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Paul MuentenerView profile

Associate Professor

• Conceptual Development
• Causal Reasoning
• Language and Thought
• +2 more

Paul Muentener is an Associate Professor of Psychology and Director of the Cognitive Development Laboratory at Tufts University, affiliated with the School of Arts and Sciences. He holds a PhD in Psychology from Harvard University, an MA in Psychology from Harvard, and a BA in Psychology from Georgetown University. His research explores the foundations of cognitive development, language-cognition interactions, and early individual differences, with specific focus on infants' causal reasoning, children's causal language, and adults' beliefs about cognitive development. Dr. Muentener's research employs diverse methodologies including eye-tracking, behavioral actions, and language analysis to investigate cognitive processes across development. His work examines how children form representations of the physical world and social environments. Research trends from recent publications show consistent focus on language development in causal reasoning, children's explanatory frameworks, and how failure experiences shape learning mechanisms. Studies span multiple cultural contexts and integrate developmental psychology with linguistic analysis. The Cognitive Development Laboratory supports graduate and undergraduate research, with opportunities for student involvement across multiple research projects exploring cognitive and language development.

Natasha KirkhamView profile

Professor

• Developmental Psychology
• Infant Cognition
• Attention Development
• +3 more

Natasha Kirkham is Professor of Developmental Psychology at the Centre for Brain and Cognitive Development, School of Psychology, Birkbeck, University of London. She serves as President-Elect of the International Congress of Infant Studies. Her research focuses on the development of selective attention in infants and preschool children, examining statistical regularities in perceptual environments, attention mechanisms, executive functions, and impacts of environmental factors like noise on cognitive development. Dr. Kirkham's educational background includes extensive training in developmental psychology and cognitive science. Her research employs diverse methodologies including corneal reflection eye-tracking, habituation/dishabituation paradigms, executive function tasks, and fNIRS recordings. Recent work investigates how home environments influence attentional development. Her publications demonstrate consistent focus on cognitive development across contexts, with recent articles examining pandemic impacts on infant development, noise effects in classrooms, and novel remote research methodologies. Her work integrates developmental psychology with education and neuroscience perspectives. Awards and Honors: President-Elect, International Congress of Infant Studies (ICIS) Dr. Kirkham leads research on child development and cognitive processes, advising graduate students and collaborating internationally. She directs laboratory research at the Centre for Brain and Cognitive Development, coordinating projects on visual statistical learning, attention reliability, and cognitive development across diverse populations.

Simone Lira CalabrichView profile

Lecturer

• Dyslexia
• Reading Development
• Eyewitness Memory
• +4 more

Dr. Simone Lira Calabrich is a Lecturer in Psychology at Bangor University's School of Psychology & Sport Science. She serves as the marketing and recruitment lead for the Department of Psychology and coordinates undergraduate teaching modules including Research Methods I and Scientific Writing and Communication II. Her role also includes overseeing the Practising Oral Presentation Skills (POPS) programme. Her academic background includes a PhD from Bangor University (2019–2023), an MSc in Clinical Linguistics from a European Erasmus Mundus consortium, an MA in Applied Linguistics from King's College London, and a postgraduate certificate in Language Teaching from the University of Nebraska at Omaha. She holds Brazilian and European teaching qualifications from Universidade Federal da Bahia and other institutions. Research interests focus on dyslexia, reading development, eyewitness memory, and literacy interventions. She contributed to the RILL programme, a pandemic-era initiative providing targeted literacy support for Key Stage 2 children. Her work bridges cognitive psychology and education, with publications on remote learning efficacy, audiovisual learning deficits in dyslexia, and memory-cueing techniques. Teaching contributions include creating Unifrog educational resources on memory psychology and delivering webinars on eyewitness reliability. She is a Fellow of the Higher Education Academy, reflecting her commitment to innovative pedagogy and inclusive learning environments. Key professional activities include academic outreach events like Bangor Science Festival stalls and the International Enterprise Educators Conference. Her research has been supported by grants and collaborations with institutions like the University of Groningen and King's College London.

• Cognitive Psychology
• Attentional Bias
• Addiction Research
• +5 more

Dr. Javad Salehi Fadardi is an Honorary Senior Research Fellow at the School of Psychology and Sport Science at Bangor University. His academic career spans over two decades with significant contributions to cognitive psychology, particularly in attentional processes and their applications in clinical settings. His research interests focus on attentional bias , addiction mechanisms , and cognitive interventions for various psychological conditions. His work bridges theoretical cognitive psychology with practical clinical applications, particularly in substance use disorders and anxiety conditions. Analysis of his recent publications (2020-2023) shows a continued focus on attentional processes across multiple domains - from migraine treatment using eye movement exercises to social anxiety interventions and Alzheimer's disease assessment. His research demonstrates a consistent thread connecting cognitive mechanisms with clinical applications across diverse populations. Dr. Salehi Fadardi has collaborated extensively with prominent researchers including W.M. Cox, J. Parkinson, and A.W. Stacy. His work has been supported by research grants, including a Conference Grant from March to July 2009. His methodological expertise spans multiple approaches including EEG, eye tracking, clinical trials, and psychometric assessment. This diverse methodological toolkit allows him to investigate cognitive processes from multiple angles and develop comprehensive intervention approaches.

Evelyne MercureView profile

Senior Lecturer

• Infant Development
• Neurocognition
• Language Acquisition
• +2 more

Evelyne Mercure is Senior Lecturer in Psychology at Goldsmiths, University of London and Independent Research Fellow at UCL. Research focuses on infant neurocognitive development, particularly how language and communicative experience shape brain specialization for social stimuli. Investigates neural mechanisms of voice/face processing using fMRI, ERPs and fNIRS. Specializes in bilingual development studies, including hearing infants with deaf mothers (bimodal bilinguals). Public engagement includes science workshops and school governance. Publications demonstrate consistent focus on neurodevelopmental trajectories of social processing. Recent work emphasizes bilingualism's impact on attention and perceptual development. Earlier foundational research established neural markers for autism risk and typical voice processing development. Research supported by ESRC, Wellcome Trust, UCL, Birkbeck, Canadian Institute of Health Research, and Fond de la recherche en santé du Québec.

Sinead RochaView profile

Lecturer

• Developmental Cognitive Neuroscience
• Music Perception and Production
• Infant Development
• +3 more

Sinead Rocha is a Lecturer in Psychology at Goldsmiths, University of London, where she has been since 2023. She is co-director of the Goldsmiths InfantLab and previously held a Lecturer position at Anglia Ruskin University, directing the ARU Kids Lab. Her research focuses on developmental cognitive neuroscience, particularly the neural and behavioral foundations of rhythm perception, motor development, and language acquisition in infants and young children. She employs advanced methodologies such as EEG, EMG, motion capture, and creative arts interventions to explore how rhythmic abilities relate to broader cognitive and linguistic outcomes. Education: PhD in Developmental Science (2018), Postdoctoral research at Birkbeck College and the University of Cambridge Research Themes: Infant sensorimotor synchronization, rhythm production, arts for maternal and childhood wellbeing, and the intersection of movement and language development Her recent work emphasizes longitudinal studies examining how early rhythmic abilities predict language acquisition and executive function. Key projects include the Cambridge UK BabyRhythm Cohort, which tracks infants' neural and behavioral responses to rhythmic stimuli over time. Publications highlight methodologies like automated movement extraction via platforms like Lookit and deep learning models for analyzing infant behavior. Rocha’s lab, the Goldsmiths InfantLab, explores embodied cognition and the role of rhythmic experience in shaping neural pathways critical for communication. Current research also investigates dance and group drumming interventions for adolescent and maternal wellbeing, bridging neuroscience with creative arts practices.

• Information Retrieval
• Deep Reinforcement Learning
• Conversational Agents
• +2 more

Dr. Grace Hui Yang is Professor and Director of Graduate Studies in the Department of Computer Science at Georgetown University, where she leads the InfoSense (Information Retrieval and Sense-Making) research group. She holds a Ph.D. in Computer Science and Language Technologies from Carnegie Mellon University and has received prestigious recognition including an NSF CAREER Award. Her research focuses on: Deep reinforcement learning for interactive systems Privacy-preserving information retrieval Proactive conversational agents Neural information retrieval architectures Publication analysis reveals three primary research trajectories: (1) Fundamental advances in neural IR infrastructure including segment-based indexing and gaze-incorporated models; (2) Theoretical frameworks for understanding search behavior and task classification; (3) Applied systems for conversational AI and proactive interaction. Recent work increasingly examines LLM integration in information systems. Dr. Yang has led major initiatives including TREC Dynamic Domain tracks and SIGIR workshops on privacy-preserving IR. She currently supervises multiple PhD students working on large language models and conversational agents. Her research has been supported by DARPA and NSF, including a recent $599,974 grant for developing proactive conversational systems.

Lucas C. ParraView profile

Professor

• Machine Learning
• Brain-Computer Interfaces
• Transcranial Electrical Stimulation
• +7 more

Lucas C. Parra is the Harold Shames Professor of Biomedical Engineering at the City College of New York (CCNY) within the City University of New York system. He also serves as an Affiliate Member of Radiology at Memorial Sloan Kettering Cancer Center since 2020. As Ph.D. Program Director, he leads research at the intersection of neuroscience, machine learning, and medical applications, with a focus on understanding brain function and developing novel neurotechnology. Dr. Parra received his Ph.D. in Physics from Ludwig-Maximilians-Universität in 1996, conducting research in machine learning under Gustavo Deco. Prior to joining CCNY, he worked on medical imaging at Siemens Corporate Research (1995-1997) and acoustic signal processing at Sarnoff Corporation (1997-2003). Dr. Parra's research spans multiple cutting-edge areas in neural engineering and AI. His lab investigates how brain activity synchronizes across individuals during shared experiences like watching videos or listening to stories, with applications to education and media. They develop AI models for early breast cancer detection in MRI, reaching state-of-the-art performance. The lab also studies the effects of transcranial electrical stimulation on learning and cognitive processes, exploring potential therapeutic applications. Their work on eye tracking and physiological signals has demonstrated how these measures can predict learning outcomes in online education, potentially transforming how we assess student engagement. Analysis of Dr. Parra's recent publications reveals a strong focus on applying AI to medical imaging, particularly for breast cancer detection in MRI. His lab has developed foundation models trained on tens of thousands of breast MRIs that can detect cancer up to one year in advance. There's also significant work on understanding neural dynamics during naturalistic stimulation, showing how brain, heart, and eye activity synchronize across individuals during shared experiences. The lab maintains a strong neuroscience foundation with ongoing research on transcranial electrical stimulation mechanisms and applications, particularly for motor learning enhancement. National Cancer Institute 5-year grant for machine learning in breast cancer screening NIH R01 grant to study effects of electric brain stimulation on motor learning NSF funding for assessing student attentional engagement during STEM instruction NSF project studying remote video-based instruction Dr. Parra mentors numerous PhD students and postdocs whose work spans neural engineering, medical AI, and cognitive neuroscience. His lab has produced significant findings on intersubject correlation of physiological signals, demonstrating how synchronized eye movements predict learning outcomes. The lab maintains strong collaborations with Memorial Sloan Kettering Cancer Center and other neuroscience research groups, working at the forefront of translating basic neuroscience discoveries into practical applications for healthcare and education.

Christian O'ReillyView profile

Assistant Professor

• Computational Neuroscience
• Neurodevelopmental Disorders
• EEG Analysis
• +4 more

Dr. Christian O'Reilly is an Assistant Professor in the Department of Computer Science and Engineering at the University of South Carolina's Molinaroli College of Engineering and Computing, with joint appointments at the Artificial Intelligence Institute (AIISC) and Carolina Autism and Neurodevelopmental (CAN) Research Center. He holds a B.Ing in Electrical Engineering (2008), M.Sc.A in Biomedical Engineering (2011), and Ph.D. in Biomedical Engineering (2012) from Polytechnique Montréal. His interdisciplinary research focuses on understanding the brain across spatial and temporal scales to address neurodevelopmental disorders like autism. Research approaches include: Analytical techniques: EEG source reconstruction and functional connectivity Computational modeling: Point neurons, morphologically-detailed neurons, neural masses Bayesian model-driven analyses to infer latent biological variables Novel applications of artificial intelligence in neuroscience Development of biologically-inspired neural networks His extensive publication record spans computational neuroscience, neurodevelopmental disorders, EEG analysis methods, and AI applications in neuroscience, with recent work focusing on multimodal data integration, neurobiological modeling, and translational applications for autism spectrum disorder. Dr. O'Reilly leads the Laboratory for Integrative Neuroscience Analysis (LINA) and has received the Penn Criminology Thesis Award for his work on defense counsel impacts on sentencing outcomes.

Melissa BaraltView profile

Professor

• Bilingualism
• Second Language Acquisition
• Bilingual Language Development in Children
• +7 more

Melissa Baralt is a Professor of Applied Psycholinguistics at Florida International University (FIU), specializing in bilingualism, second language acquisition, and task-based language teaching. Her research focuses on bilingual pre-K curricula, language teacher training, and minority student experiences in world language classrooms. Education: Ph.D. and M.S. in Applied Linguistics from Georgetown University (with distinction), B.A. in Spanish Linguistics from the University of Virginia Research Grants: Over $2.4 million in federal, state, and private funding for projects like Háblame Bebé (mobile app for bilingual families), FIU Tabadul (VR language learning), and HSI-HBCU collaborations for inclusive Spanish curricula Her work integrates psycholinguistic theory with practical applications, emphasizing equity in language education for Hispanic and Black communities. She has developed innovative tools like the Háblame Bebé app and Virtual Tabadul VR platform, addressing early language environments and digital pedagogy. Key publications include edited volumes on psycholinguistic approaches to technology and language learning, peer-reviewed articles on bilingual cognitive advantages, task-based teaching efficacy, and systematic reviews of culturally relevant interventions. Her research spans applied linguistics, neonatal cognitive development, and digital language pedagogy. Selected Grants: NEH-funded open-access curriculum on Blackness in Latin America NIH/HRSA-funded projects on bilingual early intervention In-house FIU grants for eye-tracking technology and literacy supports Baralt’s projects often involve interdisciplinary teams, collaborating with health professionals, technologists, and educators to bridge language education gaps in marginalized communities.

Andrea KleinsmithView profile

Associate Professor

• Affective Computing
• Human-Computer Interaction
• Nonverbal Behavior Analysis
• +1 more

Andrea Kleinsmith is Associate Professor in the Department of Information Systems at UMBC's College of Engineering and Information Technology. She directs the Affective Behavior Interaction Lab, researching measurement and modeling of affective nonverbal behavior, particularly body expressions. Her work develops training systems for high-stress professions like first responders, combining affective computing with human-computer interaction principles. Research interests include physiological synchrony analysis, stress detection in training environments, and multimodal emotion recognition. Recent publications focus on stress reflection systems, intelligent mentoring tools, and affective computing applications in paramedic training. Articles frequently incorporate physiological sensing, co-design methods, and multimodal analysis techniques. She holds a PhD in Computer Science from University College London, MSc from University of Aizu, and BA in Psychology from University of Oregon.

Anita KomlodiView profile

Associate Professor

• Human-Computer Interaction
• Human Information Behavior
• Virtual Reality Applications
• +3 more

Dr. Anita Komlodi is an Associate Professor in the Department of Information Systems at the University of Maryland, Baltimore County (UMBC), and serves as the Graduate Program Director for the Human-Centered Computing (HCC) Program within the College of Engineering and Information Technology. Her research focuses on Human-Computer Interaction (HCI) and Human Information Behavior, particularly in cross-cultural contexts and virtual reality environments. Key interests include information literacy, collaborative systems design, and immersive analytics. Dr. Komlodi’s work emphasizes understanding how users interact with information-intensive applications, especially in virtual reality (VR) and augmented reality (AR) contexts. Current projects explore VR’s role in community engagement, cultural variations in information seeking, and designing user-friendly interfaces for multilingual searchers. Her contributions bridge theory and practice, addressing real-world challenges in education, healthcare, and international collaboration. Her publications span over two decades, with recent emphases on VR applications for education and community engagement, usability in collaborative virtual environments, and multilingual search behaviors. Notable projects include the SEEe Immersive Analytics System and Holomentor AR telepresence tool. While no specific awards are listed, her sustained research output reflects significant scholarly impact. As a program director, she oversees the HCC PhD and MS programs, fostering interdisciplinary training in human-centered technologies. Her work integrates insights from computer science, cognitive psychology, and sociology to improve digital systems’ usability and inclusivity.

Yan LiuView profile

Assistant Professor

• Psychometrics
• Statistical Methods
• Cognitive Development
• +6 more

Yan Liu is an Assistant Professor in the Department of Psychology at Carleton University, within the Faculty of Arts and Social Sciences. He holds a Ph.D. from the University of British Columbia. His research focuses on developing innovative psychometric and statistical methods to address issues in well-being and cognitive development, incorporating causal models, machine learning, and natural language processing. He also explores quantitative methods for analyzing response processes, longitudinal data, and complex distributions. Liu has contributed to studies on exercise interventions for older adults, psychological mediators of behavior change, and technology-enhanced educational tools. His work spans interdisciplinary collaborations in health psychology, education, and statistical methodology. Education: Ph.D. (The University of British Columbia). Research Interests: Psychometrics, causal modeling (e.g., propensity score matching), machine learning applications in large-scale assessments, and eye-tracking studies on cognitive processes. He emphasizes translating statistical innovations into practical solutions for mental health and educational challenges. Awards: None explicitly listed. Advising/Grants: Supervised multiple student researchers (e.g., Dunlop, Ruissen, Chen) across health, education, and quantitative methodology projects. His grants likely support interdisciplinary research in aging populations and statistical innovation. Labs/Teams: Engaged with Carleton's Psychology department research groups focusing on quantitative methods and applied health psychology.

• Intelligent Human Machine Interaction
• Ubiquitous Computing in Healthcare
• Human Behavior Recognition
• +3 more

Dr. Xianta Jiang is an Associate Professor in the Department of Computer Science at Memorial University of Newfoundland, Canada. His research focuses on intelligent human-machine interaction, ubiquitous healthcare computing, and bio-signal processing. He leads the UCML (Ubiquitous Computing and Machine Learning) Lab, advancing technologies for human activity sensing, prosthetic control, and surgical training systems. Research Interests: Intelligent Human-Machine Interfaces Medical Applications of Wearable Sensors Eye Tracking & Pupil Dynamics Biomedical Signal Processing Robotics & Grasp Recognition Deep Learning for Healthcare Key Contributions: Developed novel bio-signal processing methods for prosthetic control Advanced eye-tracking techniques for surgical expertise assessment Pioneered multimodal sensor fusion approaches in robotics Labs/Teams: Director of the UCML Lab, collaborating with industry partners in medical robotics and wearable tech. Active in surgical training simulation and rehabilitation engineering projects.

Steffen StaabView profile

Professor

• Artificial Intelligence
• Knowledge Graphs
• Semantic Web Technologies
• +7 more

Steffen Staab is a Professor at the University of Stuttgart's Institute for Artificial Intelligence within the Faculty of Computer Science, Electrical Engineering and Information Technology. His research focuses on knowledge graphs, semantic web technologies, and their applications in interdisciplinary domains like construction, healthcare, and autonomous systems. He actively contributes to advancing AI fairness, geometric relational embeddings, and explainable machine learning. Staab's work bridges theoretical advancements with real-world challenges, such as improving data interoperability in building information modeling and enhancing accessibility for deaf communities via AI-driven captioning. His recent projects span semantic industrial information modeling, blockchain-based fair exchange protocols, and large language models for ontology learning in life sciences. Staab has collaborated on initiatives like the WikiMed-DE dataset for biomedical entity linking and the SEMMA knowledge graph foundation model. He also engages in digital society discussions, addressing ethical implications of AI and societal impacts of web science. Staab's research has been presented at leading conferences including Web Science and ACM SIGKDD. His team develops tools for knowledge graph reasoning, anomaly detection in zero-shot learning, and fair algorithmic decision-making. He is affiliated with the University of Stuttgart's Analytic Computing group and maintains active participation in international academic networks.