Jason Ritt is an Associate Professor of Brain Science (Research) and Scientific Director of Quantitative Neuroscience at the Robert J. and Nancy D. Carney Institute for Brain Science, Brown University. He holds affiliations with the Data Science Institute and collaborates across disciplines on quantitative research methods. Education : B.S., M.A., and Ph.D. in Neuroscience from Boston University (1997–2003). Research : Focuses on neural processing during active sensing and neuroengineering for neurostimulation. Combines electrophysiology, optogenetics, and theoretical approaches in rodent models. Develops closed-loop systems for studying sensory neural prosthetics and brain-machine interfaces. Key areas include synaptic diversity, neurocontrol algorithms, and sensory restoration. Teaching : Instructs NEUR 2100 NeuroPracticum, integrating hands-on neuroscience research training.
Larry Abbott is the William Bloor Professor of Theoretical Neuroscience at Columbia University, with joint appointments in the Department of Physiology and Cellular Biophysics (within Biological Sciences) and the Mortimer B. Zuckerman Mind Brain Behavior Institute. He serves as Co-Director of the Center for Theoretical Neuroscience and is a Senior Fellow at HHMI Janelia Farm. PhD in Physics (1977), Brandeis University His research focuses on computational and mathematical modeling of neurons and neural networks, emphasizing spike-timing-dependent plasticity, sensory encoding in olfaction, and dynamics of internally generated neural activity. He explores how chaotic neural activity is harnessed for motor output and how perception involves dynamic inference and synaptic plasticity. Recent publications highlight applications of recurrent neural networks, hierarchical control mechanisms, and sensory-motor integration. Collaborative work spans institutions like MIT, Hebrew University, and the Allen Institute for Brain Science. Awards include the NIH Director’s Pioneer Award and the Swartz Prize in Theoretical Neuroscience. NIH Director’s Pioneer Award (2004) Swartz Prize (2010) First Annual Prize in Mathematical Neuroscience (2013) Irving Institute Mentor of the Year (2013)
Alexei Koulakov is a Professor at Cold Spring Harbor Laboratory (CSHL) and the Charles Robertson Professor of Neuroscience. His research focuses on applying mathematical and computational approaches to unravel the principles of brain organization, particularly in sensory systems like olfaction and vision. Koulakov's work explores how neural circuits form during development, the role of genetic and experiential factors, and the evolutionary basis of brain architecture. Education: PhD in Physics from the University of Minnesota (1998). Key Research Areas: Olfactory system development, neural network modeling, and AI inspired by biological computation. Koulakov's recent publications emphasize cross-disciplinary integration of neuroscience and AI, including NeuroAI initiatives and DeepNose models predicting olfactory percepts. His team investigates how innate abilities are encoded genomically and how experience shapes neural networks. Scientific contributions include studies on primacy coding in olfaction, stochastic learning mechanisms , and high-throughput neural mapping . Awards include the Charles Robertson Professorship , reflecting his leadership in theoretical neuroscience. Koulakov collaborates extensively, with notable work on genomic bottlenecks , odor mixture interactions , and neural integrator models . His lab at CSHL is at the forefront of NeuroAI research, leveraging brain circuit insights to advance artificial intelligence.
Richard Axel is a University Professor at Columbia University, holding appointments in the Vagelos College of Physicians and Surgeons as Professor of Neuroscience, Professor of Biochemistry and Molecular Biophysics, and Professor of Pathology and Cellular Biology. He serves as Codirector of Columbia's Mortimer B. Zuckerman Mind Brain Behavior Institute and is an Investigator at the Howard Hughes Medical Institute since 1984. Dr. Axel earned his AB from Columbia College and MD from Johns Hopkins Medical School. His Nobel Prize-winning research identified over 1,000 odorant receptors in the nose that transmit olfactory information to the brain, revolutionizing our understanding of the sense of smell. His early work with colleagues developed groundbreaking gene transfer techniques that enabled the introduction of virtually any gene into any cell, leading to novel approaches for gene isolation and analysis of gene function. Dr. Axel's research focuses on understanding how olfactory information is processed in the brain to create internal representations of the external world. His work spans molecular neuroscience, neural circuitry, and the relationship between sensory input and behavioral output. He has pioneered techniques that have advanced our understanding of neural function and sensory processing mechanisms. Analysis of Dr. Axel's recent publications reveals a continued focus on olfactory processing systems, with increasing integration of computational approaches and machine learning to understand neural representations. His work spans from molecular mechanisms to circuit-level analyses, with particular emphasis on how odor information is encoded and transformed in the brain to produce meaningful perceptions and behaviors across multiple model systems. Nobel Prize in Physiology or Medicine (2004) Howard Hughes Medical Institute Investigator (1984-Present) The Royal Society Foreign Member (2014) Gairdner Foundation International Award (2003) American Philosophical Society Member (2003) National Academy of Sciences Member (1983) Richard Lounsbery Award (1989) American Association for the Advancement of Science Fellow (2018) American Academy of Arts and Sciences Fellow As Codirector of the Zuckerman Institute, Dr. Axel oversees one of the world's leading neuroscience research centers, fostering interdisciplinary collaboration across multiple departments. His lab continues to train the next generation of neuroscientists, investigating how sensory information is transformed into meaningful perceptions and behaviors. Dr. Axel's early work on gene transfer techniques laid the foundation for numerous advances in molecular biology and neuroscience, including the isolation and analysis of the CD4 gene, the cellular receptor for HIV. Dr. Axel leads the Axel Lab at Columbia University, which is part of the Zuckerman Institute. His research team investigates how organisms recognize olfactory information in the environment and transmit it to the brain, where it is processed to create internal representations of the external world. The lab employs multidisciplinary approaches combining molecular, cellular, and systems neuroscience to unravel the neural circuits underlying sensory processing and behavior, with particular focus on understanding how these representations translate stimulus features into appropriate innate and learned behaviors.
Gabriel Koch Ocker is an Assistant Professor in the Department of Mathematics & Statistics at Boston University, specializing in theoretical and computational neuroscience. His research investigates how neural activity encodes sensory information, shapes behavior, and evolves through learning mechanisms. Research Focus: Structure-function relationships in neuronal networks Methodology: Dynamical systems, stochastic processes, statistical physics Collaborations: Experimental validation of computational models Recent publications analyze integrate-and-fire networks, dendritic calcium spiking, inhibition-stabilized circuits, and metastability in stochastic neuronal systems. His group combines mathematical rigor with biological relevance to explore neural coding, plasticity, and functional hierarchy in cortical structures. Key contributions include tensor decomposition approaches to correlation analysis, reconciling recording technique discrepancies, and developing field-theoretic frameworks for compartmental modeling. Work spans from molecular-level channel dynamics (Kv7 channels) to brain-area-level functional organization.
Edward Kim is an Associate Professor in the Department of Computer Science at Drexel University's College of Computing & Informatics. His research spans computer vision, sparse coding, neuromorphic computing, and AI, with a focus on neuro-inspired machine learning and robust, interpretable models. Research Interests: Computer Vision Sparse Coding and Dictionary Learning Neuromorphic and Spiking Neural Networks Explainable and Adversarially Robust AI Multimodal Learning Medical Image Processing His recent publications highlight a strong trend in developing biologically inspired, robust, and interpretable machine learning models, particularly using sparse coding and spiking neural networks. Themes include adversarial robustness, model confidence calibration, and cross-modal integration. His work often bridges neuroscience and AI, aiming to create more human-like and trustworthy systems. Scientific Awards: NSF CAREER Award (2019) Longsview Fellow (collaborative project, 2021) Dr. Kim advises several graduate students in the SPARSE Lab and has secured significant research funding from the NSF, DARPA, and the Bill & Melinda Gates Foundation. His grants focus on ethical AI, racial bias in ML, and digital health platforms. He also contributes to academic leadership as a Provost Fellow at the Drexel Solutions Institute and co-chair of computer vision tracks at major conferences. Labs and Teams: He leads the SPARSE (SPiking And Recurrent SOFTwarE) Coding Lab, which investigates biologically inspired learning models beyond traditional deep learning. The lab integrates neuroscience principles to improve stability, interpretability, and robustness in AI systems.
Kyusang Lee is an Associate Professor in the Electrical and Computer Engineering and Materials Science and Engineering departments at the University of Virginia. His research focuses on optoelectronic devices, neuromorphic computing, and smart sensors, emphasizing applications in solar energy conversion and flexible electronics. He holds a B.S. from Korea University (2005), M.S. from Johns Hopkins University (2009), and Ph.D. from the University of Michigan (2014). He conducted postdoctoral research at the University of Michigan and MIT. Education: B.S., Electrical Engineering, Korea University, 2005 M.S., Electrical and Computer Engineering, Johns Hopkins University, 2009 Ph.D., Electrical Engineering and Computer Science, University of Michigan, 2014 Postdoctoral Fellowships: University of Michigan (EECS), MIT (Mechanical Engineering) His research interests span thin-film and flexible optoelectronics, neuromorphic computing architectures, and AIoT-enabled smart sensors. Notable contributions include remote epitaxy techniques for semiconductor membrane integration and solar-tracking concentrator designs. His work bridges materials science and device engineering to advance energy-efficient optoelectronics and bioinspired systems. Key Research Themes: Organic/inorganic optoelectronic devices for solar energy Flexible and stretchable electronics Neuromorphic hardware for edge computing Gas sensing and bioinspired sensor systems Lee’s publications reflect interdisciplinary innovation, with recent work on ferroelectric transistors, neuromorphic vision systems, and high-efficiency photovoltaics. He received the NSF CAREER Award (2020) and AFOSR YIP Award (2023).
Tom Wenseleers is a Professor at KU Leuven's Department of Biology within the Faculty of Science, where he leads the Laboratory of Socioecology and Social Evolution. His research spans theoretical and experimental approaches to evolutionary biology, with particular focus on social insect systems. Research spans social insects (ants, bees, wasps), microbes, viruses, and human systems Primary model organisms: social insects studying major evolutionary transitions Current projects examine caste determination, chemical communication, and evolutionary conflicts His research integrates theoretical modeling with experimental, behavioral, and comparative studies. Recent work combines genomic techniques and high-throughput GC/MS analysis to decipher chemical communication systems. Current trends show increasing interdisciplinary work spanning virology (SARS-CoV-2 variants), microbial ecology (antibiotic resistance), and robotics (pollinator behavior monitoring). The research demonstrates consistent application of evolutionary theory to diverse biological systems while maintaining social insects as the core model. Wenseleers actively mentors PhD students and postdocs, with recent graduates including Kamiel Debeuckelaere and Viviana Di Pietro. His lab receives substantial funding through multiple concurrent research projects, including Promotor roles on grants examining caste development in bee societies and microbial metabolite screening. The laboratory maintains strong international collaborations across Europe and South America. The lab operates within the Ecology, Evolution and Biodiversity Conservation unit at KU Leuven, with physical location at Naamsestraat 59, box 2466, 3000 Leuven. The research group maintains active outreach programs including science workshops for schools and public engagement events focused on insect conservation.
Anton Berg is a Postdoctoral Researcher at the University of Helsinki, affiliated with the Department of Digital Humanities within the Faculty of Arts and the Helsinki Institute for Social Sciences and Humanities (HSSH). He is also a member of the methodological unit at HSSH, focusing on interdisciplinary research at the intersection of cognitive science, religious studies, and artificial intelligence. His educational background spans computer science, cognitive science, and religious studies, enabling him to bridge technical and humanistic approaches to AI. His research primarily investigates how commercial image recognition systems interpret and categorize religious content, exploring issues of bias, representation, and the datafication of religion. Berg's research interests include computer vision systems, automatic image recognition, machine and deep learning, large language models, and the relationship between religions, worldviews, and values related to AI technologies. He particularly focuses on inequality issues, the datafication of religion, the datafication of societies, the social scientific study of religion, and the cognitive science of religion. His work combines technical analysis of AI systems with social scientific perspectives on religion and technology. His recent publications demonstrate a strong focus on examining biases in commercial image recognition services, particularly regarding religious content, with significant contributions to understanding representational silence and racial biases in these systems. He has also conducted important work on pandemic psychology, contributing to large-scale international studies on COVID-19 responses across 69 countries. Berg has been actively involved in numerous academic activities, including presentations at international conferences on topics such as computer vision in religious studies, mediatized religious populism, and biases in image recognition services. His research has been presented at venues including the International Association for the Cognitive Science of Religion. His teaching areas include religious studies, cognitive science, data science, and religion and technologies, reflecting his interdisciplinary approach to understanding the relationship between digital technologies and religious phenomena.
Prof. Dr. Andreas Stadlbauer is a medical physicist affiliated with the Clinical Institute for Diagnostic and Interventional Radiology at St. Pölten University Hospital and the Department of Neurosurgery at Friedrich-Alexander University Erlangen-Nuremberg. He holds an adjunct professorship at the University of Erlangen-Nuremberg and contributes to both clinical and academic research in biomedical imaging and AI applications in oncology. University: Friedrich-Alexander University Erlangen-Nuremberg Hospital Affiliation: St. Pölten University Hospital Department: Department of Neurosurgery Academic Rank: Adjunct Professor His research focuses on advanced MRI techniques, particularly physio-metabolic imaging of brain tumors, oxygen metabolism, and the integration of artificial intelligence in clinical diagnostics. He has led research on glioma classification, tumor microenvironment characterization, and deep learning models for radiomic analysis. The recent publications highlight a strong trend toward AI-driven diagnostic tools in neuro-oncology, particularly in differentiating glioblastomas from metastases and predicting genetic mutations using machine learning. His work emphasizes the clinical translation of complex imaging data into actionable insights. Artificial Intelligence in Oncology Medical Imaging and Radiomics Brain Tumor Metabolism Deep Learning for MRI Analysis Oxygen Metabolism Imaging Clinical Decision Support Systems Prof. Stadlbauer has been involved in seed-funded research projects developing deep learning algorithms for clinical integration. He collaborates extensively with neurosurgeons, radiologists, and oncologists across institutions, contributing to multidisciplinary tumor boards and translational research initiatives. He completed his doctorate in medical physics in 2004, habilitation in 2008, and an MBA in Health Management in 2010. His academic journey reflects a blend of technical expertise and leadership in healthcare innovation.
Timothy Holy, PhD is the Alan A. & Edith L. Wolff Professor of Neuroscience and Vice Chair of Research at Washington University School of Medicine. He leads the Holy Lab, which focuses on the neural mechanisms of olfactory coding and the development of innovative imaging technologies for neuroscience research. His educational background includes a BA in Mathematics and Physics (summa cum laude) from Rice University (1991), an MA in Physics from Princeton University (1992), and a PhD in Physics from Princeton University (1997) under thesis advisor Stanislas Leibler. Dr. Holy's research spans multiple domains of neuroscience with particular emphasis on the olfactory system of mice. His lab has pioneered light sheet microscopy for calcium imaging, enabling simultaneous recording from tens of thousands of neurons. More recently, they developed PhOTseq, a technique for optically tagging neurons for later sequencing. His work bridges physics, neuroscience, and computational approaches to understand how sensory systems process information. He is also among the world's foremost creators of the Julia programming language, which has gained exponential adoption in scientific computing. Analysis of his recent publications reveals a strong focus on neural coding in decision-making circuits, olfactory processing, and the development of computational tools for biological research. His work increasingly integrates machine learning approaches with traditional neuroscience techniques. Distinguished Teaching Service Award (2005, 2008, 2014) McKnight Technological Innovation in Neuroscience Award (2007) St. Louis Academy of Sciences Innovation Award (2009) NIH Director's Pioneer Award (2009) Society for Neuroscience Research Award for Innovation in Neuroscience (RAIN) (2009) Dr. Holy has mentored numerous students and postdoctoral researchers who have gone on to establish their own research programs. His lab has secured significant grant funding supporting technology development and fundamental neuroscience research. Current projects include investigating cellular mechanisms of individuality and plasticity, navigation using olfactory cues, and developing new mathematical tools for optimization and machine learning. The Holy Lab combines a focus on understanding neural circuits and behavior with a willingness to pioneer new technologies. It maintains strong collaborations across disciplines, particularly in the development and application of the Julia programming language for biological research.
Jonathan D. Victor is a Professor at Weill Cornell Medicine’s Graduate School of Medical Sciences, affiliated with the Department of Neurology and the Feil Family Brain & Mind Research Institute. His research focuses on understanding neural computations, sensory processing, and brain dynamics in health and disease, particularly in vision, olfaction, and disorders of consciousness. He employs interdisciplinary approaches, integrating mathematical modeling, computational techniques, and experimental neuroscience. Victor earned an undergraduate degree in Mathematics from Harvard College in 1973 and completed an MD-PhD program at Rockefeller University and Cornell University, specializing in visual neuroscience. He completed a neurology residency at The New York Hospital and has been at Weill Cornell since 1986. His research spans sensory systems (vision, gustation, olfaction), neural circuits, and sensorimotor integration. Key projects include analyzing perceptual spaces, image statistics in natural and medical contexts, active vision and olfaction, and large-scale brain dynamics in disorders of consciousness. He collaborates with Mary Conte (visual psychophysics), Keith Purpura (neurophysiology), and Nicholas Schiff (neurology and brain injury). Recent publications highlight his work on visuomotor integration, statistical properties of natural scenes, locomotor dynamics in Drosophila, spectral analysis in medical imaging, and cortical synchronization mechanisms. His lab develops methods like spike train metrics and binless embedding to analyze neural data. Scientific awards include the McKnight Scholars Award, NINCDS Teacher-Investigator Award, Klingenstein Fellowship in Neuroscience, and the Cornell Discovery Award. Victor also serves as co-Editor-in-Chief for the Journal of Computational Neuroscience and Vision Research .
Miiamaaria Kujala is an Academy Research Fellow at the Department of Psychology, University of Jyväskylä. Her research focuses on social cognition and emotionality in humans and non-human animals, particularly domestic dogs, through interdisciplinary collaboration across psychology, cognitive science, biology, veterinary medicine, and biomedical engineering. Academy Research Fellow (2024) Docent in Comparative Cognitive Neuroscience Her work employs non-invasive physiological methods such as eye gaze tracking, EEG/ERPs, thermal imaging, and fMRI to study emotional expressions, cross-species interaction, and the neural basis of social perception. Key themes include the development of expertise in decoding nonverbal cues, human-animal bond dynamics, and One Health/One Welfare frameworks. Recent publications highlight interdisciplinary approaches to canine emotionality, pharmacological behavior management in pets, and advanced sensor technologies for behavior classification. Her 2024 articles explore olfaction, empathy, and activity tracking in dogs, while older works examine contagious behaviors, social brain circuits, and developmental psychology in human-animal interactions. Scientific awards include the Academy Research Fellow fellowship. She leads the "Interaction of Dogs and Humans" research group, integrating expertise from psychology, veterinary medicine, and engineering to advance understanding of emotional and cognitive processes across species.
Miriam B. Goodman holds the Mrs. George A. Winzer Professorship of Cell Biology at Stanford University , with affiliations in the Department of Molecular and Cellular Physiology , Bio-X , and the Wu Tsai Neurosciences Institute . As Chair of the Molecular and Cellular Physiology Department (2017–present) and former Deputy Director of the Stanford Neuroscience Institute (2013–2017), she leads interdisciplinary research bridging neuroscience, biophysics, and molecular physiology . Education : Ph.D. in Neurobiology, The University of Chicago (1995) Sc.B. in Biochemistry, Brown University (1986) Her research focuses on molecular mechanotransduction using C. elegans to investigate touch sensation, temperature detection, and neuronal stress resilience . Key methodologies include quantitative behavioral analysis , CRISPR gene editing , and in vivo electrophysiology . Recent work explores mechanical forces in feeding through upconverting microgauges and chemosensory integration of plant-derived molecules. Her publications span neurobiological mechanisms , biomechanical sensor design , and ecological signal processing , with a 2025 Nature study on ingestible nanosensors and a 2024 Current Biology paper on mechanosensory complex organization . Scientific Awards : Distinguished Alumni Award, University of Chicago (2024) Landis Award for Outstanding Mentoring, NIH (2019) Michael and Kate Barany Award, Biophysical Society (2014) Klingenstein Fellow in Neuroscience (2005-2008) McKnight Scholar Award (2005-2008) Prize in Neurobiology, Eppendorf & Science (2004) As an advisor, she mentors doctoral candidates in the Biophysics , Molecular and Cellular Physiology , and Neurosciences Ph.D. programs , including Madeline Cooper and Caroline Arellano-Garcia. Her lab collaborates with UC San Diego's Vergassola Group on mechanosensory physics and Stanford Microsystems Lab on optical sensor design .
Dr. Enrique Blair is an Associate Professor in the Department of Electrical and Computer Engineering at Baylor University, where he has served since 2015, advancing to his current rank in 2021. His academic journey includes prior roles as a Military Instructor at the U.S. Naval Academy and service in the U.S. Navy submarine force. He is actively engaged in research, teaching, and mentoring within the College of Engineering. His research focuses on the theoretical and computational aspects of quantum engineering, particularly in quantum-dot cellular automata (QCA), open quantum systems, and quantum information sciences. He explores molecular computing paradigms, quantum decoherence, and the quantum mechanical basis of olfaction, aiming to develop ultra-dense, low-power nanoelectronic devices and novel quantum technologies. His interdisciplinary work bridges electrical engineering, physics, chemistry, and materials science. The recent articles highlight a strong trend in molecular QCA design, quantum simulation for NISQ devices, and the application of ab initio methods to understand counterion effects and molecular stability. His research increasingly integrates machine learning for material discovery and emphasizes robustness in quantum circuits against environmental noise and external fields. The publications reflect a consistent focus on foundational quantum phenomena with practical applications in computing, sensing, and security. Research Grant, Office of Naval Research, Code 312 Nanoscale Computing Devices and Systems (May 2020 - May 2023) Summer Sabbatical, Baylor University (Summer 2019) Senior Member, IEEE (2019) Outstanding Faculty Award (untenured, tenure-track faculty), Baylor University (2018) Proposal Development Award, Office of the Vice Provost for Research, Baylor University (2017) Rising Star Program, Baylor University (2017-2018) Undergraduate Research and Scholarly Achievement Award, Office of the Vice Provost for Research, Baylor University (2017-2018) Rising Star Program, Baylor University (2016-2017) Graduate Research Fellowship Program, National Science Foundation (2010-2015) National Defense Science and Engineering Graduate Fellowship, American Society for Engineering Education (2010-2013) Dr. Blair has advised multiple Ph.D. and Master’s students, including Colin Burdine, Nischal Gautam, and Nishat Liza, and has mentored numerous undergraduate researchers. His research is supported by competitive grants, particularly from the Office of Naval Research, reflecting the strategic importance of his work in nanoscale computing. He integrates teaching and research, offering courses such as Quantum Mechanics for Engineers and Introduction to Quantum Computing, and promotes scholarly productivity through tools like Emacs Org Mode and LyX. He leads an active research team focused on molecular QCA and quantum information, with current members including Ph.D. students and undergraduates. The team conducts simulations, theoretical modeling, and design of quantum devices, contributing to advancements in nanoelectronics and quantum computing. Collaborations with experts in chemistry, physics, and computer science further extend the impact of the research.
