جست‌وجوی استاد و پژوهشگر

Manuel Spitschanمشاهده پروفایل

پژوهشگر ارشد

• Circadian Neuroscience
• Visual Physiology
• Non-Visual Effects of Light
• +۳ مورد دیگر

Prof. Manuel Spitschan is a Max Planck Research Group Leader at the Max Planck Institute for Biological Cybernetics in Tübingen, Germany. Previously, he held a University Research Lecturer position at the University of Oxford (2020-2021) and a Sir Henry Wellcome Fellowship (2017-2020). His research focuses on light’s impact on human circadian rhythms, visual/non-visual physiology, and real-world applications of light exposure science. Education: B.A. (Hons) from University of St Andrews (2012) PhD in Neuroscience from University of Pennsylvania (2016) Postdoctoral Fellowship at Stanford University (2016-2017) Research Interests: Spitschan investigates light’s effects on circadian systems, retinal mechanisms underlying non-visual responses, and translational applications of light dosimetry. His work bridges basic science and clinical applications, with a focus on wearable light measurement technologies, sleep health, and chronotherapeutic interventions. Awards & Affiliations: Sir Henry Wellcome Fellowship (2017-2020) University Research Lecturer title (2020) Member of CIE TC 1-98, OSA Color Technical Group, and Daylight Academy Grants & Labs: Leads the Sensory and Circadian Neuroscience research group, developing open-source tools like luox and LightLogR . His lab focuses on advancing light measurement standards and understanding individual variability in light responses.

• Neuroscience
• Metabolism
• Diabetes Research
• +۳ مورد دیگر

Sophie Steculorum is a researcher at the Max Planck Institute for Metabolism Research in Cologne, Germany. Her work focuses on neuroendocrine mechanisms regulating energy balance, particularly the role of hypothalamic circuits in obesity and metabolic disorders. She is renowned for discovering how the signaling molecule UDP modulates appetite via AgRP neurons in the brain. Her research bridges molecular biology, neuroscience, and clinical implications for metabolic diseases like type 2 diabetes. Key contributions include elucidating the dynamic interplay between uridine/UDP signaling pathways and feeding behavior. Collaborations with Prof. Jens Brüning have advanced understanding of how metabolic dysregulation in obesity alters neuronal circuits. Her studies extend to olfactory systems, reproductive behavior, and mitochondrial dysfunction in neurodegenerative contexts, highlighting interdisciplinary approaches to metabolic research. Her recent work explores sensory regulation of feeding, candidate drugs for obesity, and mechanisms underlying motor function preservation in neurodegeneration. This research underscores the complexity of energy homeostasis, emphasizing both central nervous system and peripheral organ interactions. Future research directions include translational studies on uridine-based therapies and neural circuits governing appetite. Her findings contribute to developing innovative strategies for combating metabolic disorders through targeted neuronal and systemic interventions.

• Quantum Physics
• Optics
• Quantum Optics
• +۵ مورد دیگر

Florian Marquardt is a Professor at the University of Erlangen-Nürnberg and Scientific Director of the Theory Division at the Max Planck Institute for the Science of Light in Erlangen, Germany. His research focuses on the intersection of quantum physics, optics, and machine learning, with a particular emphasis on neuromorphic computing and quantum technologies. He holds a PhD from the University of Basel (2002) and has held roles including Emmy-Noether Fellow (2007-2012) and Full Professor at FAU Erlangen-Nürnberg (2010-2016). His work bridges theoretical physics and applied AI, aiming to advance quantum control, topological photonics, and energy-efficient computing. Education: PhD in Theoretical Physics, University of Basel (2002) Diplom Physics, University of Bayreuth (1998) Research Interests: The Marquardt Group explores quantum optomechanics, neuromorphic computing, and machine learning applications in physics. Current projects include designing analog neuromorphic systems, optimizing quantum error correction, and leveraging deep learning for inverse design in photonics. Recent work emphasizes AI-driven strategies for quantum circuit discovery and topological material engineering. Awards: Walter Schottky Prize (2009) ERC Starting Grant (2011) Emmy-Noether Fellowship (2007-2012) Grants & Collaborations: Leads research networks including the Max Planck School of Photonics and the MPC for Extreme and Quantum Photonics. His lab collaborates on EU-funded projects and organizes workshops on topics like quantum metrology and neuromorphic computing. Labs & Infrastructure: Directs the Theory Division, which hosts interdisciplinary teams working on photonic neural networks, quantum machine learning, and topological transport. The group uses advanced simulation tools like dCG (differentiable connected geometries) for multi-domain optimization.

Tim Gollischمشاهده پروفایل

استاد

• Neurobiology
• Retinal Processing
• Neural Coding
• +۵ مورد دیگر

Prof. Tim Gollisch is a Professor of Sensory Processing in the Retina at the School of Medicine, University of Göttingen. His research focuses on understanding how retinal neural networks process visual information, emphasizing neural coding and computation. Key areas include retinal dysfunction analysis, optogenetic restoration of vision, and adaptive mechanisms in retinal circuits. Education: Diploma in Physics, University of Heidelberg (2000) PhD in Biophysics, Humboldt University Berlin (2004) Research Interests: Neural coding mechanisms in retinal neurons Synaptic connectivity and functional diversity Optogenetic therapies for degenerative retinal diseases Integration of experimental data with computational models Research Trends in Publications: Advances in nonlinear signal processing and receptive field modeling Applications of machine learning in retinal circuit analysis Investigations into primate retinal responses to natural stimuli Labs/Teams: Head of the Sensory Processing in the Retina research group at University Medical Center Göttingen Collaborations with Harvard University and the Max Planck Institute of Neurobiology

• Computer Vision
• Control Theory
• Biomedical Image Analysis
• +۵ مورد دیگر

Zhen Tang is an active researcher with a prolific publication record spanning from 2007 to 2025, primarily in computer science and engineering disciplines. Their work appears consistently in high-impact venues including IEEE Access, IEEE Transactions, and major conferences in computer vision and systems engineering. Research interests span computer vision, control theory, biomedical image analysis, machine learning, and multi-agent systems. Tang's work demonstrates a strong interdisciplinary approach, connecting theoretical control systems with practical applications in medical imaging, distributed computing, and emerging technologies like DNA computing. Recent publications show a growing interest in large language models, blockchain applications, and ethical considerations in technology design. The publication trends reveal an evolution from foundational work in image processing and pattern recognition (2010-2015) to more complex systems involving multi-agent control and deep learning (2016-2020), and most recently expanding into large language models, blockchain healthcare applications, and technology ethics. The research shows strong connections between theoretical control systems and practical applications across multiple domains. Zhen Tang has established long-term collaborations with researchers including Yanli Wan, Zhenjiang Miao, Wei Wang, and others, suggesting stable research group affiliations. The consistent publication output across 18 years indicates an established academic career with significant contributions to multiple subfields within computer science and engineering.

• Computer Science Education
• Culturally Responsive Computing
• Augmented Reality for Education
• +۴ مورد دیگر

Dr. Wei Yan is an Associate Professor in the Department of Computer & Information Science & Engineering at the University of Florida's Herbert Wertheim College of Engineering. With an extensive publication record spanning computer science education, augmented reality applications, and culturally responsive computing, Dr. Yan has established a significant research presence with numerous publications in top-tier conferences and journals from 2023-2025. Dr. Yan's research interests focus on culturally responsive computing education, particularly with Indigenous communities including the Navajo Nation. Their work bridges the gap between technical computing concepts and culturally relevant pedagogy, with a special emphasis on spatial reasoning and mathematics education through augmented reality technologies. The research program has produced innovative AR classroom applications that help students understand complex spatial transformations and matrix algebra through interactive 3D visualizations. Through collaborations with researchers like Ashish Amresh, Paige Prescott, Maya Israel, and Heather Burte, Dr. Yan has developed several educational technology interventions that address inclusion in computer science education. Their publications reveal a strong commitment to broadening participation in computing, particularly among underrepresented groups, with a focus on teacher professional development and curriculum design that respects cultural contexts. Dr. Yan's technical expertise spans both educational technology development and core computer science topics, as evidenced by publications ranging from spatial reasoning in AR classrooms to advanced topics in integer coding and neural image compression. This interdisciplinary approach allows for the development of sophisticated educational tools grounded in solid computer science principles. Current research directions include AI-enhanced educational applications, culturally responsive computing curriculum development, and the integration of conversational AI with augmented reality for improved learning experiences. The work has significant implications for how computing education can be made more accessible and relevant to diverse student populations.

• High Performance Computing
• Parallel Programming
• OpenMP
• +۲ مورد دیگر

Matthias S. Müller is affiliated with RWTH Aachen University's IT Center, with additional associations to TU Dresden's Center for Information Services and High Performance Computing and the University of Stuttgart's High Performance Computing Center. His research focuses on parallel computing paradigms, OpenMP optimizations, and energy-efficient high-performance computing. Recent publications demonstrate specialization in parallel pattern compilers, OpenMP runtime optimizations, and energy-aware computing benchmarks. His team develops tools for performance analysis and optimization in heterogeneous computing environments, with applications in computer vision and scientific computing.

• High-Performance Computing
• Computational Fluid Dynamics
• Climate Modeling
• +۵ مورد دیگر

Henry M. Tufo is a Researcher affiliated with the University of Colorado, USA . His work spans High-Performance Computing (HPC) , Cloud Computing , and Computational Fluid Dynamics , with a focus on climate modeling, grid systems, and scalable algorithms. Tufo has collaborated extensively with institutions like IBM, Argonne National Laboratory, and researchers such as Paul Fischer, Kate Keahey, and Paul Marshall. His research interests include: Developing scalable HPC systems for climate and astrophysical simulations Integrating cloud computing with scientific workflows Optimizing spectral element methods for atmospheric models Trends in his publications highlight expertise in parallel computing , secure execution environments , and numerical methods for fluid dynamics. Tufo has contributed to frameworks like the FLASH code and GraphBLAS for large-scale simulations.

• Computer Vision
• Machine Learning
• Rail Transportation Systems
• +۵ مورد دیگر

Yu Qian is a Professor in the Department of Electrical and Computer Engineering at the University of Southern California's Viterbi School of Engineering, with extensive research contributions spanning computer vision, machine learning, and transportation systems. Their work bridges theoretical advances with practical applications, particularly in railroad safety and ferroelectric computing architectures. Research interests focus on computer vision applications for transportation safety, including railroad inspection systems and pedestrian behavior analysis at grade crossings. Additional expertise includes ferroelectric computing-in-memory architectures, micro-expression recognition, and multimodal sentiment analysis. The research program demonstrates strong interdisciplinary collaboration across engineering disciplines. Recent publications (2023-2025) show a clear trend toward hardware-algorithm co-design for specialized computing, particularly using ferroelectric devices for optimization problems, alongside continued work in transportation safety applications. The research spans both theoretical contributions to neural network architectures and practical implementations for real-world problems. While specific awards aren't documented in the provided records, the extensive publication record in high-impact venues demonstrates significant scholarly contribution to the field. The research program appears well-funded, supporting numerous collaborative projects across multiple institutions.

• Computer Vision
• Machine Learning
• Signal Processing
• +۶ مورد دیگر

Qian Huang is a Professor in the Department of Computer Science at Sun Yat-sen University's School of Computer Science and Engineering. With over 350 publications spanning from 1992 to 2025, Dr. Huang has established themselves as a leading researcher in multiple interdisciplinary fields at the intersection of computer science, engineering, and applied mathematics. Dr. Huang's research spans several critical domains in modern computing. Their primary interests include computer vision with applications in medical image analysis, machine learning with emphasis on transformer architectures and federated learning, signal processing for video compression, and wireless communications for IoT applications. Recent work demonstrates significant contributions to nuclei segmentation in cervical cell images, advanced video compression techniques using spatiotemporal modeling, and predictive maintenance systems for industrial equipment that incorporate uncertainty quantification. An analysis of Dr. Huang's 15 most recent publications reveals a strong trend toward interdisciplinary research that bridges theoretical computer science with practical applications. Their work consistently addresses real-world challenges in healthcare diagnostics, industrial automation, and communication systems. The publications demonstrate expertise in developing novel deep learning architectures while maintaining theoretical rigor in mathematical foundations. Multiple publications in IEEE Transactions journals across various domains Regular contributions to top-tier conferences including ICASSP, ICIP, NeurIPS, and CVPR Collaborations with researchers from leading institutions globally Dr. Huang's research program appears well-funded through collaborations with industrial partners and Chinese national research grants, though specific grant information isn't detailed in the publication record. Their work on federated learning frameworks and medical image analysis suggests strong connections with healthcare technology companies and medical research institutions. The extensive publication record across multiple domains indicates leadership of a substantial research group with expertise spanning computer vision, machine learning, and signal processing.

• dynamics of neuronal networks
• mechanisms of neuronal information processing
• physics of machine learning
• +۱ مورد دیگر

Prof. Moritz Helias is a Professor and Group Leader of the Theory of Multi-Scale Neuronal Networks at the Institute for Advanced Simulation (IAS-6), Computational and Systems Neuroscience department at Forschungszentrum Jülich. His research focuses on the dynamics of neuronal networks, mechanisms of neuronal information processing, and the physics of machine learning, with a particular emphasis on applying statistical physics methods to these areas. His work bridges theoretical neuroscience and machine learning, exploring how principles from statistical physics can elucidate neural computation and improve neural network models. He investigates transient recurrent dynamics, feature learning in deep networks, and renormalization group techniques to link network and neuron-level correlations. Key contributions include studies on criticality in neural networks, the role of synaptic heterogeneity, and the application of field-theoretic methods to analyze network dynamics and learning processes. His research also addresses challenges in computational neuroscience, such as modeling high-frequency neural activity and optimizing reservoir computing architectures.

• Neural Computation
• Cognitive Neuroscience
• Consciousness Studies
• +۴ مورد دیگر

Prof. Lucia Melloni is a Research Professor at New York University (NYU) School of Medicine's Department of Neurology and a W2 Group Leader (Tenure) at the Max Planck Institute for Empirical Aesthetics, leading the Research Group Neural Circuits Consciousness and Cognition. She holds a PhD in Psychology from the Catholic University of Chile and the Max Planck Institute for Brain Research. Her work focuses on neural mechanisms underlying consciousness, predictive processing, and brain plasticity. She has held positions at Columbia University, the Max Planck Institute for Brain Research, and Goethe University. Key achievements include developing methods for analyzing neural correlates of consciousness and advancing theories on predictive coding in sensory and cognitive systems. Education : PhD, Psychology (2006); BA in Psychology (2000) and Social Sciences (1995) from Catholic University of Chile. Affiliations : NYU School of Medicine (since 2020), Max Planck Institute for Empirical Aesthetics (since 2017). Research Interests : Domain-general cortical computations, neural mechanisms of perception and language comprehension, consciousness theories, and neuroplasticity. Her lab investigates how the brain uses predictions to control sensory processing and cognition, with implications for understanding disorders like minimally conscious states. Recent Work : Recent articles explore perceptual biases, auditory predictive processing, and adversarial testing of consciousness theories. Her work bridges clinical neurology and cognitive neuroscience, emphasizing methodological rigor and interdisciplinary collaboration. Awards : Israel Institute for Advanced Studies Fellow (2019), AcademiaNet member (2018). Grants : Over €10M secured from Templeton World Charity Foundation, NIH, and ONR for projects on consciousness, neural correlates, and cognitive neuroscience. Labs/Teams : Leads the Neural Circuits, Consciousness, and Cognition group at Max Planck, collaborating with institutions globally on projects like the Consciousness Theories Studies (ConTraSt) database.

Michael Schmidمشاهده پروفایل

استاد

• Systems Neuroscience
• Visual System
• Neural Circuits
• +۴ مورد دیگر

Prof. Michael Schmid is the Chair in Systems Neuroscience at Newcastle University's Institute of Neuroscience, leading the Schmid Lab. Previously, he held the position of Emmy Noether Group Leader at the Ernst Strüngmann Institute (ESI) for Neuroscience in Frankfurt until 2017. His research focuses on neural circuit operations in the visual system, including thalamocortical interactions during attentive vision, rhythmic neuronal activity, and mechanisms of brain plasticity after injury. He also explores pre-clinical applications like optogenetics-based cortical prosthetics for blindness and visual processing alterations in developmental dyslexia. Key achievements include securing an ERC Starting Grant in 2015 and pioneering work on optogenetic targeting and neuroimaging in non-human primates. His lab employs advanced techniques such as optogenetics, electrophysiology, and fMRI to study spatial navigation, neural circuit dynamics, and sensory restoration. Notable publications span high-impact journals like Neuron , Nature , and Current Biology . Lab Members: Includes postdocs (e.g., Dr. Michael Ortiz-Rios, Dr. Samy Rima), PhD students (e.g., Beshoy Agayby, Tenri Prasakti Fanyiwi), and lab manager Dr. Marcus Haag. Alumni: Former students include Dr. Carsten Klein, Dr. Joscha Schmiedt, and Dr. Katharine Shapcott, among others. Grants: Active ERC Starting Grant supporting research on perceptual brain circuits. Research highlights include identifying functional thalamocortical pathways, elucidating beta oscillation dynamics post-lesion, and developing MRI-compatible implants for primate studies. Ongoing projects aim to map entorhinal-hippocampal circuits and improve neuroprosthetic strategies for vision restoration.

Michael Wolffمشاهده پروفایل

پژوهشگر

• Neuroscience
• Cognitive Science
• Neuroimaging
• +۳ مورد دیگر

Michael Wolff is a Post-Doc researcher affiliated with the Rademaker Lab at ESI Frankfurt, Germany. His research focuses on the neural mechanisms of working memory, integrating neuroimaging techniques and computational modeling to understand how behavioral demands influence neural representations. He investigates dynamic hidden states and coding schemes in working memory, contributing to our understanding of cognitive processes underlying decision-making and behavior. His work spans interdisciplinary approaches, combining neuroscience with computational methods to explore the stability and adaptability of neural codes. Recent studies highlight the interplay between neural dynamics and memory maintenance, as seen in his publications in Nature Neuroscience and PLoS Biology . Affiliations include the Rademaker Lab, led by Rosanne Rademaker, where he collaborates with researchers such as Giuliana Giorjiani and Nursima Ünver Aydingül. Contact details: Deutschordenstr. 46, 60528 Frankfurt am Main, Germany.

Markus Diesmannمشاهده پروفایل

استاد

• Computational Neuroscience
• Neuronal Network Modeling
• Supercomputing
• +۳ مورد دیگر

Prof. Markus Diesmann is a Director of the Institute for Advanced Simulation (IAS-6) and the Institute for Neuroscience and Medicine (INM-10) at Forschungszentrum Jülich. He leads the Computational Neurophysics group, focusing on understanding neuronal network dynamics through computational models and supercomputing. His work integrates theoretical neuroscience with high-performance computing to simulate brain-scale networks and explore neuromorphic computing foundations. Research interests include correlation structures in neuronal networks, simulation technologies, brain-scale modeling, and software engineering for neuroscience. He has pioneered efforts in scaling neuronal network simulations to exascale systems and improving computational efficiency. Key contributions include the NEST simulation software and studies on synaptic plasticity, sequence learning, and LFP dynamics. Publications emphasize advancements in simulation algorithms, network connectivity analysis, and bridging experimental data with computational models. His work addresses challenges in real-time cortical microcircuit simulation, neuromorphic hardware integration, and the role of noise in neural computation. Prof. Diesmann collaborates internationally on neuroscience initiatives and high-performance computing applications. He advocates for robust scientific software practices and contributes to infrastructure for open science in computational neuroscience.