جست‌وجوی استاد و پژوهشگر

Katherine Wolfeمشاهده پروفایل

استاد آموزشی

• Economic Pedagogy
• Undergraduate Education
• Gender Retention in Economics
• +۳ مورد دیگر

Katherine Wolfe is a Teaching Professor in the Department of Economics at the University of Pittsburgh, with over 20 years of experience in economics education. She specializes in improving outcomes for students in core economics courses, particularly through pedagogical innovations, and focuses on retaining underrepresented groups in the discipline. She mentors graduate students in teaching and advises economics undergraduates, recognized for her award-winning advising role. Her research spans economic pedagogy and behavioral economics, with recent work addressing gender disparities in economics education and earlier studies exploring fairness models in game theory. She has held diverse roles beyond academia, including Peace Corps service in Samoa, sailing instruction, and accounting in a tech startup. Notable publications include studies on course content improvements for women in economics (2024, 2021), and foundational work on fairness, confirmation bias, and bargaining games (2003–1999). While specific awards are not listed, her advising role is explicitly noted as award-winning. Her teaching portfolio includes introductory microeconomics, game theory, and capstone seminars, alongside a PhD-level course on teaching economics. No grants or lab affiliations are detailed in the provided text.

Jelmer Borstمشاهده پروفایل

دانشیار

• Computational Cognitive Neuroscience
• Neuroimaging Analysis
• Cognitive Modeling
• +۵ مورد دیگر

Prof. Jelmer Borst is an Associate Professor in Computational Cognitive Neuroscience at the University of Groningen's Faculty of Science and Engineering, affiliated with the Artificial Intelligence department within the Bernoulli Institute. His research focuses on integrating computational models with neuroimaging data to understand cognitive processes like multitasking, working memory, and decision-making. He advises three PhD candidates and collaborates internationally on projects involving EEG/fMRI analysis and cognitive modeling frameworks such as ACT-R and Nengo. Research Interests: Neuroimaging analysis methods, cognitive bottlenecks in multitasking, memory retrieval localization, and adaptive learning systems. Key Projects: Developed the PREDICTOR tool for semi-automated driving response timing, and advanced models linking symbolic process stages to brain activity via MEG/EEG. Recent work includes large-scale evaluations of adaptive learning systems and interventions to mitigate mind-wandering in driving scenarios. He has received the Allen Newell Best Student-led Paper Award (2021) for contributions to cold-start adaptive learning research. His research group maintains active collaborations on datasets involving working memory, decision-making, and cognitive architecture validation through neuroimaging experiments. He also contributes to open-source tools for cognitive modeling and neuroimaging analysis.

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

Dr. David Tait is a Senior Research Fellow at the University of St Andrews' School of Psychology and Neuroscience. His research focuses on understanding cognitive processes that enable behavioral adaptation to environmental changes, employing neuroscientific techniques like psychopharmacology and chemogenetics in experimental animals. He investigates attentional focus, learning mechanisms, and the neurological underpinnings of attentional shifting in the frontal cortex and basal ganglia. Key research interests include attentional set-shifting, behavioral flexibility, and the role of specific brain regions such as the medial prefrontal cortex and orbital frontal cortex. His work spans rodent and reptile models to explore learning impairments, reversal learning, and neuropharmacological interventions. Publications highlight contributions to understanding cognitive deficits in lesion models, drug effects on learning, and comparative neuroscience across species. His methodologies emphasize innovative behavioral tasks and Bayesian analysis to disentangle complex neural processes.

Christopher Ferrallمشاهده پروفایل

استاد

• Labour Economics
• Computational Methods
• Econometrics
• +۴ مورد دیگر

Christopher Ferrall is a Professor in the Department of Economics at Queen's University, where he has been since 1990. He also serves as the Academic Director of the Queen's Research Data Centre and previously led the John Deutsch Institute for the Study of Economic Policy (2009–2016). His research focuses on computational methods applied to labour economics, dynamic programming, and structural estimation. Ferrall holds a BA from the University of Wyoming (1984) and a PhD from the University of Minnesota (1990). He has held visiting positions at institutions such as Carnegie Mellon, New York University, and the Norwegian School of Economics. Education: BA Economics & Computer Science (University of Wyoming, 1984); PhD Economics (University of Minnesota, 1990). Research Interests: Labour market dynamics, computational economics, structural estimation, and policy analysis using dynamic programming. Key Contributions: Developed the niqlow software platform for economic model design and estimation, and authored the textbook C4E: Computation for Economists . His publications span topics like unemployment insurance, physician work patterns, and the impact of education quality on labour market outcomes. He has supervised over 20 PhD students, many of whom hold academic or policy roles globally. Ferrall has secured grants totaling millions, including leadership in Canada’s Research Data Centre Network and SSHRC initiatives. His teaching spans advanced econometrics, computational methods, and labour economics at both graduate and undergraduate levels. Grants: Over $30M in funded research, including SSHRC/CIHR initiatives and Canada Foundation for Innovation grants. Administration: Extensive service on university committees, including roles in high-performance computing, graduate programs, and academic review committees. He is affiliated with the Queen’s Research Data Centre and has collaborated with institutions like Statistics Canada and the World Bank. His work bridges theoretical models with practical policy analysis, leveraging computational tools to address complex economic questions.

Francesco Bianchiمشاهده پروفایل

استاد

• Macroeconomics
• Monetary Policy
• Fiscal Policy
• +۶ مورد دیگر

Francesco Bianchi is the Louis J. Maccini Professor of Economics and Department Chair at Johns Hopkins University's Krieger School of Arts & Sciences. He holds a Ph.D. in Economics from Princeton University (2009) and a B.A. from Bocconi University. His research focuses on macroeconomic dynamics, monetary and fiscal policy interactions, and macro-finance, with a recent emphasis on machine learning applications for forecasting and belief distortions in asset markets. Bianchi is a co-editor of the *American Economic Journal: Macroeconomics*, and a research associate at the National Bureau of Economic Research (NBER) and the Center for Economic and Policy Research (CEPR). **Education**: Ph.D. in Economics (Princeton University, 2009), M.A. in Economics (Princeton, 2007), B.A. in Economics and Statistics (Bocconi University, 2003). **Research Interests**: Machine learning in macroeconomic forecasting, fiscal theory of inflation, monetary policy effects on asset prices, belief distortions, and policy coordination. His work has been published in top journals like the *American Economic Review*, *Review of Economic Studies*, and *Journal of Finance*. **Awards**: Wim Duisenberg Research Fellowship (2015), Zellner Thesis Award (2010). He has advised policymakers at institutions like the Federal Reserve and presented at high-profile events such as the Jackson Hole Symposium. **Grants & Roles**: Served as associate editor for journals including *Journal of Monetary Economics* and *Quantitative Economics*. His research has influenced central bank independence debates and fiscal-monetary policy frameworks. Current projects include fiscal inflation dynamics in OECD countries and structural models of belief-driven macroeconomic fluctuations. **Labs/Teams**: Leads research collaborations in macroeconomic theory and applied econometrics, often with interdisciplinary approaches combining machine learning and traditional econometric methods.

James Morganمشاهده پروفایل

استاد

• Early Language Development
• Spoken Word Recognition
• Phonetics
• +۳ مورد دیگر

James L. Morgan is a Professor of Cognitive and Psychological Sciences and Linguistics at Brown University. He holds dual appointments in the Department of Cognitive and Psychological Sciences and the Linguistics Program. His research focuses on early language development, particularly spoken word recognition in infants and young children, and how auditory/visual speech input interacts with infants' perceptual capacities to facilitate language acquisition. Educated at Washington University (AB 1974), UC San Diego (MA Linguistics 1976, MA Psychology 1979), and UIUC (PhD Developmental Psychology 1983), Morgan previously taught at the University of Minnesota and held visiting positions at CNRS-EHESS (Paris) and the University of Potsdam. His work is funded by NIH/NICHD grants and recognized by Fulbright and Fogarty fellowships. Research highlights include discovering infants' sensitivity to prosodic patterns in speech segmentation, demonstrating how lexical categories influence phonetic learning, and analyzing infant-directed speech properties. He directs the Metcalf Infant Research Lab and serves on editorial boards for Infancy and Language Learning and Development . Teaching focuses on language acquisition, psycholinguistics, and cognitive development. Recent studies explore how infants categorize grammatical vs. lexical words using acoustic correlates, the role of visual prosody in speech perception, and developmental trajectories of phonetic detail processing. Collaborators include Sheila Blumstein, Bertram Malle, and David Sobel in interdisciplinary projects linking linguistics, psychology, and neuroscience.

Giulio Ongaroمشاهده پروفایل

پژوهشگر

• Medical Anthropology
• Shamanism
• Ritual Efficacy
• +۸ مورد دیگر

Giulio Ongaro is a researcher at the Department of Anthropology at the London School of Economics (LSE). His work focuses on medical anthropology, shamanism, and ritual efficacy among the Akha people of highland Laos. He holds a PhD from LSE, supported by ESRC funding, and conducted extensive fieldwork in Southeast Asia. His research bridges anthropological theory with cognitive science and philosophy of mind, particularly exploring placebo effects and the 'extended mind' hypothesis. Currently, he is developing a monograph on Akha medical systems and pioneering projects on voodoo death, global psychiatry history, and the epistemic value of animistic philosophies. Education: MSc in Philosophy (University of Edinburgh), PhD in Anthropology (LSE). His interdisciplinary approach integrates ethnography with neuroscience and philosophy to address questions about ritual efficacy, medical rationality, and cultural psychiatry. Research Interests: Medical anthropology, animism, placebo science, cognitive science, philosophy of mind, global history of psychiatry, and Southeast Asian studies. Advising & Grants: ESRC-funded PhD research. Collaborations on projects exploring biomedical ethics, open-label placebo trials, and the social dimensions of mental health. Labs/Teams: Part of LSE's anthropology research unit, contributing to interdisciplinary initiatives at the intersection of culture, cognition, and health.

• Mathematics
• Applied Mathematics
• Computational Neuroscience
• +۳ مورد دیگر

Fei Chen is a Research Fellow in the School of Mathematics at Trinity College Dublin. Their work focuses on interdisciplinary research at the intersection of mathematics, neuroscience, and biomedical engineering. Current research interests include predictive coding models in auditory perception, Bayesian inference frameworks for phantom perception, and applications of machine learning in diagnosing hearing-related disorders. Fei's recent publications explore topics such as auditory illusion modeling, chronic pain mechanisms through predictive processing lenses, and systematic reviews of autoimmune-related hearing loss. Their work combines advanced mathematical modeling with clinical data analysis to advance understanding of sensory perception disorders. Advisees/PhD students: None listed at this time. No scientific awards mentioned in available records. Research activities include collaboration with clinical teams to investigate neural networks involved in auditory processing anomalies and developing computational tools for hearing diagnostics.

• Category Theory
• Compositional Game Theory
• Categorical Cybernetics
• +۷ مورد دیگر

Jules Hedges is a mathematics and computer science researcher affiliated with the Institute for Categorical Cybernetics and the Mathematically Structured Programming group at the University of Strathclyde. As of September 2024, he is working 90% for the CyberCat Institute and 10% for the University of Strathclyde, focusing on applying category theory across various interdisciplinary domains. Hedges completed his PhD at Queen Mary University of London in 2016 with a thesis titled "Towards compositional game theory," which established foundational work for his ongoing research program. His academic journey spans from early work on game theory to current developments in categorical cybernetics. His primary research involves applying category theory in economics, machine learning, and control theory, with a view towards what was historically called cybernetics. Hedges is particularly known for developing compositional game theory, which provides a categorical framework for understanding strategic interactions through concepts like "open games" and "selection functions." His other research interests include graphical calculi for visual representation of categorical structures, functional programming paradigms, proof theory, and the semantics of natural language. He has made significant contributions to understanding how complex systems can be built from simpler components while preserving mathematical properties. Hedges' publication record reveals a consistent trajectory toward developing compositional frameworks for complex systems, with recent work increasingly focused on categorical cybernetics—the application of category theory to systems with feedback loops and goal-directed behavior. His research often bridges abstract mathematical concepts with practical applications in economics and artificial intelligence, particularly in reinforcement learning where he has demonstrated connections between value iteration algorithms and categorical optic composition. Hedges is a signatory of the "No Free View? No Review!" pledge, indicating his strong commitment to open access publishing. He has publicly stated he will not peer review for closed access or hybrid venues, reflecting his principled stance on making academic research freely available. While specific details about his advising activities are not documented in the provided text, his extensive publication record with numerous co-authors suggests active research collaboration and likely mentorship of junior researchers. His work spans multiple institutions and involves collaborators across mathematics, computer science, and economics. Hedges is a founding member of the Institute for Categorical Cybernetics, formally incorporated in October 2023, which represents the primary focus of his current work (90% of his professional time as of September 2024). This institute serves as a hub for researchers working at the intersection of category theory, cybernetics, and their applications to complex adaptive systems, potentially supporting collaborative projects, workshops, and publications in this emerging field.

Paul Bainesمشاهده پروفایل

استاد

• Political Marketing
• Strategic Communication
• Propaganda
• +۳ مورد دیگر

Professor Paul Baines is Professor of Political Marketing and Head of Executive Education at the University of Leicester School of Business (ULSB) . He holds Visiting Professor positions at Cranfield and Aston Universities, and is an Associate Fellow of King's College London's Centre for Strategic Communications. His expertise lies in applying marketing principles to government communications, political campaigns, and counter-propaganda efforts. Educational Background: Bachelors, Masters, and Doctoral degrees from The University of Manchester Masters from Lancaster University Postgraduate Certificate in Higher Education from Middlesex University Research Focus: Baines is a leading authority on political marketing , strategic communication , and propaganda , analyzing how messages are crafted and received in political contexts. He also leads the GreenerFuture Leicestershire initiative, investigating sustainability strategies for SMEs . His work spans topics such as political disinformation , emotional appeal effectiveness , and security communication . Scientific Awards: Fellow of the Market Research Society (MRS) in 2016 Member of the Market Research Council (US) and ESOMAR Advising & Grants: Baines supervises a Cranfield DBA student researching political marketing to youth. He has secured grants for evaluating police social marketing in counter-terrorism and SME sustainability transitions . His consultancy, Baines Associates Ltd , advises private enterprises including IBM, 3M, and Saint Gobain.

• Cognitive Neuroscience
• Decision Making
• Attention & Cognitive Control
• +۴ مورد دیگر

Professor Nick Yeung is a Professor of Cognitive Neuroscience at the University of Oxford and Tutorial Fellow at University College, Oxford. His primary affiliation is with the Department of Experimental Psychology. His research focuses on the cognitive and neural mechanisms underlying human learning, memory, attention, and decision-making. He explores how subjective confidence in choices influences information-seeking behavior and error detection, with applications in mental health (e.g., depression) and human-machine collaboration. Yeung holds a BA (Hons) and PhD in Psychology. His work integrates computational modeling, neuroimaging (EEG/fMRI), and experimental psychology to study metacognition, task switching, and attentional control. Recent studies investigate domain-general error signals, advice utilization dynamics, and neural correlates of confidence. Research highlights include EEG-based attention monitoring for safety-critical tasks (e.g., driving) and exploring how confidence biases affect decision-making in healthcare and collaborative systems. His lab also develops practical applications of cognitive neuroscience findings, such as attention-stabilizing neurostimulation devices. Key contributions span over 80 publications, including high-impact work on decision confidence, error monitoring, and cognitive control. His 2024 perspective paper outlines foundational advancements in cognitive control research, while 2025 studies reveal shared neural error signals across sensory modalities.

Johannes Levinمشاهده پروفایل

استاد

• Parkinson's Disease
• Alzheimer's Disease
• Movement Disorders
• +۳ مورد دیگر

Prof. Johannes Levin is a Professor of Clinical Neurodegeneration and Head of the Outpatient Clinics for Cognitive Neurology and Movement Disorders at the LMU Munich Neurological Clinic. He specializes in Parkinson's disease, Alzheimer's disease, and neurological intensive care. His clinical qualifications include Neurology specialist certification, Neurological Intensive Care Medicine, genetic counseling in Neurology, EEG certification, and Botulinum Toxin Therapy certification. Research focuses on synucleinopathies (Parkinson's/Multiple System Atrophy), tauopathies (Progressive Supranuclear Palsy/Corticobasal Syndrome), Alzheimer's pathology (especially genetic forms), and developing disease-modifying therapies. Key initiatives include the Early Clinical Trial Unit (ECTU), Neurology Winter School (NWS), and collaborative research networks like the Dominantly Inherited Alzheimer Network. Publications span biomarker development, drug trials (e.g., anle138b), and clinical diagnostic criteria for neurodegenerative disorders. His work bridges clinical practice with translational research, emphasizing early intervention and precision medicine approaches.

Søren Haubergمشاهده پروفایل

استاد

• Machine Learning
• Bayesian Inference
• Geometric Deep Learning
• +۱۱ مورد دیگر

Søren Hauberg is a Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he is affiliated with the Cognitive Systems group. His research lies at the intersection of machine learning, geometry, and probabilistic modeling, with applications in life sciences and computer vision. Department: Applied Mathematics and Computer Science Research Group: Cognitive Systems Institution: Technical University of Denmark (DTU) His research interests center on geometric deep learning , Bayesian inference , and manifold-based modeling . He explores how stochastic and geometric structures can improve machine learning models, particularly in modeling complex data such as biological sequences and medical images. His work integrates Riemannian geometry, Gaussian processes, and energy-based models to build more robust and interpretable AI systems. The recent publications reveal a strong trend toward geometric representation learning , fairness in generative models , and computational methods for biological data . His team investigates latent space geometries, develops efficient GPU-based numerical algorithms, and applies foundational models to protein sequences. There is a clear emphasis on both theoretical rigor and practical implementation. Søren Hauberg actively supervises multiple PhD students and leads key projects in machine learning for life sciences. He is a project manager in the Center for Basic Machine Learning Research in Life Science , indicating leadership in interdisciplinary research. He also contributes to high-performance computing applications in statistical modeling. He is involved in several active research projects, including: Geometric Bayesian Deep Learning Stochastic Riemannian Geometry with Applications to Data Modelling The geometry of protein representations AI-driven Electron Tracking for High-Energy Radiation Detection

• Formal Epistemology
• Philosophy of Science
• Philosophy of Statistics
• +۵ مورد دیگر

Olav Benjamin Vassend is a Professor at the Inland School of Business and Social Sciences , University of Inland Norway, affiliated with the Department of Law, Philosophy and International studies . He joined INN in 2022 after serving as an assistant professor at Nanyang Technological University (Singapore) for five years. He earned his PhD in philosophy from the University of Wisconsin-Madison. His research focuses on: Formal Epistemology : Analyzing belief update strategies and rationality (e.g., Bayesian vs. abductivist approaches). Philosophy of Statistics : Addressing interpretive problems and norms for inductive inference. Decision Theory : Defending causal decision theory and exploring belief-desire interactions. Publications (2015–2024) span formal epistemology, philosophy of science, and decision theory, with recurring themes in Bayesian reasoning, verisimilitude, and statistical paradoxes. Collaborations include work with Elliott Sober and Branden Fitelson.

Philip Garnerمشاهده پروفایل

مدرس

• Speech Recognition
• Spiking Neural Networks
• Neuromorphic Computing
• +۷ مورد دیگر

Philip Garner is a Lecturer at the Laboratory for Intelligent Data Analysis and Processing (LIDIAP), part of the School of Engineering (STI) at École Polytechnique Fédérale de Lausanne (EPFL). He is affiliated with the Institute of Electrical Engineering (IEM) and conducts research in intelligent data analysis, with a focus on biologically inspired neural models for speech and cognitive processing. Despite holding a 'Guest' status, he actively supervises PhD students and co-directs doctoral research, indicating a significant academic role. His research interests lie at the intersection of neuroscience and machine learning, particularly in spiking neural networks , speech recognition , neuromorphic computing , and deep learning architectures . He investigates how biologically plausible models can replicate brain-like computation for efficient information processing. His work often explores temporal dynamics, recurrence, memory mechanisms, and neural oscillations in artificial systems. The publications associated with his advisees and research group reflect a strong trend in developing energy-efficient, brain-inspired computing models for sequential data processing. These works frequently involve surrogate gradient learning, Bayesian recurrent units, and analysis of cross-frequency coupling in neural networks, contributing to both cognitive science and practical applications in speech technology. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: Philip Garner supervises multiple doctoral students, including Abdennadher Yesmine, Chen Haolin, Coppieters De Gibson Louise Clothilde, He Mutian, and has co-supervised several completed theses such as those of Bittar Alexandre, Honnet Pierre-Edouard, Schnell Bastian, Taghizadeh Mohammadjavad, and Tong Sibo. His advising role is central to the LIDIAP lab’s research output in neural computation. While specific grants are not listed, his involvement in cutting-edge AI and neuroscience research suggests active participation in funded projects. Labs and Teams: He is based at the IDIAP Research Institute, closely affiliated with EPFL’s LIDIAP laboratory, which specializes in intelligent data analysis and processing. The lab fosters interdisciplinary research in machine learning, signal processing, and cognitive modeling, often in collaboration with neuroscience and engineering groups.