جست‌وجوی استاد و پژوهشگر

Joshua T. Vogelsteinمشاهده پروفایل

دانشیار

• Machine Learning
• Neuroscience
• Network Science
• +۴ مورد دیگر

Dr. Joshua T. Vogelstein is an Associate Professor in the Department of Biomedical Engineering at Johns Hopkins University, holding joint appointments in Biostatistics, Applied Mathematics & Statistics, Neuroscience, and Computer Science. He leads the NeuroData lab, focusing on big data science, machine learning, and connectomics. Education: PhD and MSE in Neuroscience and Applied Mathematics from Johns Hopkins (2009), BS in Biomedical Engineering from Washington University (2002). Notable achievements include co-founding the Open Connectome Project (acquired by APL) and Gigantum (acquired by NVIDIA). Recognized with the NSF CAREER Award (2020), F1000 Prime (2014), and multiple Johns Hopkins Discovery Awards. Research emphasizes statistical connectomics, network science, and applying AI to biomedical challenges. Key contributions include mapping the first insect brain connectome (Science 2023) and developing open-source tools like CloudReg and BrainLine. Collaborates with Microsoft Research and industry partners, co-founding ventures like Global Domain Partners and Mind-X. Advised over 60 trainees, teaches machine learning and data science. Promotes open science through NeuroData's ecosystem of tools and data. Current work explores organoid intelligence, prospective learning, and neural network dynamics.

Victoria Webster-Woodمشاهده پروفایل

دانشیار

• Biohybrid Robotics
• Organic Robotics
• Biologically Inspired Robotics
• +۱۰ مورد دیگر

Victoria Webster-Wood is an Associate Professor at the College of Engineering , Carnegie Mellon University , where she leads the Biohybrid and Organic Robotics Group (B.O.R.G) . Her research integrates organic materials into robotics as structures, actuators, sensors, and controllers for biohybrid robots and prosthetics. Education: Ph.D., Mechanical Engineering, Case Western Reserve University (2017) M.S., Mechanical Engineering, Case Western Reserve University (2013) B.S., Mechanical Engineering, Case Western Reserve University (2012) Research Interests focus on biohybrid robotics , biologically inspired systems , soft robotics , additive manufacturing , biomechanics , and computational modeling . Her work spans applications in medical robotics , micro/nano manufacturing , and environmental monitoring . Scientific Awards include being named to ASME’s 2025 MechE Watch List and awarded MIT Technology Review’s 35 Innovators Under 35 (2023) . Collaborations involve projects like neurodegenerative disease therapy tools and soft robotic tactile sensors for manufacturing , supported by the Manufacturing Futures Institute and NextManufacturing Center .

Yves Boubenecمشاهده پروفایل

دانشیار

• Auditory Neuroscience
• Perceptual Attention
• Sound Encoding
• +۳ مورد دیگر

Dr. Yves Boubenec is an Associate Professor at École Normale Supérieure (ENS)-PSL University, Paris, France. He serves as Head of the LSP Neuro Platform and Director of Studies at the Department of Cognitive Studies. Academic Rank: Associate Professor Institution: ENS-PSL Departments: Cognitive Studies (ENS), LSP Neuro Platform Email: yves.boubenec@ens.psl.eu Research Focus: Boubenec investigates neural mechanisms of auditory perception and cognition using integrated methodologies spanning single-neuron electrophysiology to large-scale neuroimaging. His work reveals how context, learning, and multisensory interactions shape sound encoding in mammalian neocortex. Primary Research Themes Context-dependent auditory encoding Perceptual attention mechanisms Task-driven neural plasticity Self-supervised learning models Population-level cortical dynamics Human/ferret auditory comparisons Publication Trends: Recent work (2024-2025) examines speech production networks, premotor auditory categorization, and algebraic structures in sound learning. Earlier studies (2018-2022) focus on population gating, hierarchical auditory coding, and self-voice mechanisms. 2025 Self-voice frequency analysis Hierarchical ferret auditory cortex mapping Temporal window constraints 2024 Premotor category hemodynamics Self-supervised sound structures Human speech cortical encoding Methodological Expertise: Combines awake ferret functional UltraSound, Neuropixels recordings, and computational modeling to analyze neural representations across spatial scales. Specializes in translating animal model findings to human auditory processes.

• Computational Neuroscience
• Spacetime Connectomics
• Neurotwin Models
• +۵ مورد دیگر

Jakub Vohryzek is a postdoctoral researcher at the Computational Neuroscience (CNS) Group at University Pompeu Fabra in Barcelona, supervised by Prof. Gustavo Deco. His work focuses on spacetime connectomics and whole-brain modeling, particularly in neurodegenerative disorders and psychedelic neuroscience. He holds a DPhil from the University of Oxford, where he studied under Prof. Morten Kringelbach. Research Interests: Spacetime connectomics Psychedelic-induced brain state transitions Neurotwin models for personalized medicine Cognitive and clinical applications of whole-brain dynamics Current projects include developing neurotwin models under a European grant for neurodegenerative treatments, investigating brain state dynamics in mindfulness therapy, and modeling psychedelic effects on Alzheimer’s disease. His recent work emphasizes low-dimensional brain network interactions and functional hierarchy perturbations. His research has explored connectivity profiles, oscillatory restoration in dementia, and algorithmic agent approaches to neuropsychiatric disorders. He collaborates on open-science initiatives like Brainhack and advocates for inclusive conference design.

Shimeng Yuمشاهده پروفایل

استاد

• Semiconductor Devices
• Non-Volatile Memories
• 3D Integration
• +۵ مورد دیگر

Shimeng Yu is a full professor at the Georgia Institute of Technology's School of Electrical and Computer Engineering, holding the Dean’s Professorship. He earned his B.S. from Peking University (2009) and M.S./Ph.D. from Stanford University (2011/2013). His research focuses on semiconductor devices, non-volatile memories, 3D integration, and AI hardware accelerators. Yu leads SRC/DARPA JUMP 2.0 centers on memory/storage and 3D integration, with over 400 publications and 30,000+ citations (H-index 82). He serves on flagship conference committees (e.g., IEDM, VLSI) and editorial boards (IEEE EDL, JETCAS). Education: B.S., Microelectronics, Peking University (2009) M.S./Ph.D., Electrical Engineering, Stanford University (2011/2013) Research Themes: Emerging non-volatile memories for AI Monolithic 3D integration Energy-efficient computing systems His work spans device fabrication, circuit design, and system-level co-optimization. Recent projects are funded by NSF, DARPA, DOE, and industry partners (TSMC, Intel, Samsung), totaling >$17M. His lab, located at the Pettit Microelectronics Research Center, develops prototypes with cleanroom access. Awards: IEEE Fellow (2024) ACM/IEEE DAC Under-40 Innovators Award (2020) NSF CAREER Award (2016) Multiple editorship roles and distinguished lecturer appointments (IEEE EDS/CASS) Grants & Funding: Lead of two SRC/DARPA JUMP 2.0 centers Total research funding exceeds $17M

Mehdi Khamassiمشاهده پروفایل

استاد پژوهشی

• Decision Making
• Reinforcement Learning
• Cognitive Neuroscience
• +۵ مورد دیگر

Mehdi Khamassi is a Research Director at the French National Center for Scientific Research (CNRS), assigned to the Institute of Intelligent Systems and Robotics (ISIR) at Sorbonne University in Paris, France. He holds an engineering background in computer science (specializing in AI and statistical modeling) from the National School of Computer Science for Industry and Business (2003), a Cogmaster in cognitive science from Pierre and Marie Curie University (2003), and a PhD in cognitive neuroscience from UPMC/Collège de France (2007). Recruited by CNRS in 2010, he co-organizes the Symposium of Biology of Decision-Making (SBDM) and co-directs the modeling major for the Cogmaster program. His research integrates computational modeling , neuroscience experiments , and robotic systems to study decision-making and learning mechanisms. Key interests include: Reinforcement learning in biological and artificial systems Role of social/non-social rewards in adaptive behavior Ethical implications of autonomous decision-making in AI Neuro-robotic models of hippocampal-prefrontal interactions Recent publications (2023-2025) demonstrate strong focus on reinforcement learning paradigms, AI alignment with human values, neurorobotics, and computational neuroscience. Work frequently bridges machine learning theory with empirical validation in biological systems or robotic platforms. He leads research within the ACIDE team at ISIR, exploring adaptive coordination of learning strategies in brains and robots. Current collaborations include NTUA (Greece), University of Oxford, and University of Trento.

Bipin Rajendranمشاهده پروفایل

استاد

• Intelligent Computing Systems
• Hardware-software co-design
• Novel materials & devices
• +۴ مورد دیگر

Bipin Rajendran is a Professor of Intelligent Computing Systems at King's College London, based in the Department of Engineering within the Faculty of Natural, Mathematical & Engineering Sciences. He directs the King's Laboratory for Intelligent Computing and co-leads the Centre for Intelligent Information Processing (CIIPS). Previously, he held positions at IBM Research and academic institutions in the U.S. and India. His research focuses on algorithms, devices, and systems for intelligent computing, emphasizing neuromorphic computing, memristive devices, and hardware-software co-design. Education: B.Tech, IIT Kharagpur (2000) M.S. and Ph.D., Electrical Engineering, Stanford University (2003, 2006) Research Interests: Rajendran's work spans hardware-software co-design, novel materials for neuromorphic systems, event-driven computing algorithms, and energy-efficient AI hardware. His contributions include foundational research on memristive devices for spiking neural networks and phase-change memory-based learning systems. Grants & Awards: He has received funding from EPSRC, NSF, European Commission, and industry partners like Intel and IBM. Notable awards include the IBM Faculty Award (2019) and election to the U.S. National Academy of Inventors (2019). Labs & Initiatives: Leads the King's Laboratory for Intelligent Computing and collaborates on projects like NeuroComm (6G neuromorphic comms) and SGAI (green AI systems). His work bridges nanoscale devices with AI algorithms for sustainable computing.

• Nanoelectronic Circuit Design
• Neuromorphic Computing
• Hardware Security
• +۴ مورد دیگر

Garrett Rose is a Professor and Department Head in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville (UTK). He holds a B.S. in Computer Engineering from Virginia Tech (2001), and M.S. and Ph.D. in Electrical Engineering from the University of Virginia (2003/2006). Prior to UTK, he served as Assistant Professor at NYU Polytechnic (2006–2011) and Senior Electronics Engineer at the Air Force Research Lab (2011–2014). His research focuses on nanoelectronic circuit design, neuromorphic computing, hardware security, and memristor-based systems. He leads the SENECA Research Group and the TENNLab initiative, exploring applications in neuromorphic architectures, hardware security primitives (e.g., PUF devices), and device modeling. Recent work emphasizes memristor-driven neuromorphic systems, secure FPGA designs, and in-memory computing. Grants include projects on neuromorphic target detection and nanotechnology-based security solutions. Rose actively mentors students and collaborates on co-design methodologies for real-world neuromorphic applications. Education: Ph.D. Electrical Engineering, University of Virginia, 2006 M.S. Electrical Engineering, University of Virginia, 2003 B.S. Computer Engineering, Virginia Tech, 2001 Research Interests: Dr. Rose’s work spans neuromorphic hardware design, including memristor-based neural networks and spiking systems. He investigates hardware security through nanoscale devices like memristors for PUFs and side-channel resistant circuits. His team develops novel memristor models and explores applications in reconfigurable computing and energy-efficient architectures. Recent efforts focus on neuromorphic vision systems, robotic navigation, and neuromorphic processors with co-design frameworks. Grants & Projects: "Ground-roaming autonomous neuromorphic targeter" (2020) "Secure Backup and Restore for IoT using Nanotechnology" (2020) "Physically Unclonable Reconfigurable Computing System (PURCS)" (2020)

Jane Wangمشاهده پروفایل

استاد

• Biological Physics
• Theoretical Condensed-Matter Physics
• Computational Fluid Dynamics
• +۴ مورد دیگر

Affiliations and Roles Professor Wang holds dual appointments as Professor of Physics and Mechanical and Aerospace Engineering at Cornell University. She is affiliated with the Sibley School of Mechanical and Aerospace Engineering and the College of Arts and Sciences. Education B.S. in Physics, Fudan University, Shanghai, China (1989) Ph.D. in Physics, University of Chicago (1996) NSF-NATO Postdoctoral Fellow, Theoretical Physics, Oxford University (1997) Visiting Member, Courant Institute of Mathematical Sciences, NYU (1997-1999) Research Her research focuses on the physics of living organisms, particularly insect flight dynamics , biophysics , and computational modeling . Key projects include: Dragonfly righting reflex mechanisms Neuro-mechanical control in fruit flies Unsteady aerodynamics and fluid-structure interactions Awards and Honors Simons Fellowship in Theoretical Physics (2020) Radcliffe Fellowship (2007) Cornell Provost's Award for Distinguished Scholarship (2005) David and Lucile Packard Fellowship (2002) Labs and Collaborations Her work integrates experimental and computational approaches, often conducted in collaboration with institutions like the Janelia Research Campus (HHMI) and the Joint Texas Experimental Tokamak (J-TEXT).

• Machine Learning
• Network Theory
• Data Mining
• +۳ مورد دیگر

Constantine Dovrolis is a Professor at the School of Computer Science, Georgia Institute of Technology, and Director of the Center for Computational Science and Technology (CaSToRC) at The Cyprus Institute. He holds a BEng from the Technical University of Crete (1995), M.S. from the University of Rochester (1996), and Ph.D. from the University of Wisconsin-Madison (2000). His research bridges Network Theory, Machine Learning, and Neuroscience, focusing on neuro-inspired architectures and structural adaptation in AI systems. His work includes pioneering contributions to sparse neural networks, continual learning through structural adaptation, and hierarchical modularity in neural networks. His research has been supported by NSF, NIH, DOE, DARPA, Google, Microsoft, and Cisco, resulting in over 15,000 citations and an h-index of 56. Affiliations: Georgia Tech (Primary), Cyprus Institute (Director of CaSToRC) Research Themes: Neuro-inspired AI, Network Science, Machine Learning Theory, Computational Neuroscience Grants: Over $10M in funding from U.S. agencies and industry partners

Tuukka Ruotsaloمشاهده پروفایل

دانشیار

• Brain-Computer Interfaces
• Human-Information Interaction
• Recommender Systems
• +۴ مورد دیگر

Tuukka Ruotsalo serves as Associate Professor in the Machine Learning Section at the Department of Computer Science, University of Copenhagen. His research bridges human cognition with computational systems through brain-computer interfaces and physiological computing. As Academy Research Fellow at University of Helsinki (2019-2024), he maintained dual institutional affiliations while leading cutting-edge work in neuro-linguistic modeling and affective relevance. His research focuses on brain-computer interfaces for information retrieval , where he pioneers methods to decode cognitive states from neural signals to improve search systems. Key areas include affective relevance modeling that integrates emotional states into search algorithms, and neuro-linguistic reconstruction that translates brain activity into language. His work on fairness-relevance tradeoffs in recommender systems established Pareto frontier evaluation frameworks now widely adopted in ethical AI research. Recent publications demonstrate how physiological signals like EEG and galvanic skin response can create more adaptive human-information interaction systems. Ruotsalo's scientific recognition includes the prestigious Academy Research Fellow position. His publications in IEEE Transactions on Human-Machine Systems , Journal of the Association for Information Science and Technology , and Communications Biology reveal growing interdisciplinary impact. His advising spans cognitive neuroscience and machine learning students, with notable collaborations across the SCIENCE AI Centre. Current projects include the TreeSense initiative for remote sensing of global tree resources and development of quantum-inspired neural architectures. His lab leverages the department's powerful compute cluster for large-scale physiological data analysis.

• Visual Physiology
• Neurobotics
• Insect Visual Systems
• +۴ مورد دیگر

Associate Professor Steven Wiederman holds the position of Associate Dean Research in the Faculty of Health and Medical Sciences at the University of Adelaide. He leads the Visual Physiology & Neurobotics Laboratory within the School of Biomedicine. His research focuses on understanding visual processing in insects, particularly dragonflies, and translating these insights into applications for autonomous robotics and neuro-inspired systems. Key areas include target detection, optic flow processing, and selective attention mechanisms in biological systems. His interdisciplinary work combines electrophysiological techniques, computational modeling, and robotics engineering. Current projects explore how dragonfly neurons enable precise tracking of moving targets amidst clutter, with applications in developing advanced vision systems for drones and robots. He has secured significant funding through ARC fellowships and industry partnerships, including projects on neuro-inspired deep learning for defense applications. Academic achievements include an ARC DECRA Fellowship (2011–2015) and an ARC Future Fellowship (2016–2020). He is a member of the ARC College of Experts and actively supervises PhD and Master’s students in neuroscience, robotics, and computational biology. His lab collaborates with engineers and computer scientists to bridge biological insights with technological innovation. Publications highlight breakthroughs in understanding neuronal mechanisms underlying predictive vision, selective attention, and adaptive processing in dragonfly visual systems. Media coverage includes features in Wall Street Journal , New York Times , and Science Daily , emphasizing the potential of insect-inspired solutions for future technologies.

Elizabeth S. Chenمشاهده پروفایل

دانشیار

• Biomedical Informatics
• Clinical Informatics
• Natural Language Processing
• +۷ مورد دیگر

Dr. Elizabeth S. Chen is an Associate Professor of Medical Science and Health Services, Policy and Practice at Brown University. She serves as Interim Director of the Brown Center for Biomedical Informatics (BCBI) and leads the Clinical Informatics Innovation and Implementation (CI3) Lab, focusing on improving mental health (mental health informatics) and child health (pediatric informatics) via EHR, data mining, and machine learning. Her work spans clinical decision support, standards interoperability, and NLP for biomedical discovery. Education: PhD in Biomedical Informatics, Columbia University BS in Computer Science, Tufts University Research Interests: Dr. Chen’s research integrates electronic health data with health IT to advance healthcare delivery and translational research. Key areas include EHR optimization, clinical documentation, NLP, machine learning, and health information standards. She mentors students across levels and contributes to Rhode Island’s BIBCE Core for data accessibility in clinical research. Article Trends: Dr. Chen’s 15 most recent publications emphasize NLP, machine learning, and EHR applications in mental health, pediatrics, and public health. Topics include suicide risk modeling, comorbidity analysis, clinical documentation standards, and interoperability frameworks. Scientific Awards: Fellow of the American College of Medical Informatics (FACMI) Mentoring & Governance: Dr. Chen co-instructs biomedical informatics courses, mentors junior faculty, and held leadership roles at NIH/NLM (BILDS Review Committee Chair) and journal editorial boards. Her lab focuses on inspiring future clinical informaticians. Labs & Collaborations: The CI3 Lab at BCBI drives EHR innovation, implementation, and education. Dr. Chen collaborates with Rhode Island’s BIBCE Core, the Scholarly Concentration in Biomedical Informatics, and interdisciplinary teams in mental health and pediatric informatics.

Baihan Linمشاهده پروفایل

استاد

• Neuro-AI
• Computational Psychiatry
• Deep Learning
• +۴ مورد دیگر

Baihan Lin is a tenure-track professor in AI, psychiatry, and neuroscience at the Icahn School of Medicine at Mount Sinai, directing the Bytes of Minds Lab. He holds a PhD in computational systems biology from Columbia University and master's degrees in applied mathematics, data visualization, and cellular molecular biophysics from University of Washington, Parsons School of Design, and Columbia University. His research focuses on trustworthy Neuro-AI, computational psychiatry, and real-world applications of deep learning, NLP, and reinforcement learning to improve clinical interactions for neuropsychiatric patients. Notable awards include Bell Labs Prize and XPRIZE finalist recognitions. Industry experience spans Google, IBM, Microsoft, Amazon, and BGI, with over 50 publications and patents. Lin's work bridges neuroscience-inspired AI with mental health support, advancing empathetic AI systems. He actively chairs conferences, serves on editorial boards, and teaches globally on technology-society intersections.

Pedro U. Limaمشاهده پروفایل

استاد

• Robotics
• Multi-Robot Systems
• Autonomous Systems
• +۴ مورد دیگر

Pedro U. Lima is a Full Professor at Instituto Superior Técnico (IST), University of Lisbon, and President of the Institute for Systems and Robotics (ISR/IST), coordinating the Intelligent Robots and Systems group (IRSg). He holds a Ph.D. in Electrical Engineering from Rensselaer Polytechnic Institute (1994). His research focuses on intelligent control systems, multi-robot coordination, and human-robot interaction, with contributions to robotics competitions like RoboCup and initiatives like the European Robotics League (E.R.L.). Lima has served as Vice President of the RoboCup Federation, Trustee of IEEE Portugal RAS Chapter, and Portuguese National Delegate to the European Space Agency’s Human Spaceflight Program Board. He led the Cátedra de Excelencia sabbatical at UC3M (2010), contributing decision-theoretic approaches to human-robot interaction. His work integrates robotics with societal impact, including outreach through the Pavilhão do Conhecimento and media appearances. He coordinates large projects like euROBIN (EU Horizon Europe) and CRAI (Portugal’s PRR), advancing robotics in domains like autonomous delivery and social robotics.