جستجوی استاد راهنمای دانشگاهی

• Cognitive Psychology
• Memory Research
• Misinformation Studies
• +۵ مورد دیگر

Ullrich Ecker is a Professor at the School of Psychological Science, The University of Western Australia. His research focuses on cognitive psychology, memory processes, and the impact of misinformation in social contexts. He has supervised 14 PhD students since 2015, with a strong emphasis on interdisciplinary topics such as misinformation correction, climate change communication, and executive function mechanisms. His research interests span cognitive and behavioral responses to misinformation, memory dynamics, and the intersection of psychology with environmental and public health issues. Key themes include how social media environments shape susceptibility to misinformation, neurocognitive mechanisms underlying memory persistence, and strategies to counter pseudoscientific beliefs. Dr. Ecker has published 132 works, including 2 datasets and 12 grants, and holds an h-index of 44 with 10,274 citations. He actively mentors researchers and is involved in initiatives to address misinformation through evidence-based interventions. His work integrates experimental psychology, computational modeling, and real-world applications, contributing to fields like climate change communication and wildlife conservation through behavioral insights.

Max Garagnaniمشاهده پروفایل

مدرس ارشد

• Computational Neuroscience
• Neural Networks
• Cognitive Science
• +۷ مورد دیگر

Dr. Max Garagnani is a Senior Lecturer in Computer Science at Goldsmiths, University of London, and co-director of the MSc in Computational Cognitive Neuroscience. He holds dual PhDs from the University of Cambridge (Computational Cognitive Neuroscience, 2009) and the University of Durham (Artificial Intelligence, 1999). His primary research focuses on developing biologically realistic neural networks to model cognitive functions like language acquisition, decision-making, and perception-action integration. Garagnani's work emphasizes neurocomputational models constrained by brain anatomy and physiology, particularly exploring how language emerges from neural activity in the cortex. He has collaborated with institutions like the Free University of Berlin’s Brain Language Lab and the MRC Cognition and Brain Sciences Unit. His research combines computational modeling with neuroimaging (MEG/fMRI) and robotics (e.g., iCub). Key achievements include modeling semantic dementia breakdown, explaining visual cortex recruitment in blind individuals, and simulating prediction-error signals in cortical hierarchies. Awards include the EU Marie Curie Fellowship and EPSRC grants. He teaches courses in computational neuroscience and supervises projects in neural network modeling and cognitive systems.

Sarika Khushalani-Solankiمشاهده پروفایل

دانشیار

• Smart Grids
• Power Systems
• Electrical Engineering
• +۶ مورد دیگر

Sarika Khushalani-Solanki is an Associate Professor in the Lane Department of Computer Science and Electrical Engineering at West Virginia University , affiliated with the Benjamin M. Statler College of Engineering and Mineral Resources . She holds a Ph.D. in Electrical and Computer Engineering from Mississippi State University (2006), an M.E. in Electrical Engineering from Mumbai University (2000), and a B.E. in Electrical Engineering from Nagpur University (1998). Education: Ph.D., Electrical and Computer Engineering, Mississippi State University, 2006 M.E., Electrical Engineering, Mumbai University, 2000 B.E., Electrical Engineering, Nagpur University, 1998 Her research interests focus on smart grids , microgrid modeling and control , data mining , and AI applications in power systems . She has pioneered work in cyber-physical microgrids , resilient frequency control , and intelligent power system restoration , with a strong emphasis on integrating renewable energy and electric vehicles into modern grids. Her recent publications highlight advancements in microgrid control under cyber disruptions , AI-driven disturbance identification , and stochastic optimization for resilient power systems. She actively employs machine learning , dynamic time warping , and neuroscience-based control to address challenges in smart grid technologies . Scientific Awards: NSF CAREER Award Honda Fellowship As an IEEE Senior Member and former IEEE PES Subcommittee Chair , she contributes to grid resilience standards and workforce development . She serves as an Editor for IEEE Transactions on Smart Grid and MDPI Energies , and has taught courses like Electric Distribution Systems , Power Markets , and Smart Grid Technologies .

Saria Awadallaمشاهده پروفایل

استادیار

• Epidemiology
• Biostatistics
• Pharmacoepidemiology
• +۴ مورد دیگر

Saria Awadalla is an Assistant Professor in the Department of Epidemiology and Biostatistics at the School of Public Health, University of Illinois Chicago. Her academic work bridges advanced biostatistical methodology with critical public health applications, focusing on health disparities and evidence-based interventions across diverse populations. Her research spans pharmacoepidemiology, women's health, and social determinants of health, with particular expertise in case-crossover study designs, medication adherence analysis, and health services research. She investigates bias in observational studies, cervical cancer screening disparities across sexual orientation and racial groups, and the impact of food insecurity on oral health outcomes. Her methodological rigor extends to developing predictive tools for emergency department triage and analyzing longitudinal pain trajectories. Analysis of her recent publications reveals a consistent emphasis on real-world evidence generation using national datasets, with strong attention to vulnerable populations including older adults, cisgender women, and urban underserved communities. Her work demonstrates sophisticated integration of epidemiological methods with clinical questions, particularly in chronic disease management and preventive health services. Key thematic areas include LGBTQ+ health equity, environmental exposures, and the intersection of social factors with biological outcomes.

• Deep Learning
• Neural Networks
• Artificial Intelligence
• +۹ مورد دیگر

Elena Mocanu is an Assistant Professor in the Department of Datamanagement & Biometrics and a faculty member of the Digital Society Institute. Her research focuses on advancing neural network architectures, particularly through dynamic sparse training techniques to enhance computational efficiency and model performance. Key areas include deep reinforcement learning applications in building energy optimization, federated learning for collaborative data environments, and sparse connectivity models that reduce resource usage without sacrificing accuracy. She has contributed to frameworks like the Digital Twin for autonomous driving and energy systems, emphasizing sustainability and scalability. Her work bridges theoretical advancements and practical implementations, addressing challenges in energy-efficient AI, robust noise filtering in reinforcement learning, and feature selection for medical imaging tasks. She actively organizes conferences such as ICLR workshops on sparsity in neural networks and IJCAI events, fostering interdisciplinary collaboration in artificial intelligence. Awards : Best Paper Award at AAMAS 2022 Workshop, ICML 2022 Outstanding Reviewer Award Conference Leadership : Organized ICLR 2023 Sparsity Workshop, EPIA 2023/2022 Conferences Research highlights include scalable training methods inspired by network science, energy optimization in smart buildings, and sparse ensembling techniques that achieve efficiency gains without overhead. Her contributions span foundational machine learning theory to applied domains like smart grids and autonomous systems.

Sukbin Limمشاهده پروفایل

دانشیار

• Neuroscience
• Computational Neuroscience
• Neuronal Systems
• +۳ مورد دیگر

Sukbin Lim is an Associate Professor of Neural Science at NYU Shanghai and a Global Network Associate Professor at New York University's Center for Neural Science. She holds a PhD in Mathematics from NYU and a BS from Seoul National University. Her research focuses on computational models of neuronal systems, particularly exploring working memory, synaptic plasticity, and neural circuit dynamics. She has contributed to understanding mechanisms underlying visual response dynamics, learning rules in cortical neurons, and noise tolerance in neural networks. Lim's work spans theoretical neuroscience and experimental analysis, with publications in Nature Neuroscience , Journal of Neuroscience , and eLife . Her recent studies investigate sensory-memory interactions, decision biases in visual working memory, and unsupervised learning frameworks. She teaches courses on mathematical tools for life sciences and theoretical neuroscience at NYU. Her research highlights include modeling corrective feedback control in spatial working memory and analyzing cerebellar granule neuron signaling during visuomotor tasks. Lim’s interdisciplinary approach bridges computational methods with experimental neurobiology to uncover fundamental principles of neural processing.

Claudius Grosمشاهده پروفایل

استاد

• Physics of AI
• Complex Systems Theory
• Self-Organized Robotics
• +۶ مورد دیگر

Claudius Gros is a Professor of Theoretical Physics at Goethe University Frankfurt. He holds a PhD from ETH Zurich and has held academic positions at Indiana University, University of Dortmund, and Saarland University. His research focuses on complex systems theory, physics of AI, self-organized robotics, and the Genesis Project, an interstellar mission concept for establishing life on exoplanets. His work bridges theoretical physics with interdisciplinary applications, including epidemiology modeling and societal dynamics analysis. Key contributions include the textbook Complex and Adaptive Dynamical Systems (Springer) and foundational studies on attention mechanisms in AI architectures. Education: Bachelor/Master: ETH Zurich, Theoretical Condensed Matter Physics PhD: ETH Zurich, 1985 (Advisor: T. Maurice Rice) Postdoc: Indiana University, 1988–1990 (With Steve Girvin and Allan MacDonald) Research Interests: Physics of AI : Analysis of transformer models, attention mechanisms, and neural scaling laws. Complex Systems : Epidemic models, dormancy dynamics in cellular automata (Spore Life), and self-organized robotics. Genesis Project : Feasibility of interstellar probes to seed life on exoplanets, magnetic sail deceleration. Societal Dynamics : Strategy condensation, envy-driven class stratification, and pandemic policy modeling. Articles Overview: Recent work spans AI physics (attention mechanisms, neural scaling), complex systems (epidemic oscillations, dormancy models), and robotics (self-organization principles). Themes include theoretical frameworks for embodied systems, computational models of societal behavior, and interdisciplinary applications of dynamical systems theory. Advising & Grants: Claudius Gros has advised multiple researchers, with co-authored papers featuring collaborators like O. Neumann, D.H. Nevermann, and B. Sandor. His grants include funding for Genesis Project studies and robotics research. Labs & Teams: His research group focuses on Physics of AI and Self-Organized Robotics , with active projects on embodied robots, neural network dynamics, and interstellar mission feasibility.

John Rinzelمشاهده پروفایل

استاد

• Computational Neuroscience
• Biophysical Mechanisms
• Neural Computations
• +۳ مورد دیگر

John Rinzel is a Professor of Neural Science and Mathematics at New York University, affiliated with the Center for Neural Science and the Courant Institute. He holds academic positions within the College of Arts and Science and the Graduate School of Arts and Science. His research focuses on computational neuroscience, integrating biophysical mechanisms with mathematical modeling to understand neural computations at cellular and network levels. Education: Ph.D. in Mathematics (1973) and M.S. in Mathematics (1968) from NYU’s Courant Institute, and a B.S. in Engineering from the University of Florida (1967). Ph.D. in Mathematics, NYU Courant Institute (1973) M.S. in Mathematics, NYU Courant Institute (1968) B.S. in Engineering, University of Florida (1967) Research Interests: Rinzel studies biophysical mechanisms underlying neural computations, including dendritic computation, neuronal excitability, auditory pathway modeling, perceptual bistability, and rhythmic timing. His work combines theoretical models with experimental collaborations, emphasizing reduced biophysical models for cellular and network-level dynamics. Recent research trends include auditory streaming, perceptual dynamics, and rhythmic beat generation in music. His models explore gamma oscillations, thalamic spindle rhythms, and sleep-related neural excitability. Publications highlight applications in sensory processing, neural network oscillations, and cognitive functions like attention and memory coordination. No scientific awards explicitly listed. Rinzel leads a research group focusing on computational neuroscience, collaborating on projects involving auditory neuroscience, mathematical biology, and theoretical neurophysiology. His lab is part of NYU’s Center for Neural Science, fostering interdisciplinary approaches to brain function.

Carla Shatzمشاهده پروفایل

استاد

• Neuroscience
• Neurodevelopment
• Synaptic Plasticity
• +۳ مورد دیگر

Carla Shatz is the Sapp Family Provostial Professor and Professor of Biology and Neurobiology at Stanford University, serving as Director of Bio-X. Her research focuses on neural circuit development, synaptic plasticity, and the interplay between the nervous and immune systems. She earned her B.A. in Chemistry from Radcliffe College (1969), M.Phil in Physiology from University College London (1971), and Ph.D. in Neurobiology from Harvard Medical School (1976). Key roles include Chair of Harvard Medical School's Neurobiology Department (2000–2007) and leadership in prestigious institutes like the Howard Hughes Medical Institute. Education: B.A., Chemistry, Radcliffe College (1969) M.Phil, Physiology, University College London (1971) Ph.D., Neurobiology, Harvard Medical School (1976) Research Interests: Dr. Shatz investigates how neural activity shapes brain circuits during development and adulthood. Her lab discovered that MHC class I molecules (typically immune-related) play critical roles in synaptic plasticity, Alzheimer's disease, and recovery from brain injury. Key areas include: Critical period plasticity in visual systems Role of immune molecules in neurodevelopment Mechanisms of synaptic pruning and circuit refinement Awards: Kavli Prize in Neuroscience (2016) Champalimaud Vision Prize (2016) Harvey Prize in Science & Technology (2018) Gruber Prize in Neuroscience (2015) Advising & Labs: Advised doctoral students including Clara Bacmeister and Xiaochen Xiong. Her lab (Shatz Lab) explores molecular mechanisms underlying brain plasticity and neurodegenerative diseases. Current research includes Alzheimer's-related pathways and immune-neural interactions.

• Neuroscience
• Computational Neuroscience
• Neuronal Modeling
• +۳ مورد دیگر

Nicholas Carnevale is a Senior Research Scientist in Neuroscience at Yale School of Medicine, affiliated with the Department of Neuroscience. His work focuses on computational neuroscience, neuronal modeling, and electrophysiological simulations using the NEURON software. He holds an MD and PhD from Duke University, with postgraduate training at Stanford University and the University of California, San Diego. Carnevale collaborates extensively with Michael Hines on advancing the NEURON simulation environment, including its porting to multiple platforms and integration with real-time dynamic clamp systems. His research emphasizes understanding neuronal structure-function relationships, particularly in hippocampal and olfactory bulb neurons. He has developed educational tools and short courses to disseminate simulation methodologies. Key contributions include the Electrotonic Workbench, ModelDB database, and the Neuroscience Gateway, promoting open-access computational resources. His work bridges theoretical models with experimental neuroscience, advancing drug screening methods and understanding neural network dynamics.

Paul R. Adamsمشاهده پروفایل

استاد

• Computational Neuroscience
• Theoretical Neuroscience
• Synaptic Plasticity
• +۳ مورد دیگر

Paul R. Adams is a Professor in the Department of Neurobiology and Behavior at Stony Brook University's Renaissance School of Medicine. He joined Stony Brook in 1981 as an Associate Professor and was promoted to Professor in 1984, following prior faculty service at the University of Texas (1977-1981). His educational background includes a B.A. in Physiology and Pharmacology from Cambridge University (1968) and a Ph.D. in Pharmacology from London University (1974). Professor Adams' research centers on computational neuroscience , investigating how synaptic modifications underlying learning are compromised by crosstalk between densely packed synapses. His pioneering Synaptic Darwinism theory proposes evolutionary mechanisms for cortical learning, including "Hebbian proofreading" where layer 6 neurons detect and correct learning errors. He further explores how inter-brain communication networks overcome individual learning failures through information sharing. His publication trajectory reveals consistent focus on mathematical modeling of neural plasticity, evolving from foundational Synaptic Darwinism concepts (1997-2002) to recent work on crosstalk minimization (2013-2014). Key themes include Hebbian learning constraints, cortical circuitry reinterpretation, and theoretical solutions to neural network limitations. Notable honors include the MacArthur Foundation Prize (1986), election as Fellow of the Royal Society (1991), and Howard Hughes Medical Institute Investigatorship (1987-1995). Professor Adams leads theoretical neuroscience research through the Kalypso Mind/Brain Center, collaborating with Research Assistant Professor Kingsley Cox. His HHMI-funded work has influenced understanding of cortical function relevant to autism, schizophrenia, and epilepsy. He serves on editorial boards including Frontiers in Neural Circuits . The Synaptic Darwinism project maintains active investigation into cerebral cortex principles, exploring implications for both neurological disorders and fundamental questions about consciousness.

• Computational Thinking
• Neural Networks
• Machine Learning
• +۶ مورد دیگر

Anthony V. Robins is a Professor in the Department of Computer Science at the University of Otago, New Zealand. His work spans neural networks , computational cognition , computer science education , and machine learning . With over three decades of publication history, Robins has contributed to understanding catastrophic forgetting , prototype analysis , and reinforcement learning in artificial neural networks. His recent research (2024-2025) focuses on Hopfield networks and Boltzmann neural networks , particularly their applications in state classification , supra-classical logic , and sequential learning . Collaborations with researchers like Hayden McAlister and Lech Szymanski highlight his work in memory systems and Hebbian learning . Robins has also made significant contributions to high school computing education in New Zealand, analyzing curriculum implementation , student perspectives , and teacher challenges through publications in venues like ACM Trans. Comput. Educ. and ACE. His work on self-regulated learning and educational frameworks addresses systemic issues in computing education.

Anders Lansnerمشاهده پروفایل

استاد

• Computational Neuroscience
• Neuromorphic Engineering
• Brain-like Computing
• +۴ مورد دیگر

Anders Lansner is a Professor of Computer Science at Stockholm University and holds an affiliated professorship at KTH Royal Institute of Technology. He leads the Lansner Lab (Computational Biology and Neurocomputing) at the Department of Computational Science and Technology (CST) within the School of Computer Science and Communication (CSC) at KTH. His research focuses on computational neuroscience and brain-like computing, emphasizing mathematical and computational models of neuronal networks in the neocortex and basal ganglia. Key projects include developing neuromorphic algorithms for supercomputers and FPGA-based hardware implementations. Lansner manages the computational neuroscience platform for the Stockholm Brain Institute (SBI) and the neuroinformatics platform for StratNeuro (Karolinska Institutet). His lab contributes to EU projects such as FACETS and NEUROChem, and collaborates with KTH’s Electronics Department on modular brain-inspired FPGA designs. Research interests span synaptic plasticity mechanisms, memory systems (episodic, semantic, and working memory), and applications in neuromorphic computing. He supervises graduate students and teaches courses in computational neuroscience. Lansner’s work bridges theoretical neuroscience with engineering, aiming to advance brain-inspired AI and hardware systems. His lab’s StreamBrain framework supports heterogeneous computing architectures for brain-like neural networks. Notable collaborations include cross-disciplinary efforts in neuromorphic hardware development (e.g., memristor-based learning engines) and olfactory system modeling. Lansner’s research addresses both fundamental brain mechanisms and technical applications in data analysis and neurorobotics.

• Robotics
• Neuroscience
• Artificial Intelligence
• +۳ مورد دیگر

Tom Knowles is a Lecturer in Robotics at the University of the West of England , affiliated with the School of Engineering . He teaches technical subjects such as C Programming , Matlab Programming , Python Programming , Robotics , and Neuroscience . Education: MSc BSc Mr. Knowles bridges conventional and spiking neural networks to develop artificial systems that mimic biological capabilities, particularly in navigation. His work explores how mammalian brains represent space and models robust, flexible behaviors through robotics, intersecting Robotics , Neuroscience , and Artificial Intelligence . Publications trends highlight biomimetic robotics , spiking neural networks , and computational neuroscience . His research integrates machine learning , bio-inspired models , and sensory reconstruction to solve navigation challenges, with collaborations at the University of Sussex and Technische Universität München (TUM) . Collaborations: University of Sussex TUM (Technische Universität München) Contact: Email: Tom.Knowles@uwe.ac.uk Phone: +44117 965 6261

Charan Ranganathمشاهده پروفایل

استاد

• Human Memory
• Executive Control
• Neuroimaging
• +۷ مورد دیگر

Charan Ranganath is a Professor in the Department of Psychology at the University of California, Davis, and director of the Dynamic Memory Lab. He is affiliated with the UC Davis Center for Neuroscience and the Center for Mind and Brain, focusing on human memory and executive control using neuroimaging, electrophysiology, and behavioral methods. His research explores the neural basis of memory, including roles of the prefrontal cortex, medial temporal lobes, and hippocampus in working and long-term memory. Education: Ph.D., Clinical Psychology, Northwestern University M.S., Clinical Psychology, Northwestern University B.A., Psychology, University of California, Berkeley Ranganath’s research spans hippocampal-cortical interactions, curiosity-driven memory enhancement, schizophrenia-related memory deficits, and computational models of memory systems. His work reveals overlapping prefrontal and medial temporal lobe activity in memory tasks and investigates how stress, aging, and neuromodulation affect memory dynamics. His research trends emphasize episodic memory , relational memory , neural oscillations , cortico-hippocampal networks , curiosity-driven learning , and clinical applications in mental illness. Scientific Awards: Guggenheim Fellowship Leverhulme Trust Visiting Professorship Laird Cermak Award Samuel Sutton Award Chancellor’s Fellow award Young Investigator Award (Cognitive Neuroscience Society) National Security Science and Engineering Faculty Fellowship (2015) He has secured grants from the Department of Defense, National Institute of Mental Health, and private foundations. His lab collaborates with Andy Yonelinas on recognition memory and with others on field potentials in hippocampal studies. He teaches Human Learning, Cognitive Neuroimaging, and Current Research in Psychology.