling, with applications ranging from virtual reality educational systems to medical imaging analysis. He has published the influential book 'Culture on the Edge of Chaos - Cultural Algorithms and the Foundations of Social Intelligence' (2018) that formalized his theoretical contributions. His recent publications (2022-2024) demonstrate continued innovation in applying Cultural Algorithms to diverse challenges including STEM education through virtual reality environments, medical image analysis, economic modeling, and climate change impact assessment. These works consistently show his approach of integrating social dynamics into computational problem-solving frameworks. Among his notable scientific contributions is the development of the Cultural Algorithms framework as a comprehensive approach to optimization and problem-solving that incorporates social dimensions often missing in traditional evolutionary algorithms. His work has influenced numerous researchers in the field of computational intelligence. Professor Reynolds has mentored numerous students who have become frequent collaborators, including Mostafa Z. Ali, Thomas Palazzolo, Sarah Saad, and Chencheng Zhang. His research has been supported by various grants that have enabled the development of projects like the Deep Dive Virtual Reality system for exploring ancient submerged civilizations and educational applications of AI. His laboratory work focuses on developing the Cultural Algorithms Toolkit (CAT) and applying it to real-world problems through virtual environments that simulate social and cultural dynamics. Current projects include extending Cultural Algorithms for trustable AI systems and developing more sophisticated models of cultural knowledge transmission.

Roman Hornungمشاهده پروفایل

پژوهشگر ارشد

• Random Forests
• Statistical Methodology for Cross-Lab Prediction
• Statistical Methodology for Multi-Omics Data
• +۵ مورد دیگر

Dr. Roman Hornung is a Research Fellow at the Institute of Medical Informatics, Biometry and Epidemiology (IBE) within Ludwig-Maximilians-Universität Munich (LMU), specializing in Biometry in Molecular Medicine. His work focuses on advancing statistical and machine learning methodologies for biomedical applications. Academic Rank: Research Fellow Key Research Areas: Random Forests, Multi-Omics Data Integration, Interpretable Machine Learning, Cross-Validation Techniques, Biomedical Informatics, and Meta-Research on Prognostic Modeling Teaching: Leads seminars on applied statistics in epidemiology and contributes to clinical epidemiology tutorials Software Contributions: Maintainer of R packages like diversityForest and prioritylasso His research bridges statistical theory and practical biomedical applications, with a particular emphasis on reproducibility and robustness in predictive modeling. He has developed novel random forest variants for multi-class outcomes and multi-omics data. Hornung’s recent publications and submitted work highlight trends in benchmarking machine learning methods, improving cross-study prediction, and understanding the impact of data preparation on model performance. Notably, he has contributed to methodologies for generalization error estimation, variable importance measures, and diversity-enhanced forest algorithms. As a member of the Biometry in Molecular Medicine working group, he collaborates on interdisciplinary projects integrating clinical and omics data. His habilitation at LMU Munich (2024) and editorial roles at Biometrical Journal and Scientific Reports underscore his academic leadership in statistical methodology for health sciences.

• Medical Image Analysis
• Computer Vision
• Multimedia Systems
• +۶ مورد دیگر

Steven Alexander Hicks is an Associate Professor in the Department of Computer Science at Oslo Metropolitan University's Faculty of Technology, Art and Design. His research focuses on applying computer vision and machine learning techniques to medical imaging problems, particularly in gastrointestinal endoscopy and reproductive medicine. He is an active contributor to the medical AI community through his involvement in organizing challenges and workshops such as ImageCLEFmedical and MediaEval. Hicks' research interests center on medical image analysis, with particular emphasis on gastrointestinal endoscopy and reproductive health applications. His work spans medical image segmentation, polyp detection, sperm tracking systems, and explainable AI for medical applications. He has developed innovative approaches for medical image analysis including diffusion models for synthetic data generation, visual question answering systems for gastrointestinal tract analysis, and frameworks for evaluating explanation methods in neural networks. His research bridges the gap between computer science and clinical medicine, aiming to develop practical AI tools that can be integrated into medical workflows. Analysis of Hicks' recent publication record reveals a strong focus on medical image analysis challenges, particularly in gastrointestinal endoscopy and reproductive medicine. He has been instrumental in organizing and contributing to the ImageCLEFmedical challenges, which have become important benchmarks in the field. His work shows a progression from basic image analysis techniques to more sophisticated approaches incorporating explainability, multimodal learning, and generative models. The consistent publication output across top venues demonstrates his active role in advancing medical AI research. Hicks has made significant contributions through his leadership in organizing evaluation campaigns and challenges that have shaped research directions in medical image analysis. His work with the MediaEval and ImageCLEF communities has provided valuable benchmarks and datasets for researchers worldwide. He has been involved in several large-scale collaborative projects addressing important problems in medical imaging, including polyp segmentation, instrument detection in endoscopy, and sperm analysis. As part of the broader research ecosystem at Oslo Metropolitan University, Hicks contributes to a vibrant research environment focused on applying computing technologies to healthcare challenges. His work often involves interdisciplinary collaboration between computer scientists, medical professionals, and domain experts to ensure that technical solutions address real clinical needs. His research group appears to focus on developing practical AI solutions that can be translated into clinical practice, with particular attention to validation methodologies and explainability requirements in medical contexts.

Dennis Doblerمشاهده پروفایل

استادیار

• Resampling methods
• Survival and event time analysis
• Multivariate analysis
• +۱ مورد دیگر

Dennis Dobler is an Assistant Professor in Statistics at TU Dortmund University, appointed in September 2023. His academic foundation includes a doctorate from Ulm University (2016) and dual bachelor's degrees in Mathematics and Computer Science from Heinrich-Heine-Universität Düsseldorf (2011). Research expertise centers on Resampling methods, Survival analysis, Multivariate techniques, and Asymptotic statistics, with applications spanning clinical trials, risk modeling, and nonparametric inference. His methodological work frequently addresses censored data challenges in survival contexts. Prior roles include Assistant Professorship at Vrije Universiteit Amsterdam, postdoctoral research at Ulm University, and a research stay at the University of Copenhagen. Current publications reflect sustained focus on survival analysis innovations, particularly in competing risks and resampling methodologies.

Sebastian Wallotمشاهده پروفایل

استاد

• Nonlinear Dynamics in Psychology
• Methodology and Evaluation Research
• Reading Processes and Comprehension
• +۷ مورد دیگر

Sebastian Wallot is a Professor of Psychology at Leuphana University of Lüneburg, Germany, specializing in Methodology and Evaluation Research. His academic position centers on developing and applying advanced analytical methods to psychological phenomena, particularly focusing on nonlinear dynamics and time series analysis. Wallot maintains an active research profile with 90 publications spanning from 2011 to 2025, demonstrating consistent scholarly productivity across multiple domains of psychological science. Wallot's research interests bridge complex systems theory with empirical psychological research. He has developed significant expertise in recurrence quantification analysis (RQA) and its applications to diverse psychological phenomena including reading processes, joint action coordination, and psychophysiological measures. His work demonstrates how nonlinear dynamics can reveal patterns in human cognition and behavior that traditional linear methods might miss. Wallot approaches psychological research from a complex systems perspective, emphasizing the interconnectedness of cognitive, physiological, and social processes across multiple timescales. His publication record shows a clear trajectory from foundational work on reading processes and eye movement dynamics toward increasingly interdisciplinary applications in sports science, music cognition, developmental psychology, and occupational health. Recent publications highlight methodological innovations in analyzing time series data of unequal length, multivariate approaches to leader-follower relationships, and applications of nonlinear methods to practical problems in sports medicine and employee recovery. Wallot leads several significant research projects including 'The role of (non-)synchronous coordination and its quantification in joint action,' 'Regularities in Reading Times,' and the prestigious Heisenberg Professorship project 'Quantifying nonlinear dynamics in psychological data.' His collaborative work spans multiple institutions and disciplines, as evidenced by his co-authorship on the MusicLab Copenhagen Dataset and various cross-institutional studies in sports medicine and developmental psychology.

Harry Hauptمشاهده پروفایل

استاد

• Statistics
• Econometrics
• Data Analytics
• +۴ مورد دیگر

Prof. Dr. Harry Haupt is a full Professor of Statistics and Data Analytics at the University of Passau's School of Business, Economics and Information Systems, where he leads the Chair of Statistics and Data Analytics. He has held this position since 2012 and previously served as Vice President for Research (2014-2020) and Deputy President (2017-2020) of the university. His research focuses on developing and applying flexible regression methods, with emphasis on semi-parametric techniques, quantile regression, and spatio-temporal modeling. Key application areas include environmental statistics, econometrics, marketing research, and machine learning. His work bridges theoretical statistics with computational implementations for real-world problems. Prof. Haupt's publications demonstrate strong interdisciplinary collaboration, particularly in climate science, environmental health, and econometrics. Recent work emphasizes quantile methods, election forecasting, air pollution modeling, and educational pedagogy in econometrics. Awards & Recognition: Habilitation Award, University of Regensburg PhD Supervision (Selected): Matthias Wild (2024): Machine learning applications Svenia Behm (2022): Environmental statistics Markus Fritsch (2019): Panel data methods 7 additional doctoral students in statistics and econometrics He leads a research team focusing on computational statistics and maintains active collaborations across economics, environmental science, and social research domains.

Pimrapat Gebertمشاهده پروفایل

پژوهشگر

• Public Health
• Medical Biometrics
• Biostatistics
• +۲ مورد دیگر

Dr. Pimrapat Gebert, MPH, is a Researcher at the Institute of Biometry and Clinical Epidemiology at Charité - University Medicine Berlin. Her expertise spans Public Health, Medical Biometrics, and Biostatistics, with a focus on observational studies and registry data analysis. Education: Doctorate (Dr. rer. medic.) in Medicine, Charité - University Medicine Berlin (2023) Master of Public Health (Biostatistics), Faculty of Public Health, Khon Kaen University, Thailand (2005–2007) Bachelor of Nursing, Khon Kaen University (2001–2005) Her research activities include biometric consulting, study planning and evaluation, caseload planning, and methodological research. She specializes in statistical techniques such as generalized estimating equations (GEE), Poisson regression, survival analysis, missing value patterns, and multiple imputation. Dr. Gebert is proficient in programming with SPSS, STATA, and R, supporting her work in statistical modeling and data analysis. She contributes to the Working Group on Observational Studies and Registry Data at Charité, located at the Campus Charité Mitte (Sauerbruchweg 3, 10117 Berlin).

• Artificial Intelligence
• Electrical Power Supply
• Power Grid Monitoring
• +۲ مورد دیگر

Andreas Winter is a researcher at the Institute of Electrical Energy Systems within Saarland University of Applied Sciences . His work focuses on integrating artificial intelligence (AI) into modern power grids, particularly for monitoring, optimization, and control of electrical networks. His contributions span real-time grid optimization , state estimation , and earth fault detection using machine learning techniques. Research Trends: Winter’s publications emphasize AI applications in power systems , combining neural networks with traditional methods to enhance grid reliability. Key areas include low-voltage grid monitoring , missing data handling , and fault diagnostics , reflecting a focus on smart grid technologies and grid modernization . Collaborations: He frequently collaborates with colleagues like M. Igel and P. Schegner , presenting at conferences such as CIRED , VDE/ETG Congress , and NEIS . His work bridges academic research with practical implementation in real-world power distribution systems.

Julian B. Axenfeldمشاهده پروفایل

پژوهشگر

• Survey Methodology
• Data Science
• Imputation
• +۲ مورد دیگر

Julian B. Axenfeld serves as Head of the German Social Cohesion Panel (SCP) at the Socio-Economic Panel (SOEP) within the German Institute for Economic Research (DIW Berlin). His research focuses on advancing survey methodology through innovative applications of data science, nonresponse analysis, and machine learning to improve social survey accuracy and efficiency. He holds a doctorate in Sociology from the University of Mannheim, where he completed his undergraduate studies. Prior to joining DIW Berlin, he worked as a Research Associate at the Socium Research Institute (University of Bremen) and Mannheim Centre for European Social Research (MZES) on DFG-funded projects involving modular questionnaire design. Dr. Axenfeld specializes in developing computational solutions for survey data challenges, particularly imputation techniques for missing data in split questionnaire designs and mixed-mode data collection systems. His work bridges statistical theory with practical survey implementation, emphasizing machine learning applications to reduce nonresponse bias and optimize resource allocation in large-scale panel studies. His recent publications reveal a clear trajectory toward integrating AI-driven methodologies with traditional survey science, with significant contributions to split questionnaire designs, imputation quality assessment, and longitudinal data monitoring systems. These studies consistently address the tension between respondent burden reduction and data integrity preservation in contemporary social research. No scientific awards were documented in the source materials. As SCP Director, Axenfeld manages a major longitudinal infrastructure project tracking social cohesion across Germany, though specific grant details remain undisclosed. He previously contributed to the DFG-funded 'Modular Questionnaire Designs for Social Surveys' project but shows no formal student advising relationships in available records. The Social Cohesion Panel operates as a specialized research unit within SOEP, implementing cutting-edge mixed-mode survey protocols (including push-to-web transitions) and sophisticated imputation frameworks. This infrastructure generates nationally representative longitudinal data through annual waves, employing experimental design changes to evaluate methodological innovations in real-world survey contexts while maintaining continuity with established social indicators.

• Statistical Relational Learning
• Probabilistic Reasoning
• Graph Neural Networks
• +۵ مورد دیگر

Manfred Jaeger is a Professor in the Department of Computer Science at Aalborg University's Faculty of Engineering and Science. With an extensive publication record spanning over three decades from 1993 to 2025, he has established himself as a leading researcher in statistical relational learning and probabilistic reasoning. His collaborative work extends across multiple institutions, with frequent co-authorship with researchers from Aalborg University including Kim G. Larsen, Thomas D. Nielsen, and others. Professor Jaeger's research primarily focuses on the intersection of artificial intelligence, machine learning, and probabilistic modeling. His work centers on developing methods for learning and reasoning with relational and graph-structured data, with particular emphasis on Graph Neural Networks, Bayesian Networks, and Statistical Relational Learning. His contributions span both theoretical foundations and practical applications, addressing challenges in representation learning, knowledge extraction, and uncertainty modeling in complex relational domains. Analysis of his recent publications (2020-2025) reveals a strong trend toward integrating neural approaches with traditional probabilistic reasoning frameworks. His work increasingly focuses on explainable AI within graph learning contexts, meta-path learning for heterogeneous networks, and bridging theoretical guarantees with practical implementations. Jaeger has made significant contributions to understanding projectivity in statistical relational models and developing algorithms for learning from coarse or incomplete data. Professor Jaeger has maintained a highly productive research trajectory, with numerous publications in top-tier AI venues including JMLR, Artificial Intelligence journal, UAI, IJCAI, and ECML/PKDD. He has developed influential frameworks for relational Bayesian networks and statistical relational learning, with applications spanning knowledge graph reasoning, network analysis, and decision-making under uncertainty.

Philipp Marekمشاهده پروفایل

پژوهشگر

• Household finance
• Microeconometrics
• Foreign Direct Investment (FDI)
• +۳ مورد دیگر

Philipp Marek is an Economist in the Research Centre of the Deutsche Bundesbank, affiliated with the Directorate General Financial Stability. His research focuses on household finance, microeconometrics, foreign direct investment (FDI), and regional economic dynamics. He has contributed to studies analyzing FDI impacts in transition economies, regional wealth estimation, and multinational enterprise strategies. His work often involves collaboration with institutions like the Halle Institute for Economic Research (IWH). Affiliations: Deutsche Bundesbank, Halle Institute for Economic Research (IWH) Key Research Areas: FDI location patterns, regional convergence, household financial behavior, and microdata analysis His publications explore topics such as functional upgrading in subsidiaries, agglomeration effects of FDI in East Germany, and the role of human capital in income convergence. He has co-authored studies using advanced statistical methods, including the Fay-Herriot model for regional wealth estimation. His work on the Panel on Household Finances (PHF) surveys provides insights into savings behavior under low-interest environments. Publications highlight interdisciplinary approaches, combining econometric analysis with regional and international business perspectives. He has contributed to policy-relevant research, including FDI micro-database development for CEECs and Germany.

Timo Wiedemannمشاهده پروفایل

پژوهشگر

• Financial Engineering
• Asset Pricing
• Econophysics
• +۳ مورد دیگر

Dr. Timo Wiedemann is a Researcher at the Chair of Derivatives and Financial Engineering at the University of Münster, affiliated with the School of Business and Economics. His academic background includes a PhD in Economics (2024) and an M.Sc. in Physics with a Finance minor (2019), both from the University of Münster. He has conducted study abroad programs at the University of York (UK) and Universidad de Sevilla (Spain). Research Focus: Financial Engineering, Econophysics, Asset Pricing, and Quantitative Methods Teaching: Leads Asset Pricing courses in the Master's program, including PhD seminars on Advanced Asset Pricing His research bridges physics-based methodologies with financial markets, evidenced by publications in Physical Review E and Scientific Reports . Current work addresses high-frequency financial data analysis, volatility measurement, and empirical asset pricing challenges. He actively contributes to working papers exploring missing data imputation and beta estimation biases in financial models.

• Causal Analysis
• Statistics
• Econometrics
• +۵ مورد دیگر

Professor Martin Huber is a leading academic in Applied Econometrics and Policy Evaluation at the University of Fribourg within the Faculty of Management, Economics and Social Sciences . He also serves as a Research Associate at the Centre for European Economic Research (ZEW) and collaborates with institutions like Soda Labs (Monash Business School) . PhD in Economics and Finance (2010) Assistant Professor at University of St.Gallen (2010-2014) Research stays at Harvard University (2011/2012) and University of Sydney (2014, 2019) His research focuses on data-based causal analysis , machine learning , and policy evaluation in labor, health, and education economics. He has pioneered the integration of causal machine learning with traditional econometric methods, developing novel algorithms for bid-rigging detection and dynamic treatment effect analysis. Recent publications demonstrate methodological advancements in graph attention networks , doubly robust estimation , and causal mediation analysis . His work addresses practical challenges like sample selection bias , instrument validation , and high-dimensional data in policy evaluation. Huber actively engages in teaching through initiatives like the ZEW summer school and Fribourg Winter School , where he instructs on R , Python , and causal analysis tools . He maintains a strong online presence through @causalhuber.bsky.social and has published Impact Evaluation in Firms and Organizations (MIT Press, 2023).

• Spatial Databases
• Big Spatial Data
• Location-Based Services
• +۶ مورد دیگر

Mohamed F. Mokbel is a Professor at the University of Minnesota with a distinguished career in spatial databases and big spatial data management. His research spans over two decades with more than 260 publications in top-tier venues including ICDE, SIGMOD, VLDB, and GIS conferences. He has established himself as a leading researcher in trajectory data management, mobility data science, and scalable spatial processing systems. Dr. Mokbel's research interests focus on the intersection of spatial databases, big data, and machine learning. He has pioneered work in trajectory data management systems, developing frameworks like ST-Hadoop for processing spatio-temporal data at scale. His recent research explores the application of large language models to trajectory analysis, spatial data cleaning systems with spatial awareness, and innovative approaches to mobility data science. His work addresses fundamental challenges in handling massive spatial datasets while maintaining efficiency and accuracy. His publication record reveals significant trends toward integrating machine learning with spatial data management, particularly in trajectory imputation using BERT models (KAMEL system) and applying NLP techniques to trajectory analysis. Recent work shows increasing focus on urban mobility applications, privacy considerations in location data, and the development of specialized frameworks for processing different types of spatial data at scale. Dr. Mokbel has mentored numerous students who have become productive researchers in their own right, with many publications featuring students as first authors. His collaborative network is extensive, with frequent co-authorship with researchers like Walid G. Aref, Ahmed Eldawy, and Amr Magdy, indicating strong research group leadership. He has contributed significantly to the spatial database community through systems like RASED for monitoring OpenStreetMap updates, KAMEL for trajectory imputation, and Sparcle for spatial data cleaning. His work bridges theoretical database concepts with practical applications in transportation, urban planning, and location-based services.

• Production Engineering
• Sustainable Manufacturing
• Life Cycle Engineering
• +۴ مورد دیگر

Lanza, G. is a faculty member at RWTH Aachen University, serving as a Professor within the Faculty of Mechanical Engineering . His research and advisory roles span the critical intersection of production engineering, sustainable manufacturing, and life cycle engineering. He has notably served as a thesis advisor for doctoral research in function-oriented ecology analysis, underscoring his commitment to advancing environmentally conscious industrial practices. His fields of interest are centered on creating sustainable and efficient production ecosystems. This includes: Production Engineering: Optimizing manufacturing processes for efficiency and quality. Sustainable Manufacturing: Developing methods to minimize environmental impact across the product life cycle. Life Cycle Engineering: Analyzing and improving the environmental footprint of products from design to disposal. Digital Twins: Leveraging digital models to simulate and optimize real-world manufacturing systems. The body of his published work demonstrates a significant contribution to both high-energy particle physics and advanced manufacturing. While a substantial portion of his articles are in collaboration with the CMS experiment at CERN—focusing on the discovery and measurement of fundamental particles and forces—his recent thesis advising role highlights a primary dedication to engineering innovation. The trend in his publications reveals a dual expertise, blending rigorous experimentation in physics with the practical application of sustainable engineering principles. In his capacity as an educator and mentor, Professor Lanza has guided doctoral students, such as C. Ruschitzka, in groundbreaking research that integrates ecological analysis into the machinery and plant engineering sectors. His work is pivotal in shaping the future of environmentally responsible industrial systems.