جستجوی استاد راهنمای دانشگاهی

Russell Epstein is a Professor and Director of Graduate Studies in the Department of Psychology at the University of Pennsylvania. He is affiliated with the Center for Cognitive Neuroscience and Goddard Labs. His research focuses on neural mechanisms underlying visual scene perception, spatial navigation, and memory. Epstein holds a BA in Physics from the University of Chicago and a PhD in Applied Mathematics from Harvard University. Epstein’s research interests include high-level vision, spatial cognition, and the neural basis of environmental representations. His lab uses functional MRI and cognitive neuroscience techniques to study how scenes, objects, landmarks, and spaces are encoded in brain systems such as the parahippocampal place area and retrosplenial cortex. Recent work explores cognitive maps, grid-like neural representations, and the role of multisensory cues in navigation. His articles emphasize spatial navigation strategies, hierarchical cognitive maps, and the interplay between perception and memory. Notable contributions include investigations into hippocampal spatial metrics, olfactory navigation, and the neural underpinnings of environmental learning. Epstein teaches courses on cognitive neuroscience, including PSYC 149 and PSYC 600. He advises two graduate students in Psychology and has no listed scientific awards. His work is supported by grants (unspecified) and conducted within collaborative teams at the Center for Cognitive Neuroscience. Epstein’s research extends to labs focused on spatial cognition and neuroimaging, advancing understanding of how humans mentally map environments through visual and sensory integration.

• Cognitive Neuroscience
• Behavioral Neuroscience
• Spatial Navigation
• +۴ مورد دیگر

Joseph Kable is the Jean-Marie Kneeley President's Distinguished Professor of Psychology at the University of Pennsylvania's Department of Psychology within the School of Arts and Sciences. His research integrates experimental economics, cognitive neuroscience, and personality psychology to investigate the neural and psychological mechanisms underlying decision-making. He explores how subjective value is represented in the brain, deviations from rational choice theory, and individual differences in decision processes. His lab employs fMRI and interdisciplinary methods to study topics like risk tolerance, impulsivity, and neural plasticity. Education: BS in Chemistry from Emory University (undergraduate), PhD in Neuroscience from the University of Pennsylvania (doctoral). Research focuses on behavioral and cognitive neuroscience mechanisms of choice, including studies on temporal predictions, value signals, and brain structure-function relationships. His recent work examines how neural markers correlate with decision-making variability across individuals. He currently advises graduate students in Psychology and has post-doctoral researchers in his lab. Associated with MindCORE, Penn's hub for integrative mind research. Lab activities include undergraduate research programs and active studies exploring decision neuroscience and neuroeconomics.

• Behavioral Neuroscience
• Cognitive Neuroscience
• Decision Processes
• +۳ مورد دیگر

Evrim Acar Ataman is a Research Professor and Chief Research Scientist at Simula Metropolitan, where she serves as Head of the Department of Data Science and Knowledge Discovery. Her research focuses on advanced data mining techniques for complex, multi-modal datasets across biomedical and network domains. Her primary research interests include Data Mining , Matrix and Tensor Factorizations , and Data Fusion for multi-modal data analysis. She develops constrained and coupled factorization methods to extract interpretable patterns in applications spanning neuroimaging, metabolomics, and mobile network analysis, with emphasis on dynamic and longitudinal data structures. Her work integrates mechanistic models with data-driven approaches to enhance biological and system understanding. Analysis of her recent publications (2024-2025) reveals a dominant trend applying tensor and coupled matrix-tensor factorizations to biomedical data for biomarker discovery, particularly in metabolomics and neuroimaging. Key innovations include tracking evolving patterns in temporal data (tPARAFAC2), constrained fusion methods (dCMF), and integration of mechanistic models with tensor decompositions for longitudinal analysis. As Head of the Department of Data Science and Knowledge Discovery, she leads research in developing novel data mining methodologies and their real-world applications at Simula Metropolitan, with significant contributions to interpretable AI for complex systems.

• Data Mining
• Matrix Factorizations
• Tensor Factorizations
• +۲ مورد دیگر

Professor Rebecca Lawson is a Professor of Neuroscience and Computational Psychiatry at the University of Cambridge, affiliated with the Department of Psychology and Bye-Fellow at Peterhouse College. Her research focuses on understanding how humans learn to make predictions under uncertainty, with applications to mental health conditions such as anxiety and depression. She leads the Prediction and Learning (PaL) Lab, which combines computational modeling, neuroimaging (e.g., 7T MRI), and behavioral experiments to study cognitive processes in typical and atypical populations. Key research interests include computational psychiatry, autism spectrum disorders, neuroimaging techniques, and the neurochemical basis of learning mechanisms. She has received significant funding, including a £4.3m Wellcome Mental Health Award and the Sir Henry Dale Fellowship. Notable contributions include advancing theories of neural gain and sensory expectations in autism, as well as studies on uncertainty processing in anxiety and depression. Professor Lawson holds academic awards such as the BNPA Lishmann Prize and the BAP Psychopharmacology Award. She actively contributes to public engagement, including initiatives like Knit-a-Neuron and involvement with PrideinSTEM. Her lab collaborates internationally, with projects like the CamRAA study investigating autism-anxiety overlaps and the Chemical and Brain Basis of Uncertainty (CBBU) study exploring pharmacological interventions. Education: PhD in Cognitive Neuroscience (University of Cambridge), BA (Hons) Psychology & Philosophy (University of Glasgow). Grants: £4.3m Wellcome Award, Parke-Davis Fellowship, Lister Institute Prize. Labs/Teams: Principal Investigator of the PaL Lab; collaborations with MRC Cognition and Brain Sciences Unit, Yale University, and Brown University.

• Computational Neuroscience
• Neuroscience
• Psychiatry
• +۶ مورد دیگر

Adrien Desjardins is a Professor at the University of British Columbia, jointly appointed in the Department of Mechanical Engineering and Department of Electrical and Computer Engineering within the Faculty of Applied Science. He joined UBC in 2024 after serving as a Full Professor at University College London from 2019-2024, following 13 years on faculty there. His educational background includes a B.Sc. from UBC (2001) and a Ph.D. from MIT and Harvard University (2007). Dr. Desjardins' research program focuses on interdisciplinary development of imaging and sensing modalities and autonomous robotics with marine and biomedical applications. His work integrates photonics, ultrasound, machine learning, and robotics to create innovative diagnostic tools and sensing systems, particularly in optical coherence tomography, diffuse optical spectroscopy, and photoacoustic imaging. His publication record reveals an evolution from foundational neuroimaging work (2001) toward increasingly sophisticated optical systems culminating in breakthroughs like ultrasensitive optical microresonators for ultrasound sensing (2017), demonstrating consistent innovation in biomedical optics with growing emphasis on machine learning integration and real-world applications. His scientific contributions have been recognized through prestigious awards: Research Chair from the Royal Academy of Engineering Healthcare Technologies Challenge Award from EPSRC Starting grants from ERC, EPSRC, and Royal Society World Economic Forum Young Scientist (2015) UCL Provost Teaching Prize (2013) Dr. Desjardins actively mentors graduate students and secures major research funding through competitive grants, with current openings for January/September 2025 intakes. His program involves close industry collaboration indicating strong translational focus, though specific lab names aren't mentioned. The interdisciplinary nature of his work suggests teams spanning engineering, computer science, and medical disciplines working on next-generation imaging systems for healthcare and marine exploration.

• Imaging and sensing
• Robotics
• Machine learning
• +۶ مورد دیگر

Tülay Adali is a Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). She has held this position since 1992 and was named a Distinguished University Professor in 2015 for her contributions to statistical signal processing and machine learning. Currently serving as Editor-in-Chief of the IEEE Signal Processing Magazine, she has also held leadership roles in IEEE committees and conferences. Her research focuses on statistical signal processing, machine learning, and their applications in medical imaging and data fusion. Dr. Adali earned her Ph.D. in Electrical Engineering from North Carolina State University in 1992. Her work integrates foundational signal processing techniques with biomedical applications, addressing challenges in neuroimaging analysis. She leads the Machine Learning for Signal Processing laboratory, supported by grants from NSF and NIH. Her lab develops algorithms for analyzing complex signals in medical contexts, emphasizing reproducibility and interdisciplinary collaboration. Recognition includes IEEE Fellow, AIMBE Fellow, AAIA Fellow, Humboldt Research Award, and NSF CAREER Award. She has authored numerous papers on fMRI analysis, independent component analysis, and multimodal data fusion. Her editorial leadership and service to technical communities reflect her commitment to advancing signal processing and education. Education: Ph.D. in Electrical Engineering, North Carolina State University (1992) Grants: NSF, NIH-funded projects on medical imaging and signal processing Awards: SPS Meritorious Service Award, SPIE Pioneer Award

• Statistical Signal Processing
• Machine Learning
• Medical Image Analysis
• +۳ مورد دیگر

Antonio Rangel is the Bing Professor of Neuroscience, Behavioral Biology, and Economics at the California Institute of Technology (Caltech), where he also serves as Head Faculty in Residence. He is a faculty member in the Division of Humanities and Social Sciences (HSS) with research focusing on the computational and neurobiological basis of value-based decision-making. Dr. Rangel received his educational training at prestigious institutions: B.Sc. from Caltech in 1993 M.S. from Harvard University in 1996 Ph.D. in 1998 Professor Rangel's research lies at the intersection of neuroscience, economics, and psychology, with a focus on understanding how the brain makes decisions. His work investigates the neural mechanisms underlying value computation, choice processes, self-control, and social decision-making. Using a multidisciplinary approach that combines functional magnetic resonance imaging (fMRI), eye-tracking, computational modeling, and behavioral experiments, his lab has made significant contributions to the field of neuroeconomics. His research has revealed how value signals are represented in the brain, how attention influences choice, and the neural basis of self-control failures. Professor Rangel has pioneered methods for studying decision processes with high temporal resolution using eye-tracking data, demonstrating how fixation patterns relate to value computations and choice outcomes. His work spans from theoretical frameworks of value-based decision-making to practical applications in behavioral public economics. Professor Rangel's work has been recognized with prestigious awards: 2019 NOMIS Distinguished Scientist Award 2018 Fellow of the Association for Psychological Science As an academic leader, Professor Rangel has mentored numerous students and researchers who have gone on to make their own contributions to neuroscience and economics. His lab, the Rangel Neuroeconomics Laboratory at Caltech, serves as a hub for interdisciplinary research, bringing together students and scholars from neuroscience, economics, psychology, and computer science. The lab has received significant funding to support its research on the neural basis of decision-making, including support from the NOMIS Foundation. The Rangel Neuroeconomics Laboratory is equipped with state-of-the-art facilities including fMRI analysis capabilities, eye-tracking systems, and computational resources for modeling decision processes. The lab fosters a collaborative environment where researchers apply methods from experimental economics and cognitive neuroscience to unravel the complexities of human decision-making.

• Neuroeconomics
• Decision Neuroscience
• Behavioral Economics
• +۴ مورد دیگر

Franz Heukamp is the Director General of IESE Business School at the University of Navarra and Professor in the Department of Decision Analysis. He holds the Antonio Valero Chair of Business Administration since 2016 and previously served as Vice Dean of the MBA program and Secretary General of IESE. His academic background includes a Ph.D. in Engineering from the Massachusetts Institute of Technology, an Ingénieur Civil des Ponts et Chaussées from Ecole Nationale des Ponts et Chaussées in Paris, and a Diplom-Ingenieur from Technische Universität München. Professor Heukamp's research focuses on behavioral decision-making with a special interest in neuroeconomics, examining how people make decisions under uncertainty, particularly regarding time-probability trade-offs. His work bridges business decision theory with neuroscience, as evidenced by his publications in journals like Management Science , European Neuropsychopharmacology , and British Journal of Pharmacology . In recent years, he has expanded his research interests to include artificial intelligence applications in management and sustainability. His recent scholarly output shows a progression from fundamental decision theory research to practical applications of AI in business leadership and management education. He has published extensively on risk preferences, intuition in decision making, and the neurological basis of economic choices, with increasing focus on how digital transformation and AI are reshaping management practices. As Director General, Heukamp has championed several strategic initiatives including internationalization efforts across North America, Latin America, and Central Europe, development of new degree programs like the Master in Management and Munich Executive MBA, and integration of digital learning technologies while maintaining the school's commitment to in-person educational excellence. Professor Heukamp is an active member of both the Institute for Operations Research and the Management Sciences and the Neuroeconomics Society, reflecting his interdisciplinary approach to business education and research. His leadership prioritizes supporting faculty research, advancing lifelong learning solutions for executives, and maintaining IESE's mission of service to society through academic innovation.

• Decision Analysis
• Behavioral Decision-Making
• Neuroeconomics
• +۵ مورد دیگر

Sam Yam is a Professor and Head of the Management & Organisation Department at the National University of Singapore's (NUS) Business School. He holds the Jardine Cycle & Carriage Professorship and serves as Associate Dean. His research spans strategy, organisational behaviour, applied psychology, ethics, and human-technology interaction. Yam has contributed to over 125 publications, focusing on AI ethics, workplace dynamics, and cross-cultural management. He explores topics like robot replacement effects, abusive supervision, and financial anxiety's intergenerational impacts. His work bridges psychology, ethics, and organisational theory, addressing contemporary challenges in automation, leadership, and human-AI collaboration. Appointments: Head of Management & Organisation (2022–present), Joint Appointment in Arts & Social Sciences (2023–present). Key Research Themes: Future of work, human-technology interaction, ethical leadership, and Machiavellian dynamics. Notable Contributions: Multimodal ADHD diagnostics, leader biases toward robot-using employees, and cultural impacts on team performance. Yam’s interdisciplinary approach integrates psychology, technology, and management, addressing societal issues like AI ethics and workplace automation. His research frequently appears in top journals like the Journal of Management and Annual Review of Organizational Psychology .

• Strategy
• Management
• Organisational Behaviour
• +۱۰ مورد دیگر

Pierre Vandergheynst is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) in the Department of Electrical Engineering, with a courtesy appointment in Computer and Communication Sciences. He serves as EPFL’s Vice-Provost for Education since 2015 and leads the Signal Processing Laboratory 2 (LTS2). His research spans harmonic analysis, sparse approximations, mathematical data processing, and applications in signal/image processing, computer vision, machine learning, and graph-based data analysis. PhD in Mathematical Physics (1998), Université catholique de Louvain Postdoctoral Researcher at EPFL (1998-2001) Assistant Professor at EPFL (2002-2007) His research explores geometry/symmetry in high-dimensional data, redundant dictionaries for dimensionality reduction, and computational harmonic analysis on manifolds. Recent work focuses on protein structure modeling, geometric deep learning, and graph-based signal processing. Key article trends include graph neural networks for protein analysis, geometric deep learning in neuroscience, and structured knowledge priors in neural models. His 2023-2025 publications emphasize interpretable AI, long-range dependencies in graphs, and molecular representation learning. Scientific Awards: IEEE Signal Processing Magazine Best Paper Award (2023) Signal Processing Society Best Paper Award (2022) Apple ARTS Award (2007) De Boelpaepe Prize, Royal Academy of Sciences of Belgium (2009-2010) He has supervised over 30 PhD theses and contributed to foundational work in graph signal processing, compressive sensing, and geometric deep learning. His lab develops tools for data science on non-Euclidean structures, with applications in medicine, astronomy, and wireless systems.

• Data Science
• Machine Learning
• Computational Harmonic Analysis
• +۶ مورد دیگر

Belkis Ezgi Arikan is a researcher at Justus Liebig University Giessen , affiliated with the Department of Psychology and Sports Science . Her work focuses on sensorimotor integration, tactile suppression, and neural mechanisms underlying action-outcome monitoring. She collaborates with Prof. Dr. Fiehler and Dr. Voudouris on project A4 (Predictive somatosensory processing during voluntary movements). Department: Psychology and Sports Science Collaborators: Dr. Dimitris Voudouris, Prof. Dr. Katja Fiehler Her research investigates: How the brain processes self-generated vs. externally generated sensory feedback Neural correlates of tactile suppression networks Role of cerebellum and angular gyrus in temporal recalibration Modulation of BOLD responses during action monitoring Recent publications reveal trends in predictive coding , sensorimotor recalibration , and neural suppression mechanisms across tactile, visual, and multisensory domains. Her work employs neuroimaging techniques like fMRI and behavioral paradigms to study self-motion perception and feedback processing. She contributes to understanding how the brain distinguishes self-initiated from external sensory events, with implications for motor learning and perceptual timing mechanisms.

• Cognitive Neuroscience
• Neurophysiology
• Psychology
• +۴ مورد دیگر

Christopher Thomas Sege, PhD is an Assistant Professor in the Department of Psychiatry and Behavioral Sciences at the Medical University of South Carolina (MUSC) College of Medicine. His research focuses on the neurophysiological mechanisms underlying anxiety disorders, particularly examining escape, avoidance, and approach behaviors in the anxiety disorder spectrum. Dr. Sege's primary research interests include: Anxiety Disorders and Emotional Processing Defensive Behaviors and Coping Mechanisms Neurophysiological Correlates of Emotional Anticipation Startle Response and Autonomic Nervous System Functioning Transcranial Magnetic Stimulation (rTMS) Applications His work bridges clinical psychology with neuroscience to understand how individuals with anxiety disorders process and respond to emotional stimuli. Dr. Sege leads the research study "Modeling and Modulating Mechanisms of Escape, Avoidance, and Approach in the Anxiety Disorder Spectrum" which investigates whether repetitive transcranial magnetic stimulation (rTMS) can alter how people with anxiety cope with emotional situations. This study involves measuring brain activation while participants view emotional pictures and undergo rTMS procedures. His publication record demonstrates expertise across multiple domains: Neurophysiological mechanisms of defensive behaviors Emotional anticipation and perception Startle modulation in anxiety disorders Borderline personality disorder research Development of experimental paradigms for emotional processing studies His research employs multiple methodologies including psychophysiology, event-related potentials, and neurostimulation techniques. Dr. Sege collaborates with researchers including Lisa McTeague, Bernadette Cortese, Carla Danielson, Thomas Uhde, and Aicko Schumann. His work appears in journals such as Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, Biological Psychology, and Psychophysiology.

• Anxiety Disorders
• Neurophysiology
• Emotional Processing
• +۴ مورد دیگر

Prof. Dr. Dennis Säring is a faculty member at the University of Applied Sciences Wedel , specifically affiliated with the School of Engineering. His academic and research activities focus on Deep Learning , Medical Image Analysis , and applications of Artificial Intelligence in healthcare and biomedical imaging. He has led seminars on Deep Learning topics and supervised student projects in Autonomous Driving at Audi's AADC 2018 competition. Research Highlights : Cardiovascular imaging, forensic age estimation via MRI, neural network-based bone segmentation, and cerebrovascular aneurysm analysis. Technical Expertise : Cardiac MRI, 3D/4D image processing, parametric mapping, and spatiotemporal data fusion. His recent publications (2018-2023) emphasize 3D MR segmentation for age assessment, CMR strain analysis in athletes, and T1/T2 mapping for myocarditis. Key collaborations include institutions like the University Medical Center Hamburg-Eppendorf and Wedler Hochschulbund, with funding for autonomous vehicle research. While no explicit scientific awards are listed, his work spans clinical cardiology, forensic radiology, and AI-driven medical diagnostics.

• Deep Learning
• Medical Image Analysis
• Artificial Intelligence
• +۵ مورد دیگر

Prof. Dr. Kenan Aycan is a Professor of Anatomy at Ahi Evran University's Faculty of Medicine, Department of Basic Medical Sciences, where he has served since 2019. He also holds the position of Department Head since 2020. Previously, he worked at Erciyes University as a School Director from 2011-2016. His academic career spans over four decades with significant contributions to anatomical sciences. Dr. Aycan earned his PhD in Basic Medical Sciences from Ege University's Faculty of Medicine (1983-1986) following his Bachelor's degree in Science from Ege University's Faculty of Science (1970-1975). He also holds a Certificate of Use of Experimental Animals from Erciyes University (2008). His research focuses on anatomical morphology, vascular structures, and developmental processes. Dr. Aycan has pioneered anatomical techniques including the 'Aycan's method' for corrosion preparations. His work spans comparative anatomy across various species, morphometric analyses of anatomical structures, and investigations into teratogenic effects and protective agents. He has extensively studied the foramen magnum using golden ratio principles, vascular anatomy of reproductive organs in ruminants, and auditory ossicles in sheep. Analysis of his recent publications (2021-2025) reveals consistent focus on anatomical methodology development, morphometric studies of key anatomical structures, vascular anatomy investigations, and research on developmental processes and teratology. His work often employs plastic injection and corrosion techniques, CT imaging, and comparative approaches across human and animal models. Dr. Aycan has mentored numerous graduate students, serving as primary advisor for over 20 Master's and PhD theses covering diverse anatomical topics from vascular variations to developmental studies. His collaborative network includes researchers like Tufan Ulcay, Burcu Kamaşak, and others across Turkish institutions.

• Anatomy
• Morphology
• Vascular Anatomy
• +۴ مورد دیگر

Mayank R. Mehta is a Professor at the University of California, Los Angeles (UCLA), holding joint appointments in the Departments of Physics & Astronomy, Neurology, and Neurobiology. He is a member of the Brain Research Institute and the W. M. Keck Center for Neurophysics at UCLA. His research bridges experimental and theoretical neuroscience, focusing on how neuronal networks encode space-time, the role of brain rhythms in learning and memory, and the impact of sleep and virtual reality on neural dynamics. His recent publications highlight breakthroughs in understanding hippocampal spatiotemporal selectivity, dendritic activity during behavior, and the causal influence of visual cues on memory neurons. Notable findings include the discovery that dendrites generate ten times more spikes than neuronal cell bodies and the modulation of hippocampal theta rhythms in virtual reality. Research Themes: Neurophysics of spatial-temporal coding Dendritic contributions to learning Virtual reality and brain plasticity Neural oscillations in memory consolidation Key Collaborators: Bert Sakmann (Max Planck Florida Institute) Thomas Hahn (Bernstein Center Heidelberg/Mannheim) Maryam Ghorbani (UCLA) Mehta's lab at UCLA trains graduate and postdoctoral researchers in cutting-edge techniques combining hardware development, electrophysiological recordings, and biophysical modeling. His work has significant implications for treating learning and memory disorders like Alzheimer's disease.

• Neurophysics
• Synaptic Plasticity
• Learning and Memory
• +۳ مورد دیگر