Mark Steedman is a Professor in the School of Informatics at the University of Edinburgh, where he conducts research in Artificial Intelligence, Computational Cognitive and Social Science, and Natural Language and Speech Processing. He is affiliated with the Institute for Language, Cognition and Computation (ILCC), the Centre for Speech Technology Research (CSTR), and the Human Communications Research Center (HCRC). He also holds an adjunct professorship in Computer and Information Science at the University of Pennsylvania. His research focuses on Combinatory Categorial Grammar (CCG) , computational linguistics , prosody and intonation , temporal semantics , gesture in communication , and computational music analysis . He has authored foundational books including Surface Structure and Interpretation , The Syntactic Process , and Taking Scope . The recent publications reflect a strong trend toward integrating formal grammatical frameworks like CCG with modern neural and distributional models, particularly in semantic parsing, entailment reasoning, and cognitive modeling. His work bridges symbolic and statistical approaches in NLP, often focusing on robust, wide-coverage parsing and semantic interpretation. Best Paper Award at AACL/IJCNLP 2023 for 'Smoothing Entailment Graphs with Language Models' Best Paper Award at ACL 2023 for 'Extrinsic Evaluation of Machine Translation Metrics' Influential Paper Award 2017 from IFAAMAS for 'Animated Conversation' Mark Steedman has supervised numerous PhD students and collaborated widely across institutions. He leads research in formal grammar applications to cognitive modeling, dialogue, and multimodal communication. His lab contributes to CCG software and semantic parsing tools, and he continues to be actively involved in advancing the integration of symbolic and neural AI.
Maria Gouskova is a Professor of Linguistics at the Department of Linguistics, New York University (NYU). She is affiliated with the College of Arts and Science and holds editorial roles as an Associate Editor of Language and board member of NLLT and Phonology . Her research focuses on phonology, morphology, and lexicon, with a particular emphasis on morphophonological interactions, sublexicons, and phonotactic constraints. She earned her Ph.D. in Linguistics from the University of Massachusetts, Amherst (2003) and a B.A. in English Linguistics and German Language/Literature from Eastern Michigan University (1998). Her work bridges theoretical phonology and experimental methods, addressing questions such as how phonological patterns interact with morphology, the role of sublexical phonotactics in grammatical processes, and the learnability of complex segmental inventories. Recent research includes studies on Russian diminutive affixes, gradient phonological constraints, and the phonological properties of compounds. Her publications span topics like allomorphy, lexical phonology, and the typology of morpheme structure constraints. She frequently collaborates on projects investigating the interplay between syntax, phonology, and morphology, as seen in studies of Russian prepositions and compound stress patterns. Her contributions to phonological theory include advancing models of sublexicon theory and nonlocal constraint induction.
Pranav Anand is a Professor in the Department of Linguistics at the University of California, Santa Cruz (UCSC). He currently serves as the Faculty Director of the Humanities Institute at UCSC since July 2023. His research focuses on the interplay between context, interpretation, and grammatical perspective, particularly in areas like de re/de se contrasts, evaluative predication, and indexical shift. He has contributed to studies on narrative structures, evidential restrictions, and the syntax-semantics interface in sluicing. Dr. Anand has taught a variety of courses including Ling 119: Narratives , Ling 231: Semantics A , and special topics like Invented Languages: From Elvish to Esperanto . His work bridges theoretical linguistics with computational methods, evidenced by collaborations in projects such as the Santa Cruz sluicing dataset and analyses of political discourse in online commentary. His research has been published in journals like Linguistics and Philosophy , Language , and Discourse and Society , with a focus on semantics, pragmatics, and narrative linguistics. He has also contributed to computational linguistics initiatives, including the development of annotated corpora for sentiment analysis and argumentation studies. Dr. Anand's academic contributions span both theoretical exploration and applied computational linguistics, reflecting his interdisciplinary approach to understanding language structure and usage.
Dylan Hadfield-Menell is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology, holding the Bonnie and Marty (1964) Tenenbaum Career Development Professorship. His research focuses on AI alignment and human-AI interaction within MIT's School of Engineering. His research interests center on agent alignment problems in AI systems, particularly examining uncertainty in objective optimization for human-robot teams and societal oversight of machine learning systems. Key areas include the principal-agent alignment problem , assistance games frameworks , and robust preference learning that accounts for hidden contextual factors in reinforcement learning from human feedback. His recent publications reveal strong trends toward multi-agent cooperation , formal contract mechanisms for resolving social dilemmas, and advanced evaluation methodologies for AI safety. The research spans theoretical frameworks like open-universe assistance games while addressing practical challenges in language model alignment and cultural bias assessment. Scientific awards include: AI2050 Early Career Fellowship from Schmidt Futures Berkeley Fellowship NSF Graduate Research Fellowship C.V. Ramamoorthy Distinguished Research Award His work bridges theoretical computer science with real-world AI governance challenges, as demonstrated through MIT's participation in AI policy white papers. Current research directions include developing frameworks for transparent AI systems and addressing fundamental limitations in aligning recommender systems with human values through interdisciplinary synthesis.
Brian Hie is an Assistant Professor of Chemical Engineering at Stanford University , a Dieter Schwarz Foundation Stanford Data Science Faculty Fellow , and an Innovation Investigator at Arc Institute . He leads the Laboratory of Evolutionary Design , focusing on the intersection of biology and machine learning . His prior roles include a Stanford Science Fellow in the Stanford University School of Medicine and a Visiting Researcher at Meta AI . Education: Ph.D. , Electrical Engineering and Computer Science , Massachusetts Institute of Technology (2021) Bachelor’s Degree , Stanford University Research Interests: Brian’s work bridges machine learning and computational biology , with a focus on protein engineering , single-cell RNA sequencing , and viral evolution . His Evolutionary velocity framework predicts protein evolutionary dynamics across timescales, while his Scanorama algorithm enables efficient integration of heterogeneous single-cell datasets. He also develops structure-informed language models for antibody optimization and uncertainty-aware ML for biological discovery. Publication Trends: His recent work (2023) emphasizes structure-based inverse folding for antibody evolution, evolutionary scale modeling , and unsupervised optimization . Earlier studies (2022-2021) cover evolutionary velocity , multi-modal single-cell analysis , and viral escape prediction using natural language analogies. Scientific Awards: Stanford Science Fellow (2021) National Defense Science and Engineering Graduate Fellowship (2019) Advising: He mentors doctoral students including Brandon Ameglio , Garyk Brixi , and Chang M. Yun , with a focus on biological design and computational methods . Labs & Collaborations: His lab collaborates with Bio-X and the Institute for Human-Centered Artificial Intelligence (HAI) , and he maintains affiliations with Sarafan ChEM-H and Stanford Data Science .
Jon Brennan is an Assistant Professor in the Department of Linguistics at the University of Michigan, affiliated with the College of Literature, Science, and the Arts (LSA). His research focuses on neurolinguistics, computational linguistics, and psycholinguistics, particularly investigating how the brain processes language structure and meaning. He leads the Computational Neurolinguistics Lab, which develops neurocomputational models to study language comprehension mechanisms. Brennan received an NSF Grant for collaborative research with Christophe Pallier (Paris) on neurocomputational models of natural language processing. His work integrates EEG, fMRI, and MEG techniques to decode linguistic features in neural signals. Key research areas include syntax-semantics interfaces, multilingual processing, and developmental disorders like dyslexia. Notable contributions include studies on hierarchical syntactic structure, minimal pairs in language models, and neural correlates of theory of mind in children. Brennan collaborates internationally, exemplified by the US-French NSF-CRCNS grant. He has published extensively on topics like neural decoding of grammatical features, LLM internal representations, and bilingual processing mechanisms. Scientific awards include the NSF Collaborative Research in Computational Neuroscience (CRCNS) Grant (2016). His research bridges computational modeling and experimental neuroscience, aiming to reveal how language mechanisms are implemented in neural systems.
Patrick Sturt is a Reader in Psychology at the University of Edinburgh's School of Philosophy, Psychology and Language Sciences. His research focuses on syntactic processing in language comprehension, computational models of incremental parsing, anaphor resolution, and eye movements in reading. With over 100 publications and more than 3,300 citations, he is a recognized expert in psycholinguistics and language processing. Dr. Sturt's research interests span multiple areas of language processing. He investigates how humans comprehend sentences in real-time, with particular focus on syntactic structures, agreement phenomena, and anaphoric reference. His work often employs eye-tracking methodologies to examine the moment-by-moment processing of linguistic information. He has made significant contributions to understanding how readers handle syntactic ambiguities, garden-path sentences, and the role of prediction in language comprehension. His recent publications demonstrate a strong focus on cross-linguistic studies, particularly examining language processing in Mandarin Chinese and Korean. Many of his studies investigate how syntactic and semantic information interact during comprehension, and how linguistic structures like honorifics, classifiers, and non-canonical word orders are processed. His work bridges theoretical linguistics with experimental psycholinguistics, providing empirical evidence for models of sentence processing. Dr. Sturt actively supervises PhD students including Carine Abraham, Wenjia Cai, Chiuchou Hao, Ruomeng Zhu, and Christy Gu. He teaches Psychology of Language 1 and 2 at the MSc level, as well as Data Analysis for Psychology in R for first-year undergraduates. His teaching reflects his research expertise, providing students with both theoretical knowledge and practical analytical skills. Based in Room G29 of the Psychology Building at 7 George Square, Edinburgh, Dr. Sturt maintains regular office hours on Tuesdays from 3-4pm, providing accessibility to students and colleagues. His email address is patrick.sturt@ed.ac.uk.
Jeffrey Heinz is a Professor at Stony Brook University, with a joint appointment in the Department of Linguistics and the Institute for Advanced Computational Science. He holds a Ph.D. from UCLA (2007) and previously served on the faculty at the University of Delaware from 2007–2017. His research bridges theoretical linguistics, computational learning theory, and formal language models, focusing on phonology, linguistic typology, and grammatical inference. He has contributed to influential works on computational phonology and edited volumes on topics like phonological stress and learning theory. Key academic achievements include the 2017 Linguistic Society of America Early Career Award for contributions to computational inference in language. His work emphasizes the intersection of formal models and empirical linguistics, with applications to reduplication, phonological processes, and machine learning benchmarks like MLRegTest. Heinz has co-authored a book on grammatical inference and guest-edited special issues in Machine Learning and Phonology . His research also extends to interdisciplinary applications, such as modeling human-robot interaction and pediatric motor rehabilitation through grammatical inference techniques.
Yoshiko Matsumoto is the Yamato Ichihashi Professor in Japanese History and Civilization and Professor of East Asian Languages and Cultures at Stanford University, with a courtesy appointment in Linguistics. She has been a faculty member at Stanford since 1992, progressing from Assistant Professor to her current distinguished position. Matsumoto also serves as coordinator of the Japanese Language Program and has held significant administrative roles including Chair of the Department of Asian Languages (2003-2005) and Interim Chair of the Department of East Asian Languages and Cultures (2016). Matsumoto earned her Ph.D. in Linguistics from the University of California, Berkeley (1989), following M.A. degrees in Linguistics from UC Berkeley and General and Applied Linguistics from the University of Tsukuba, an M.I.A. in American Studies from the University of Tsukuba, and a B.A. in English Language & Literature from Japan Women's University. Professor Matsumoto's research focuses on linguistic pragmatics from cross-linguistic perspectives, with particular expertise in Japanese language. Her work spans structural and sociocultural aspects of language in use, including noun-modifying clause constructions, honorifics, discourse markers, and the intersection of language with gender and aging. She has pioneered research on conversational narratives of older adults, examining how ordinary framing strategies help individuals navigate difficult experiences. Her current projects explore intergenerational communication through haiku, communicative abilities of people with dementia, and noun-modifying constructions across Eurasian languages. Matsumoto's scholarship consistently bridges theoretical linguistics with practical applications for understanding human communication in diverse social contexts. Matsumoto's recent publications reveal a growing focus on practical applications of linguistic research for social benefit, particularly in intergenerational communication and dementia care. Her work increasingly integrates arts-based approaches, especially haiku poetry, to bridge generational divides and enhance communication with elderly populations. The research shows a consistent trajectory from theoretical linguistic frameworks toward applied, human-centered language studies that address real-world challenges in aging societies, with particular attention to how ordinary language practices help individuals navigate life transitions and difficult experiences. Dean's Award for Distinguished Teaching, School of Humanities and Sciences, Stanford University (2000) Richard E. Guggenhime Faculty Scholar, Stanford University (2000-2003) Violet Andrews Whittier Fellow, Stanford Humanities Center (2019-2020) Faculty Research Fellow, Michelle R. Clayman Institute for Gender Research (2014-2015) Research Fellow, Japan Foundation (2002) Internal Fellow, Stanford Humanities Center (2005-2006) Presidential Fund for Innovation in the Humanities, Stanford University (2009-2011) Professor Matsumoto has mentored numerous students through her teaching in Japanese language and linguistics courses, including specialized offerings on language and aging, points in Japanese grammar, and haiku-based communication. Her research has been supported by prestigious grants from the National Endowment for the Humanities, the Japan Foundation, and Stanford's Presidential Fund for Innovation in the Humanities. She has served on multiple editorial boards including the Journal of Pragmatics since 1992, demonstrating long-standing leadership in her field. Matsumoto has also advised students through individual studies and thesis projects in East Asian Languages and Cultures. Matsumoto leads several collaborative research initiatives including the 'Sharing Conversations' project which examines intergenerational communication through haiku, and research on communicative abilities of people with dementia. Her work often involves interdisciplinary teams spanning linguistics, gerontology, and creative arts, with fieldwork conducted in both Japan and the United States. The 'Noun-Modifying Constructions in Languages of Eurasia' project represents a major international collaboration examining linguistic structures across cultural boundaries. She also directs the 'Language, Old Age and Gender in Japan' project supported by the Stanford University/Japan Foundation, and the 'Difficult Conversations Continue: Memories of the 3.11 Disaster and Bereavement Narratives' project focused on post-disaster communication.
Richard Futrell is an Associate Professor at the University of California, Irvine (UCI), affiliated with the Department of Language Science. He leads the Language Processing Group, focusing on computational models of human and machine language processing. His work bridges information theory, Bayesian cognitive modeling, and natural language processing (NLP) interpretability. University of California, Irvine Department of Language Science Language Processing Group leader His research examines how linguistic structures emerge from cognitive and communicative pressures. Key areas include dependency locality, surprisal theory in sentence processing, and efficiency-driven language evolution. He investigates how memory constraints, predictability, and information density shape syntactic and morphological patterns across languages. Recent publications analyze code-switching efficiency, syntactic priming, ERP component modeling, and agent-based language contact simulations. His work frequently employs Bayesian modeling, neural network analysis, and cross-linguistic corpora to uncover universal principles in language processing. ACL Best Paper Award (2024) Best Paper Award for Computational Modeling of Language (2023) Marr Prize for Best Student Paper (2017) He has developed datasets like SPACER for error repair analysis and contributed to phonotactic learning frameworks. His collaborations span cognitive scientists, computational linguists, and neuroscientists, advancing understanding of language production, comprehension, and structural optimization.
Prof. dr. Enoch O. Aboh is Professor of Linguistics at the University of Amsterdam , Faculty of Humanities, Department of Literature and Linguistics. His office is located at Spuistraat 134, room 647, and he can be contacted at e.o.aboh@uva.nl . Research Interests: Prof. Aboh’s work lies at the intersection of formal syntax, language contact, and learnability. He investigates creole formation , multilingual ecologies , Gbe and Kwa syntax , cartographic approaches to clause structure , and sign language morphosyntax . A recurring theme is the emergence and evolution of grammatical systems under contact, approached through both descriptive fieldwork and formal theoretical modelling. His publications reveal a methodological breadth spanning experimental studies with kindergarteners on statistical learning, computational detection of loanwords, and fine-grained syntactic analyses of serial verb constructions, predication patterns, and determiner systems. This spectrum underscores his commitment to integrating cognitive, typological, and formal perspectives on language. Awards & Recognition: While no specific prizes are listed in the current material, Prof. Aboh’s extensive editorial and collaborative work—evidenced by numerous co-edited volumes and Festschrift contributions—attests to his standing in the field. Students & Grants: Details on PhD advisees or funded projects are not provided in the source text. Labs & Teams: No dedicated laboratory or research group names are mentioned, yet his affiliation with the Amsterdam Center for Language and Communication (ACLC) can be inferred from the institutional context.
Adam Jardine is an Associate Professor in the Department of Linguistics at Rutgers University. He serves as the Graduate Program Director, overseeing the academic affairs of the department's graduate programs. His research focuses on computational and mathematical approaches to phonological theory, with emphases on formal language theory, learnability, and the application of computational models to phonological phenomena. Jardine holds a PhD in Linguistics from the University of Delaware (2016) and has held prior academic positions. His research integrates theoretical linguistics with computational methods, exploring topics such as autosegmental phonology, subregular complexity classes, and phonological learning algorithms. He has organized major conferences including AMP 2024 and workshops on subregular phonology. Jardine leads NSF-funded research on subregular inference of morpho-phonology (Award #2416184), collaborating with Jane Chandlee and Jeffrey Heinz. Key contributions include work on autosegmental representations, computational phonology, and the logical foundations of phonological theory. He has advised numerous PhD students, including Huteng Dai (2024) and Hyunjung Joo (expected 2026), whose research spans phonological learning and computational modeling. Jardine teaches advanced courses such as Phonology Seminar and maintains active participation in interdisciplinary projects linking linguistics to computer science and artificial intelligence. Recent work includes contributions to The Cambridge Handbook of Phonology (2026) and co-organizing sessions on computational learnability at the LSA Annual Meeting. His lab focuses on formal phonology, with ongoing projects exploring the computational boundaries of phonological systems and the application of recursive schemes to linguistic analysis.
Paul Cohen is a Professor of Computer Science at the University of Pittsburgh's School of Computing and Information (SCI), where he also directs the Modeling and Managing Complicated Systems Institute (MOMACS). Previously, he served as the founding Dean of SCI from 2017 to 2020. Before joining Pitt, he was a Program Manager at DARPA (2013–2017), leading initiatives like Big Mechanism and Communicating with Computers. Earlier roles include founding director of the University of Arizona’s School of Information: Science, Technology and Arts (SISTA), and professor at the University of Southern California’s Information Sciences Institute and the University of Massachusetts. Education: PhD in Computer Science and Psychology (Stanford University), MS in Psychology (UCLA), BS in Psychology (UC San Diego). Research Interests: Focuses on artificial intelligence, machine learning, natural language processing, and modeling complex systems like cell signaling pathways and socio-environmental interactions. His work emphasizes explainable AI, human-computer communication, and interdisciplinary problem-solving. Key Contributions: Authored Empirical Methods for Artificial Intelligence and over 200 peer-reviewed articles. His research spans robotics, education technology (e.g., the AnimalWatch tutoring system), and collaborative analysis tools like COLAB. He has won a Telly Award for his video on systemic challenges and a Best Paper award for spatial language learning frameworks. Awards & Recognition: Elected Fellow of the AAAI, recipient of the Telly Award, and winner of the Best Paper Award at the IEEE Conference on Development and Learning. Leadership & Outreach: Advocates for polymathy in education to address global challenges. His work includes developing curricula for complex systems thinking and promoting diversity in STEM through initiatives like AnimalWatch.
Professor Adrian Hilton is a distinguished faculty member at the University of Surrey, serving as Director of the Centre for Vision, Speech and Signal Processing (CVSSP) and Director of the Surrey Institute for People-Centred AI. He is affiliated with the School of Computer Science and Electronic Engineering and leads the Visual Media Research Lab (V-Lab). His research focuses on pioneering next-generation 4D computer vision technologies that enable machines to understand and model dynamic real-world scenes. Key areas include 3D/4D shape capture, computer vision, machine learning, graphics, and animation for applications in sports analysis, film/TV production, virtual reality, and medical imaging. His work bridges the gap between real and computer-generated imagery, with notable contributions in volumetric capture, motion capture, and free-viewpoint video. Hilton's recent publications demonstrate a strong trend toward multimodal integration, particularly combining audio and visual processing for spatial audio applications, while advancing 4D reconstruction techniques for human performance capture. His work increasingly incorporates transformer architectures and neural rendering techniques for improved illumination estimation, shadow modeling, and multi-view consistency. Scientific Awards and Recognition Two EU IST Innovation Prizes Manufacturing Industry Achievement Award Royal Society Industry Fellowship (2008-2011) Royal Society Wolfson Research Merit Award in 4D Vision (2013-2018) Fellow of the Royal Academy of Engineering (FREng) Fellow of the International Association for Pattern Recognition (FIAPR) Fellow of the Institution of Engineering and Technology (FIET) Hilton actively mentors PhD and post-doctoral researchers through his leadership of CVSSP, which has a grant portfolio exceeding £31M and comprises 170 researchers. He has successfully commercialized several technologies, including systems used by the BBC for sports commentary visualization. His research collaborations span major industry partners including BBC, BT, Sony, Framestore, and The Foundry. He co-founded the G3 Games forum and the CVMP Conference on Visual Media Production, demonstrating strong engagement with the creative industries. Current research projects include the S3A Programme Grant in Future Spatial Audio and InnovateUK's ALIVE project for 360 video reconstruction.
Dr. Alan Bale is a Professor in the Department of Classics, Modern Languages and Linguistics at Concordia University, specializing in semantics and syntax-semantics interfaces. His work integrates generative linguistics frameworks with experimental methods to explore grammatical representations of meaning, particularly focusing on number, comparison, and scalar competition across languages like Mi’gmaq, English, and Western Armenian. He investigates how conceptual development interacts with linguistic structures through both linguistic and non-linguistic meaning analysis. His research emphasizes teasing apart contributions from pure semantics, psychological categories, and pragmatic reasoning. Key topics include the mass-count distinction, scalar implicature, and experimental methodologies. He maintains a website and has published extensively on topics ranging from linguistic pragmatics to formal syntax. His CV provides additional details on his academic contributions. Bale's publications (2020–2025) reflect a sustained focus on scalar implicature mechanisms, mass-count distinctions, and cross-linguistic syntax-semantics interactions. Recent work explores how online experimental methods and cognitive load influence pragmatic reasoning. His research bridges formal linguistic theory with empirical cognitive science approaches.
