جست‌وجوی استاد و پژوهشگر

Ruijin Luمشاهده پروفایل

استادیار

• Biostatistics
• Alzheimer's Disease Research
• Neurodegenerative Disorders
• +۵ مورد دیگر

Ruijin Lu serves as Assistant Professor of Biostatistics at Washington University School of Medicine, affiliated with both the Institute for Informatics, Data Science and Biostatistics (I 2 DB) and the Center for Biostatistics and Data Science (CBDS). With 238 citations across 24 research publications spanning from 2017 to 2025, Dr. Lu maintains an active research program focused on statistical applications in neurodegenerative disease research. Dr. Lu's research interests center on biostatistical methods applied to Alzheimer's disease research, particularly in biomarker analysis for autosomal dominant Alzheimer's disease. Key research areas include: Statistical modeling of cerebrospinal fluid and plasma biomarkers Longitudinal analysis of neurodegenerative disease progression Development of sensitivity analysis methods for complex exposure interactions Comparative studies of tau pathology across different patient populations Integration of genetic and clinical data for dementia prediction Recent publications demonstrate significant contributions to high-impact journals including Nature Medicine , Nature Communications , and The Lancet Neurology , with multiple studies gaining substantial media attention (83 news outlets for one publication) and academic engagement (47 Mendeley readers). Dr. Lu actively collaborates with major research networks including the Dominantly Inherited Alzheimer Network and Alzheimer's Biomarker Consortium–Down Syndrome, working alongside prominent researchers such as Bateman, Morris, and Holtzman. Current research output shows accelerating productivity with 12 publications in 2024 and 2 in 2025, indicating an expanding research program with significant clinical implications for understanding and treating Alzheimer's disease.

Eric Leggeمشاهده پروفایل

دانشیار

• Spatial Cognition
• Animal Behavior
• Mnemonics
• +۴ مورد دیگر

Dr. Eric Legge is an Associate Professor in the Department of Psychology at MacEwan University since 2016. He holds a PhD and MSc from the University of Alberta and a BA (Honours) from Memorial University of Newfoundland. His research focuses on spatial cognition, navigation, and comparative animal behavior, with secondary interests in companion animal welfare and human-animal interaction. He teaches courses including Cognitive Psychology, Principles of Behaviour, and Comparative Cognition. Education: PhD (Alberta), MSc (Alberta), BA (Memorial University) His primary research investigates spatial memory mechanisms across species, including human navigation strategies, desert ant navigation systems, and the Method of Loci mnemonic technique. He also studies companion animal behavior, training ethics, and the therapeutic benefits of human-animal bonds. Dr. Legge has published extensively on spatial cognition, animal behavior, and mnemonic strategies, with recent work exploring empathy in pet owners and video game impacts on spatial tasks. He actively supervises student research projects in these areas. His publications span topics from pigeon cue integration to ant navigation algorithms, demonstrating cross-species applications of cognitive principles. While no specific awards are listed, his work contributes to foundational understanding of spatial and comparative cognition. His teaching and research emphasize bridging theoretical insights with practical applications in animal welfare and human cognition.

Mehrdad Jazayeriمشاهده پروفایل

استاد

• Computational Neuroscience
• Neural Dynamics
• Sensory-Motor Integration
• +۵ مورد دیگر

Mehrdad Jazayeri is a Professor in the Department of Brain and Cognitive Sciences at MIT and an Investigator at the McGovern Institute for Brain Research. He directs educational initiatives in the department and teaches 9.014: Quantitative Methods and Computational Models in Neuroscience , focusing on mathematical tools for analyzing neurobiological data. His research centers on the neural mechanisms of timing, sensorimotor integration, and decision-making, with contributions to understanding how the brain encodes time and generates flexible behaviors. Education and Background: Jazayeri earned his PhD in Neuroscience from New York University (NYU) and completed postdoctoral training there, supported by the Helen Hay Whitney Foundation. His work has been honored with prestigious awards including the Howard Hughes Medical Institute Investigator designation (2024), McKnight Scholars Award (2017), and multiple teaching awards from MIT. Research Interests: His lab explores how neural circuits in the frontal cortex and other regions support temporal processing, mental simulation, and hierarchical reasoning. Key topics include neural dynamics underlying interval timing, Bayesian computation in sensory-motor systems, and mechanisms of cognitive control. Recent work highlights the role of entorhinal cortex in mental navigation and the dynamics of working memory. Awards: Over 20 awards/fellowships span career milestones, including the 2024 HHMI Investigatorship, Sloan Research Fellowship (2014), and recognition for both research and teaching excellence. His early career was supported by the Goldsmith Fellowship (NYU) and NSERC Scholarship (Canada). Teaching and Education: As Director of Education in BCS, he shapes curricula for MIT’s Brain and Cognitive Sciences programs. His 9.014 course emphasizes computational modeling, MATLAB programming, and analysis of neurobiological data, reflecting his commitment to bridging theory and experiment. Lab and Collaborations: The JazLab develops innovative techniques like MEDiCINe for neural recordings and leverages primate models to study decision-making circuits. Collaborations integrate experimental neuroscience with computational modeling, addressing questions in temporal processing and neural plasticity.

Hongjing Luمشاهده پروفایل

استاد

• Cognitive Psychology
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Professor Hongjing Lu is a faculty member in the Department of Psychology at the University of California, Los Angeles (UCLA). Her research explores how humans learn and reason under conditions of sparse, ambiguous, and noisy data, with applications to understanding intelligent systems in both biological and artificial contexts. Her work integrates theories and methods from Psychology , Statistics , Computer Vision , Machine Learning , and Computational Neuroscience . Key research themes include: Exploiting generic priors for learning/inference Manipulating structured representations in perception and cognition Interdisciplinary approaches to motion perception, action recognition, and object recognition Connections between human cognition and AI systems Her publications span journals like Nature Communications , Nature Human Behavior , Psychological Review , and PLoS Computational Biology , focusing on analogical reasoning, causal learning, and neural mechanisms of perception. She leads the Computational Vision and Learning Lab at UCLA.

• Neuroimaging Statistics
• Bayesian Modelling
• Multiple Testing Inference
• +۳ مورد دیگر

Thomas Nichols is Professor of Neuroimaging Statistics at the University of Oxford's Big Data Institute. His research develops advanced statistical methods for brain imaging data analysis, focusing on Bayesian modelling, multiple testing inference, and large-scale data integration. He serves as Associate Head for Innovation and Training at the BDI. Research Focus: Methodological innovations in neuroimaging statistics Meta-analysis of large-scale brain imaging datasets Open science tools for data sharing Key Contributions: Development of FEMA algorithm for large-sample whole-brain imaging analysis Foundational work on multiple testing correction in neuroimaging

Cédric Foucaultمشاهده پروفایل

پژوهشگر ارشد

• Computational Neuroscience
• Cognitive Modeling
• Decision-Making
• +۴ مورد دیگر

Cédric Foucault is a Junior Research Fellow at Christ Church College, University of Oxford, affiliated with the Department of Experimental Psychology. His research focuses on computational models of human cognition and decision-making in dynamic environments, leveraging tools from computer science, neuroscience, and machine learning. Prior to academia, he worked as a software engineer at Apple in Silicon Valley, contributing to iOS development. Education: PhD in Cognitive Computational Neuroscience (2023), University of Paris-Saclay/Sorbonne University MSc in Cognitive Neuroscience (2020), École Normale Supérieure de Paris MSc in Computer Science (2014), École Normale Supérieure Paris-Saclay His research integrates fMRI , M/EEG , and Bayesian modeling to study learning under uncertainty, probabilistic inference, and decision-making processes. Collaborations include work with Laurence Hunt’s research group at Oxford. He maintains an active GitHub profile with repositories on neural networks and computational cognitive science. Recent work emphasizes expanding research into broader decision-making domains while continuing to develop computational tools for neuroscience applications.

Michael S Landyمشاهده پروفایل

استاد

• Sensory Decision-Making
• Multisensory Integration
• Spatial Vision
• +۵ مورد دیگر

Michael S Landy is a Professor of Psychology and Neural Science at New York University, affiliated with the College of Arts & Science. He holds a Ph.D. in Computer and Communication Sciences from the University of Michigan (1981) and has been at NYU since 1981, progressing through academic ranks to full Professor in 1997. His research focuses on visual perception, action, and computational modeling, with key areas including sensory decision-making, multisensory integration, spatial vision, depth perception, and motor control. His lab explores how humans combine sensory cues (e.g., visual, tactile, auditory) under uncertainty and how these processes interact with neural mechanisms. Recent work addresses causal inference in multisensory perception, neural coding of motion signals, and Bayesian models of perception. He serves as director of the Landy Lab, advancing interdisciplinary research at the intersection of psychology, neuroscience, and computer science. Education: Ph.D., Computer and Communication Sciences, University of Michigan (1981) M.S., Computer and Communication Sciences, University of Michigan (1976) B.S., Columbia University (1974) Research Interests: Sensory Decision-Making: How humans set perceptual criteria and estimate confidence under uncertainty. Cue Integration: Optimal combination of visual, tactile, and auditory information (e.g., depth cues). Visual Action: Visually guided movements (e.g., eye/limb control under risk and cost constraints). Depth Perception: Neural and computational mechanisms for estimating 3D layout from binocular, motion, and pictorial cues. Neural Models: Linking psychophysics with neural responses in visual cortex (e.g., fMRI studies of texture and motion processing). Lab & Collaborations: The Landy Lab conducts interdisciplinary research with collaborations in neuroscience, computer science, and mathematics. Current projects include studying causal inference in multisensory systems, neural mechanisms of motion perception, and Bayesian models of perception-action cycles.

Joshua Plotkinمشاهده پروفایل

استاد

• Evolutionary Biology
• Ecology
• Mathematical Biology
• +۴ مورد دیگر

Joshua Plotkin is a Professor of Biology and holds the Walter H. and Leonore C. Annenberg Professorship of Natural Sciences at the University of Pennsylvania. He is a core member of the University’s Center for Mathematical Biology, which focuses on interdisciplinary research at the intersection of mathematics, biology, and ecology. His work integrates evolutionary theory, game theory, and computational models to study topics such as cooperation evolution, population dynamics, and the evolution of social norms. Plotkin’s research interests span evolutionary biology and ecology, with a particular emphasis on mathematical approaches to understanding biological systems. He leads initiatives in the Center for Mathematical Biology, including organizing seminar series and mentoring postdoctoral fellows funded by the Simons Foundation. These fellows are typically advised by mentors from both the Department of Mathematics and the Department of Biology, reflecting the Center’s interdisciplinary mission. His recent work explores topics like the evolution of cooperation, the dynamics of social learning, and the impact of network structures on strategic interactions. Plotkin’s publications frequently address game-theoretic models of human and microbial behavior, including studies on indirect reciprocity, reputation systems, and the evolutionary consequences of environmental feedbacks. His research also examines historical constraints on biological systems, such as the evolution of color naming in human cultures. Plotkin advises graduate students in the Department of Biology and collaborates with researchers across disciplines. He is actively involved in initiatives to model the spread of diseases, such as the multi-strain analysis of the SARS-CoV-2 pandemic, and has contributed to theoretical frameworks explaining evolutionary rescue and the role of phenotypic plasticity in adaptation. No scientific awards are explicitly mentioned in the provided texts, but his contributions to mathematical biology and evolutionary theory are widely recognized. The Center for Mathematical Biology, under his leadership, serves as a hub for collaborative research and training, fostering innovation in quantitative biological sciences.

Alice Tonioloمشاهده پروفایل

مدرس

• Artificial Intelligence
• Argumentation Theory
• Human-AI Interaction
• +۷ مورد دیگر

Dr. Alice Toniolo is a Lecturer in Computer Science at the University of St Andrews' School of Computer Science. Her research focuses on artificial intelligence, argumentation theory, human-AI interaction, and multi-agent systems. She leads projects exploring deliberation dialogues, planning frameworks, and collaborative intelligence analysis tools like CISpaces.org. Her work bridges formal models with real-world applications in education technology, healthcare, and spatial cognition. Research interests include argumentation schemes, provenance tracking, and normative systems in AI. She supervises PhD students in topics like diverse planning and AR-based learning. Toniolo has published over 40 peer-reviewed works, with recent contributions in CHI, IEEE Transactions, and AI journals. Her projects are funded by grants from the Scottish Funding Council and the Carnegie Trust. She collaborates internationally on tools for collaborative analysis, AR learning environments, and explainable AI systems. Notable contributions include the VocabulARy AR vocabulary tool, Deb8 dialogue analysis software, and the AIF-EL ontology for argument interchange. Current work emphasizes mitigating algorithmic loafing in AI teams and adaptive guidance in AR educational systems. She actively participates in initiatives like the UN Sustainable Development Goals through AI-driven solutions. Key Projects : Argumentation-Based Training (2020), Don't Make a Wave (2017), CISpaces Toolkit Tools : CISpaces.org, Deb8, Write Reason, AIF-EL ontology Grants : Scottish Funding Council, Carnegie Trust Labs/Teams: Core contributor to the CISpaces collaboration platform and the School's AI research group. Engages in interdisciplinary work across computer science, philosophy, and education.

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.