جست‌وجوی استاد و پژوهشگر

Katharina Dobsمشاهده پروفایل

پژوهشگر

• Visual Cognition
• Computational Neuroscience
• Face Perception
• +۴ مورد دیگر

Dr. Katharina Dobs is a faculty-level researcher at Justus Liebig University Giessen, based in the Department of Psychology and Sports Science and soon moving to the Department of Mathematics & Computer Science. She leads the Visual Cognition & Computational Neuroscience (VCCN) Lab, where she integrates state-of-the-art machine-learning models with human behavioural and neural data to decipher the computations underlying rapid visual scene and face perception. Education & Career: While explicit degree details are not provided, Dr. Dobs currently holds a prestigious LOEWE Start-Professorship and an ERC Starting Grant (DEEPFUNC), underscoring her leading role in computational vision research. Research Interests: Computational principles of human visual perception Functional organisation of the visual cortex Deep neural network models of object and face recognition Integration of facial form and motion cues Temporal dynamics of face perception using MEG and fMRI Across her recent publications, a clear trend emerges toward leveraging deep convolutional networks to explain category-selective responses in the human brain, particularly for faces. Her work repeatedly demonstrates how artificial networks trained on ecologically relevant tasks spontaneously develop brain-like functional specialisation, providing computational evidence against domain-specific expertise hypotheses. Scientific Awards & Funding: ERC Starting Grant “DEEPFUNC” LOEWE Start-Professorship Supervision & Collaboration: Dr. Dobs currently supervises PhD students Elaheh Akbarifathkouhi and Hilal Nizamoglu. She co-leads multiple SFB (Collaborative Research Centre) projects, including Project S “Deep Learning: Unleashing the Potential” with Prof. Roland Fleming and Project C9 “Factors influencing categorical face processing” with Dr. Benjamin de Haas. The VCCN Lab is additionally affiliated with the CRC Cardinal Mechanisms of Perception, the research cluster The Adaptive Mind, the Center for Mind, Brain and Behavior, and the Center for Applied Computer Science and Data Science (ZAD).

Nadira Sophie Faberمشاهده پروفایل

استاد

• Social Psychology
• Morality
• Cooperation
• +۵ مورد دیگر

Nadira Sophie Faber is a Professor of Social Psychology and Work & Organisational Psychology at the University of Bremen since mid-2023. She previously led a lab at the Universities of Oxford and Exeter and remains an Associated Faculty Member at Oxford's Philosophy department. Her interdisciplinary research spans psychology, behavioral economics, neuroscience, and philosophy. University of Munich (Psychology studies) University of Göttingen (PhD in Social Psychology) University of Oxford (Postdoc in Experimental Psychology and Philosophy) Her research focuses on how humans balance self-interest with cooperation, morality, and speciesism. She investigates situational/physiological influences on prosocial behavior, moral judgment determinants, and human-animal value hierarchies. Teaching includes B.Sc. and M.Sc. lectures/seminars in social psychology. Key collaborations include work with neuroscientists and philosophers on decision-making mechanisms. Publications in top journals like Psychological Review and Nature demonstrate her cross-disciplinary impact.

• Cortex Functional Organisation and Development
• Neural Coding and Dynamics
• Neural Data Analysis
• +۳ مورد دیگر

Matthias Kaschube is a Senior Fellow at the Frankfurt Institute for Advanced Studies (FIAS) and a Professor of Computational Neuroscience and Computational Vision at Goethe University Frankfurt. His research focuses on the functional organization and development of the neocortex, particularly exploring how cortical networks form and adapt through developmental and experiential processes. He leads a research group studying dynamic neural representations, including the emergence of cortical maps, flexible cognitive processes, and advanced neural data analysis techniques. Education: Studied physics at Goethe University Frankfurt and Georg-August-University Göttingen (B.Sc. 2000, Ph.D. 2005). Postdoctoral work at the Max Planck Institute and Princeton University. Academic roles include Professorships at Goethe University since 2011 and FIAS Fellowships since 2011, becoming Senior Fellow in 2020. Research Interests: Developmental neurobiology, neural coding mechanisms, computational models of cortical circuits, and the interplay between innate and experiential factors shaping cortical organization. Notable projects include studying modular network dynamics in early visual cortex, cuttlefish skin patterning, and predictive models of cognitive abilities from brain connectivity. Awards: Lewis Sigler Theory Fellowship (2006–2011), Bernstein Fellowship (2005). Students: Supervises multiple PhD students in computational neuroscience and neuroimaging. Active in mentoring and graduate programs at FIAS and Goethe University. Labs/Teams: Heads the Kaschube Research Group at FIAS, collaborating with institutions like MPFI, UC Denver, and MIT. Develops tools for neural data analysis and computational modeling.

Romy Lorenzمشاهده پروفایل

استاد پژوهشی

• Cognitive Neuroscience
• Neurotechnology
• Brain-Computer Interfaces
• +۷ مورد دیگر

Dr. Romy Lorenz is a Max Planck Research Group Leader at the Max Planck Institute for Biological Cybernetics in Tübingen, Germany, where she leads the Cognitive Neuroscience & Neurotechnology research group. She previously held postdoctoral positions at the University of Cambridge, Stanford University, and the Max Planck Institute for Human Cognitive & Brain Sciences from 2018 to 2023 as a Sir Henry Wellcome Postdoctoral Fellow. Dr. Lorenz's educational background includes: BSc in Psychology from Leuphana University (2009) MSc in Human-Machine Interaction from TU Berlin (2012) PhD in Neurotechnology from Imperial College London (2017) Her research focuses on understanding the frontoparietal brain network mechanisms that underpin high-level cognition and adaptive behavior. She employs an interdisciplinary approach combining subject-specific brain-computer interface technology, fMRI at standard and ultrahigh magnetic field strengths (3T, 7T, and 9.4T), EEG, non-invasive brain stimulation, computational modeling, and machine learning techniques. A key innovation in her work is the development of neuroadaptive Bayesian optimization methods that allow for real-time, closed-loop experimental design in cognitive neuroscience. Dr. Lorenz's recent publications demonstrate a clear trend toward investigating brain function at increasingly fine-grained spatial scales, particularly exploring layer-specific processing in the prefrontal cortex using ultrahigh-field fMRI. Her work bridges computational neuroscience, cognitive psychology, and neurotechnology, with applications ranging from basic cognitive science to clinical rehabilitation. A significant portion of her research focuses on closed-loop systems that integrate real-time brain imaging with adaptive experimental design and stimulation protocols. Her notable scientific achievements include: Outstanding Contributions in AI Innovation Award Sir Henry Wellcome Postdoctoral Fellowship Funding from the German Scholar Organisation Fellowships from the Wellcome Trust and Engineering and Physical Sciences Research Council Dr. Lorenz has secured significant research funding for her work, including support from the Wellcome Trust and German funding agencies. She has mentored several students, including Master's students like Pedro who has presented their collaborative work at conferences. Her research group actively recruits postdoctoral researchers and students interested in cognitive neuroscience and neurotechnology. Dr. Lorenz leads the Cognitive Neuroscience & Neurotechnology research group at the Max Planck Institute for Biological Cybernetics, which focuses on developing and applying advanced neuroimaging and computational methods to understand high-level cognitive functions. She has also co-initiated interest groups such as CoCoNUT (Computational Cognitive Neuroscience at the Max Planck Institute) to foster interdisciplinary collaboration. Her lab utilizes cutting-edge technologies including real-time fMRI at ultrahigh field strengths, EEG, and non-invasive brain stimulation to investigate frontoparietal network function.

Christian Schifferمشاهده پروفایل

پژوهشگر ارشد

• Deep Learning
• Medical Imaging
• Artificial Intelligence
• +۷ مورد دیگر

Dr. Christian Schiffer is a Research Fellow at the Research Centre Jülich, leading the 'Large-scale AI for Brain Mapping' team within the Institute of Neuroscience and Medicine (INM-1). His work focuses on developing deep learning algorithms for automated analysis of cytoarchitectonic brain structures using high-resolution histological data. He heads the Helmholtz AI Young Investigator Group, which integrates contrastive learning, graph neural networks, and high-performance computing (HPC) workflows to advance brain mapping technologies. His research applies methods like convolutional neural networks (CNNs) and generative models to extract microstructural features from petabyte-scale microscopy datasets. Key contributions include 3D cytoarchitectonic mapping, integrating topology with histological data, and improving computational efficiency for large-scale neuroimaging analysis. Schiffer also collaborates with the 'Big Data Analytics' group, advancing tools for 3D reconstruction and interactive AI applications in neuroscience. Notably, he received the Helmholtz AI Award 2023 for his contributions to AI-driven neuroscience. His work bridges artificial intelligence, supercomputing, and neuroanatomy to create data-driven brain atlases, enabling deeper insights into brain connectivity and function. Schiffer’s group actively contributes to open-source tools like the Julich-Brain platform and participates in initiatives such as the 'BigBrain' project.

• Neuroscience
• Brain Mapping
• Medical Imaging
• +۳ مورد دیگر

Katrin Amunts is a University Professor at the Cécile and Oskar Vogt Institute for Brain Research of Heinrich-Heine-University Düsseldorf and heads the Institute for Neuroscience and Medicine (INM-1) at Forschungszentrum Jülich. Her research focuses on creating multi-level brain atlases through cytoarchitectonic, molecular, and fiber architecture analysis. Director of the Cécile and Oskar Vogt Institute for Brain Research Head of INM-1 at Forschungszentrum Jülich Pioneered the BigBrain ultra-high-resolution 3D human brain model Research Interests: Combining image analysis, high-performance computing, and big data analytics to map brain structure-function relationships, intersubject variability, and interspecies differences. Her work underpins the Human Brain Project. Key Publications: Her research output includes probabilistic cytoarchitectonic atlases, comparative studies of avian and human brain circuits, and ultra-high-resolution models. These focus on cortical organization, receptor mapping, and neuroethical frameworks.

• Cortex Functional Organisation and Development
• Neural Coding and Dynamics
• Neural Data Analysis
• +۷ مورد دیگر

Matthias Kaschube is a Professor in the Faculty of Computer Science and Mathematics at Goethe University Frankfurt and a Senior Fellow at the Frankfurt Institute for Advanced Studies (FIAS). His research group focuses on understanding how the brain forms efficient representations of sensory environments and internal states through dynamic neural processes. He maintains active collaborations with leading neuroscience institutions including the University of Minnesota, Max Planck Florida Institute for Neuroscience, and Technion. Dr. Kaschube completed his physics studies at Goethe University Frankfurt and Georg-August-University Göttingen, graduating in 2000 and earning his doctoral degree in physics in 2005. His doctoral work was conducted at the Max Planck Institute for Dynamics and Self-Organization under Fred Wolf and Theo Geisel. He then held a Bernstein Fellowship before becoming a Theory Fellow at Princeton University's Lewis Sigler Institute from 2006-2011. In 2011, he joined Goethe University as Professor for Computational Neuroscience. His research spans four primary areas: the developmental emergence of cortical representations, flexible representations underlying learning and creativity, cognitive maps and representational spaces, and analysis methods for neural data. His group combines dynamic models of neural circuit function with neural data modeling techniques in close collaboration with experimental groups, creating an interdisciplinary interface between computer science, physics, biology, and AI. Notably, his work has revealed highly structured cortical networks prior to sensory experience that share similar architectural principles across sensory and association cortices. Analysis of his recent publications shows a consistent focus on understanding how endogenous neural activity patterns develop into reliable cortical representations through experience. His work spans multiple scales from molecular and cellular mechanisms to whole-brain functional organization, with particular emphasis on developmental processes in visual and auditory cortices. His methodological contributions include advanced techniques for analyzing chronic imaging data, tracking chromatophores in cuttlefish, and characterizing latent spaces in deep neural networks. Lewis Sigler Theory Fellowship (2006-2011) Bernstein Fellowship (2005) Professor Kaschube actively mentors a large group of PhD students including Lorenzo Butti, Jonas Elpelt, Santiago Galella, Deyue Kong, Maurycy Miekus, Ana Pamela Osuna Vargas, and Sigrid Trägenap. His research has been supported by multiple grants including NIH grants EY011488 and EY026273, Bernstein Focus Neurotechnology grant 01GQ0840, and BMBF project D-USA-Verbund: SpontVision. His group currently pursues three major research directions: the origin of distributed modular activity in neocortex, quantitative growth models for cuttlefish based on physical models, and the role of self-organization in linking endogenous cortical networks to sensory input.