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F. Sayako EarleView profile

Associate Professor

• Speech Perception
• Memory Consolidation
• Language-Based Learning Disorders
• +5 more

F. Sayako Earle is an Associate Professor in the Department of Communication Sciences and Disorders at the University of Delaware's College of Health Sciences. Her research focuses on the interplay between declarative and procedural memory systems in speech perception, memory consolidation, and language-based learning disorders in adults and adolescents. Ph.D., University of Connecticut, 2016 M.A., University of Virginia, 2008 B.A., Kenyon College, 2003 Dr. Earle investigates how sleep and wakeful rest influence the consolidation of speech-motor and speech-perceptual learning, particularly in individuals with developmental language disorders and dyslexia. Her work integrates behavioral methods with sleep polysomnography, actigraphy, and neuroimaging (fMRI, ERP) to map the time course of speech sound representation. Current trends in her publications highlight applications of memory theory to reading acquisition, the role of chronobiology in perceptual learning, and neurocognitive mechanisms of non-native speech adaptation. Scientific awards include: Early Career Contributions in Research Award (ASHA, 2020) Conference Fellow (ASHA/NIDCD co-sponsored, 2016) New Century Doctoral Scholarship (ASH Foundation, 2013) She has secured multiple NIH and institutional grants as principal investigator, including an R21DC016391 on perceptual learning and memory consolidation. As head of the MAPS Lab (Memory and Perception of Speech), her team explores time-of-day effects on perceptual category learning and speech-motor generalization across talkers using polysomnography and web-based platforms.

Jordan R. GreenView profile

Professor

• Speech Production Disorders
• Oromotor Skill Development
• Quantification of Speech Motor Performance
• +6 more

Jordan R. Green, PhD, CCC-SLP, FASHA, is the Matina Souretis Horner Professor in Rehabilitation Sciences and Chief Scientific Advisor at the MGH Institute of Health Professions. He leads the Speech and Feeding Disorders Lab, focusing on speech motor control, neurodegenerative diseases, and AI-driven speech analysis. His roles include strategic research planning and mentoring. Education: PhD in Speech and Hearing Sciences from the University of Washington, Seattle. Notable awards include the Willard R. Zemlin Award and ASHA’s highest honor, the Honors of the Association (2024). Research interests span ALS biomarkers, autism speech patterns, and AI applications in speech disorders. Collaborations include Harvard, MIT, and industry partners like Google. His work has generated over $50M in research funding and 20+ NIH grants. Recent articles highlight innovations in personalized ASR systems, ALS progression tracking, and speech biomarkers for Alzheimer’s. His lab’s 4D tongue motion imaging and AI tools aim to revolutionize communication access for disordered speech patients. Advising: Mentored over 30 PhD/postdoc trainees. Grants focus on speech analytics, facial transplantation recovery, and ALS monitoring. Lab collaborations extend to Brigham and Women’s Hospital and global institutions like the University of Toronto.

Paul SowmanView profile

Professor

• Neuroscience
• Psychology
• Cognitive Science
• +3 more

Paul Sowman is a Professor and Honorary Professor at Macquarie University's School of Psychological Sciences. His affiliations include the Hearing Research Centre, Perception in Action Research Centre, and Centre for Language Sciences (CLaS). He holds an ORCID ID (0000-0002-3863-6675) and specializes in neuroscience, psychology, and cognitive science. His research focuses on neuroimaging techniques like magnetoencephalography (MEG), auditory perception, and brain mechanisms underlying speech disorders, sensory processing, and psychedelic effects. He leads projects on microdosing psilocybin for depression, interoceptive abilities in aesthetic appreciation, and computational neurosurgery. Key research interests include auditory environments' impact on autism traits, prefrontal cortex roles in learning acoustics, and hallucinogen-induced perceptual disorders. He has been awarded the Henry Guze Award (2018) for hypnosis-related research. His work spans collaborations in Australia, Europe, and beyond, addressing topics like sport-related concussion assessment, neurosurgical innovations, and language processing during natural conversations. Recent projects include MQRC Data Research Centre collaborations and studies on brain activity in rugby players. He has supervised 1 student and contributed over 119 research outputs, including peer-reviewed articles and datasets on auditory surprise decoding and room acoustics learning.

Simon RigoulotView profile

Associate Professor

• Affective Neuroscience
• Neuroscience of Emotion
• Auditory and Visual Perception
• +3 more

Simon Rigoulot is an Associate Professor in the Department of Psychology at Université de Montréal, affiliated with the Faculté des arts et des sciences. He holds dual appointments with the Centre de Recherche sur le Cerveau, la Langue et la Musique (CRBLM) and the BRAMS International Laboratory for Brain, Music and Sound Research. His work focuses on affective neuroscience, particularly the neural mechanisms underlying emotional perception in auditory and visual modalities. He employs EEG, MEG, and psychophysiological techniques to study how cultural background and individual differences influence emotional processing. Recent projects explore links between musical expertise and emotional competence, as well as the impact of regional accents on emotional prosody recognition. Education: PhD in Cognitive Sciences from Université de Lille (2008) Postdoctoral work at McGill University and under Dr. Simone Dalla Bella Director of the COGNAC lab (COGnition, Neuroscience, Affect et Comportement) Research interests include: multimodal emotion integration, neural correlates of vocal and musical emotions, and the development of psychopathologies. He has supervised multiple master's theses on topics like neurostimulation-music mood effects and cultural influences on prosody perception. Key grants include leadership in the CRBLM strategic research group (2017-2023) and collaborations with Quebec Bio-imaging Network projects. His lab actively seeks participants for studies exploring social neuroscience and emotion processing.

Leah DingView profile

Assistant Professor

• Trustworthy Machine Learning
• Cybersecurity
• Adversarial Attacks
• +2 more

Leah Ding is an Assistant Professor in the Department of Computer Science at American University. Her research focuses on trustworthy machine learning with applications in cybersecurity and scientific data analytics. Her expertise spans: Adversarial attacks on multimedia systems Deepfake detection techniques Secure AI for climate science applications Robust machine learning models Her publications demonstrate significant contributions to audio security, deepfake detection, and AI applications in atmospheric science. Recent work shows increasing focus on adversarial robustness in AI systems. Awards include: IEEE Computational Intelligence Society Outstanding Paper Award

Ian StevensonView profile

Associate Professor

• Computational Neuroscience
• Statistical Analysis of Neural Data
• Neural Dynamics
• +5 more

Ian Stevenson is an Associate Professor in the Department of Psychological Sciences at the University of Connecticut. He earned his Ph.D. from Northwestern University in 2011 and leads the UConn Statistical Neuroscience Lab. His affiliations include the Behavioral Neuroscience Program, Department of Biomedical Engineering, and CT Institute for the Brain and Cognitive Sciences. Stevenson's research bridges computational neuroscience and neural data analysis with two primary focus areas: Developing computational tools to model neural dynamics, interactions, and adaptation mechanisms Linking neural activity patterns to perception, behavior, and learning processes His recent publications demonstrate strong trends in Bayesian neural decoding methods and synaptic plasticity modeling, with applications spanning auditory processing, neural connectivity inference, and educational neuroscience. Computational approaches dominate his research, particularly for analyzing high-dimensional neural datasets. He maintains active collaborations through shared data repositories and tools for neural data analysis. His lab is currently seeking undergraduate and graduate students interested in computational neuroscience research.

• Classical Philology
• Latin Language
• Latin Stylistics
• +3 more

Anni Polding (born March 26, 1998) is a Junior Research Fellow in Classical Philology at the Institute of Foreign Languages and Cultures, Faculty of Arts and Humanities, University of Tartu, a position held since August 29, 2022. She concurrently pursues doctoral studies under Janika Päll, analyzing neohumanist ideas and Latin stylistics in historical student corpora from Imperial Tartu University. Her academic foundation includes: Master's Degree in Classical Philology (2022), University of Tartu, thesis: A Comparative Analysis of Karl Morgenstern's Inauguration Speech and the Pedagogical-Philological Seminar's Students' Exercises Bachelor's Degree in English Language and Literature with Classical Philology minor (2020), University of Tartu Polding's research bridges philological analysis with cultural history, emphasizing Latin linguistic evolution during neohumanist educational reforms. Her work investigates pedagogical methodologies in 19th-century Tartu academic settings and explores underrepresented themes like childhood representations in antiquity, revealing intersections between linguistic practice and socio-educational frameworks. Her publications demonstrate methodological rigor in archival reconstruction and literary analysis, with the 2025 study tracing institutional memory formation in Tartu's Philological Seminar archives and the 2021 collaborative work decoding childhood symbolism in ancient texts through material culture lenses. Her recognition includes: Estonian Research Council's 2nd Prize for University Student Research in Humanities and Arts (2022) As an early-career researcher, Polding focuses on completing her doctoral dissertation while contributing to the Institute's archival projects. No formal advising roles or external grants are documented, though her doctoral work involves corpus analysis of historical student exercises from Tartu's imperial-era academic institutions.

Jie CaoView profile

Assistant Professor

• Natural Language Processing
• Machine Learning
• Dialogue Systems
• +4 more

Dr. Jie Cao is an Assistant Professor at the School of Computer Science, University of Oklahoma, affiliated with the Gallogly College of Engineering and the Data Science and Analytics Institute. He holds a PhD from the University of Utah, and MS/BS degrees from Huazhong University of Science and Technology. His research focuses on Natural Language Processing (NLP), Machine Learning (ML), dialogue systems, structured prediction, and trustworthy AI applications in education and healthcare. Prior to OU, he was a postdoctoral researcher at the NSF AI Institute for Student-AI Teaming (iSAT) at the University of Colorado Boulder, and worked as a research scientist at WeChat AI, Amazon, and in software engineering at Alibaba and Baidu. Education: PhD in Computer Science, University of Utah MS in Computer Science, Huazhong University of Science and Technology BS in Information Security, Huazhong University of Science and Technology Research Interests: Dr. Cao’s work spans multi-party/multimodal dialogue analysis, LLM alignment, efficient structured prediction, and robust AI deployment. Notable projects include AI partners for collaborative classrooms, discourse analysis in psychotherapy, and database workload characterization. His research bridges foundational NLP/ML advancements with interdisciplinary applications in education, healthcare, and material science. Key Achievements: Recipient of the iSAT Trainee Grant (2023) and Alternative Textbook Grant (2025) Lead developer of MatterChat, a multi-modal LLM for material science Published over 20 peer-reviewed papers in top venues like ACL, COLING, and IEEE TVCG Co-instructor for NLP courses integrating LLM and agentic AI materials Labs & Teams: Leads the OUNLP lab at OU, focusing on advancing dialogue systems and trustworthy AI solutions.

Alexander GenevskyView profile

Associate Professor

• Consumer Neuroscience
• Neuroeconomics
• Affective Neuroscience
• +7 more

Alexander Genevsky is an Associate Professor in the Department of Marketing Management at the Rotterdam School of Management (RSM), Erasmus University Rotterdam. His interdisciplinary research bridges psychology, neuroscience, and marketing to understand the emotional and cognitive mechanisms underlying consumer decisions. Institution: Erasmus University Rotterdam School: Rotterdam School of Management (RSM) Department: Department of Marketing Management Academic Rank: Associate Professor Dr. Genevsky holds a Ph.D. in Psychology from Stanford University, with a focus on decision-making and affective neuroscience, and a bachelor’s degree in Computer Science from Binghamton University. His research employs behavioral experimentation, neuroimaging (e.g., fMRI), and market-level data analysis to explore how emotions shape preferences and choices. He is particularly known for his work on neuroforecasting —using brain activity to predict aggregate market behaviors such as crowdfunding success and video engagement. His recent publications reveal a consistent trajectory in consumer neuroscience and neuroeconomics , with a focus on decoding affective responses, modeling emotional integration over time, and applying neural insights to real-world marketing challenges. Articles like "When brain beats behavior" and "Neural affective mechanisms predict market-level microlending" highlight his pioneering role in demonstrating that neural data can outperform traditional self-reports in forecasting consumer behavior. Dr. Genevsky’s work has been widely covered in international media, including features on brain scans predicting crowdfunding success and the psychological underpinnings of fast fashion consumption. He has collaborated extensively with researchers from Stanford University and the University of Michigan. Scientific Focus: Consumer Neuroscience, Affective Neuroscience, Decision-Making, Neuroforecasting Media Impact: Coverage in Medical Xpress, Morningstar, and documentaries on fast fashion Collaborators: Brian Knutson (Stanford), Carolyn Yoon (Michigan), Maarten Boksem (RSM) He teaches courses in marketing management and leads research that integrates computational modeling with neuroscience to scale lab findings to market-level predictions. There is no indication of advisory roles for students in the provided text, nor any mention of formal scientific awards.

Barry DevereuxView profile

Senior Lecturer

• Machine Learning
• Natural Language Processing
• Cognitive Science
• +5 more

Dr. Barry Devereux is a Senior Lecturer at Queen's University Belfast's School of Electronics, Electrical Engineering and Computer Science, part of the Faculty of Engineering and Physical Sciences. His research focuses on interdisciplinary applications of machine learning, natural language processing (NLP), cognitive science, and healthcare analytics. He leads projects like the AIDE_NICYBER2025 initiative, exploring cybersecurity and AI integration. Dr. Devereux teaches the CSC2062 Artificial Intelligence and Machine Learning course and advises four PhD students, including those researching semantic vectors, deep learning in healthcare, and neurosciences ontology. His research interests include model interpretation in NLP, lexical semantics, and applying AI to neuroimaging and bioinformatics. Notable achievements include the British Council/IAESTE Employer Award (2018) and contributions to datasets like SynFinTabs and LAB-KG. He collaborates on projects funded by NIO New Deal, PWC, and Liberty IT, emphasizing AI's role in solving complex societal challenges. Dr. Devereux's work bridges computational models with human cognition, leveraging large-scale data analytics to address issues in healthcare, cybersecurity, and environmental science. His lab, affiliated with the Institute of Electronics, Communications & Information Technology (ECIT), fosters innovation in AI-driven solutions.

• Second Language Acquisition
• Psycholinguistics
• Language Teaching Methodology
• +9 more

Vanda Marijanovic is an Associate Professor in the Department of Language Sciences at the University of Toulouse - Jean Jaurès, where she conducts research and teaching in linguistics and second language acquisition, particularly in the context of French as a Foreign Language (FLE). She is affiliated with the NeuroPsychoLinguistics Laboratory (LNPL, EA 4156) and serves as co-responsible for the master's program ADFLE. Her academic work spans both theoretical and applied aspects of language learning, with a focus on early learners and psycholinguistic mechanisms. Her research interests include L2 literacy development, pronunciation, memory in language acquisition, and contrastive analysis. She explores how learners, especially children and adolescents, process and acquire a second language, drawing on methodologies from psycholinguistics and cognitive didactics. Her work investigates the interplay between oral and written language, phonological representations, and the influence of orthographic input on spoken output. The recent publications highlight a consistent focus on early language learners, particularly Croatian children acquiring French. Themes such as phonological processing, reading aloud, word identification, and the role of memory recur across her work. She frequently collaborates with researchers like M. Billières, C. Solier, and A. Krönert, contributing to international conferences such as EUROSLA, AFLS, and DIDCOG. Her publications appear in peer-reviewed journals, book chapters, and conference proceedings, reflecting active engagement in the academic community. She has contributed to educational outreach, including a MOOC on pronunciation teaching in FLE and public lectures such as 'Le cerveau à deux langues' during the Brain Awareness Week. While no specific awards are listed, her sustained research output and leadership in academic programs underscore her contributions to the field. In terms of teaching, she offers courses in FLE methodology, technology in language classrooms (TICE), cultural anthropology, general linguistics, and critical approaches to language teaching. She also teaches sociolinguistics and linguistic diversity within the broader linguistics curriculum. Her academic journey includes a PhD in Language Sciences from the University of Toulouse-Le Mirail, supported by a strong educational foundation from Sorbonne Nouvelle – Paris 3 and INALCO, where she studied Italian, Arabic, and foreign language pedagogy.

• Speech Intelligibility
• Phonetics and Phonology
• Otorhinolaryngology
• +13 more

Virginie Woisard is an Associate Professor and Hospital Practitioner affiliated with Toulouse - Jean Jaurès University and Toulouse University Hospital. She is a key member of the NeuroPsychoLinguistics Laboratory (LNPL) and holds clinical and research roles in the ENT Department at Larrey Hospital and the Oncorehabilitation Unit at the Toulouse University Cancer Institute. She also directs the University Training Center for Speech Therapy in Toulouse. Her research interests lie at the intersection of speech science, otorhinolaryngology, and oncology rehabilitation. She focuses on speech intelligibility (acoustic, prosodic, and phonetic dimensions), aerodigestive functions (physiology, pathophysiology, and assessment), and cancer care (patient pathways, sequelae, disability, and quality of life), particularly for upper aerodigestive tract cancers. Her methodological approaches include experimental psycholinguistics, automatic speech processing, functional assessments, and clinical trials. Her recent publications (2022–2024) demonstrate a strong trend in developing and validating objective, automated tools for assessing speech and swallowing disorders in cancer patients. These include the Carcinologic Speech Severity Index, pseudo-word intelligibility tests, holistic communication scores, and psychometric guidelines. Her work bridges clinical practice with advanced linguistic and acoustic analysis, particularly in French-speaking populations. Scientific Awards and Recognition: President of the French Society for Swallowing and Dysphagia (2021–2024) Member of the European Society of Swallowing Disorders Research Group (since 2021) Member of the European Union of Phoniatrists’ Dysphagia Committee Editorial board member for multiple journals: Revue de laryngologie Otologie Rhinologie , Folia Phoniatrica et Logopedia , Annals of Otolaryngology , Case Reports in Otolaryngology , and European Archives of Otorhinolaryngology Virginie Woisard actively leads and coordinates major research projects such as PhLES-NID (ANR-funded) and VULCANO (Institut National du Cancer) , and participates in multi-center trials like Voice4PD-MSA and PETAL . She has supervised numerous university diplomas (DU/DIU) in phonology, voice, speech, swallowing, and neurolaryngology since 1998, indicating a long-standing commitment to training and academic mentorship. Her work emphasizes therapeutic education, rehabilitation, and improving patient quality of life. She leads the Parolothèque scientific interest group and is deeply involved in interdisciplinary research connecting linguistics, medicine, and technology. Her lab, the NeuroPsychoLinguistics Laboratory (LNPL), serves as a hub for clinical and experimental research in speech and language disorders.

• Artificial Intelligence
• Deep Learning
• Medical Imaging
• +5 more

Mohamed F. Tolba is a Professor at Ain Shams University in Cairo, Egypt, affiliated with the Department of Scientific Computing within the Faculty of Science. He also maintains a research affiliation with Nile University's NISC Research Center in Giza, Egypt. His career spans over two decades of active research and publication in computational intelligence and its applications. Dr. Tolba's research interests focus on the intersection of artificial intelligence and practical applications, particularly in medical imaging, bioinformatics, and computer vision. His work demonstrates expertise in deep learning architectures, neural networks, and image processing techniques applied to medical diagnostics and biological data analysis. He has made significant contributions to cancer detection systems, EEG signal processing, and hyperspectral image classification. His recent publications (2023-2025) reveal a strong trend toward developing specialized deep learning models for medical applications, including cancer classification, Parkinson's disease diagnosis, and medical image analysis. He frequently collaborates with researchers in Egypt, particularly with Howida A. Shedeed, Hala Mousher Ebeid, and Aboul Ella Hassanien, producing numerous high-impact publications in international conferences and journals. Dr. Tolba has been instrumental in organizing and contributing to major conferences in his field, including the International Conference on Advanced Intelligent Systems and Informatics (AISI), where he has served as editor and contributor to conference proceedings.

Yuxing FangView profile

Researcher

• Neuroscience
• Cognitive Science
• Neuroimaging
• +5 more

Dr. Yuxing Fang is a Researcher at the Centre for Speech, Language, and the Brain (CSLB) within the Department of Psychology at the University of Cambridge. His work explores the neural mechanisms underlying language processing, semantic representation, and brain connectivity. Affiliation: University of Cambridge, Department of Psychology Contact: yf292@cam.ac.uk Research Interests: Dr. Fang’s studies focus on neurocognitive dynamics during speech comprehension, brain hubs in lesion models, and semantic networks in healthy and pathological conditions. His expertise spans neuroimaging, cognitive science, and clinical psychology. Publications: He has contributed to understanding Alzheimer’s disease, semantic dementia, and tool use through multimodal neuroimaging and lesion-behavior mapping. His work integrates neuroscience with computational approaches to language and cognition.

• Neurocognitive Linguistics
• Speech and Language Processing
• Cognitive Neuroscience
• +6 more

Dr. Andrew Thwaites is a Research Fellow at the Centre for Speech, Language, and the Brain (CSLB) within the Department of Psychology, University of Cambridge. His work focuses on the neurocognitive mechanisms underlying speech and language processing, integrating cortical activity analysis with machine learning approaches. Research Interests: Neurocognitive linguistics EEG/MEG signal analysis Cortical entrainment to speech Human-machine speech recognition comparisons Auditory perception Computational modeling of neural systems Publication Trends: Recent work (2025) includes frameworks for cortical processing mapping and unified EEG/MEG models. Earlier studies explore tonotopic representations (2017-2018), cross-domain comparisons between AI and neural networks (2016-2017), and sensory entrainment mechanisms (2015-2023). Keywords span Neuroscience , Machine Learning , and Speech Processing . Labs & Teams: Affiliated with the CSLB, a multidisciplinary research hub focusing on speech, language, and neuroimaging technologies.