جست‌وجوی استاد و پژوهشگر

Mark Fiolkaمشاهده پروفایل

استاد

• Finite Element Method
• Virtual Product Development
• Structural Analysis
• +۳ مورد دیگر

Prof. Dr.-Ing. Mark Fiolka serves as a Professor in the Department of Mechanical Engineering at South Westphalia University of Applied Sciences, Iserlohn campus, specializing in computational mechanics and virtual engineering solutions. His research centers on advanced applications of the Finite Element Method for structural optimization, with key interests including: Virtual product development and parameterized prototyping Structural lightweight construction techniques Nonlinear FEM analysis for complex assemblies Resource-efficient product optimization from technical, ecological, and economic perspectives His work enables early identification of critical design weaknesses through simulation, reducing physical testing requirements while enhancing product quality and sustainability. Professor Fiolka actively supervises student research, exemplified by Jonas Schröder's structural analysis project focusing on component simulation and optimization of physical properties using FEM visualization techniques.

Ke Thia Yaoمشاهده پروفایل

استاد پژوهشی

• Artificial Intelligence
• Machine Learning
• Quantum Computing
• +۳ مورد دیگر

Ke-Thia Yao is a Research Professor at the University of Southern California's Information Sciences Institute (ISI), where he leads initiatives in the Artificial Intelligence Division. With a Ph.D. in Computer Science from Rutgers University and a B.S. in Electrical Engineering and Computer Science from UC Berkeley, his career spans 25+ years in research and development across artificial intelligence, machine learning, and multidisciplinary engineering design. B.S., Electrical Engineering and Computer Science, UC Berkeley (with honors) M.S. and Ph.D., Computer Science, Rutgers University His research focuses on leveraging AI and machine learning for predictive modeling in engineering systems, quantum computing applications for neural networks, and data-driven approaches in environmental and medical contexts. He has authored influential work on knowledge graph augmentation, simulation data analysis, and automated machine learning pipelines. Recent trends in his publications highlight interdisciplinary applications, including quantum-assisted Boltzmann machines for materials science, environmental toxicology studies linking PM2.5 exposure to corneal diseases, and scalable cyber warfare test environments. His work bridges high-performance computing, neuromorphic systems, and quantum architectures for advanced AI solutions. Ke-Thia Yao is affiliated with the University of Southern California's Information Sciences Institute, collaborating with experts in computer science, quantum computing, and environmental health. His projects emphasize scalable simulation frameworks, failure prediction systems, and semantic interoperability in data management.

Jeanne Rubnerمشاهده پروفایل

عضو هیئت علمی

• Science Communication
• Journalism
• Physics
• +۴ مورد دیگر

Jeanne Rubner is an Honorary Professor at the Technical University of Munich (TUM) since 2024, affiliated with the TUM School of Social Sciences and Technology. She serves as the Vice President of Global Communication and Public Engagement at TUM since April 2022, leading the Corporate Communications Center. Prior to this role, she had a distinguished career in journalism, heading the science and education desk at Bayerischer Rundfunk from 2012 to 2022 and working as a senior editor at Süddeutsche Zeitung. Dr. Rubner's educational background includes: Studied physics in Regensburg, Strasbourg, and Seattle, USA Received her Ph.D. in theoretical biophysics from TUM in 1989 Jeanne Rubner's expertise spans science communication, journalism, physics, and brain research. Her work focuses on making complex scientific concepts accessible to the general public, bridging the gap between academia and society. She has particular expertise in communicating about energy policy, neuroscience, and the intersection of science and politics, with a career that evolved from theoretical physics research to science journalism and academic communication leadership. Her publications demonstrate consistent focus on translating scientific knowledge for broader audiences, with a progression from technical scientific writing to accessible science communication. Her work shows particular strength in neuroscience communication, energy policy analysis, and political systems explanation, reflecting her dual background in physics and journalism. Dr. Rubner has received numerous prestigious awards for her contributions to science communication: Universitas Prize for Science Journalism from Hanns-Martin-Schleyer Foundation (2009) Medal for Science Writing from the German Physical Society (2019) Digital Journalism Fellowship from Hamburg Media School (2020/21) Medal Bene Merenti from the University of Regensburg (2007) As Vice President of Global Communication and Public Engagement at TUM, Dr. Rubner leads initiatives to enhance the university's public outreach and engagement. She has been teaching science communication at the Hochschule für Politik since 2017, mentoring emerging science communicators. Her leadership focuses on shaping TUM's strategic communication direction and building stronger connections between academic research and public understanding. While specific laboratory affiliations aren't mentioned in her current role, Dr. Rubner collaborates extensively with researchers across TUM to translate scientific findings for public consumption. Her work bridges multiple disciplines and focuses on creating effective communication channels between academic research and the broader public, positioning her as a key figure in contemporary science communication in Germany.

• Computational Social Science
• Econophysics
• Political and Media Debates
• +۵ مورد دیگر

Raphael Heiko Heiberger is a Professor and Head of the Computational Social Science department (Sowi VII) at the Institute for Social Sciences, University of Stuttgart. His research bridges social and computer sciences, applying computational methods to analyze social phenomena. He maintains active collaborations across disciplines including computer science, political science, communication science, public health, educational research, civil engineering, and natural history. Professor Heiberger's research focuses on computational social science with specific interests in econophysics, political and media debates, and the science of science. Methodologically, he specializes in natural language processing, machine learning, social network analysis, and agent-based simulations. His work demonstrates how computational approaches can provide new insights into complex social systems and phenomena ranging from economic crises to scientific careers. His recent publications reveal a strong trend toward interdisciplinary computational approaches to social science questions, with particular emphasis on text analysis, network analysis, and simulation methods. His work spans political science, sociology, education, and public health, demonstrating the versatility of computational social science methods across traditional disciplinary boundaries. Professor Heiberger is an active member of the DGS Social Network Analysis Section and has co-organized the Trier Summer School on Social Network Analysis for several years. He leads a research project on ABD funded by the Federal Ministry of Education and Research (BMFB) and is part of the EU Horizon 2020 project 'Protect' focused on international refugee protection. His research has secured significant funding from national and international sources, supporting interdisciplinary collaborations that bridge computational methods with substantive social science questions. His work demonstrates how computational approaches can transform traditional social science research paradigms. The Computational Social Science department under his leadership maintains strong connections with various research communities, fostering innovative approaches to studying social phenomena through computational lenses. His research group actively engages with both theoretical social science frameworks and cutting-edge computational methodologies.

Patrick Kaminskiمشاهده پروفایل

پژوهشگر

• Sociology
• Social Network Analysis
• Complex Systems
• +۵ مورد دیگر

Patrick Kaminski is a researcher at the University of Stuttgart in the Department of Computational Social Science . He specializes in sociological research using quantitative methods and social network analysis to study complex systems. His research interests include: Public health dynamics of the opioid epidemic Adaptive strategies for pandemic response Climate change adaptation in agricultural systems Computational sociology using large language models Patent network analysis Recent publications highlight his work on: Social network analysis for healthcare outcomes Formation of cultural myths through network dynamics Network-based interventions for opioid treatment access Intersectional identity analysis using machine learning Comparative studies of professional communities He teaches Multivariate Data Analysis with R in both summer and winter semesters.

• Neurosurgery
• Neurocritical Care
• Machine Learning in Medical Prognostics
• +۳ مورد دیگر

Jennifer Sauvigny is a Physician Scientist at the Department of Neurosurgery , Universitätsklinikum Hamburg-Eppendorf, affiliated with the Medical Faculty of the University of Hamburg. Her work bridges clinical neurosurgery, medical technology evaluation, and neurocritical care research, with a focus on improving outcomes for patients with complex neurological conditions. Key research areas include neurocritical care analytics, brain tumor classification, cerebrovascular surgery, and digital microscopy applications in neurosurgical workflows. She has developed machine learning models for predicting shunt dependency and intracranial hypertension using intensive care unit data. Her publications emphasize technical innovations (e.g., 3D4K exoscope systems) and clinical challenges in subarachnoid hemorrhage, glioblastoma treatment, and postoperative bone resorption. Recent articles demonstrate expertise in machine learning integration for neurological outcome prediction and DNA methylation-based tumor classification . Collaborations span neurosurgery, radiology, and critical care medicine across multiple institutions.

• Structural Biology
• Mass Spectrometry
• Protein Folding
• +۴ مورد دیگر

Antonio Calabrese is an Associate Professor at the Faculty of Biological Sciences, University of Leeds, UK. His research focuses on structural biology, protein dynamics, and membrane protein folding using advanced mass spectrometry and cryo-EM techniques. Current Position: Associate Professor (2024–) Previous Roles: University Academic Fellow (2020–2024), Postdoctoral Research Fellow (2013–2019) Education: PhD in Chemical Sciences from University of Adelaide (2009–2013) His work explores protein conformational changes, amyloid aggregation, and viral RNA chaperone mechanisms. He has developed PyXlinkViewer for crosslinking data visualization and contributed to understanding SurA/BAM complex interactions in bacterial membrane protein assembly. Recent publications address RNA oligonucleotide inhibition of TDP-43 aggregation, structural mapping of amyloid intermediates, and biophysical characterization of protein-nanodisc systems. He actively reviews for journals like Nature Communications and Angewandte Chemie.

Hélène Choquetمشاهده پروفایل

پژوهشگر

• Human Genetics
• Genetic Epidemiology
• Pharmacogenomics
• +۶ مورد دیگر

Hélène Choquet, PhD , is a Research Scientist II at the Kaiser Permanente Northern California Division of Research , where she focuses on human genetics and genomics to identify genetic factors influencing complex traits, diseases, and treatments. She holds a PhD in Human Genetics from Lille 2 University of Health and Law, France, and completed postdoctoral training in genetic epidemiology and statistical genetics at the University of California, San Francisco (UCSF). Research Interests: Dr. Choquet investigates the genetic basis of eye diseases (e.g., myopia, glaucoma, cataract), nonmelanoma skin cancers , and abdominal hernias . Her work emphasizes multi-ethnic genetic analyses , ancestry-specific risk prediction, and integrating genetic and non-genetic factors to improve disease prevention and treatment outcomes. Recent studies include polygenic risk scores for glaucoma , gene-environment interactions in skin cancer , and genetic disparities in metabolic diseases . Scientific Awards: American Heart Association Award (during postdoc at UCSF) Grants: Funded by National Eye Institute , National Cancer Institute , National Institute of Arthritis and Musculoskeletal and Skin Diseases , and TPMG Delivery Science Projects Program . Her projects address vision screening for amblyopia , genetic risk prediction for keratinocyte carcinoma , and micronutrient deficiency management post-bariatric surgery . Collaborative Teams: Affiliated with the Behavioral Health and Aging and Genes, Environment and Health research programs at Kaiser Permanente. She collaborates with multidisciplinary teams in ophthalmology , dermatology , and metabolic surgery to translate genetic findings into clinical practice.

Emily A-Izzeddinمشاهده پروفایل

پژوهشگر

• Visual Perception
• Cognitive Psychology
• Psychophysics
• +۳ مورد دیگر

Dr. Emily A-Izzeddin is a researcher at the Department of Psychology and Sports Science, Justus Liebig University Giessen. She contributes to project C1, 'Visual Categorization of Complex, Naturalistic Materials,' collaborating with Prof. Dr. Fleming and Dr. Schmidt. Research Focus: Visual perception and cognitive processing of naturalistic materials Collaboration: Works with senior researchers in interdisciplinary projects Her work bridges psychology, computational modeling, and sensory analysis. Contact details include office location in Building F2, Room 341, though no explicit email address is provided.

Katja Fiehlerمشاهده پروفایل

استاد

• Sensorimotor Processing
• Tactile Suppression
• Spatial Perception
• +۵ مورد دیگر

Prof. Dr. Katja Fiehler is a full professor at the Department of Psychology and Sports Science , Justus Liebig University Giessen , where she leads research on sensorimotor processing. She was promoted to W3 Professor for Experimental Psychology "Perception & Action" in 2020 and previously held a Heisenberg Professorship (W2) from 2011-2016. Scientific leadership roles: Speaker for Cluster Project "The Adaptive Mind" and DFG IRTG-1901, Member of DFG Senate and SFB/TRR-135 directorate Research focuses on sensorimotor predictions, tactile suppression mechanisms, and visuomotor control in naturalistic environments Methodological expertise: Psychophysics, motion tracking, EEG/fMRI, VR experiments Her research interests center on understanding how the brain integrates sensory feedback and motor commands to enable adaptive behavior. Key projects investigate tactile suppression during movement, predictive processing in complex motor tasks, and cross-modal metacognition. Her work spans from basic sensorimotor mechanisms to applications in aging and clinical populations (schizophrenia). She has received scientific recognition through: Heisenberg Professorship (W2) for Experimental Psychology Leadership roles in major research consortia Her advising team currently includes: PhD students Yanina Tena Garcia and Fabian Wachsmann Research associates: Belkis Ezgi Arikan, Dimitris Voudouris (Commissioner for Young Scientists)

Hermann Müllerمشاهده پروفایل

استاد

• Motor Learning
• Neurophysiology
• Human Movement Analysis
• +۳ مورد دیگر

Prof. Dr. Hermann Müller is a W3 Professor of Training Science at the Faculty of Psychology and Sports Science, Justus Liebig University Giessen, and Head of the Training Science Department. His research focuses on human motor strategies, predictive error processing, and neurophysiological correlates of movement kinematics through virtual and real-life sports scenarios. Bachelor's degree in Sports and Mathematics, Saarland University (1988) Doctorate in Sports Science (1995), Saarland University Habilitation in Sports Science (2001), Saarland University His work bridges Motor Learning , Neurophysiology , and Human Movement Analysis , often involving novel data collection methods. Recent publications emphasize error valuation , predictive modeling , and sensory signal integration in motor tasks, with applications in sports like basketball and throwing mechanics. Scientific awards include: Doctoral scholarship from the German Academic Scholarship Foundation (1992-1993) Habilitation scholarship from the German Research Foundation (1997-1999) Müller leads the junior research group Neuroprocessing in Movement and Training and has collaborated extensively with Dr. Lisa Maurer and Prof. Dr. Hegele on projects funded by the DFG priority program. His research group develops innovative methods for analyzing timing variability and error processing in motor learning.

Lisa Maurerمشاهده پروفایل

پژوهشگر

• Motor Learning
• Neuroscience
• Cognitive Psychology
• +۳ مورد دیگر

Lisa Maurer is a Research Associate and Junior Research Group Leader at the Justus Liebig University Giessen , affiliated with the Faculty of Psychology and Sports Science and the Department of Training Science. Her research explores complex motor learning processes , focusing on the role of internal forward models , predictive error processing , and metacognitive representations in motor tasks, utilizing kinematic analysis and EEG techniques. Education: 2001–2002: Study of Sports Science (STAPS) at Marc-Bloch University, Strasbourg 2002–2007: Diploma in Sports Science at University of Saarland, Saarbrücken 2003–2008: 1st State Examination in German Studies at Saarland University 2008–2012: Doctoral studies in Sports Science at Justus Liebig University Giessen (PhD in 2012) Research Interests center on neural correlates of motor error processing , predictive modeling in motor tasks , and metacognition in movement control . Her work bridges neuroscience and sports psychology , with applications to conditions like Parkinson's disease and schizophrenia . She investigates how attentional focus and sensorimotor feedback influence learning outcomes. Publications highlight trends in predictive error processing , gaze behavior in sports , and neural indicators of motor learning . Recent papers examine eye movement patterns in basketball, sensorimotor insoles for Parkinson's patients , and metacognitive sharpening through motor outcomes. Scientific Awards: Scholarship holder of the German Academic Scholarship Foundation (during doctoral studies) Grants include funding for projects like Predictive Error Perception in Complex Natural Environments (Project B6) , focusing on expertise development in sports and clinical applications. She collaborates with Prof. Dr. Mathias Hegele and Prof. Dr. Hermann Müller . Labs & Teams include the Junior Research Group Neuroprocessing in Movement and Training at Justus Liebig University Giessen and the German Association for Sports Science (dvs) center for Mind, Brain, and Behavior (CMBB) .

Benjamin Straubeمشاهده پروفایل

استاد

• Neuroscience
• Cognitive Neuroscience
• Psychiatry
• +۵ مورد دیگر

Benjamin Straube is a Professor at the Department of Psychiatry and Psychotherapy at Philipps University Marburg , where he leads translational imaging research. He completed his psychology degree at Saarland University (2005) and his doctorate at RWTH Aachen University (2008), followed by postdoctoral work at the University of Pennsylvania (2008-2009). He was promoted to Heisenberg Professor for Translational Imaging in 2016 after leading the Experimental Neuroscience Group at Marburg (2014-2016). Research Interests: Straube investigates neural correlates of predictive perception processes in multisensory contexts action-monitoring deficits in schizophrenia gesture-speech integration mechanisms temporal recalibration during active/passive movements neural dynamics of self-other distinction His work combines EEG, fMRI, and tDCS to study basic perception-action loops and their clinical implications in psychiatric disorders. Scientific Awards: Von Behring-Röntgen Young Talent Award (2014) DGPPN Prize for Mental Illness Research (2014) Multiple best poster awards (2006-2016) Grants & Collaborations: Funded by DFG (Heisenberg Professorship), RHÖN-KLINIKUM AG, and the Else Kröner-Fresenius Foundation. Leads subproject A3 in the SFB/TRR 135 collaborative research center on perceptual prediction mechanisms.

• Deep Learning
• Business Intelligence
• Computer Vision
• +۴ مورد دیگر

Aseem Behl is a researcher at the School of Business and Economics, University of Tübingen. His work bridges deep learning methodologies with business intelligence, focusing on extracting insights from visual and textual data. He teaches practical courses on deep learning for visual and language processing, including 'Practical Deep Learning from Visual Data' in Summer Semester 2022 and 'Practical Deep Learning for Language Processing' in Winter Semester 2021/22. His research interests include: Deep Learning Business Intelligence Computer Vision Natural Language Processing Publications reveal a focus on autonomous driving, scene flow estimation, and data aggregation techniques. He has also explored interdisciplinary work in corpus linguistics using complex network theory for Bollywood song lyrics analysis. Contact: aseem.behl@uni-tuebingen.de

Simon Geerkensمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Railway Systems
• Explainable AI
• +۳ مورد دیگر

Simon Geerkens serves as a Researcher and PhD candidate at Hochschule Düsseldorf's Faculty of Electrical Engineering & Information Technology under Prof. Dr. Braun. Currently embedded in the EU-funded safe.trAIn. consortium, he works on developing safety architectures for driverless regional trains through AI integration. His research focuses on explainability and trustworthiness of AI systems , data quality assurance , and practical AI applications in safety-critical railway environments. With expertise spanning artificial intelligence, deep learning, and data security, his work bridges theoretical machine learning with industrial railway automation requirements. Analysis of his recent publications reveals a strong emphasis on data quality tools (QI², ECS) and safety assurance pipelines for ML systems in rail transport. His research consistently addresses the challenge of implementing verifiable AI in safety-critical infrastructure, particularly through dataset validation methods and complexity assessment frameworks. Professional activities include active participation in the safe.trAIn. project consortium comprising railway industry leaders, technology suppliers, and standardization organizations working toward GoA4 automation standards.