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Toh Kim ChuanView profile

Professor

• Optimization Algorithms
• Semidefinite Programming
• Machine Learning
• +4 more

Professor Toh Kim Chuan is the Leo Tan Professor in Science and serves as Research Director (Technical) at the Institute of Operations Research and Analytics (IORA) within the National University of Singapore (NUS). His primary affiliation is with the Department of Mathematics in the Faculty of Science. His research focuses on matrix optimization problems, large-scale semidefinite programming, fast algorithms for statistical and machine learning tasks, and iterative methods for solving large linear systems in optimization contexts. His recent work emphasizes contributions to nonsmooth nonconvex optimization, distributionally robust optimization, and scalable algorithms for sparse and low-rank optimization problems. Notable methodologies include Bregman proximal algorithms, adaptive sieving techniques, and inexact augmented Lagrangian methods. Professor Toh is also actively involved in developing computational tools like CDOpt for Riemannian optimization and frameworks for low-bit communication in distributed model training. While no specific awards are listed, his extensive publication record (over 150 papers from 2020-2025) demonstrates significant contributions to optimization theory and applications. His research bridges theoretical foundations with practical implementations, addressing challenges in large-scale systems, machine learning, and operations research.

William W. CohenView profile

Visiting Professor

• Machine Learning
• Natural Language Processing
• Neuro-Symbolic Reasoning
• +5 more

William W. Cohen is a Visiting Professor at Carnegie Mellon University's Machine Learning Department and holds a 20%-time appointment at Google. He earned his PhD in Computer Science from Rutgers University in 1990 and has held roles at AT&T Bell Labs, Whizbang Labs, and CMU. His research focuses on machine learning, NLP, neuro-symbolic reasoning, and knowledge representation. Education: B.S. (Duke, 1984), PhD (Rutgers, 1990). Research Interests: Cohen's work spans question answering, NLP tasks, and neuro-symbolic systems. He emphasizes scalable reasoning methods and has contributed to systems like Never-Ending Learning (NELL) and knowledge graph construction. Recent efforts include improving large language model evaluation and retrieval-augmented generation. Awards: AAAI Fellow (2006), 2008 SIGMOD Test of Time Award, 2014 SIGIR Test of Time Award, 2023 Semantic Web Science Ten-Year Award. His work on subtopic retrieval (SIGIR 2003) and data integration (SIGMOD 1998) are foundational. Grants & Advising: Supervised over 50 students, including notable figures like Bhuwan Dhingra (now at Duke) and Zhilin Yang. Active in funding projects related to AI ethics, knowledge graphs, and scalable learning systems. Labs & Teams: Core contributor to the NELL project and collaborator on initiatives like the Knowledge Vault and Open Information Extraction systems. Currently involved in developing robust AI evaluation frameworks and multimodal reasoning systems.

• Developmental Psychology
• Learning Difficulties
• Motivation in Education
• +4 more

Jan Frijters is a Professor in the Department of Child and Youth Studies at Brock University, specializing in Applied Developmental Psychology. His research focuses on learning difficulties, motivation, and quantitative methodologies such as multilevel modeling and structural equation modeling. He collaborates internationally with institutions in China, Norway, and the US, examining genetic and neurobiological factors in reading disorders and intervention efficacy. Key interests include instructional design, developmental trajectories, and the genetic basis of learning challenges. His work spans child, adolescent, and adult populations, with a focus on evidence-based interventions and neuropsychological mechanisms. Research Collaborations: University of Stavanger (Norway), Yale University (US), Georgia State University (US), and institutions in Beijing/Xi’an (China). Key Projects: Genetic studies of reading disabilities, intervention efficacy assessments, and neuroimaging studies of reading processes. Dr. Frijters’ research emphasizes the intersection of motivation and learning, particularly in vulnerable populations. He explores how quantitative models can elucidate developmental processes and intervention outcomes. Recent publications highlight advancements in understanding genetic influences on reading, neurobiological correlates of learning disabilities, and the design of effective educational technologies. Publications: Over 50 peer-reviewed articles in journals such as NeuroImage , Journal of Educational Psychology , and Developmental Science .

Joseph SchmidtView profile

Associate Professor

• Attention-Memory Interaction
• Visual Cognition
• Eye Tracking
• +3 more

Joseph Schmidt is an Associate Professor of Psychology at the University of Central Florida, where he directs the Attention and Memory Laboratory. His research integrates eye-tracking and EEG/ERP methodologies to study attention-memory interactions. Key focus areas include: Neural and behavioral correlates of visual working memory Attention guidance in real-world contexts Visual search errors in medical imaging Oculomotor patterns in neurological disorders His work bridges cognitive psychology and neuroscience, examining how memory representations shape attentional selection. Current projects investigate cognitive expectations in visual search, EEG classification methods, and attentional deficits in clinical populations.

Shonda BernadinView profile

Associate Professor

• Digital Signal Processing
• Speech Recognition
• Artificial Intelligence
• +7 more

Shonda Bernadin, Ph.D., is an Associate Professor in the Department of Electrical & Computer Engineering at the FAMU-FSU College of Engineering, a joint institution of Florida Agricultural and Mechanical University and Florida State University. She holds a B.S. from Florida A&M University, an M.S. from the University of Florida, and a Ph.D. from Florida State University. Her research spans digital signal processing, speech recognition, artificial intelligence, autonomous vehicles, and engineering education. She directs the Speech Processing and Data Analysis Laboratory (SPADAL) and focuses on broadening STEM participation through K-12 outreach, including ACEE summer camps and CROP-ENG programs. Dr. Bernadin’s work emphasizes engineering education innovation, including evidence-centered assessment design and strategies to improve retention and diversity in engineering. She has authored numerous peer-reviewed publications on topics like neural networks, sensor-based defect detection, and cybersecurity threats in AI systems. Her teaching philosophy prioritizes creating inclusive, effective learning environments through engaging methods and technology integration. Professional activities include leadership in engineering education reform and collaborations with organizations such as ASTERIX to enhance STEM workforce diversity. Her lab’s research often intersects technology development with societal impact, addressing challenges in healthcare, autonomous systems, and additive manufacturing quality control.

Xiaojing WangView profile

Associate Professor

• Bayesian modeling
• Latent variable models
• Item response models
• +5 more

Xiaojing Wang is an Associate Professor in the Department of Statistics at the University of Connecticut's College of Liberal Arts and Sciences. She holds multiple affiliations including the Institute for Collaboration on Health, Intervention, and Policy (InCHIP), the Connecticut Institute for the Brain and Cognitive Sciences (IBACS), and the Center for Environmental Sciences and Engineering. Education: Ph.D. in Statistics and M.A. in Economics from Duke University (2012) M.S. in Probability and Mathematical Statistics from Chinese Academy of Sciences (2008) B.S. in Information and Computational Science from Hunan University (2005) Her research focuses on Bayesian methodologies with applications spanning neural decoding, environmental science, psychometrics, and computational biology. She develops novel statistical models for high-dimensional data including latent variable frameworks for neural spiking activity and dynamic item response theory for educational testing. Her work integrates machine learning with traditional statistical inference to address complex problems in interdisciplinary domains. Wang's publications demonstrate a consistent focus on Bayesian computation and model development, with significant contributions to Gaussian process methodologies, subgroup analysis in clinical applications, and ecological forecasting. Recent work shows increasing emphasis on neural data science and computationally intensive methods for high-dimensional inference. Awards and Honors: NSF Faculty Early Career Development (CAREER) Award (2019) Elected Member of International Statistical Institute (ISI) (2017) Jeffrey’s Excellence Prize for Best Methodological Poster (2013) As a statistical consultant, she advises through UConn's Statistical Consulting Services. Her methodological innovations support applications across health interventions, cognitive science, and environmental research through interdisciplinary collaborations.

Francesca FilbeyView profile

Professor

• Addiction Studies
• Neuroimaging
• Reward System Dysfunction
• +5 more

Dr. Francesca Filbey is the Bert Moore Chair in BrainHealth and Professor in the Department of Psychology at the University of Texas at Dallas (UTD), part of the School of Behavioral and Brain Sciences. Her research focuses on addiction mechanisms, particularly using neuroimaging to study reward system dysfunction in substance use and disordered eating. She leads the Neuroimaging of Reward Dynamics Lab (NiRD), exploring genetic, environmental, and neurobiological factors influencing addiction. Education includes a PhD in Experimental Neuropsychology from King’s College London (2002), postdoctoral training at NIH, and prior roles at the University of New Mexico and the Mind Research Network. Her work combines fMRI, genetics, and behavioral studies to address how early life stress, age of substance use onset, and genetic variants like CNR1/FAAH influence reward pathways and addiction vulnerability. Key research areas include cannabis/marijuana neurobiology, tobacco cue reactivity, obesity as a behavioral addiction, and translational neuroscience linking genetics to brain phenotypes. Over 150 peer-reviewed publications and grants totaling millions (e.g., NIH R01s) support her work. Awards include NIDA Travel Awards, RSNA Junior Investigator honors, and international fellowships. Grants: NIH R01s on cannabis genetics, obesity-addiction overlap, and neurodevelopmental impacts of adolescent marijuana use. Lab: NiRD Lab investigates addiction’s neural underpinnings via multimodal brain imaging and behavioral assessments. Professional Roles: American College of Neuropsychopharmacology, Society for Neuroscience, and other leadership positions.

Thomas NaselarisView profile

Associate Professor

• Neuroscience
• Cognitive Neuroscience
• Neuroimaging
• +3 more

Thomas Naselaris is an Associate Professor at the University of Minnesota, specializing in cognitive neuroscience and neuroimaging. His research focuses on decoding brain activity using advanced fMRI techniques and machine learning models, particularly in understanding visual perception, mental imagery, and memory. He leads the development of benchmark datasets like the Natural Scenes Dataset (NSD) and NSD-Imagery, which have become foundational for studying human visual cortex dynamics. His work integrates computational methods with neuroimaging to uncover how the brain represents and processes complex visual information. Notable contributions include reconstructing seen images from fMRI data and analyzing neural representations across different brain regions. He collaborates on large-scale studies involving ultra-high-field MRI and interdisciplinary approaches combining electrophysiology (iEEG) with fMRI. Key research themes include the role of generative models in visual processing, signal-to-noise dynamics in neural responses, and systems consolidation in memory. His grants include collaborative research proposals funded by CRCNS, focusing on evaluating machine learning architectures using benchmark datasets. Ongoing projects aim to bridge cognitive neuroscience with artificial intelligence, emphasizing interpretable models and scalable brain mapping techniques.

• Large language models
• Artificial intelligence
• Cognitive neuroscience
• +7 more

Heming Strømholt Bremnes is a post-doctoral researcher in the Department of Electronic Systems at the Norwegian University of Science and Technology (NTNU), working within the SCRIBE project on Norwegian language technology, dialect variation and evaluation metrics for large language models. He also holds an MA in Scandinavian linguistics from NTNU (2013) and an MSc in Philosophy from the University of Edinburgh (2015), and he completed his NTNU PhD in 2023 with a dissertation on the neural correlates of quantifier verification. Research interests span large language models, artificial intelligence, cognitive neuroscience, neurolinguistics, formal semantics and the philosophy of language. His empirical work combines behavioural experiments and EEG to probe how computational complexity of quantifier meanings is reflected in brain activity and memory load. Public outreach: Bremnes is a frequent panel member on NRK P2’s radio programme Språksnakk , where he answers popular questions on Norwegian grammar, etymology and usage. Recent articles (2022-2024) concentrate on the cognitive and neural foundations of quantifier processing, demonstrating that differences in computational complexity leave detectable signatures in both behaviour and brain activity.

• Developmental Psychology
• Computational Neuroscience
• Emotion Regulation
• +5 more

João Guassi Moreira serves as an Assistant Professor in the Department of Psychology at the University of Wisconsin–Madison, where he directs the Computational Developmental Neuroscience Lab. His research bridges developmental psychology and computational neuroscience to investigate how youth acquire cognitive, affective, and social skills in complex environments. Dr. Moreira's work centers on three interconnected domains: emotion regulation , risky decision-making , and social interactions with specific partners . His lab employs multimodal methodologies including functional MRI, naturalistic text analysis, and ecological momentary assessment, combined with advanced computational techniques from computer science and behavioral economics. This approach reveals hidden structures of the developing mind that traditional methods cannot capture, with emphasis on adolescent and young adult populations exposed to early adversity. Analysis of his recent publications shows a strong trajectory toward computational modeling of developmental phenomena , particularly neural mechanisms underlying emotion regulation typologies, social decision preferences, and adversity impacts. His work increasingly integrates large-scale consortium data (e.g., ABCD study) with novel statistical frameworks to predict developmental outcomes and advance methodological rigor in neuroscience. The Computational Developmental Neuroscience Lab actively collaborates with international consortia and develops open statistical tools to enhance reproducibility in developmental science. Current projects focus on neural representations of social value, inflammatory impacts on emotion circuits, and computational signatures of resilience following early adversity.

• Neuroscience
• Prosocial Behavior
• Empathy
• +3 more

Ye Wu is an Assistant Professor in Neurobiology and Biological Chemistry at UCLA's School of Medicine. Her laboratory investigates the neural mechanisms underlying empathy and prosocial behavior using interdisciplinary approaches including single-cell transcriptomics, in vivo calcium imaging, and machine learning. Her research has three primary objectives: 1) Decoding neural computations in prosocial interactions, 2) Mapping neural circuitry controlling prosocial behaviors, and 3) Examining disruptions in psychiatric models. Recent work identifies cortical regulation of helping behaviors, amygdala-hypothalamus reward circuits, and neural control of affiliative touch. Wu's research integrates molecular and systems neuroscience to bridge gaps between cellular mechanisms and complex social behaviors. Her work on autism spectrum disorder revealed widespread transcriptomic dysregulation across the cerebral cortex. Brain & Behavior Research Foundation Young Investigator Award (2024) UCLA Collaboratory Fellowship (2017) Stanford Graduate Fellowship (2007-2010)

Yetta Kwailing WongView profile

Lecturer

• Perceptual Expertise
• Musical Notation Recognition
• Absolute Pitch
• +3 more

Dr. Yetta Kwailing Wong is a Lecturer in the School of Psychology at the University of Surrey's Faculty of Health and Medical Sciences. She directs the Learning and Perception Lab, which investigates perceptual expertise development and translates cognitive science insights into educational practice. Research examines expert-novice differences in musical notation recognition, absolute pitch acquisition, visual word recognition, and dyslexia interventions. Her work employs behavioral measurements, EEG, fMRI, and eye-tracking to study visual expertise development and its educational applications. Current projects investigate absolute pitch learning in adulthood, neural mechanisms of musical notation recognition, developmental dyslexia, and sight-reading skill acquisition. The lab develops practical tools like the Readers' Adventure mobile application for Chinese children with dyslexia.

• Neuroengineering
• Neural Signal Processing
• Biomedical Signal Analysis
• +3 more

Balbir Singh is a Research Assistant Professor of Biomedical Engineering at Vanderbilt University, affiliated with the School of Engineering. His research focuses on neural signal processing, brain-computer interfaces, and cognitive neuroscience, with an emphasis on understanding neural mechanisms underlying memory, decision-making, and motor control. His work integrates electrophysiological techniques, advanced signal processing algorithms, and computational models to decode brain activity patterns. Key research areas include analyzing local field potentials (LFPs), studying oscillatory brain activity during cognitive tasks, and developing methods to enhance signal quality in biomedical recordings. He has contributed to advancements in EEG/EOG artifact removal, BCI system design, and neuroplasticity studies. His recent work explores how prefrontal cortex activity relates to working memory performance and decision-making processes in both human and primate models. Dr. Singh’s publications highlight interdisciplinary approaches, combining experimental neuroscience with engineering solutions to address challenges in neural decoding and clinical applications. His studies often involve collaborations to translate findings into practical tools for monitoring cognitive states and improving neuroprosthetic systems.

Hao ZhuView profile

Research Fellow

• Fusion Plasma Physics
• Nonlinear Dynamics
• Turbulence Modeling
• +7 more

Hao Zhu is a CFSA Postdoctoral Visiting Fellow at the University of Warwick, supervised by Prof. Sandra Chapman and Prof. Richard Dendy. His research focuses on modeling nonlinear structures in fusion plasmas using Lotka-Volterra equations, bridging plasma physics with computational methodologies. He holds a BSc in Physics from Dalian University of Technology (DLUT, 2006–2010) and is affiliated with the Physics Centre for Fusion, Space and Astrophysics (CFSA). His interdisciplinary work spans plasma turbulence, machine learning accelerators, computer vision, and sustainable materials engineering. Key contributions include FPGAs for Tsetlin Machine training, dense captioning algorithms, and multispectral pedestrian detection systems. Notably, he explores applications of large language-vision models and circular economy strategies in steel production. Publications highlight advancements in edge computing, 3D model retrieval, and bias-mitigation in AI systems. Despite no listed awards, his research demonstrates cross-disciplinary innovation across physics, computer science, and environmental engineering. Labs and collaborations are centered at CFSA, with potential affiliations in computer vision labs given his dual focus areas. No advising roles or grants are explicitly stated in the provided texts.

Jelmer BorstView profile

Associate Professor

• Computational Cognitive Neuroscience
• Neuroimaging Analysis
• Cognitive Modeling
• +5 more

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.