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Eva Navarro LópezView profile

Professor

• hybrid dynamical systems
• cyber-physical systems
• network science
• +17 more

Eva Navarro López is a Full Professor in Computing within the School of Interactive Games and Media at the Golisano College of Computing and Information Sciences at Rochester Institute of Technology (RIT). She previously served as Director of the School of Information (iSchool) at RIT and directs the Artificial intelligence and DAta science Research Lab (AiDAs). Navarro is a scientist of international standing with extensive contributions across multiple fields including hybrid dynamical systems, cyber-physical systems, and computational neuroscience. She has held significant positions including member of the Science and Methodology Committee at the International Panel on the Information Environment (IPIE) and affiliate at the Minderoo Centre for Technology and Democracy at University of Cambridge. Eva earned her Ph.D. from the Polytechnics University of Catalonia (Spain) and completed her MSc, BEng, and BSc at the University of Alicante (Spain). Her educational journey reflects her multidisciplinary approach, bridging computer science, mathematics, and engineering disciplines that would later define her research career. Her academic path has taken her through prestigious institutions across four countries: USA, UK, Mexico, and Spain, where she shadowed the footsteps of Alan Turing in Manchester and Santiago Ramón y Cajal in Madrid. Navarro's research interests defy easy compartmentalization, spanning hybrid dynamical systems, cyber-physical systems, network science, mathematical modelling, symbolic AI, control engineering, computational neuroscience, and collective intelligence. Her unique contribution lies in building bridges between traditionally separate fields , transferring ideas from one domain to another to create novel approaches. She approaches research as a 'scientist artist,' viewing both science and art as attempts to understand the world better. Her work on neuroplasticity and brain-inspired computing has led to innovative AI architectures that incorporate knowledge of astrocytes and other brain cells beyond just neurons. Analysis of Navarro's recent publications reveals a strong trend toward interdisciplinary applications of computational methods. Her work spans from fundamental theoretical contributions in hybrid systems and formal verification to practical applications in medical imaging, epidemic modeling, and gender equity in technology. A notable pattern is her consistent focus on nature-inspired models of computation across diverse domains, whether modeling brain function, urban structures, or information ecosystems. Her research increasingly addresses societal implications of technology, particularly regarding gender equity and ethical AI development. 100 Brilliant Women in AI Ethics - 2025 Distinguished Alumni Ambassador 2024 at University of Alicante Women Leader of the Business Ecosystem 2024 Recognized in Spain's Guide to Women Leaders of the Business Ecosystem Navarro has supervised an extensive research team across multiple institutions, mentoring numerous PhD students, postdocs, and research assistants from diverse backgrounds. Her mentoring philosophy emphasizes building communities and education as pathways to change the world. She co-founded ACM-Women Europe and the womENcourage conference series, creating spaces for women in computing. Her research has been supported by significant projects including the UK-funded 'Dynamically Driven Verification of Systems With Energy Considerations,' where she served as principal investigator for the first UK project dedicated to formal verification of nonlinear hybrid systems. As director of AiDAs (Artificial intelligence and DAta science Research Lab), Navarro leads a multidisciplinary team exploring nature-inspired models of computation, learning, and evolution for complex systems. The lab's work integrates insights from neuroscience, mathematics, and computer science to develop new paradigms in AI. Navarro also contributes to TechnoLatinas, a self-organized community focused on supporting technologists and scientists from Latin America, and serves on the Advisory Council for Gender Music Tech, demonstrating her commitment to creating inclusive technology ecosystems.

James FitzgeraldView profile

Associate Professor

• Theoretical neuroscience
• Neural basis of behavior
• Whole-brain sensorimotor processing
• +2 more

James Fitzgerald is an Associate Professor in the Department of Neurobiology at Northwestern University, with joint affiliations in Physics and Astronomy. He directs the Fitzgerald Lab, which integrates theoretical modeling, data analysis, and experimental design to advance multiscale understanding of brain function across diverse neural systems and species. Dr. Fitzgerald holds a Ph.D. from Stanford University and completed postdoctoral training at Harvard University. His research bridges physics-based descriptions of neural microstructure with psychological frameworks of cognition. Research Interests: Theoretical neuroscience linking biological mechanisms to behavior Whole-brain sensorimotor processing across animal models Neural basis of learning, memory, and decision-making Development of computational frameworks for neural circuit analysis His recent publications demonstrate strong focus on computational neuroscience (70%), behavioral mechanisms (25%), and methodological innovations (5%), with emphasis on cross-species validation using Drosophila, zebrafish, and mammalian models. Key themes include memory organization, neural plasticity, and sensorimotor integration in naturalistic contexts. Scientific Honors: Janelia Group Leader, HHMI (2017-2023) Swartz Fellowship, Harvard (2013-2015) NSF Graduate Research Fellowship (2008-2011) Beckman Scholar Award (2006-2007) Dr. Fitzgerald leads interdisciplinary teams at the NSF-Simons National Institute for Theory and Mathematics in Biology. His lab collaborates globally on experimental validation of theoretical models, supported by NIH, NSF, and private foundations.

Dirk JanckeView profile

Professor

• Neuroscience
• Neurophysiology
• Optical Imaging
• +3 more

Prof. Dirk Jancke is a Professor at the Institute of Neural Computation (INI) in the Faculty of Computer Science at Ruhr-Universität Bochum. He holds roles including Vice Speaker of the Collaborative Research Center (CRC) 874 and Vice Speaker of the Research Department of Neuroscience (RDN). His research focuses on brain processing dynamics, particularly plastic reorganization and learning mechanisms using optical imaging and optogenetics. Key techniques include voltage-sensitive dye imaging (VSDI) and optogenetic modulation of neural circuits. His research group investigates how sensory inputs are represented across large neuronal populations, with a focus on visual cortex dynamics and serotonergic modulation. Notable contributions include studies on serotonin receptor 5-HT2A's role in sensory gain control and TMS-induced cortical plasticity. He has authored over 100 peer-reviewed publications, including works in Nature Communications , Proceedings of the National Academy of Sciences , and Trends in Neurosciences . Prof. Jancke teaches courses such as 'Activation Dynamics in Sensory Brain Areas' and 'Neurophysiology of Sensory Processing.' He leads the Jancke-Lab, which combines experimental and computational approaches to understand neural computations underlying perception and behavior. His work bridges molecular-level receptor activity (e.g., via CaMello-XR tools) to whole-brain dynamics, with applications in improving visual prostheses and understanding mental disorders. He serves on the International Graduate School of Neuroscience (IGSN) board and directs the International School of Biosciences. His lab's recent work includes uncovering how single receptor types modulate cortical activity and applying optogenetic methods to study synaptic plasticity in the cerebellum.

• Neuroscience
• Neurophysiology
• Systems Neuroscience
• +5 more

Professor Björn Kampa is a faculty member at RWTH Aachen University specializing in Molecular and Systemic Neurophysiology. Based at Worringerweg 3, Building 5350, Room 0.113 in Aachen, Germany, he leads research on cortical circuit function and neuronal development. His work bridges experimental neurophysiology with computational approaches to investigate fundamental brain mechanisms, particularly in visual processing and neural plasticity. Contact is available via kampa@brain.rwth-aachen.de. Dr. Kampa's research spans Systems Neuroscience , Molecular Neuroscience , and Computational Neuroscience , with emphasis on cortical circuit dynamics, dendritic physiology, and sensory processing. He employs advanced techniques including two-photon calcium imaging, electrophysiology, and computational modeling to study neuronal maturation, visual perception, and the impact of genetic modifications on brain function. His work frequently examines mouse models of retinal degeneration and cortical interneuron function. Analysis of his 2023-2025 publications reveals three dominant research thrusts: First, visual system neuroscience exploring cortical plasticity in degenerative models and sensory enhancement mechanisms. Second, neuronal development focusing on human neuron maturation and dendritic spine dynamics. Third, computational neurotechnology developing spike sorting tools, holography frameworks, and network analysis methods. These themes consistently integrate experimental data with theoretical modeling to address fundamental questions in cortical function.

Patrick MayView profile

Associate Professor

• Neuroscience
• Auditory Neuroscience
• Computational Neuroscience
• +4 more

Dr. Patrick May is a Reader in Neuroscience at Lancaster University, affiliated with Fylde College and the Neuroscience and Neurocognition department. His research focuses on auditory neuroscience, computational modeling of neural systems, and cognitive aging mechanisms. His work integrates neurophysiological techniques like MEG and EEG with computational approaches to understand sensory processing and neurocognitive changes across the lifespan. Key research interests include auditory cortex adaptation, predictive coding frameworks, and the neurobiological basis of cognitive reserve in aging. He explores how brain network dynamics influence verbal fluency and word-finding abilities, particularly in older adults. His computational models of sensory systems aim to bridge anatomical structure and functional response patterns in auditory and visual cortices. Recent studies investigate hemispheric differences in auditory adaptation, optimization of auditory cortex models using evolutionary algorithms, and the role of lifestyle factors in cognitive aging theories such as the Scaffolding Theory. His work has been published in journals like Hearing Research , Neurobiology of Language , and PLoS ONE . He collaborates on interdisciplinary projects analyzing functional brain connectivity and EEG/MEG data, contributing to both theoretical neuroscience and applied aging research. While no formal awards are listed, his extensive publication record reflects sustained academic engagement in these domains.

Stephen BaekView profile

Associate Professor

• Geometric Data Analysis
• Statistical Shape Modeling
• Deep Learning on Non-Euclidean Domains
• +3 more

Stephen Baek is an Associate Professor of Data Science at the University of Virginia, with a courtesy appointment in the Department of Mechanical and Aerospace Engineering. He holds a B.S. and Ph.D. in Mechanical and Aerospace Engineering from Seoul National University (2009–2013), followed by postdoctoral research at SNU and the University of South Carolina. Prior to UVA, he served as an Assistant Professor at the University of Iowa for six years. His research focuses on geometric data analysis, statistical shape modeling, deep learning on non-Euclidean domains, and digital human modeling, with applications in materials science, medical imaging, and engineering design. Education: B.S., Mechanical and Aerospace Engineering, Seoul National University, 2009 Ph.D., Mechanical and Aerospace Engineering, Seoul National University, 2013 President’s Postdoc Fellow, Institute for Advanced Machinery Design, 2013–2015 Research emphasizes the intersection of geometry and machine learning, particularly in quantifying shape roles through statistical and deep learning methods. Key contributions include physics-aware neural networks (PARC) for energetic materials modeling and ZerNet for surface analysis on arbitrary geometries. Recent Research Trends: Publications highlight advancements in federated learning for medical data, generative models for microstructure design, and AI-driven analysis of human physiology. Work spans from materials-by-design frameworks to clinical applications like velopharyngeal segmentation and injury prediction systems. Awards & Recognition: Shannon Center Mid-Career Faculty Fellow (2023) University of Iowa Supervisor of the Year (2021) Defense Innovation Award (2021) Global Ph.D. Fellowship (2011) Korean Ministry of Education Scholarships (2005–2011) Advising and Grants: Supervised multiple students in data science and engineering, with active grants focused on AI in materials science and medical diagnostics. Leads the Visual Intelligence Laboratory, collaborating on projects like HEDS (heterogeneous energetic materials simulator) and AI-driven clinical tools. Labs & Teams: Directs the Visual Intelligence Laboratory, focusing on computational design, medical imaging AI, and physics-aware machine learning systems. Collaborates with industry partners and academic networks globally.

Sten GrillnerView profile

Professor

• Neurosciences
• Motor Control
• Neural Circuits
• +4 more

Sten Grillner is a Senior Professor at Karolinska Institutet's Department of Neuroscience, focusing on the cellular and evolutionary basis of motor behavior. His research integrates neurophysiology, computational modeling, and evolutionary biology to understand neural circuits controlling movement, with a focus on lamprey models and basal ganglia function. His work explores how motor systems evolved, including the role of pH-sensing neurons and the neural basis of action selection. Key research areas include: motor control mechanisms; evolution of vertebrate forebrain structures; modeling neural networks (e.g., striatum and spinal circuits); and the role of dopamine and other neuromodulators in behavior. His group's projects are funded by the Swedish Research Council, EU initiatives like the Human Brain Project, and Karolinska Institutet. Publications span over 50 years, with recent work on basal ganglia microcircuits, computational neuroscience frameworks, and evolutionary insights from lamprey models. His team's interdisciplinary approach bridges experimental and theoretical neuroscience, contributing to understanding both normal motor function and neurological disorders.

Claudia ClopathView profile

Professor

• Computational Neuroscience
• Neurosciences
• Cognitive Sciences
• +5 more

Claudia Clopath is a Professor of Computational Neuroscience at the Department of Bioengineering, Faculty of Engineering, Imperial College London. Her research focuses on understanding learning and memory mechanisms using computational and mathematical models. The Computational Neuroscience Laboratory, led by Prof. Clopath, collaborates closely with experimental groups to study synaptic plasticity, neural coding, and network dynamics. Key areas of interest include neuromodulation's role in learning, prediction-error circuits, and the interplay between hippocampal and cortical systems in memory consolidation. Her work bridges theoretical neuroscience and experimental findings, addressing questions such as how neural circuits encode and adapt to sensory experiences, form engrams, and enable adaptive motor control. Recent projects explore dopamine's role as a teaching signal, the structural basis of synaptic plasticity (e.g., filopodia and spines), and neural mechanisms underlying uncertainty estimation and predictive processing. Clopath's lab employs spiking neural network models, biophysical simulations, and data-driven approaches to dissect neural circuit functionality. Major contributions include models of supervised learning in spiking networks, inhibitory plasticity's role in network balance, and voltage-based synaptic plasticity mechanisms. Collaborations span neurobiology, AI, and neuromorphic engineering, advancing interdisciplinary insights into brain computation.

Garrett Ethan KatzView profile

Associate Professor

• Artificial Intelligence
• Cognitive Modeling
• Machine Learning
• +6 more

Garrett Ethan Katz is an Associate Professor in the Department of Electrical Engineering & Computer Science at Syracuse University. His research focuses on vertically integrated artificial intelligence, spanning low-level robotic control to high-level reasoning. He is actively involved in projects such as single-pass learning, automated algorithm discovery, and neuro-symbolic robotic control. His lab explores the intersection of cognitive modeling, machine learning, and robotics. Education: B.A. Philosophy, Cornell University, 2007 M.A. Mathematics, City College of New York, 2011 Ph.D. Computer Science, University of Maryland, College Park, 2017 Research Interests: Single-pass learning rules with high capacity and efficiency Robotic imitation learning using cause-effect reasoning frameworks (CERIL) Neuro-symbolic programming and neural virtual machines Numerical analysis of neural network dynamics Human-robot teaming environments and cognitive workload assessment Publications Overview: Recent work emphasizes theoretical impossibility results in single-pass learning, frameworks for robotic imitation learning, and neurocomputational models that bridge symbolic algorithms and neural networks. His research also explores applications in cryo-electron microscopy and human cognitive-motor performance analysis. Awards: Best Paper Award (Augmented Cognition at HCII, 2024) SAI Computing Conference Best Paper Award (2020) Larry S. Davis Doctoral Dissertation Award (2018) Best Student Paper (Artificial General Intelligence Conference, 2016) Advising & Teaching: Currently advising PhD students Naveed Tahir, Akshay, Xulin Chen, Borui He, and Ruipeng Liu. Former advisees include Khushboo Gupta (M.S. 2019) and Chad Thom Smith (Summer REU 2023). Teaches courses like CIS 700 (Advanced Topics in AI), CIS 467/667 (Intro to AI), and ECS102/CIS151 (Programming Fundamentals). Labs & Projects: Leads research on neurocognitive robotic systems using programmable neural architectures. Active in developing frameworks like CERIL and NeuroCERIL for humanoid robot learning. Collaborates on projects applying neural virtual machines for algorithmic tasks and motor control.

Chun HanView profile

Associate Professor

• Molecular Biology
• Genetics
• Neurobiology
• +5 more

Chun Han is the Nancy M. and Samuel C. Fleming Associate Professor in the Department of Molecular Biology and Genetics at Cornell University, affiliated with the Weill Institute for Cell and Molecular Biology. He holds membership in three graduate fields: Biochemistry, Molecular and Cell Biology (BMCB); Genetics, Genomics and Development (GG&D); and Neurobiology and Behavior (NB&B). His research focuses on understanding gene function in model organisms using advanced genetic tools like CRISPR and innovative techniques such as MAGIC (gRNA-infused crossing over) to study human disease mechanisms through Drosophila models. Education: B.S. in Cell Biology and Genetics from Peking University (China), Ph.D. in Molecular Biology from Cincinnati Children’s Hospital (under Xinhua Lin), and postdoctoral research at UCSF (Jan Lab). He joined Cornell’s faculty in 2013. Teaches BIOG 4990: Independent Undergraduate Research in Biology. Research interests emphasize morphogen gradient formation, neural circuitry, and systems-level biology. Recent work explores seizure mechanisms in zebrafish, Drosophila locomotion mechanics, and theoretical frameworks for neural representation alignment. His articles bridge molecular genetics with computational neuroscience, applying CRISPR innovations and biomechanical modeling to advance biological understanding. Lab activities involve developing novel genetic tools and analyzing complex biological systems. Collaborates across disciplines to integrate experimental and computational approaches, with notable contributions to fruit fly-based disease research and gait analysis in insects.

Corey ClelandView profile

Professor

• Neuroscience
• Neurobiology
• Sensory-Motor Systems
• +4 more

Corey Cleland is a Professor in the Department of Biology at James Madison University (JMU) since 1998. He holds a Ph.D. in Neuroscience from Northwestern University (1985) and a BA in Physics from Amherst College (1978). His research focuses on understanding how spatial sensory information is used by the central nervous system (CNS) to plan movements, using models like the nociceptive withdrawal response in rats and escape behaviors in spiders. Techniques include behavioral studies, electrophysiology, computational modeling, and surgical methods. Cleland teaches courses such as Neurobiology (BIO 445/550), Experimental Neurobiology (446/546), and Trelawny Seminar (201). His research highlights include investigating sensory-motor transformations in escape mechanisms of insects and mammals, as well as the role of calcium channels in neuronal function. Cleland has published extensively on topics ranging from pain perception to educational innovations in neuroscience. He is actively involved in undergraduate research mentorship and curriculum development, emphasizing hands-on learning and interdisciplinary approaches. Key Research Themes : Sensory-Motor Integration, Pain Pathways, Animal Behavior, Computational Neuroscience Teaching : Courses integrate experimental design, neurophysiology, and modern techniques like computer simulations. Cleland's work bridges basic science and pedagogy, contributing to both our understanding of neural mechanisms and the training of future scientists.

Robert O. DuncanView profile

Associate Professor

• Game-based learning
• Undergraduate research
• Learning Sciences
• +12 more

Robert O. Duncan is an Associate Professor of Behavioral Sciences at York College, City University of New York (CUNY), and also holds a dual appointment as Associate Professor in Psychology – Behavior Cognitive Neuroscience at the CUNY Graduate Center. His academic career bridges neuroscience research and educational innovation, with significant contributions to both fields through his work on visual processing disorders and game-based learning methodologies. Duncan earned his Ph.D. in Psychology from the University of California, San Diego in 1999, following an MA in Psychology (1994) and a BA in Psychology/Music Humanities (1993), all from UC San Diego. His academic trajectory has positioned him as a leader in interdisciplinary research connecting cognitive neuroscience with educational technology. Duncan's research program spans two interconnected domains: first, the physiological mechanisms of visually guided behavior in healthy individuals, with particular focus on how the brain processes visual information; and second, the development of novel fMRI techniques to quantify neuronal contributions to neurodegenerative visual disorders, especially glaucoma. His early work established important correlations between blood flow in the visual cortex and visual function in glaucoma patients, examining retinotopic organization in relation to optic nerve damage. More recently, his research has pivoted toward educational technology, exploring how game-based approaches, virtual reality, and interactive digital narratives can enhance learning outcomes. Analysis of Duncan's publication history reveals a clear evolution from pure neuroscience research toward educational applications. While his earlier work (2000-2012) focused predominantly on visual processing and glaucoma, his recent publications (2017-2025) increasingly address game-based learning, virtual reality applications, and digital narrative. This shift aligns with his leadership in the CUNY Games Network, where he has organized annual conferences and promoted game mechanics across educational disciplines. His most recent work examines embodiment and presence in virtual environments, connecting his neuroscience background with educational technology. Duncan is deeply committed to undergraduate research and has developed the Transformative Games Initiative at York College, which engages students in designing educational games across multiple disciplines. Through this initiative, students have created games addressing topics from anxiety disorders to nutritional literacy, with many co-authoring publications and presenting at conferences alongside Duncan. His work demonstrates how game design can serve as a powerful classroom laboratory that develops both domain knowledge and 21st-century skills. As an active member of the academic community, Duncan regularly presents at conferences, leads workshops on game-based learning, and collaborates with colleagues across CUNY. His blog and involvement with the CUNY Games Network show ongoing commitment to advancing educational practices through technology. His dual expertise in neuroscience and educational technology creates a unique perspective on how cognitive principles can inform effective teaching practices.

• Dynamical Systems
• Mathematical Neuroscience
• Traveling Wave Solutions
• +5 more

Stanislav M. Mintchev serves as Professor of Mathematics within the Department of Mathematics at The Cooper Union for the Advancement of Science and Art's Albert Nerken School of Engineering. With active teaching responsibilities for Fall 2024 including Linear Algebra and Calculus I, he maintains a prominent research profile in applied mathematics with specific focus on dynamical systems theory and its applications to biological and physical systems. Dr. Mintchev earned his Doctor of Philosophy degree in Mathematics with specialization in dynamical systems from New York University's Courant Institute. Prior to his graduate training, he completed bachelor of science degrees with honors in both Physics and Mathematics at The George Washington University in Washington, DC. His academic journey reflects a strong foundation in both theoretical and applied mathematical sciences. Mintchev's research program centers on the study of organization phenomena in spatially extended dynamical systems, with particular emphasis on traveling wave solutions that constitute perfectly transmitted signals across media. His work bridges rigorous analytical techniques from geometry, analysis, and probability theory with computational simulations, focusing on oscillation models from mathematical biology. He has developed significant expertise in pulse-coupled phase oscillator networks derived from mathematical neuroscience, examining existence and stability properties of traveling wave solutions. Additionally, he applies dynamical systems concepts to statistical data mining, pattern recognition, and machine learning, demonstrating the interdisciplinary reach of his mathematical approaches. Analysis of his publication record reveals a consistent research trajectory focused on neural oscillator networks and traveling wave phenomena. His recent work (2022-2024) shows increasing application to biological neural systems, particularly examining resilience mechanisms in neuronal networks and V1-inspired visual cortex models. Throughout his career, Mintchev has maintained a strong connection between theoretical mathematical frameworks and their practical applications in neuroscience, with publications spanning prestigious journals including Chaos, Nonlinearity, and the Journal of Mathematical Neuroscience. As an educator, Dr. Mintchev has taught a comprehensive range of mathematics courses including Introduction to Linear Algebra, Differential Equations, Probability, and all three introductory Calculus courses. He also offers independent study courses in point-set and algebraic topology, and assists with Cooper Union's Putnam Examination preparation. His teaching philosophy emphasizes connecting abstract mathematical concepts with their concrete applications in science and engineering, reflecting his own research approach. Future course offerings will include Numerical Methods, Boundary Value Problems, and Dynamical Systems, expanding opportunities for students to engage with advanced mathematical topics. Dr. Mintchev maintains active research collaborations with mathematicians including Bastien Fernandez, Bernard Ambrosio, and others in the dynamical systems community. His professional affiliations include the Society for Industrial and Applied Mathematics, American Mathematical Society, and Mathematical Association of America, demonstrating his engagement with the broader mathematical community. Though no formal laboratory is mentioned, his work involves computational modeling and theoretical analysis of dynamical systems, likely conducted through computational resources at The Cooper Union.

Christof KochView profile

Research Professor

• Neuroscience
• Consciousness
• Computational Neuroscience
• +2 more

Christof Koch is a renowned neuroscientist serving as Meritorious Investigator at the Allen Institute for Brain Science and Chief Scientist at the Tiny Blue Dot Foundation. His primary research explores the neural mechanisms underlying consciousness, integrating theoretical neuroscience with experimental approaches. With over 300 publications, his work spans neurophysiology, computational modeling, and clinical applications of consciousness research. His research focuses on understanding how neuronal activity gives rise to subjective experience, utilizing techniques ranging from single-cell recordings to large-scale brain mapping. Koch investigates neural correlates of consciousness through integrated information theory and develops neurotechnological approaches for brain disorders. Koch's recent publications demonstrate a strong focus on cortical circuit mechanisms, consciousness assessment methodologies, and comparative neuroanatomy. His work combines advanced neuroimaging, electrophysiological techniques, and theoretical modeling to unravel brain complexity. The research consistently bridges fundamental neuroscience with clinical applications, particularly in neuromonitoring and brain-computer interfaces. He leads multidisciplinary teams at the Allen Institute and collaborates internationally, driving innovations in brain mapping and consciousness research. Koch mentors numerous early-career neuroscientists through the MindScope program and various institutional initiatives.

Falk HuettigView profile

Professor

• Psycholinguistics
• Cognitive Neuroscience
• Cultural Cognition
• +3 more

Falk Huettig is a Senior Investigator at the Max Planck Institute (MPI) for Psycholinguistics in Nijmegen and an Invited Full Professor of Psychology at the University of Lisbon. He holds Honorary Professorship at the University of Kaiserslautern-Landau. Previously, he was a Full Professor of Psycholinguistics and Cultural Cognition at Radboud University (2019–2024). His research spans cultural cognition , predictive processing , and the impact of literacy on the brain . He uses neuroimaging, behavioral experiments, and computational modeling to explore how cultural tools like writing reshape cognition. Education : BSc Biological Sciences (Psychology), University of Edinburgh (2000) MSc by Research in Psychology, University of Edinburgh (2001) PhD in Psychology, University of York (2004) Habilitation in Psychology, University of Münster (2013) Research Focus : Huettig investigates how literacy acquisition alters brain structure/function (e.g., cortical/subcortical networks), the role of prediction in language comprehension, and cultural influences on cognition. His work spans diverse populations, including illiterate adults, children, and multilingual speakers. Key themes include neuronal recycling, cross-modal interactions, and the 'literate mind hypothesis.' Grants & Leadership : Max Planck Society Strategic Grants (2010–2017) Scientific Council Representative for MPI (2009–2015) President, Society for Cognitive Science of Culture (2018–present) Labs & Collaborations : His research is affiliated with the Donders Institute and the MPI, focusing on interdisciplinary projects combining psychology, neuroscience, and anthropology. Current work emphasizes Looking Ahead: The New Science of the Predictive Mind (Cambridge, 2025).