جستجوی استاد راهنمای دانشگاهی

• Dynamical Systems
• Mathematical Neuroscience
• Traveling Wave Solutions
• +۵ مورد دیگر

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

Mark Daleyمشاهده پروفایل

استاد

• Artificial Intelligence
• Computational Neuroscience
• Biomarker Discovery
• +۵ مورد دیگر

Mark Daley is a full Professor in the Department of Computer Science at Western University, with cross-appointments in Biology, Epidemiology & Biostatistics, Electrical & Computer Engineering, Applied Mathematics, and Statistics & Actuarial Science. He holds affiliations with the Rotman Institute of Philosophy, Western Institute for Neuroscience, and Vector Institute for Artificial Intelligence. As Vice-President, Research at CIFAR, he leads strategic research initiatives and advises on national research priorities. His research bridges theoretical computer science, artificial intelligence, and neuroscience, focusing on computational methods for understanding natural and machine intelligence. Research Interests: His work emphasizes biomarker discovery for critical illnesses (e.g., sepsis, traumatic injury), AI-driven diagnostics, and modeling complex biological systems using connectome-based approaches. He explores interdisciplinary applications of machine learning in healthcare, neuroscience, and pandemic response. Notable projects include developing metabolomic and proteomic signatures for disease diagnosis and predicting clinical outcomes. Key Projects: Directs the Computational Convergence Lab, advancing AI and computational tools for biomedical research. Leads the COMPASS-COVID-19-ICU study and contributes to global efforts analyzing SARS-CoV-2 variants and long-COVID biomarkers. Has pioneered computational frameworks integrating connectome data into reservoir computing architectures. Administrative Roles: Previously served as Associate Vice-President (Research) at Western University and chaired Compute Ontario. Current roles include strategic leadership in national research infrastructure and digital governance. His work emphasizes ethical AI implementation and data privacy in healthcare tech.

Friedmann Pulvermuellerمشاهده پروفایل

استاد

• Neuroscience of Language
• Cognitive Neuroscience
• Semantic Grounding
• +۳ مورد دیگر

Friedmann Pulvermueller serves as Professor of Neuroscience of Language and Pragmatics at the Free University of Berlin and leads the Brain Language Laboratory. His work bridges neurobiology, linguistics, and computational modeling to decode the neural basis of human language processing. His research centers on category-specific semantic circuits distributed across cortical areas, where modality-specific neural networks enable correlation-based semantic learning. Key emphases include: Action-related mechanisms for symbol understanding Neurophysiological evidence for grounded cognition Feature correlation models in conceptual representation Multimodal integration in language processing Neuropsychological validation of semantic theories Brain-constrained neural network simulations Recent work explores how brain-inspired neural architectures explain semantic grounding in large language models, demonstrating how object-type feature correlations form concrete concept representations through interlinked neuronal assemblies. His laboratory actively investigates the transition from perceptual experience to verbal labeling in cognitive systems. As Head of the Brain Language Laboratory, Pulvermueller directs experimental and theoretical research using combined neuroimaging, neuropsychological, and computational approaches to model language acquisition and processing. The lab focuses on translating neural circuit principles into biologically plausible AI frameworks while maintaining empirical grounding in human cognition.

Ksenia Fedorovaمشاهده پروفایل

مدرس

• aesthetics
• art theory
• art and science
• +۸ مورد دیگر

Ksenia Fedorova is a University Lecturer at Leiden University's Centre for the Arts in Society (LUCAS), where she is affiliated with the Modern and Contemporary cluster and the Environmental Humanities sub-cluster. Her academic work bridges philosophy, media studies, and artistic practice, with a particular focus on the intersections between aesthetics, digital culture, and technological experience. Her educational background is impressive and international, with a PhD in Philosophy/Aesthetics from institutions in St. Petersburg and Ekaterinburg, Russia (2014), and a PhD in Cultural Studies from the University of California, Davis (2017). She also holds an MA in Philosophy from Ural State University, Russia (2005) and an MA in Art History from the University of Colorado, Boulder (2007). Dr. Fedorova's research interests span aesthetics , art theory , art and science , digital culture , media art , new media , phenomenology , and the human digital world , with particular emphasis on affect theory , the technological sublime , and locative media . Her work explores how digital technologies reshape human perception, cognition, and our relationship with the environment. Her recent publications demonstrate a consistent focus on the intersections between artistic practice and scientific inquiry, particularly examining how media technologies create new forms of perception and experience. Her book "Tactics of Interfacing: Encoding Affect in Art and Technology" (MIT Press, 2020) represents a significant contribution to the field, while her numerous journal articles explore topics ranging from sensory substitution to proprioception in digital environments. Her work frequently examines the "technological sublime" - the awe-inspiring, boundary-pushing experiences created by new media technologies. Short-listed for Innovation and Kandinsky awards for "Media: Between Magic and Technology" (2014) As an educator, Dr. Fedorova teaches across multiple programs including Art History (BA), Arts, Media and Society (BA), Urban Studies (BA), and several MA programs including Arts and Culture, Contemporary Art in a Global Perspective, and Museums and Collections. She has supervised PhD candidates including Jose Hopkins Brocq and Anders Ottosson. Her research projects often involve interdisciplinary collaborations that bridge artistic practice with scientific inquiry, particularly examining how locative media, augmented reality, and other digital technologies create new forms of perception and environmental awareness. Her work suggests that these technologies don't merely mediate our experience but fundamentally transform how we relate to ourselves, each other, and the world around us.

• Computational Neuroscience
• Reinforcement Learning
• Machine Learning
• +۳ مورد دیگر

Peter Dayan serves as Managing Director and Professor in the Department of Computational Neuroscience at the Max Planck Institute for Biological Cybernetics in Tübingen, Germany, where he leads research initiatives at the intersection of neuroscience and artificial intelligence. His position encompasses strategic oversight of the department's scientific direction and operational management. Dr. Dayan's research program fundamentally explores computational principles of brain function, with concentrated expertise in reinforcement learning algorithms, decision-making processes, and neural network modeling. His work bridges theoretical machine learning frameworks with biological plausibility, particularly investigating how Bayesian inference and predictive coding operate in neural systems. This cross-disciplinary approach has established foundational contributions to understanding learning mechanisms in both biological and artificial agents. As head of the Department of Computational Neuroscience, he directs a research ecosystem focused on decoding neural computation through mathematical modeling, simulation, and experimental collaboration. The department maintains strong ties with Tübingen's neuroscience community and international AI research networks, fostering innovation in neuro-inspired computing architectures and cognitive modeling frameworks.

• Experimental Condensed Matter Physics
• Quantum Spintronics & Nanomagnetism
• Energy-efficient Nanodevices
• +۶ مورد دیگر

Himanshu Fulara serves as Assistant Professor in the Department of Physics at Indian Institute of Technology Roorkee. His research focuses on experimental condensed matter physics with emphasis on quantum spintronics, nanomagnetism, and energy-efficient nanodevices. He leads the department's Condensed Matter Physics Lab and holds multiple administrative roles including Professor-in-charge of the lab and Coordinator for BSMS Physics program. His research interests span Experimental Condensed Matter Physics , Quantum Spintronics & Nanomagnetism , and Energy-efficient Nanodevices . Key specialties include Spin-Torque and Spin Hall Nano-Oscillators, Skyrmions, Micromagnetic Modelling, Brain-Inspired Computing, and Thin Films/Multilayers. His work demonstrates significant innovation in neuromorphic computing applications and voltage-controlled spintronic devices. Analysis of his recent publications reveals strong focus on spin Hall nano-oscillators (SHNOs) for neuromorphic applications, skyrmion dynamics, and voltage-controlled magnetism. His work frequently appears in high-impact journals including Nature Materials, Nature Nanotechnology, and Science Advances, with notable contributions to memristive control of SHNO synchronization and ultra-low current nano-oscillators. Honors and awards include: Max-Planck Postdoctoral Fellowship (2014) University of Gothenburg Postdoctoral Scholarship (2017) Best Project Award at IIT Delhi (2013) Senior Research Fellowship from CSIR-UGC (2011) He actively supervises PhD students including Shikhar Jugran (Novel Spin-Orbit Torque Materials), Hind Prakash (Magnetic skyrmions), and Arunima TM (Nano-constriction based SHNOs). His current research is funded by SERB (INR 33.11 lakh) and SRIC, IIT Roorkee (INR 20 lakh), both as sole PI. He has delivered numerous invited talks at international conferences including APS March Meetings and INTERMAG. Administratively, he serves on multiple committees including Departmental Purchase Committee, Institute Level Tinkering Committee, and Anti-Ragging Squads. He coordinates departmental initiatives for Tinkering & Mentoring and BSMS Physics programs.

Chenchen Liuمشاهده پروفایل

استادیار

• High-Performance Computing
• Machine Learning
• Brain-Inspired Computing
• +۳ مورد دیگر

Dr. Chenchen Liu is an Assistant Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). She leads the Computing Compass Laboratory and focuses on high-performance computing for machine learning, brain-inspired computing with hardware-software co-design, and novel non-volatile memory technologies. Education: Ph.D., Electrical and Computer Engineering, University of Pittsburgh, 2017 M.S., Electrical and Computer Engineering, Peking University, 2013 Research Interests: Her work emphasizes optimizing neural networks for efficiency, enhancing AI security against adversarial attacks, and advancing neuromorphic systems using memristor and ReRAM technologies. Recent publications address multi-tenant DNN inference, edge computing frameworks, and latency-aware GPU optimization. Her articles highlight innovations in neuromorphic hardware, federated learning, and defense against adversarial attacks. Ongoing efforts include designing robust AI systems for autonomous driving and edge environments.

• Biomedical Engineering
• Medical Robotics
• Neural Engineering
• +۳ مورد دیگر

Giacomo Severini is a Lecturer in Biomedical Engineering at University College Dublin, Ireland. He holds a MSc in Electronic Engineering (2008) and a PhD in Biomedical Engineering (2012) from Roma Tre University. His postdoctoral research included roles at Harvard Medical School and Spaulding Rehabilitation Hospital in the USA. His work focuses on applying robotics to upper/lower limb rehabilitation, with contributions to muscle synergy analysis and tremor suppression systems. He participated in the EU-FP7 'TREMOR' project (2008–2011). Key research areas include EMG signal processing, cortico-muscular coherence, and rehabilitation technology. His projects emphasize clinical applications such as functional electrical stimulation and real-time movement intent detection. He has published widely in journals like IEEE Transactions on Neural Systems and Rehabilitation Engineering, and conferences including the International Society of Electrophysiology and Kinesiology (ISEK). His work also extends to bio-inspired neural models for tremor control and wearable robotic systems for mobility assistance. Lab affiliations include BioLab³ at Roma Tre University and the Motion Analysis Laboratory at Spaulding Hospital. His research integrates robotics, neuroscience, and biomechanics to enhance rehabilitation outcomes. Current efforts center on synergy-based approaches to movement analysis and robotic-assisted therapies.

Tony Gengمشاهده پروفایل

استادیار

• Computer Architecture
• Machine Learning
• Generative AI
• +۶ مورد دیگر

Tony Geng is an Assistant Professor at the University of Rochester, jointly appointed in the Departments of Electrical and Computer Engineering and Computer Science. He directs the IntelliArch Lab and holds secondary affiliations with the Goergen Institute for Data Science. His research spans computer architecture, generative AI, graph intelligence, and high-performance computing, focusing on sustainable and efficient artificial general intelligence. His work integrates hardware-software co-design for accelerating machine learning workloads, with applications in scientific discovery, fintech, and social media. Recent projects explore dynamical systems for graph learning and diffusion models for scientific simulations. Notable recognitions include multiple DOE and NSF research awards, the Excellence in Graduate Teaching Award, and substantial GPU allocations from NERSC for generative AI research. He actively mentors PhD students and postdocs in sustainable AI development.

Ruimin Chenمشاهده پروفایل

استادیار

• Data Analytics
• Statistical Learning
• Uncertainty Quantification
• +۴ مورد دیگر

Ruimin Chen is an Assistant Professor in the Department of Mechanical Engineering at the University of Connecticut (UConn), joining in August 2022. She holds a Ph.D. in Industrial Engineering from The Pennsylvania State University (2021), dual M.S. degrees in Industrial Engineering and Operations Research from the same institution (2018), and a B.S. in Industrial Engineering from Southeast University, China (2016). Her research focuses on data analytics, statistical learning, system identification, and uncertainty quantification, with applications in advanced manufacturing systems, privacy/security, and human-machine teaming. Key areas include quality monitoring in smart manufacturing, additive manufacturing process optimization, and interpretable AI for safety compliance. Her recent work explores federated hyperdimensional computing for distributed quality monitoring, cognitive data fusion in hybrid manufacturing, and brain-inspired computing for melt pool characterization. She also investigates educational strategies integrating Six Sigma principles with hands-on engineering practice. Notable contributions include methodologies for heterogeneous quality characterization in additive manufacturing, causal inference frameworks using Bayesian networks, and ontology-driven process analytics. She emphasizes translational research bridging theoretical computer science with industrial applications.

Farhad Imaniمشاهده پروفایل

استادیار

• Data Analytics
• Machine Learning
• Artificial Intelligence
• +۷ مورد دیگر

Farhad Imani is an Assistant Professor at the University of Connecticut's Department of Mechanical Engineering, part of the School of Engineering. He leads the Intelligent Systems and Control Laboratory (ISCL), focusing on AI-driven advancements in manufacturing systems, robotics, and quality control. His work integrates machine learning, federated learning, and hyperdimensional computing to address challenges in additive manufacturing, privacy preservation, and smart manufacturing processes. Education: He holds a Dual-title Ph.D. in Industrial Engineering and Operations Research from Pennsylvania State University (2020). His research is supported by grants from NSF and CCAT, and he has authored over 50 peer-reviewed articles in top journals and conferences. Research Interests: Core areas include data analytics, machine learning applications in manufacturing, federated learning, privacy-preserving techniques, robotics, and anomaly detection. His lab develops tools for intelligent process monitoring, defect localization, and cooperative robotic systems in advanced manufacturing. Awards: Recipient of the NSF Graduate Research Fellowship Honorable Mention (2024), CCAT Faculty Fellow (2024), and multiple fellowships from Penn State. He serves as an Associate Editor for ASME's Journal of Autonomous Vehicles and Systems (JAVS). Students & Collaborations: Advises PhD and Master's students (e.g., Zhiling Chen, Danny Hoang) and collaborates with institutions like NIST, UC Irvine, and CCAT. His lab actively participates in STEM outreach programs and industry partnerships. Labs/Teams: ISCL focuses on cognitive computing, digital twins, and edge-based learning systems. Recent projects include the Robotics Dataset ScanBot and privacy-preserving frameworks for manufacturing data.

• Spiking Neural Networks
• Brain-Computer Interfaces
• EEG Analysis
• +۴ مورد دیگر

Gang Pan is a Professor at Zhejiang University's College of Computer Science and Technology, where he leads research in neural networks, brain-computer interfaces, and neuromorphic computing. His work bridges computer science, neuroscience, and biomedical engineering, focusing on developing novel AI approaches inspired by biological neural systems. He maintains extensive collaborations with researchers including Shijian Li, Qian Zheng, and Huajin Tang. Professor Pan's research centers on spiking neural networks (SNNs) and their applications in brain-computer interfaces, medical diagnostics, and efficient neuromorphic computing. His work explores how SNNs can model biological neural processes while offering energy-efficient alternatives to traditional deep learning. Recent projects include EEG-based mental health diagnostics, neural decoding of visual perception, and battery-free neural recording systems. His approach integrates computational neuroscience with practical AI applications, particularly in healthcare contexts. Analysis of his 15 most recent publications reveals strong trends in neuromorphic computing, with particular emphasis on spiking neural networks for medical applications. His work spans from theoretical advances in SNN architectures to practical implementations in EEG analysis, mental health diagnostics, and neural interface hardware. The interdisciplinary nature of his research connects computer science, neuroscience, and biomedical engineering, with increasing focus on clinical applications of neural decoding technologies. Professor Pan actively mentors students and researchers, as evidenced by his numerous collaborative publications across multiple labs. His research is supported by significant grants enabling work on neuromorphic hardware, brain-computer interfaces, and medical AI applications. The consistent high-impact output demonstrates sustained funding support for his innovative research directions. His laboratory focuses on neuromorphic computing systems, brain-computer interface development, and neural signal processing. The research environment integrates theoretical AI development with practical hardware implementation, creating a pipeline from algorithm design to clinical application. The lab maintains strong connections with neuroscience researchers and medical professionals to ensure clinical relevance of their technological innovations.

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.

Michael Schmukerمشاهده پروفایل

استاد

• Neuromorphic Computing
• Chemical Informatics
• Machine Learning
• +۲ مورد دیگر

Michael Schmuker is Professor of Neural Computation at the University of Hertfordshire, leading research at the intersection of neuroscience-inspired computing and chemical informatics. He develops neuromorphic algorithms for low-power, low-latency chemical sensing systems and applies machine learning to decode olfactory perception. Key research areas: Neuromorphic olfaction for robotic navigation Machine learning for odor prediction Electronic nose technology development Computational modeling of olfactory receptors Schmuker participates in major neuroscience initiatives including the Human Brain Project and NeuroNex consortium, with publications in Science Advances and Nature Machine Intelligence.

Pier Luigi Gentiliمشاهده پروفایل

دانشیار

• Chemical Artificial Intelligence
• Neuromorphic Engineering
• Photochromic Materials
• +۴ مورد دیگر

Prof. Pier Luigi Gentili is an Associate Professor of Physical Chemistry at the University of Perugia's Department of Chemistry, Biology, and Biotechnology. His research focuses on integrating chemistry with artificial intelligence, neuromorphic engineering, and complexity science. Key areas include chemical artificial intelligence (CAI), photochromic materials, oscillatory reactions, and sustainability-driven innovations. He explores how biological mechanisms can inspire novel computing paradigms and advocates for interdisciplinary education to address 21st-century challenges. His work bridges molecular-level studies with macroscopic systems, emphasizing emergent properties in out-of-equilibrium systems. Recent projects involve neuromorphic engineering in 'wetware' (biological-inspired computing), quantum AI development, and microheterogeneity analysis in materials. He also designs courses merging chemistry, complexity science, and global citizenship education to prepare future scientists for sustainability and ethical challenges. Publications highlight contributions to photochromic oscillators, chemical neural networks, and the application of fuzzy logic in molecular systems. While no awards are listed, his research has advanced unconventional computing approaches using minimal resources and earth-abundant materials. Grants and collaborations are implied through his active publication record but not explicitly detailed in the provided text.