جستجوی استاد راهنمای دانشگاهی

• Artificial Intelligence
• Deep Learning
• Computer Vision
• +۵ مورد دیگر

Lior Wolf is a Professor at the School of Computer Science, Tel Aviv University. Previously, he was a postdoctoral researcher at MIT's Center for Biological and Computational Learning (CBCL) under Prof. Tomaso Poggio and earned his PhD from Hebrew University of Jerusalem with Prof. Amnon Shashua. His educational background includes: PhD in Computer Science, Hebrew University of Jerusalem Postdoctoral Research, MIT CBCL Prof. Wolf's research centers on artificial intelligence with seminal contributions to deep learning, computer vision, and natural language processing. His work bridges theoretical foundations (e.g., attention mechanisms, transformer analysis) with practical applications in medical imaging, speech processing, and sign language technology. He pioneered methods for neural network interpretability, efficient sequence modeling, and multimodal fusion. Analysis of his 2023-2025 publications reveals dominant trends in large language model optimization (neuron pruning, attention analysis), efficient video generation, and cross-modal learning. His work increasingly integrates medical applications (fMRI/EEG analysis) while maintaining theoretical rigor in model architecture design. His scientific achievements include: Best paper award at EMNLP 2024 for 'Backward Lens: Projecting Language Model Gradients into the Vocabulary Space' Best paper award at SCIA 2023 for 'Gradient Adjusting Networks for Domain Inversion' Best paper award at FG 2021 for 'Generating Master Faces for Dictionary Attacks' Prof. Wolf mentors graduate students in the School of Computer Science and leads research at the ICRC building laboratory. His team collaborates with 'the friends of TAU' on projects spanning biometric security, medical imaging, and generative AI. Current work focuses on efficient transformers, neural network interpretability, and multimodal medical diagnostics.

• Computational Neuroscience
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Raul Vicente Zafra is a Professor of Data Science at the University of Tartu, Faculty of Science and Technology, Institute of Computer Science, where he has been working since 2013. His research spans computational neuroscience, artificial intelligence, and data science, with a particular focus on bridging biological and artificial models of intelligence. Education: PhD in Physics (2001-2006), University of the Balearic Islands BSc in Physics (1997-2001) Professor Zafra's research interests center on computational neuroscience and artificial intelligence, with specific expertise in brain-computer interfaces, reinforcement learning, neural modeling, and explainable AI. His work bridges the gap between biological and artificial intelligence systems, exploring how neural principles can inform machine learning algorithms and vice versa. He has made significant contributions to understanding neural coherence, time interval learning in neural systems, and the application of information theory to brain-computer interfaces. His research often involves interdisciplinary collaboration between computer science, neuroscience, and medicine. Analysis of Zafra's recent publications reveals a strong focus on the intersection of artificial intelligence and neuroscience. His work spans explainable AI methods, brain-computer interfaces, reinforcement learning models that mimic cognitive processes, and neurophysiological studies of brain activity. A notable trend is his exploration of how biological principles of neural computation can inform and improve artificial intelligence systems, particularly in areas like time-based learning, consciousness modeling, and neural coherence. Scientific Awards: 2012: Attendee at the 62nd Lindau Nobel Laureate Meeting 2007: Quantum Electronics and Optics Division Prize of the European Physical Society for the best PhD Thesis in Applied Optics in Europe 2006: PhD Extraordinary Award of the Physics Department of the University of the Balearic Islands 2001: Physics Degree Extraordinary Award (First Class Honors, best GPA) 1997: Bronze Medal in the "8th Spanish Physics Olympiad" Professor Zafra has been principal investigator on numerous significant research projects including the Estonian Centre of Excellence in Artificial Intelligence, Cardiovascular Stress Impacts On Neuronal Function, and Bridging biological and artificial models of vision. His grant portfolio demonstrates strong funding support from the Estonian Research Council, European Commission, and other major funding bodies. He has supervised multiple PhD students and mentored early-career researchers in computational neuroscience and AI. His laboratory work focuses on developing computational models of neural systems and applying these insights to artificial intelligence. Current research directions include explainable AI methods, brain-computer interfaces, modeling of consciousness and cognitive processes, and the application of AI to healthcare challenges.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۶ مورد دیگر

Timothy M. Hospedales is a Professor of Artificial Intelligence at the Institute of Perception, Action and Behaviour within the School of Informatics at the University of Edinburgh . He also serves as VP AI and Head of Samsung AI Research Centre Europe . His research focuses on efficient and robust AI , emphasizing meta-learning , lifelong transfer-learning , and domain adaptation in both probabilistic and deep learning frameworks. Applications span computer vision , vision and language , reinforcement learning for robotics , and finance . Professor at University of Edinburgh (2020–present) ELLIS Fellow (2021) Head of Samsung AI Research Europe (2020–present) Founding Director of Applied Machine Learning Lab at QMUL (2012–2016) His work includes pioneering contributions to meta-learning , few-shot learning , and self-supervised methods , with notable awards such as the Best Paper Prize at ICML AutoML 2018 and Best Student Paper at ICPR 2018 . He has co-authored 15+ recent papers on topics like Vision-Language Models , Medical AI Fairness , and Diffusion Model Optimization . He served as Program Co-Chair for BMVC 2018 and AAAI 2022 , and authored a book on Visual Adaptation in the Deep Learning Era (2022). Co-Chair, BMVC 2018 Guest Editor, IET CV Special Issue (2016) Keynote Speaker at TASK-CV Workshop (ECCV 2016) Special Issue on Fewer Labels (IEEE PAMI 2020) His leadership extends to organizing workshops like the Learning-to-Learn Workshop at ICLR 2021 , Meta-Learning Workshop at NeurIPS 2020 , and Domain Generalisation Workshop at ICLR 2023 . Current projects include Meta-Omnium (CVPR 2023) for general-purpose meta-learning and MetaAudio (ICANN 2022) for few-shot audio classification benchmarks.

Aleksandar Jeremicمشاهده پروفایل

دانشیار

• Biomedical Signal Processing
• Statistical Signal Processing
• Biometrics
• +۷ مورد دیگر

Aleksandar Jeremic is an Associate Professor in both the Electrical & Computer Engineering department and the McMaster School of Biomedical Engineering at McMaster University. His research focuses on biomedical signal processing, statistical signal processing, and biometrics, with applications in healthcare and biomedical systems. He holds a Dipl. Ing. from the University of Belgrade, and an M.S. and Ph.D. from the University of Illinois at Chicago. His expertise spans physiological signal analysis (e.g., ECG/EEG), medical imaging, and machine learning for biomedical applications. He has authored over 50 technical articles and two book chapters, and has been recognized with a teaching award from the McMaster Electrical and Computer Engineering Society (2018). He supervises graduate students in both theoretical and applied research areas. Dr. Jeremic teaches advanced courses such as Biomedical Signal Modeling and Processing and Advanced Probability and Random Processes , emphasizing practical applications of signal processing in healthcare. His work includes clinical implementations like neonatal seizure monitoring software and microwave imaging for breast cancer detection.

John P. O'Dohertyمشاهده پروفایل

استاد

• Decision Neuroscience
• Reward-related Learning
• Computational Modeling
• +۳ مورد دیگر

John P. O'Doherty serves as the Fletcher Jones Professor of Decision Neuroscience within Caltech's Division of Humanities and Social Sciences, holding continuous faculty appointments since 2004 (Assistant Professor 2004-07, Associate Professor 2007-09, Professor 2009-present, Fletcher Jones Professor 2021-present). He previously directed the Caltech Brain Imaging Center (2013-17) and maintains affiliations with the T&C Chen Center for Social and Decision Neuroscience. His educational background includes a B.A. from University of Dublin, Trinity College (1996) and D.Phil. from University of Oxford (2000). His research focuses on computational and neural mechanisms of reward-based learning and decision-making , employing fMRI, intracranial recordings, and mathematical modeling to investigate how the brain solves complex decision problems through evolutionarily conserved algorithms. Key areas include Reinforcement learning systems (model-based/model-free arbitration) Observational and social learning mechanisms Neural representation of value, risk, and uncertainty Computational phenotyping of mental disorders Temporal dynamics of goal persistence Analysis of his 2023-2025 publications reveals dominant trends in computational psychiatry (problem gambling, autism traits), hierarchical decision-making, and neuroeconomic modeling of social behavior. His work consistently integrates cross-species computational frameworks with human neuroimaging to identify transdiagnostic mechanisms. While specific awards beyond his endowed professorship aren't detailed, his leadership as Brain Imaging Center Director and prolific high-impact publications demonstrate significant recognition. Current advising includes graduate researcher Sneha Aenugu on goal-persistence projects, with administrative support from Mary A. Martin (mmartin@caltech.edu). His active research program continues to pioneer computational approaches to understanding decision pathologies.

• Self-organized criticality
• Neuronal network dynamics
• Excitation/Inhibition balance
• +۵ مورد دیگر

Anna Levina is an Assistant Professor for Computational Neuroscience at the University of Tübingen , affiliated with the Department of Computer Science under the Faculty of Science. Her research focuses on the self-organization of neuronal activity, critical dynamics in neural networks, and the excitation/inhibition balance in cortical circuits. Current positions: Assistant Professor (since 2018), Group Leader (2017-2018), Equality Officer (Computer Science) Previous roles: IST Fellow (2015-2017), Associated Researcher (2011-2015), Postdoc/PI (2011-2015), Postdoc (2008-2011) Her research integrates mathematical modeling , statistical physics , and computational neuroscience to study criticality phenomena, neural avalanches, and adaptive network dynamics. Key interests include: Self-organized criticality in neural systems Excitation/Inhibition balance mechanisms Network topology and dynamics Timescale analysis in neural processing Stochastic modeling of neural activity Recent publications reveal trends in understanding critical dynamics across biological and artificial networks, with applications to memory systems, sensorimotor integration, and disease modeling. She has received recognition as an IST Fellow .

Vibhu Sahniمشاهده پروفایل

استادیار

• Neuroscience
• Neuroregeneration
• Motor Circuit Development
• +۳ مورد دیگر

Vibhu Sahni, Ph.D., is an Assistant Professor of Neuroscience and Lab Director of the Laboratory for Cell Fate Specification and Circuit Development at the Burke Neurological Institute, an affiliate of Weill Cornell Medicine. His research focuses on understanding molecular mechanisms underlying corticospinal circuit development and regeneration, particularly after injuries like spinal cord injury or stroke. His work integrates developmental neuroscience principles to identify strategies for repairing neural circuits involved in motor control. Research interests include axon guidance, segment-specific neural circuit formation, and the molecular basis of neural regeneration decline during development. Key projects investigate how genes like Cbln1 direct axon targeting to thoraco-lumbar regions and why long-distance regenerative ability varies across spinal segments. Recent publications highlight discoveries in segmental axon targeting specificity and the dynamic loss of regenerative capacity in corticospinal neurons. His lab employs advanced techniques such as single-cell RNA sequencing, in vivo electroporation, and microsurgical lesion models to study these processes. Current grants include funding from the Craig H. Neilsen Foundation and Wings for Life Spinal Cord Research Foundation to advance molecular strategies for corticospinal circuit repair.

Casper Hoogenraadمشاهده پروفایل

استاد

• Molecular Neuroscience
• Neuronal Development
• Intracellular Protein Trafficking
• +۷ مورد دیگر

Prof. Casper Hoogenraad is a full professor in Molecular Neuroscience at the Department of Cell Biology, Faculty of Science, Utrecht University. His research focuses on understanding how intracellular protein trafficking underlies neuronal development and function, with particular emphasis on the microtubule cytoskeleton, synaptic cargo trafficking, and synaptic plasticity. He leads an active research group within Utrecht University's Cell Biology department and collaborates extensively with other neuroscience research groups. Education: PhD, Erasmus University Rotterdam (1996-2001) Postdoc, Massachusetts Institute of Technology (2002-2005) Hoogenraad's research spans three main themes: cytoskeleton dynamics during neurodevelopment and synaptic plasticity, motor proteins and adaptors as regulators of synaptic transport, and psychiatric and neurologic disease disorders linked to intracellular transport. His work combines genetics, biochemistry, molecular, and cellular biology methods in in vitro (neuron cultures), ex vivo (brain slices), and in vivo (mice) systems, along with advanced microscopy techniques including immunofluorescent confocal microscopy, high-resolution live cell imaging, and photo-activated localization microscopy (PALM). Analysis of Hoogenraad's recent publications reveals a strong focus on microtubule organization, neuronal polarity, and the molecular mechanisms underlying synaptic function and dysfunction. His work frequently explores how disruptions in intracellular transport contribute to neurological disorders including Alzheimer's disease, schizophrenia, and autism spectrum disorders, with particular attention to the relationship between cytoskeletal organization and cargo transport in neuronal compartments. Scientific Awards and Memberships: ZonMW-VIDI (2004) European Young Investigators (EURYI) award (2005) NWO-ALW VICI (2011) ERC Consolidator grants (2013) FENS-Kavli Network of Excellence (2014) European Molecular Biology Organization (EMBO) (2015) Young Academy of Europe (YAE) (2015) IBRO Kemali Prize (2016) Hoogenraad leads a research group studying neuronal development and function, with a particular focus on how intracellular transport mechanisms contribute to both normal brain function and neurological disorders. His laboratory employs a multidisciplinary approach combining molecular, cellular, and systems neuroscience techniques to investigate the molecular basis of neuronal polarity, synaptic plasticity, and the pathogenesis of neurological disorders. He has secured significant research funding through prestigious grants including ERC Consolidator grants. The Hoogenraad lab operates within the Cell Biology department at Utrecht University, collaborating with other research groups focusing on cellular dynamics, biophysics, and neurobiology. The lab utilizes advanced microscopy techniques including immunofluorescent confocal microscopy, high-resolution live cell imaging (spinning disc microscopy and total internal reflection fluorescence microscopy), and quantitative analysis using advanced high-resolution microscopy (photo-activated localization microscopy). Current lab technicians include Phebe Wulf and Bart de Haan.

Craig R. Forestمشاهده پروفایل

استاد

• Neuroengineering
• BioMEMS Technologies
• High-Throughput Genomics
• +۴ مورد دیگر

Dr. Craig R. Forest is a Professor at the Georgia Institute of Technology's Woodruff School of Mechanical Engineering, specializing in bioMEMS, neuroengineering, and high-throughput instrumentation. He leads the Precision Biosystems Laboratory, focusing on developing robotic tools for neuroscience and genomics. His research bridges mechanical engineering with biological systems, creating innovations like the PatcherBot for automated electrophysiology. Forest earned his Ph.D. (2007) and M.S. (2003) from MIT and B.S. (2001) from Georgia Tech. He has been recognized with awards including the 2013 Georgia Tech Class of 1940 W. Roane Beard Outstanding Teacher Award and Engineer of the Year (2013). His work emphasizes interdisciplinary collaboration, particularly through initiatives like CREATE-X and the Invention Studio, fostering student entrepreneurship and maker culture. Key contributions include ultra-high-throughput genomics tools, microfluidic systems, and acoustic reporter genes for medical imaging. Forest’s lab explores emerging fields like intracellular robotics in neuroscience and molecular communication networks, with applications in drug discovery and personalized medicine. Scientific awards highlight his impact in education and engineering innovation. His grants and collaborations span academic and industrial partnerships, advancing both theoretical and applied research in bioengineering and nanotechnology.

Arie E. Kaufmanمشاهده پروفایل

استاد

• Computer Graphics
• Visualization
• Virtual Reality
• +۴ مورد دیگر

Arie E. Kaufman is a Distinguished Professor in the Department of Computer Science at Stony Brook University, serving as Chief Scientist of the Center of Excellence in Wireless and Information Technology (CEWIT) and Director of the Center of Visual Computing (CVC). He additionally holds a Distinguished Professorship in Radiology, with a 40+ year career at Stony Brook since joining in 1985 and chairing the CS department from 1999-2009. His seminal research spans computer graphics, visualization, and virtual reality with biomedical applications, pioneering breakthroughs including 3D Virtual Colonoscopy (FDA-approved colon cancer screening), Cube hardware architectures (commercialized as VolumePro), the Reality Deck (1.5 billion-pixel immersive display), and foundational work in volume visualization. His interests focus on real-time rendering, medical imaging, and immersive analytics, with recent work integrating machine learning for healthcare and environmental risk visualization. Recent publications demonstrate continued leadership in high-resolution immersive displays (Silo), XR analytics with LLMs, storm surge visualization, and neural reconstruction techniques. His work bridges theoretical innovation with practical applications, particularly in pancreatic cancer prognosis and disaster preparedness. Major honors include: IEEE Visualization Career Award (2005) Fellow of the National Academy of Inventors (2017) ACM Fellow (2009) IEEE Fellow (1998) Long Island Technology Hall of Fame (2013) European Academy of Sciences membership (2002) As PI on 100+ research grants, Kaufman's work has generated 300+ refereed papers, 40+ patents, and extensive media coverage (New York Times, Science, Wall Street Journal). He leads the Center of Visual Computing with focus on translational research, including VolVis software (5,000+ installations) and Reality Deck deployments for big data analytics. His lab develops cutting-edge visualization infrastructure for medical diagnostics and environmental modeling, with current projects advancing immersive storm surge analytics, neural structure extraction, and VR-based risk communication systems. Future work emphasizes AI-enhanced visualization for precision medicine and climate resilience planning.

Jianping Fuمشاهده پروفایل

استاد

• Stem Cell Bioengineering
• Developmental Bioengineering
• Mechanobiology
• +۴ مورد دیگر

Jianping Fu is a Professor in the Department of Mechanical Engineering at the University of Michigan , with joint appointments in Biomedical Engineering and Cell and Developmental Biology. His research integrates micro/nanoengineering , mechanobiology , and stem cell biology to model human development and disease. Education: PhD (MIT, 2007), BE (University of Science and Technology of China, 2000) His research interests focus on stem cell bioengineering , developmental bioengineering , and mechanobiology , particularly in modeling early post-implantation human development, neural tube formation, and pluripotent stem cell mechanoregulation. His work combines biomimetic culture systems with microfluidic gradients to study embryogenesis and organogenesis. Recent publications highlight advances in human embryo modeling (2024 Cell, Nature, Cell Stem Cell), neural tube patterning (2024 Nature), and mechanobiology of stem cells (2024 Nature Reviews Physics). These studies emphasize computational methods , single-cell analysis , and standardization of embryo models . Scientific honors include: Friedrich Wilhelm Bessel Research Award (2022) ISSCR Merit Award (2024) Fellow, American Institute for Medical and Biological Engineering (2019) NSF CAREER Award (2012) Life Member, World Association of Chinese Biomedical Engineers (2024) Dr. Fu mentors extensively, with 20+ alumni including PhD students and postdocs now in academic and industry positions. His lab has received $3M NIH funding for immunological diagnostics and MTRAC grants for translational research. Collaborations with institutions like Cincinnati Children's Hospital and Rice University enhance his interdisciplinary approach to regenerative medicine.

Lin Yangمشاهده پروفایل

استادیار

• Reinforcement Learning Theory
• Learning for Control
• Non-convex Optimization
• +۱ مورد دیگر

Lin Yang is an Assistant Professor in the Electrical and Computer Engineering Department at the University of California, Los Angeles (UCLA). His research focuses on reinforcement learning theory and applications, learning for control, non-convex optimization, and streaming algorithms. Education: PhD in Computer Science and PhD in Physics & Astronomy - Johns Hopkins University (simultaneously) Bachelor's degree in Math and Physics - Tsinghua University Research Interests: His work spans reinforcement learning theory and its applications, particularly in learning for control systems. He also investigates non-convex optimization techniques and streaming algorithms for efficient data processing. Scientific Awards: Dean Robert H. Roy Fellowship - Johns Hopkins University Previous Positions: Before joining UCLA, he was a postdoc at Princeton University working with Professor Mengdi Wang.

Mikail Rubinovمشاهده پروفایل

استادیار

• Network Neuroscience
• Computational Neuroscience
• Brain Connectivity
• +۴ مورد دیگر

Mikail Rubinov serves as Assistant Professor of Biomedical Engineering (primary appointment), Computer Science, Psychiatry, and Psychology at Vanderbilt University's School of Engineering. His interdisciplinary work bridges computational neuroscience, network science, and clinical applications. His research focuses on integrative statistical models of large-scale neural data , exploring brain network organization across species and scales. Key interests include evolutionary principles of brain networks, transcriptomic basis of neural individuality, information transfer in neural systems, and neuropsychiatric connectivity phenotypes. The Rubinov Lab develops computational frameworks for analyzing complex neural systems and integrates neuroscientific knowledge with multi-omics data. Recent publications reveal strong trends in network neuroscience methodology development (circular analysis frameworks, unbiased sampling techniques) and translational applications (epilepsy networks, autism spectrum connectomics, gut-brain axis interrogation). His work increasingly incorporates transcriptomic data with neuroimaging at biobank scale. NIH Grant Writing Workshop (June 2022) NIH Workshop Short Talks (April 2023) Rubinov actively mentors graduate and undergraduate students across Biomedical Engineering and Computer Science. His lab maintains collaborations with UCSF, HHMI Janelia Research Campus, Weizmann Institute, and international neuroscience consortia. Current projects include integrative models of large-scale neural data and transcriptomic basis of neural individuality. The Rubinov Lab operates within Vanderbilt's Department of Biomedical Engineering with extensive cross-school collaborations. Technical resources include GitHub repositories for constraint network models (cnm-code), volumetric segmentation (voluseg), and brain connectivity toolboxes.

Yongjie Jessica Zhangمشاهده پروفایل

استاد

• Finite Element Method
• Isogeometric Analysis
• Image-Based Geometric Modeling
• +۵ مورد دیگر

Dr. Yongjie Jessica Zhang is a Professor at Carnegie Mellon University, holding appointments in both the Department of Mechanical Engineering and the Department of Biomedical Engineering . She received her B.S. and M.S. in Engineering Mechanics from Tsinghua University, followed by an M.S. in Aerospace Engineering and a Ph.D. in Computational Engineering and Sciences from the University of Texas at Austin. After a postdoctoral fellowship at ICES, she joined CMU in 2007, advancing from assistant to full professor by 2016. Research Interests : Image-based geometric modeling, mesh generation, finite element analysis (FEA), isogeometric analysis, and applications in computational biomedicine, materials science, and computer-assisted surgery. Leadership Roles : Chair of Solid Modeling Association (2019-2020), USACM Executive Committee Member-at-Large (2017-2021), and ELATE Fellow (2017-2018). Her work addresses the critical challenge of automating high-fidelity geometric modeling and mesh generation for complex domains (e.g., human anatomy), which traditionally consumes ~80% of FEA time. Her group develops AI-driven methods for multiscale modeling (molecular to organ), with applications in neuroscience , biomechanics , and 4D printing . Notable awards include the Presidential Early Career Award (PECASE) , NSF CAREER Award , and ASME Van C. Mow Medal (2025) . Dr. Zhang’s publications span over 170 peer-reviewed articles, focusing on truncated hierarchical B-splines , polycube meshing , and neurite transport modeling . She has advised more than 40 students, including PhD candidates and postdoctoral fellows. Her editorial roles include Associate Editor of Computer Aided Geometric Design and editorial board memberships in Computer-Aided Design and Engineering with Computers .

Peter R. MacLeishمشاهده پروفایل

استاد

• Retinal Neuroscience
• Neural Regeneration
• Electrophysiology
• +۵ مورد دیگر

Peter R. MacLeish serves as Professor of Neurobiology at Morehouse School of Medicine's School of Medicine, where his laboratory in the Multidisciplinary Research Center (F-22614) investigates fundamental mechanisms of retinal function and regeneration. Education: Undergraduate: Bachelor of Engineering Science (Electrical) from University of Western Ontario Graduate: Doctor of Philosophy from Harvard University Postdoctoral Training: Neurobiology at Harvard Medical School Research Focus: Dr. MacLeish's program encompasses two interconnected domains. First, he characterizes functional properties of mature retinal neurons in salamanders and primates using electrophysiology and optical imaging to dissect phototransduction cascades, synaptic connectivity, and compartmentalized ionic conductances. Second, he investigates retinal regeneration mechanisms in adult newts, examining how retinal pigment epithelial (RPE) cells undergo depigmentation, proliferation, and differentiation to form functional retinas after injury. His innovative approaches include antibody-immobilized substrates to enhance neuronal adhesion for in vitro studies. Publication Trends: His 25-year publication record reveals consistent contributions to vision science, evolving from foundational studies on retinal development (1990s) to contemporary work on primate retinal circuitry (2010s), with his 2015 BRAIN Initiative paper highlighting leadership in large-scale neuroscience collaboration. The body of work demonstrates methodological progression from immunolabeling to advanced electrophysiological techniques across multiple model organisms. Scientific Recognition: Elected Member of the Institute of Medicine (National Academies) Dana Alliance for Brain Initiatives membership NIH BRAIN Initiative Working Group participant Research Operations: While specific grant details aren't provided, his laboratory maintains active research programs in retinal neuroscience with demonstrated capacity for multi-institutional collaboration as evidenced by the BRAIN Initiative publication. The absence of student listings suggests either independent research focus or unreported mentoring activities. Facilities: Research is conducted within the Multidisciplinary Research Center at Morehouse School of Medicine, utilizing specialized equipment for cellular electrophysiology and optical imaging of retinal tissues.