جست‌وجوی استاد و پژوهشگر

Moein Khajehnejadمشاهده پروفایل

پژوهشگر ارشد

• Active Inference
• Reinforcement Learning
• Computational Neuroscience
• +۴ مورد دیگر

Moein Khajehnejad is a Research Fellow in the Computational & Systems Neuroscience Laboratory at the Turner Institute for Brain and Mental Health, Monash University, working with Prof. Adeel Razi. His research integrates Active Inference, Reinforcement Learning, and Network Science to study biological mechanisms of cognition using in-vivo and in-vitro brain recordings. He holds a PhD in Data Science/AI from Monash University, focusing on task allocation in multi-agent systems via Game Theory and Evolutionary Graph Theory. He has conducted visiting research at institutions including Max Planck Institute, EPFL, and HKUST. Education: PhD in Data Science and AI (Monash University, 2023), specializing in Game Theory and Multi-Agent Systems. Prior academic qualifications include a focus on computational methodologies. Research Interests: Active Inference, Reinforcement Learning, Computational Neuroscience, Graph Representation Learning, and Multi-Agent Systems. His work bridges theoretical frameworks with experimental neuroscience, exploring how biological systems achieve lifelong learning through network dynamics and decision-making processes. Key Contributions: Published 10+ peer-reviewed articles across journals/conferences, including work on social learning dynamics, fairness in node representation, and neuronal network analysis. His research has been recognized through awards like the Best Reviewer Award at LoG 2022 and travel grants to NeurIPS 2019. Grants & Advising: Accepting PhD students since 2017. Collaborates internationally with teams in Switzerland, Singapore, and Hong Kong. Active in interdisciplinary projects linking AI, neuroscience, and complex systems. Labs & Affiliations: Core member of Monash’s Computational & Systems Neuroscience Lab. Affiliated with Turner Institute’s brain and mental health research programs.

Deena Schmidtمشاهده پروفایل

دانشیار

• Mathematical Biology
• Applied Probability and Statistics
• Network Science (Neuroscience Networks)
• +۲ مورد دیگر

Deena Schmidt is an Associate Professor in the Department of Mathematics and Statistics at the University of Nevada, Reno (UNR). She also serves as the Faculty Director for the TechWise Program, a collaboration with Google and other institutions, and is affiliated with the Integrative Neuroscience Graduate Program. Her research focuses on mathematical biology, applied probability, networks in neuroscience, and evolutionary genetics. She develops stochastic models to study biological systems' dynamics, including complexity reduction in network models, contagion dynamics, sleep-wake regulatory networks, and carcinogenesis. Education: Ph.D. Applied Mathematics, Cornell University (2007) M.S. Applied Mathematics, Cornell University (2005) B.S. Mathematics, Biology, and Dance, University of Akron (2001) Research Interests: Her work integrates mathematics with biology to analyze stochasticity, structural complexity, and evolutionary processes. Key areas include: Stochastic network models in neuroscience Contagion dynamics on adaptive networks Mathematical models of sleep-wake cycles Population genetics and evolutionary processes Grants and Advising: She has mentored numerous graduate and undergraduate students, supervising projects on topics ranging from cancer modeling to sleep dynamics. Her advising spans interdisciplinary collaborations, including work on homelessness statistics and psychomotor vigilance tasks. Labs/Teams: Active in the TechWise Program and collaborates with interdisciplinary teams on projects like modeling amphibian pathogen dynamics and retinal degeneration.

Maria Giulia Pretiمشاهده پروفایل

مدرس

• Neuroimaging
• Brain Connectivity
• Alzheimer's Disease
• +۳ مورد دیگر

Maria Giulia Preti is a Senior Scientist at MIPLAB, Research Staff Scientist at the CIBM Center for Biomedical Imaging (Switzerland), and a Lecturer (Maître-Assistante) at the University of Geneva. She holds a PhD in Bioengineering from Politecnico di Milano (2013) and completed postdoctoral research in Dimitri Van De Ville’s group at EPFL. Her work focuses on integrating neuroimaging techniques (fMRI, DTI, EEG, fNIRS) with graph signal processing to study brain function-structure relationships in health and disease. Key clinical applications include Alzheimer’s disease, epilepsy, multiple sclerosis, and stroke. She has pioneered methods like groupwise fMRI-guided tractography to study neurodegenerative pathologies. Education: PhD in Bioengineering (2013), Politecnico di Milano MSc in Biomedical Engineering (2009), Politecnico di Milano BSc in Biomedical Engineering (2007), Politecnico di Milano Research emphasizes multimodal neuroimaging integration and computational neuroscience. Notable achievements include developing connectome embedding techniques and demonstrating brain fingerprinting via spectral signatures. She received the Progetto Rocca fellowship (2011) supporting her work at MIT/Harvard Medical School. Ongoing projects investigate structure-function coupling dynamics, drowsiness markers, and DBS mechanisms in depression. Labs/Teams: MIP:Lab (part of the Van De Ville group at EPFL and University of Geneva collaborations).

Zhaoyu Liمشاهده پروفایل

پژوهشگر ارشد

• Neuroscience
• Neural Circuits
• Behavioral Genetics
• +۳ مورد دیگر

Dr. Zhaoyu Li is a Group Leader at the Queensland Brain Institute, The University of Queensland. His research focuses on understanding neural circuit function and behavior using the model organism Caenorhabditis elegans . Dr. Li employs advanced techniques including calcium imaging, optogenetics, and behavioral genetic screens to study how neural networks operate in healthy and diseased states, particularly in the context of neurodegenerative diseases. His work bridges molecular, cellular, and systems-level neuroscience to uncover fundamental principles of neural network dynamics. Key research interests include neural circuit architecture, the impact of aging on locomotion behaviors, and the development of computational models to predict neural connectivity. Collaborative projects involve interdisciplinary approaches combining neuroscience with artificial intelligence for spatio-temporal behavior analysis. Dr. Li's publications highlight contributions to understanding sensory processing (e.g., chemosensation, mechanosensation), neural network plasticity, and the molecular mechanisms underlying neurodegenerative conditions. His team's work has implications for translational research in Alzheimer's disease and other neurological disorders. Current research emphasizes leveraging the fully mapped C. elegans connectome to dissect neural circuit dysfunction, with potential applications in therapeutic development. The Li Group actively develops novel tools for in vivo imaging and behavioral analysis to advance systems neuroscience.

Robert Kassمشاهده پروفایل

استاد

• Computational Neuroscience
• Bayesian Inference
• Graphical Models
• +۷ مورد دیگر

Robert Kass is the Maurice Falk University Professor of Statistics and Computational Neuroscience at Carnegie Mellon University, with affiliations in the Department of Statistics & Data Science, Machine Learning Department, and Neuroscience Institute. He has served as Department Head of Statistics for 9 years and held leadership roles in academic societies. Ph.D. in Statistics (University of Chicago, 1980) B.A. in Mathematics (Antioch College) Postdoctoral training at Princeton University His research bridges statistics and neuroscience, focusing on: Statistical methods for neural data analysis Bayesian inference and graphical models Point process modeling of spike trains High-dimensional and time-series analysis Computational neuroscience education Recent publications span computational neuroscience, emphasizing phase coupling analysis, latent variable modeling, and cross-population dynamics, with methodological innovations in neural data interpretation. As founding Editor-in-Chief of Bayesian Analysis and Executive Editor of Statistical Science , he shaped academic discourse in statistics. Key scientific recognitions: Outstanding Statistical Application Award (ASA) Distinguished Achievement Award (COPSS) National Academy of Sciences member (2023) He co-authored the influential textbook Analysis of Neural Data and developed international workshops like Statistical Analysis of Neuronal Data (2002-2017), advancing neuroscience methodology globally.

Chunming Zhangمشاهده پروفایل

استاد

• Neuroinformatics
• Bioinformatics
• Machine Learning
• +۷ مورد دیگر

Chunming Zhang is a Professor of Statistics at the University of Wisconsin-Madison, affiliated with the Department of Statistics within the School of Computer, Data & Information Sciences. He holds academic leadership roles and has been a faculty member since 2000. His research focuses on statistical learning theory and applications in neuroinformatics, bioinformatics, financial econometrics, and imaging data analysis. Key areas include high-dimensional inference, robust statistics, and functional data analysis. Education: Ph.D. in Statistics (University of North Carolina-Chapel Hill, 2000), M.S. in Computational Mathematics (Academia Sinica, 1995), B.S. in Mathematical Statistics (Nankai University, 1992). His research interests span computational neuroscience, medical imaging, and financial time series analysis. Notable contributions include methodologies for neuroimaging data analysis, robust statistical estimation, and large-scale multiple testing. He has authored over 50 peer-reviewed publications and serves on editorial boards of leading journals such as the Annals of Statistics and Journal of the American Statistical Association. Professional service includes roles as Associate Editor for multiple top-tier journals and leadership in statistical societies. Teaching focuses on advanced statistical theory, including courses on Mathematical Statistics and Nonparametric Methods.

Anthony Hudetzمشاهده پروفایل

استاد

• Neurophysiology
• Consciousness Mechanisms
• Anesthesia Effects
• +۳ مورد دیگر

Anthony Hudetz, Ph.D., is a Professor in the Department of Anesthesiology and Biomedical Engineering at the University of Michigan. His primary research focuses on experimental neurophysiology, particularly large-scale neuronal ensemble activity using high-density microelectrode arrays. His work aims to understand state-dependent cortical processing in wakefulness, anesthesia, and consciousness. The Hudetz Lab explores mechanisms underlying conscious perception and anesthesia effects through advanced signal analysis and modeling. Research interests include neural engineering, consciousness theories, anesthesia-induced brain state transitions, and thalamocortical interactions. Recent studies highlight the role of thalamic nuclei in conscious perception and the application of low-intensity focused ultrasound neuromodulation. His work also addresses philosophical and clinical questions, such as detecting covert consciousness in unresponsive patients using an Aristotelian-Thomistic framework. Key contributions include identifying disrupted functional connectivity patterns during anesthesia, modeling synaptic effects of anesthetics, and investigating psychedelic and sleep states' neural correlates. Articles emphasize translational insights into consciousness dimensions, brain network dynamics, and clinical applications like anesthesia monitoring. Grants and advising details are not explicitly provided in the text, but his lab’s interdisciplinary approach integrates neurophysiology, computational modeling, and clinical research. Future directions likely involve refining consciousness metrics and advancing neurotechnology for anesthesia and neurological disorders.

M. Drew LaMarمشاهده پروفایل

دانشیار

• Mathematical Biology
• Computational Neuroscience
• Quantitative Ecology
• +۷ مورد دیگر

M. Drew LaMar is an Associate Professor of Biology at William & Mary, with interdisciplinary expertise spanning mathematics, statistics, biology, and computational sciences. His work integrates quantitative methods to advance biological research and education. PhD in Mathematics (2005), University of Texas at Austin BSc in Mathematics (1997), University of Texas at San Antonio Research Interests: Quantitative Ecology Computational Neuroscience Mathematical Biology Education Open Source Software Development Network Analysis & Dynamical Systems Biostatistics & Data-Driven Modeling Key Projects: Co-PI and Director of Cyberinfrastructure for QUBES (Quantitative Undergraduate Biology Education and Synthesis), focusing on open-source tools for biology educators Contributor to PyDSTool, a Python-based simulation environment for dynamical systems Development of educational tools like Serenity Viz, DnDSelectR, and LuminophoR Publications: Focus on trait-based ecological modeling, mathematical biology education frameworks, and computational neuroscience applications.

Shannon McWeeneyمشاهده پروفایل

استاد

• Computer Science
• Biostatistics
• Genetics
• +۳ مورد دیگر

Dr. Shannon McWeeney is a Professor at Oregon Health & Science University (OHSU) and a leading methodologist at the intersection of computer science, biostatistics, and genetics. She holds dual roles as the inaugural Chief Data Officer and Associate Director of Data Science for the OHSU Knight Cancer Institute, and Director of Medical Bioinformatics for the Knight Precision Oncology Program. Her research focuses on developing computational methods for precision medicine, particularly in cancer, and she has pioneered frameworks for target identification and therapeutic prioritization. Dr. McWeeney has been recognized with prestigious awards, including the 2010 Kavli Frontiers Fellowship and the 2018 Medical Research Foundation of Oregon Mentor Award. Education: Ph.D., 2000, University of California. Research Interests: Her work integrates multi-omics data to understand cancer progression and drug resistance. Key areas include computational modeling of biological networks, AI-driven data analysis, and translational research bridging basic science and clinical applications. She emphasizes data sharing and ethical data governance, contributing to initiatives like the NIH Bridge2AI project. Grants & Collaborations: She leads the NCI-funded Acquired Resistance to Therapy Network (ARTNet) and co-leads the NIH-funded AI-READi Project. Her work often involves large-scale collaborations, such as the OHSU BeatAML program and the Biden Cancer Initiative’s Data Sharing workstream. Awards: In addition to her Kavli and Mentor Awards, she has contributed to foundational studies on Sarbecovirus pathogenesis and AML survivorship predictors. Her lab develops tools for FAIR/CARE-compliant data systems and explores biomarkers for therapy response in solid tumors and blood cancers.

Anca Radulescuمشاهده پروفایل

دانشیار

• Dynamical Systems
• Mathematical Biology
• Neuroscience
• +۳ مورد دیگر

Anca Radulescu is an Associate Professor in the Department of Mathematics at the State University of New York at New Paltz. Her research focuses on dynamical systems, mathematical biology, and complex networks, with applications to neuroscience and epidemiology. She has contributed to understanding phenomena such as fractal geometry in brain networks, synchronization in neural systems, and modeling disease spread dynamics. Her work integrates mathematical modeling with interdisciplinary challenges, including analyzing brain connectivity patterns, exploring glucose metabolism in microglia, and investigating population dynamics in ecological systems. Recent studies also address the impact of vaccination adherence on epidemic models and the role of time delays in neuronal networks. Dr. Radulescu has published extensively on topics like Wilson-Cowan systems, quadratic networks, and fractal geometry. Her research bridges abstract mathematical concepts with real-world applications, particularly in health and biological systems.

• Geometry
• Mathematical Physics
• Complex Systems
• +۴ مورد دیگر

Jürgen Jost is an Honorary Professor in the Department of Mathematics at the University of Leipzig and a retired Director at the Max Planck Institute for Mathematics in the Sciences. He is a member of multiple prestigious academies including the Leopoldina and the Santa Fe Institute for the Sciences of Complexity. His research focuses on geometric analysis, complex systems, network science, and applications in chemistry and neuroscience. Key areas include Ricci curvature in networks, Dirac-harmonic maps, and the evolution of chemical knowledge systems. Jost has received major awards such as the Leibniz Prize (1993) and an ERC Advanced Grant (2010). His work bridges pure mathematics with interdisciplinary applications, including topological data analysis and mathematical biology. He leads the Max Planck School of Cognition and has contributed to foundational studies on minimal surfaces, geometric flows, and information geometry.

Yuan Panمشاهده پروفایل

استادیار

• Neuroscience
• Cancer Biology
• Neuro-Oncology
• +۴ مورد دیگر

Dr. Yuan Pan is an Assistant Professor at The University of Texas MD Anderson Cancer Center, holding dual appointments in the Department of Symptom Research (Division of Internal Medicine) and the Department of Neuro-Oncology (Division of Cancer Medicine). Her research bridges neuroscience and cancer biology, focusing on neuron-cancer interactions and neurofibromatosis type 1 (NF1)-related gliomas. She leads the Pan Lab, which investigates how neuronal activity, glial biology, and chemotherapy impact nervous system cancers, aiming to improve patient outcomes through novel therapeutic strategies. Dr. Pan trained with renowned scientists Drs. David Gutmann and Michelle Monje, contributing seminal work on NF1 mutations and optic glioma pathogenesis (Pan et al., Nature, 2021). Since establishing her independent lab in 2022, she has secured CPRIT Scholar funding and advanced studies on neuroimmune interactions, tumor microenvironment dynamics, and activity-dependent tumor initiation. Her work highlights the critical role of neural circuits in cancer progression. Key Awards: CPRIT Scholar in Cancer Research (2022) Research Themes: Neuronal-glial crosstalk, neurofibromatosis, tumor immunology, chemotherapy effects Labs/Teams: Pan Lab (MD Anderson Cancer Center) Her publications reveal a focus on low-grade glioma mechanisms, chemokine signaling, and systems biology approaches to identify tumor-specific networks. Clinical and translational efforts aim to translate these findings into improved diagnostics and therapies.

Konstantinos Michmizosمشاهده پروفایل

دانشیار

• Neuromorphic Computing
• Artificial Intelligence
• Computational Neuroscience
• +۴ مورد دیگر

Konstantinos Michmizos is an Associate Professor in the Department of Computer Science at Rutgers University, with affiliations to the Rutgers Center for Cognitive Science, Brain Health Institute, and Center for Computational Biomedicine Imaging and Informatics. His research focuses on neuromorphic computing, neuro-AI integration, and brain-inspired robotics, emphasizing systems that interact with brain activity across neural and behavioral scales. He leads the Michmizos Lab and ComBra Lab, pioneering projects like robotic nurses powered by neuromorphic chips and astrocyte-driven computational models. Key research interests include neuromorphic hardware-software co-design, spiking neural networks, and applications in neurorehabilitation. Notable achievements include securing grants from NIH, Intel, and VCRI, along with awards for best papers on Neuro-AI and computational astrocyence. His work bridges artificial intelligence with biological systems, aiming to translate neural mechanisms into advanced robotic and biomedical technologies. Michmizos teaches graduate courses like Brain-Inspired Computing and has advised students such as Ioannis Polykretis. His research spans neuromorphic SLAM systems, EEG decoding frameworks, and biohybrid robotic systems, with recent breakthroughs in calcium wave modeling for astrocyte-neuron interactions. Collaborations emphasize multi-scale computational biology and clinical applications in neurology.

Zheng Wangمشاهده پروفایل

دانشیار

• Bioinformatics
• Genomics
• Epigenetics
• +۳ مورد دیگر

Zheng Wang is an Associate Professor in the College of Arts and Sciences at the University of Miami, with a secondary faculty role in Biology. His research focuses on computational genomics, epigenetics, and structural biology, particularly leveraging machine learning to develop tools for analyzing chromatin interactions, protein structures, and single-cell data. He leads the Z. WANG LAB, which develops innovative bioinformatics methods such as scHiGex, PANDA-3D, and HiC4D to advance understanding of genome architecture and function. Key research interests include chromatin conformation dynamics, protein function prediction, and applying deep learning to solve challenges in genomics and proteomics. His work bridges computational methods with experimental insights, contributing to cancer biology, neuroregeneration, and epigenetic mechanisms. Zheng Wang has published over 50 peer-reviewed articles, including high-impact work in Nature Neuroscience , Proceedings of the National Academy of Sciences , and NAR Genomics and Bioinformatics . His lab collaborates widely, addressing topics like axonal regeneration, histone methylation in cancer, and the functional implications of TADs. No formal awards are listed in the provided materials, but his research has significant community impact through tool development and open-source software. Zheng Wang’s grants and collaborations are central to his work, though specific funding details are not detailed here. He actively mentors researchers and students in computational biology, driving innovation at the intersection of biology and artificial intelligence.

• 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.