Mark Y. Liberman is the Christopher H. Browne Distinguished Professor of Linguistics and Trustee Professor at the University of Pennsylvania. He holds a joint appointment in the Department of Linguistics and the Department of Computer and Information Science. His roles include Director of the Linguistic Data Consortium (LDC), Faculty Director of Ware College House, and former Director of the Institute for Research in Cognitive Science. Education: A.B. in Linguistics and Applied Mathematics from Harvard University (1965–1969), M.S. (1972) and Ph.D. (1975) in Linguistics from MIT. Research focuses on corpus-based phonetics, clinical linguistics applications, tonal phonology, formal models for linguistic annotation, and computational linguistics. He explores speech production, prosody, and interdisciplinary topics like language evolution and neurobiology of speech. Recent articles highlight advancements in speech biomarkers for neurodegenerative diseases, autism analysis, and computational linguistics. Awards include Fellowships from the AAAS and Linguistic Society of America. He advises graduate students and leads large-scale language resource initiatives like LDC, contributing to open-access linguistic datasets. Labs/Teams: Linguistic Data Consortium (LDC), Institute for Research in Cognitive Science (IRCS), and collaborations in computational linguistics and neuroscience.
Jennifer Olsen, PhD, is an Assistant Professor of Computer Science at the University of San Diego since 2020. She holds a PhD, MS, and BS in Human-Computer Interaction and Cognitive Science from Carnegie Mellon University, followed by postdoctoral research at the Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland. Her research focuses on the intersection of human-computer interaction, cognition, and education, emphasizing collaborative learning and educational technology design from both learner and instructor perspectives. Education: PhD in Human-Computer Interaction, Carnegie Mellon University MS in Human-Computer Interaction, Carnegie Mellon University BS in Cognitive Science, Carnegie Mellon University Research Interests: Dr. Olsen explores how collaboration supports learning, designs technologies to enhance educational practices, and investigates gaze-based metrics for understanding collaborative problem-solving. Her work spans gamified robotics, AI-driven orchestration systems, and virtual reality applications in vocational training. She emphasizes learner-centered design and the integration of social robots and virtual agents in pedagogical settings. Grants/Advising: While no specific grants or advisees are listed, her prolific publication record indicates active involvement in educational technology research and development. Her work addresses challenges in classroom orchestration, multimodal data analysis, and accessibility in educational robotics. Labs/Teams: Collaborates with interdisciplinary teams focused on educational technology, human-robot interaction, and adaptive learning systems. Her research leverages tools like FROG orchestration graphs and eye-tracking technologies to develop practical classroom solutions.
Hanjie Chen is an Assistant Professor in the Department of Computer Science at Rice University, affiliated with the Ken Kennedy Institute. She holds a Ph.D. from the University of Virginia and a Master's from the University of Science and Technology of China. Her research focuses on Natural Language Processing, Interpretable Machine Learning, and Trustworthy AI, emphasizing model explainability, alignment with human needs, and applications in healthcare, sports, and medicine. She has advised numerous students and led initiatives in AI ethics and education. Education: Ph.D. (Computer Science, UVA 2023), M.Sc. (USTC 2018), B.Sc. (Nanjing University of Aeronautics and Astronautics 2015). Awards include the Outstanding Doctoral Student Award (UVA 2023) and John A. Stankovic Research Award (UVA 2023). She has organized workshops like BlackboxNLP and served on program committees for ACL, NAACL, and EMNLP. Her recent work includes developing benchmarks like SPORTU for multimodal LLMs, evaluating medical question-answering systems, and advancing methods for robust rationale evaluation (RORA). She teaches courses on Natural Language Processing and Trustworthy NLP, emphasizing pedagogical innovation recognized by teaching awards at UVA. Research collaborations include internships at Microsoft Research, IBM, and the Allen Institute for AI. She mentors students in SURF programs and advocates for diversity in tech, serving as a mentor in UVA's CSGSG Council.
Dr. Kaya de Barbaro is an Associate Professor in the Department of Psychology at the University of Texas at Austin (College of Liberal Arts). She holds a Ph.D. from the University of California San Diego. Her research focuses on bridging computer science and developmental/clinical psychology, particularly maternal mental health and infant social-emotional development. She directs the Daily Activity Lab, which uses mobile/wearable sensors and machine learning to analyze real-world interactions, aiming to develop just-in-time interventions for new mothers. Key research areas include maternal-infant dynamics, physiological synchronization, and the impact of environmental chaos on development. She has pioneered methods for analyzing high-density data, such as Granger causality and machine learning algorithms to detect behaviors like crying and holding. Recent work emphasizes leveraging ecological momentary assessment surveys and 24-hour LENA audio recordings to understand proximal mechanisms of development. Dr. de Barbaro teaches Psychology 333D (Introduction to Developmental Psychology) and has developed curricula for both in-person and online formats. She is actively involved in training students through the Eureka program at UT Austin. Her lab collaborates on tools like chatbots for postpartum mental health and has published extensively on sensor-based methodologies in developmental science.
Brenden Lake is an Associate Professor of Computer Science and Psychology at Princeton University, starting Fall 2025. Previously, he was an Associate Professor of Psychology and Data Science at New York University. He is the principal investigator of the lab for Human & Machine Intelligence, which moved from NYU to Princeton in 2025 and is jointly affiliated with the Department of Computer Science and the Department of Psychology. His lab is located in Princeton's Peretsman Scully Hall, rooms 117, 120, and 121. Ph.D., Massachusetts Institute of Technology, 2014 Lake's research focuses on the intersection of human and machine intelligence, specifically examining human cognitive abilities that elude current AI systems. His work centers on few-shot learning of new concepts, learning by generating new goals, learning by asking questions, and learning by producing novel combinations of known components. He employs modern neural network modeling approaches including meta-learning, fine-tuning LLMs, neuro-symbolic modeling, and learning from child headcam videos. His research aims to advance both psychology and computer science by exploring what makes human intelligence unique and using those insights to develop more powerful AI systems. Lake's recent publications demonstrate significant trends in grounded language acquisition through child perspectives, systematic generalization in neural networks, and the intersection of developmental psychology with AI. His work has appeared in top-tier venues including Science (2024) and Nature (2023), with multiple publications exploring how insights from human cognition can improve machine learning systems. His research shows how incorporating human cognitive ingredients can make AI systems more powerful and human-like while addressing longstanding debates about neural network capabilities. Science publication (2024) on Grounded language acquisition through the eyes and ears of a single child Nature publication (2023) on Human-like systematic generalization through a meta-learning neural network Multiple publications covered by major media outlets including New York Times and Washington Post Lake advises Ph.D. students in computer science, psychology, and related fields through his lab. His research is supported by publications in top venues across computer science and cognitive science. He teaches courses including Computational Cognitive Modeling and Advancing AI through Cognitive Science, bridging the theoretical and practical aspects of his research. Lake leads the lab for Human & Machine Intelligence, which studies the ingredients of intelligence in humans and machines. The lab investigates human cognitive abilities that current AI systems cannot replicate, with the dual goal of advancing psychological understanding of human intelligence while developing more capable artificial intelligence systems. Current research focuses on few-shot concept learning, learning through goal generation, and learning by asking questions.
Noah D. Goodman is Associate Professor of Psychology and Computer Science, and Linguistics (by courtesy) at Stanford University. He directs the Computation & Cognition Lab (CoCoLab) at Stanford, where he leads research on computational models of cognition, integrating logic and probability. His work spans cognitive psychology, linguistics, and computer science. Primary Appointment: Psychology Department By Courtesy: Computer Science Department and Linguistics Department Director: Computation & Cognition Lab (CoCoLab) Goodman's research focuses on computational models of cognition, with particular interest in probabilistic approaches to understanding human thought. His work integrates logic and probability to model concepts, categorization, intuitive theories, causal learning and reasoning, social cognition (including reasoning about others' goals, beliefs, and actions), cognitive development (especially acquisition of abstract knowledge), and natural language semantics and pragmatics. He has made significant contributions to the development of probabilistic programming languages as tools for cognitive modeling. His recent publications demonstrate a strong trend toward integrating probabilistic modeling with linguistic theory and social cognition. The articles span computational cognitive science, natural language processing, and artificial intelligence, with a consistent theme of using probabilistic frameworks to understand complex cognitive phenomena. Many papers explore how humans make inferences under uncertainty across different domains. Goodman teaches several courses at Stanford including Language and Thought (Psych 132), Computation and Cognition: the Probabilistic Approach (Psych 204/CS 428), Foundations of Cognition (Psych 205), and Introduction to Cognitive Science. He has also led seminars on topics ranging from natural and artificial intelligence to the science of meditation.
Natalie H. Brito is an Associate Professor of Applied Psychology at New York University (NYU), affiliated with the Steinhardt School of Culture, Education, and Human Development. Her research focuses on how early social and cultural contexts shape neurocognitive development in infants and toddlers, particularly in areas of attention, memory, and socio-emotional skills. Prior to NYU, she completed a postdoctoral fellowship at Columbia University Medical Center and was a Robert Wood Johnson Health and Society Scholar. Dr. Brito’s work bridges developmental psychology, neuroscience, and public policy, emphasizing the need for equitable environments that support healthy child development. She has received prestigious awards such as the APS Rising Star Award and NIH grants, reflecting her impactful contributions to understanding developmental trajectories. Her research also extends to policy implications, such as the effects of paid maternal leave on infant brain function and the role of structural inequities in maternal mental health. Key themes include early life stress, gut microbiome influences, and the neurobiological underpinnings of cognitive development. Dr. Brito has published extensively in journals like Child Development , Developmental Cognitive Neuroscience , and JAMA Psychiatry , with a focus on innovative methodologies (e.g., the OWLET gaze-tracking tool). She teaches courses on developmental psychology and the principles of applied psychology, fostering interdisciplinary approaches to human development. Her scientific accolades include recognition from the International Society of Developmental Psychobiology and the American Psychological Association, underscoring her leadership in advancing developmental science and equity-focused research.
Ceren Budak is an Associate Professor at the University of Michigan School of Information and holds a joint appointment as Associate Professor of Electrical Engineering and Computer Science in the College of Engineering. Her work bridges computer science, statistics, and social sciences through computational social science approaches. Her educational background includes a PhD in Computer Science from the University of California, Santa Barbara (2012) and a Bachelors degree in Computer Science from Bilkent University in Turkey (2007). Prior to joining the University of Michigan faculty, she was a Postdoctoral Researcher at Microsoft Research New York. Professor Budak's research centers on computational social science, with particular emphasis on analyzing large-scale datasets to address questions with social, political, and policy implications. Her work spans several interconnected domains: News Media Production & Consumption (examining bias in news outlets and reader preferences), Social Movements & Media (using social media data to study collective action), Social Networks (understanding information diffusion processes), and Measuring and Promoting the Quality of Online Discussions (developing tools to improve online conversations). She teaches SI 608 (Networks) and SI 618 (Data Manipulation and Analysis) at the School of Information. Her publication record demonstrates consistent contributions to understanding how online information ecosystems operate, with recent work focusing on AI-human collaboration, misinformation dynamics, social movement framing, and the application of computational methods to political communication. Her research shows a clear trajectory from foundational work on social network diffusion to increasingly sophisticated analyses of contemporary information challenges. Among her service activities, she has served as Registration chair for COSN (ACM Conference on Online Social Networks) 2015 and as Program Committee Member for numerous prestigious conferences including WWW, ICWSM, WebSci, AAAI, and others. She has also been involved in organizing the MSR NYC Data Science Seminar Series and instructing the Microsoft Research Data Science Summer School.
Kristin Bernard, Ph.D. (University of Delaware, 2013), is an Associate Professor of Clinical Psychology at Stony Brook University’s Department of Psychology. Her research focuses on the neurobiological consequences of early adversity, parent-child relationships, and the efficacy of early parenting interventions such as Attachment and Biobehavioral Catch-up (ABC). She collaborates with Power of Two, a NYC-based nonprofit, and leads the Developmental Stress and Prevention Lab. Current grants include studies on epigenomics, maternal attachment representation, and community-based ABC interventions. Dr. Bernard’s work bridges clinical and developmental psychology, emphasizing prevention science and translational research. Education: Ph.D. in Clinical Psychology, University of Delaware, 2013. Research Interests: Child maltreatment, neurobiological impacts of early adversity, parenting interventions, psychobiology of attachment, and intervention efficacy. Her lab investigates mechanisms linking caregiving quality to child outcomes, including cortisol regulation, brain development, and behavioral compliance. Lab & Partnerships: Directs the Developmental Stress and Prevention Lab, focusing on high-risk populations. Collaborations include Power of Two for ABC implementation and NYC agencies like the Administration for Children’s Services. Current projects explore neighborhood influences on parenting (Geography of Parenting Study) and neural correlates of parenting behavior (SNAP study). Grants & Funding: Principal Investigator on NIH-funded projects (R03 HD099372, R01 MH119310) and co-PI on Stony Brook’s seed grants. Projects examine accelerated aging biomarkers and intervention effectiveness in community settings. Publications: Over 50 peer-reviewed articles and a co-authored book on ABC interventions. High-impact journals include Psychoneuroendocrinology , Development and Psychopathology , and Child Development .
Dr. Soonja Choi is a Research Professor in the Comparative Psycholinguistics Group at the University of Vienna and Director of the Korean Studies Program at San Diego State University (SDSU), where she holds the title of Professor Emerita of Linguistics. She earned her Ph.D. in Linguistics from the University of Buffalo (1986) and holds advanced degrees from institutions in France and South Korea. Her research focuses on language and thought, semantics, spatial language, and Korean linguistics, with a particular emphasis on cross-linguistic studies of spatial categorization and its cognitive implications. Research Contributions: Her work has explored how language influences spatial perception and cognition, notably through long-term collaborations with the Max Planck Institute for Psycholinguistics and the National Science Foundation. Key contributions include studies on spatial term development in Korean/English children and cross-linguistic comparisons of spatial semantics between German, Korean, and English. Grants & Awards: Secured over $1.4M in grants including a EUR 600,000 Vienna Science Fund grant (2016-2020) and multiple NSF awards. Elected to the Academy of Europe (2019) as an Ordinary Member. Professional Roles: Served as Chair of SDSU's Department of Linguistics and Oriental Languages (1997-2000, 2001-2002), Graduate Advisor (multiple terms), and editorial board member for Language, Interaction, and Acquisition . Active in academic governance and international linguistic organizations.
David W. Jacobs is a Professor in the Department of Computer Science at the University of Maryland, with a joint appointment at the University of Maryland Institute for Advanced Computer Studies (UMIACS). He also served as the interim Director of the University of Maryland Center for Machine Learning starting in 2018. University: University of Maryland School: College of Computer, Mathematical, and Natural Sciences Department: Department of Computer Science Academic Rank: Professor Education: He received his B.A. from Yale University, and M.S. and Ph.D. in Computer Science from MIT. Research Interests: His research primarily focuses on computer vision and machine learning, particularly visual object recognition, lighting variation modeling, 3D reconstruction, perceptual organization, motion understanding, and the integration of vision with graphics and human-computer interaction. A major applied contribution is the development of Leafsnap , an electronic field guide app for plant identification, which has been downloaded over 1.5 million times and used in biodiversity and educational contexts. Publication Trends: His recent scholarly output centers on deep learning, convolutional networks, residual architectures, generative models (especially GANs), and interpretability. His work often bridges theoretical insights with practical applications in vision and AI. Scientific Awards: Honorable Mention, Best Paper Award, CVPR 2000 Best Student Paper Award, UIST 2003 Best Paper Award, Eurographics 2016 2011 Edward O. Wilson Biodiversity Technology Pioneer Award for Leafsnap Teaching and Advising: He has taught advanced courses such as CMSC 422 (Introduction to Machine Learning) and CMSC 828L (Deep Learning). He mentors students through course projects and research, though specific advisees are not listed. He has collaborated with institutions like Columbia University and the Smithsonian on impactful interdisciplinary projects. Labs and Teams: He is affiliated with UMIACS and leads research efforts in vision and learning, contributing to the University of Maryland Center for Machine Learning. His team has developed several mobile applications including Leafsnap, Birdsnap, and Dogsnap, demonstrating a strong focus on real-world deployment of vision technology.
Professor Sara Baker is a Professor of Developmental Psychology and Education at the University of Cambridge's Faculty of Education and a Fellow at Darwin College, where she served as Vice Master from 2021-2024. She is also a 2022 Senior Fellow in the Science of Learning with UNESCO-IBE/IBRO. Her work bridges cognitive science and educational practice, focusing on how children develop executive functions and self-regulation skills. Baker leads the Early Years Library project, co-founded the Research Centre for Play in Education, Development and Learning (PEDAL), and is a founding member of the Global Executive Functions Initiative. Sara Baker specializes in the science of learning, with research aimed at improving children's lives by identifying factors at home and school that support their agency over learning. Her work emphasizes developing executive functions and self-regulation through playful learning approaches. She uses lab-based experiments and collaborates with educators to translate cognitive science research into educational contexts. Her research spans multiple countries including the UK, USA, Mexico, Denmark, Slovakia, South Korea, Ghana, Rwanda, Nigeria, Kenya, South Africa, and more, reflecting a strong commitment to culturally relevant and globally applicable educational practices. Analysis of Professor Baker's recent publications reveals a strong focus on executive functions, self-regulation, and playful learning across diverse cultural contexts. Her work increasingly addresses cultural adaptation of assessment tools and educational practices, particularly for Global Majority countries. There's a clear trajectory from basic cognitive research toward practical applications in educational settings, with growing emphasis on teacher training, culturally relevant assessment, and the role of play in early childhood development. Her interdisciplinary approach connects developmental psychology, educational theory, and practical classroom applications. 2022 Senior Fellow in the Science of Learning with UNESCO-IBE/IBRO Professor Baker leads the doctoral program in the Faculty of Education and serves as an Academic Project Director in the School of Humanities and Social Sciences. She is the Cambridge academic lead on the Close the Gap partnership between Cambridge and Oxford, focused on postgraduate widening participation. Her research has been funded by prestigious organizations including the Newton Trust, Cambridge Humanities Research Grant, Economic and Social Research Council, LEGO Foundation, Nuffield Foundation, and Office for Students/Research England. She actively supervises doctoral students and seeks highly motivated candidates for October 2026 entry whose research aligns with her focus areas. Professor Baker co-founded and is actively involved with the Research Centre for Play in Education, Development and Learning (PEDAL), which investigates how children's play affects their development. She leads the Early Years Library project, a curated collection of evidence-based practices for early years educators, and is a founding member of the Global Executive Functions Initiative. Her work with the Connections international professional learning network focuses on self-regulation, while the Close the Gap project addresses widening participation in postgraduate education between Oxford and Cambridge.
Ekaterina Shutova is an Associate Professor at the Institute for Logic, Language and Computation (ILLC) within the Faculty of Science at the University of Amsterdam. She concurrently holds a Visiting Associate Professor position in the Computer Science Department at Stanford University. She leads the Amsterdam Natural Language Understanding Lab and heads the NLP & Digital Humanities research unit at ILLC. An ELLIS Scholar, she earned her PhD from the University of Cambridge Computer Laboratory and Pembroke College. Her research has been funded by ERC, Innovate UK, British Academy, Leverhulme Trust, Google, Meta, and Deloitte. Her research spans natural language processing and machine learning, with core interests in: Few-shot learning for NLP Multilingual and cross-lingual systems Joint modeling of language and vision Cognitive processing and semantic representation Figurative language interpretation Computational social science applications Her recent publications (2024-2025) predominantly focus on multimodal learning, cultural alignment in AI, metaphor processing, and evaluation methodologies for language models. These works reflect a trend toward integrating cognitive science with multilingual systems and ethical considerations. Awards & Fellowships: ERC Consolidator Grant (2025) ELLIS Scholar Outstanding Paper Award at ACL 2023 Finalist for Outstanding Certification by TMLR Runner-up Best Paper Award at NAACL-HLT 2016 Research Leadership: She directs the Amsterdam Natural Language Understanding Lab, supervising 8 PhD students, 1 MSc student, and 34 alumni. Her projects include an ERC-funded initiative on globally accessible language technology and an AI Democratization grant for hate speech detection.
Henriëtte Hendriks is a Professor in Language Acquisition and Cognition at the University of Cambridge, affiliated with the Faculty of Modern and Medieval Languages and Linguistics and the Theoretical and Applied Linguistics (TAL) department. She leads the Cambridge Processing and Acquisition of Language lab (CAMPAL) and serves as Deputy Director of the Centre for Lifelong Learning and Individualised Cognition (CLIC). Education: Sinology at Leiden University Early Career: Coordinator at Max-Planck Institute for Psycholinguistics (DFG/ESF projects) Her research explores how languages encode concepts (person, time, space, causality) and their impact on language acquisition. Key themes include: Cognitive Linguistics and Language-Cognition Interfaces First and Second Language Acquisition Mechanisms Multilingualism and Cognitive Flexibility Typological Variation in Semantic Structures Deictic Terms and Dynamic Space Representation Recent publications focus on motion event typology across English, German, French, and Uyghur, examining cross-linguistic influences in acquisition processes. Her work combines experimental methods with syntactic analysis to study semantic mapping and grammaticalization in multilingual contexts. Current projects include: Centre for Lifelong Learning and Individualised Cognition (CLIC) - Principal Investigator MEITS Project (Strand 5) - Co-Investigator Cambridge Language Sciences Incubator projects on cognitive pacing and parental linguistic contributions International DeicTeS project on deictic meaning processes Labs & Teams: Cambridge Processing and Acquisition of Language lab (CAMPAL) Collaborations with Zoe Kourtzi, Vicky Leong, and Clare Hughes
Tara McAllister is an Associate Professor and Director of the Doctoral Program in Communicative Sciences and Disorders at New York University’s Steinhardt School. She leads the Biofeedback Intervention Technology for Speech (BITS) Lab , focusing on speech learning mechanisms and biofeedback treatments for speech disorders. Her work emphasizes acoustic and ultrasound biofeedback efficacy in resolving residual speech sound disorders, particularly in children. McAllister directs development of the staRt iOS app, expanding access to biofeedback training. She holds degrees from Harvard, MIT, and Boston University, with clinical expertise in speech-language pathology. Education: A.B./A.M., Linguistics, Harvard University (2003) M.S., Communication Disorders, Boston University (2007) Ph.D., Linguistics, MIT (2009) Research Interests: Speech motor control, perception-production links, bilingual phonological development, and technology-driven interventions. Her NIH-funded studies investigate biofeedback applications for speech disorders and crowdsourcing methodologies for perceptual analysis. Grants & Labs: NIH/NIDCD-funded BITS Lab research staRt app development since 2014 Teaching: Courses include Critical Evaluation of Research and Speech Science Instrumentation , emphasizing evidence-based practices in communication sciences.
