Professor Janet B. Pierrehumbert is a leading academic in computational linguistics and natural language processing, holding the position of Professor of Language Modelling at the Oxford e-Research Centre, University of Oxford. She is also a Senior Research Fellow at Trinity College and affiliated with the Faculty of Linguistics, Philology and Phonetics. Her work bridges interdisciplinary research in phonology, sociolinguistics, and computational models of language dynamics. Education: PhD in Linguistics from MIT (1980), A.B. in Linguistics from Harvard University (1975). Research Interests: Focuses on computational linguistics, dialect variation, language dynamics, and the societal impacts of NLP. Her group develops algorithms for analyzing social media discourse, forecasting trends, and modeling language evolution. Recent work includes studies on dialect fairness in LLMs and semantic shifts in political discourse. Key Projects: EPSRC-funded research on online forum dynamics, the Wordovators project on lexical innovation, and collaborations with institutions like the Oxford Man Institute. Her work emphasizes robust NLP systems and theoretical linguistics. Awards: ISCA Medal (2020), National Academy of Sciences membership (2019), Fellowships from the American Academy of Arts and Sciences and Cognitive Science Society. Grants & Advising: Over £6M in research funding, including EPSRC and Templeton grants. Supervised over 30 PhD students and postdocs, many now leading academics and industry researchers in NLP and linguistics. Labs/Teams: Leads the Pierrehumbert Language Modelling Group, collaborating with the Oxford e-Research Centre and international partners like Stanford and the University of Canterbury.
Sara Stymne is a Senior Lecturer in Computational Linguistics at the Department of Linguistics and Philology, Uppsala University, where she has been working since 2012. She initially joined as a post-doc (2012-2015), then worked as a researcher (2015-2017), and served as an assistant professor (2017-2023) before her current position as Senior Lecturer. Prior to Uppsala, she was a researcher at Linköping University's Department of Computer and Information Science. Dr. Stymne earned her PhD in Computational Linguistics from Linköping University in 2012 with the thesis 'Text Harmonization Strategies for Phrase-Based Statistical Machine Translation,' following a Licentiate degree in Computational Linguistics (2009) and a Master's degree in Cognitive Science (2006), both also from Linköping University. During her doctoral studies, she spent the autumn of 2010 and spring of 2009 at Xerox Research Centre Europe in Grenoble, France. Her primary research interests focus on cross-lingual natural language processing and digital humanities, with particular emphasis on multilingual dependency parsing. Dr. Stymne is passionate about applying computational linguistics to solve research questions in other fields, including language history, literary analysis, and political science. Her earlier work concentrated on machine translation, with specific interests in discourse-aware translation, compound processing, and error analysis. She has made significant contributions to the development of language technology tools for analyzing dialogue, narrative, and stylistic features in literature. Analysis of Dr. Stymne's recent publications reveals a strong focus on cross-lingual and cross-domain natural language processing. Her work spans multiple subfields including dependency parsing across genres and topics, discourse relation analysis in low-resource languages like Egyptian Arabic, direct speech identification in Swedish literature, and causality detection in governmental documents. A notable trend is her application of NLP techniques to digital humanities problems, particularly in analyzing literary texts and historical language change. Her research often involves creating and utilizing specialized datasets for specific linguistic phenomena across multiple languages. Dr. Stymne actively supervises graduate students, having guided numerous master's and bachelor's theses on topics ranging from speech recognition to multilingual parsing and causality detection. She leads or participates in several research projects including 'Fictional prose and language change' (funded by VR, 2021-2023) and 'Enabling climate-resilient development' (funded by Marianne and Marcus Wallenberg Foundation, 2023-2027), demonstrating her commitment to interdisciplinary research with practical applications. Her work has resulted in several notable software resources including uuPronPred for cross-lingual pronoun prediction, uuparser for dependency parsing, and Docent for document-level machine translation. Within the Computational Linguistics and Language Technology group at Uppsala University, Dr. Stymne contributes to multiple research initiatives focused on developing language technology tools for digital humanities applications. Her team works closely with literary scholars and historians to create computational methods for analyzing large corpora of literary texts, particularly focusing on Swedish literature across different historical periods. Her research bridges the gap between theoretical computational linguistics and practical applications in the humanities, creating new methodologies for quantitative analysis of literary and historical texts.
Ben Eischens is an Assistant Professor in the Department of Linguistics at UCLA. His work focuses on the intersection of phonology and phonetics, particularly in San Martín Peras Mixtec, an Otomanguean language spoken in Oaxaca, Mexico and diaspora communities. He holds a Ph.D. from UC Santa Cruz (2022) and collaborates extensively with Indigenous communities on language documentation and description. His research explores tone systems, laryngeal features, speech rate effects, and negative nominal structures, emphasizing empirical fieldwork and community partnerships. Recent work includes studies on vowel reduction, polar question formation, and the phonetic grounding of phonological theories. Eischens has presented at major conferences such as the International Congress of Phonetic Sciences (ICPhS), Annual Meeting on Phonology (AMP), and workshops on Languages of the Americas. His publications appear in journals like the International Journal of American Linguistics and Phonological Data & Analysis. He teaches courses in linguistic theory and field methods at UCLA, maintaining active research partnerships with San Martín Peras Mixtec community members. His work bridges theoretical linguistics with applied documentation, prioritizing Indigenous language revitalization efforts.
Noah Smith is the Amazon Professor of Machine Learning at the University of Washington's Paul G. Allen School of Computer Science & Engineering. He holds adjunct appointments in Linguistics and serves as Senior Director of NLP Research at the Allen Institute for Artificial Intelligence. Previously an Associate Professor at Carnegie Mellon University, Smith earned his Ph.D. from Johns Hopkins University and dual BS/BA degrees from the University of Maryland. His research focuses on developing algorithms for language/music data processing, AI methodology improvement, and human capability augmentation. He co-directs the OLMo open language modeling initiative and leads the NSF/NVIDIA-funded 'Open Multimodal AI Infrastructure to Accelerate Science' project. Research spans machine learning foundations, NLP applications, and computational social science methodologies. Smith's publications demonstrate consistent focus on transformer architectures, language model optimization, and AI ethics. Recent work addresses length extrapolation techniques, self-supervised alignment, domain adaptation, toxicity evaluation, and linguistic transfer learning. ACL Fellow (2020) Amazon Alexa Prize (2017) UW Innovation Award (2016-2018) NSF CAREER Award (2011-2016) 15+ best paper awards at major conferences He has graduated 32 PhD students and mentored 18 postdocs, with 28 alumni in faculty positions globally. Leads the Noah's ARK research group and UW NLP lab, focusing on open-source AI infrastructure development.
Freda Shi is an Assistant Professor at the David R. Cheriton School of Computer Science at the University of Waterloo and a Faculty Member at the Vector Institute, where she holds a Canada CIFAR AI Chair. She joined the University of Waterloo in July 2024 after completing her Ph.D. at the Toyota Technological Institute at Chicago. Educational Background: Ph.D. in Computer Science, Toyota Technological Institute at Chicago (2024), advised by Professors Karen Livescu and Kevin Gimpel Bachelor's degree in Intelligence Science and Technology (Computer Science Track) with a minor in Sociology, Peking University (2018) Dr. Shi's research focuses on computational linguistics and natural language processing, particularly on deeper understandings of natural language and the human language processing mechanism. She is especially interested in learning language through grounding, computational multilingualism, and related machine learning aspects. Her work aims to inform the design of more efficient, effective, safe, and trustworthy NLP systems. She leads the CompLING Lab at the University of Waterloo, which investigates how language models process spatial relationships and acquire linguistic structures through grounded experiences. Her publication record shows a consistent trajectory of high-impact research, with recent work focusing on spatial reasoning in vision-language models, multilingual chain-of-thought capabilities, and grounded language acquisition. She has published in top-tier conferences including ACL, EMNLP, ICLR, and NAACL, with several papers receiving notable recognition including Best Paper Nominee status at multiple venues. Her research bridges theoretical linguistics with practical NLP applications, demonstrating how linguistic insights can improve AI systems. Scientific Recognition: Canada CIFAR AI Chair (2024) Google Ph.D. Fellowship Thesis of Distinction for her doctoral work Multiple Best Paper Nominee awards at major NLP conferences Dr. Shi teaches CS 784: Computational Linguistics and CS 486/686: Introduction to Artificial Intelligence at the University of Waterloo. She actively contributes to the NLP research community through conference participation, program committee service, and collaborative projects. Her research has significant implications for creating more robust, human-like language understanding systems and advancing the field of grounded language learning in artificial intelligence.
Elaine J. Francis is a Professor of English and Linguistics at Purdue University, where she also serves as the Associate Head of the Department of English. She holds affiliate appointments in the Department of Linguistics and the Department of Speech, Language, and Hearing Sciences. At Purdue, she directs the Experimental Linguistics Lab and teaches linguistics courses at both graduate and undergraduate levels. Francis completed her B.A. in Linguistics at the College of William and Mary in 1993, followed by her M.A. (1995) and Ph.D. (1999) in Linguistics at the University of Chicago under the direction of Salikoko Mufwene. Her dissertation examined variation among members of the same lexical category in English using Sadock's Autolexical Grammar framework. Prior to joining Purdue in 2003, she served as an Assistant Professor at the University of Hong Kong from 1999 to 2002, where she collaborated with Stephen Matthews on research concerning syntactic categories and relative clauses in Cantonese. Her research focuses on syntax and its interfaces with semantics, discourse information structure, and language processing in production and comprehension. Francis employs experimental methods to investigate syntactic, semantic, discourse-pragmatic, and cognitive factors underlying the grammar and usage of complex sentence structures. Her specific interests include word order alternations, filler-gap dependencies, resumptive pronouns, relative clauses, grammatical categories, and syntactic alternations. She has published extensively in top linguistics journals including Language and Cognition, Glossa Psycholinguistics, Linguistics, Lingua, Cognitive Linguistics, and the Journal of Psycholinguistic Research. Analyzing her recent publications reveals a consistent focus on experimental approaches to syntactic phenomena. Her work demonstrates a strong interest in gradient acceptability, syntactic priming, cross-linguistic comparison (particularly involving English and Cantonese), and the relationship between grammatical theory and language processing. Her 2022 book Gradient Acceptability and Linguistic Theory represents a major contribution that synthesizes experimental findings with theoretical linguistics frameworks. Francis plays an active role in the broader linguistics community. She regularly teaches short courses at the Linguistic Society of America Linguistic Institutes, serves on the LSA Ethics Committee, and is on the editorial board of Glossa Psycholinguistics. She has also edited several books and special journal issues, including Mismatch: Form-Function Incongruity and the Architecture of Grammar (2003) and Polymorphous Linguistics: Jim McCawley's Legacy (2005). As an educator and administrator, Francis has supervised numerous graduate students and currently serves as Associate Head of the Department of English at Purdue. Her Experimental Linguistics Lab provides research opportunities for students interested in the intersection of theoretical syntax and experimental methodology. While she recently announced she will not be accepting new graduate students for the 2025-2026 cycle, her established mentorship record demonstrates her commitment to training the next generation of linguists. The Experimental Linguistics Lab, which she directs, serves as a hub for research combining theoretical linguistics with experimental methods. Her collaborative work extends across disciplines, including collaborations with researchers in speech-language pathology, cognitive science, and computational linguistics, reflecting the interdisciplinary nature of modern linguistic research.
Kyle Johnson is a Professor and Curriculum Committee Chair in the Department of Linguistics at the University of Massachusetts at Amherst, located in Integrative Learning Center N440. He holds a BA in Psychology from the University of California-Irvine (1981) and a PhD in Linguistics from MIT (1985). His research focuses on formal syntactic theory, particularly the interaction between syntax and prosody/semantics, with attention to real-time sentence processing evidence. He has taught at multiple institutions including UC-Irvine, UCLA, University of Wisconsin-Madison, and McGill University before joining UMass. Current courses include LING 301 (Introduction to Syntax) and LING 601 (Transformational Grammar). His work emphasizes syntactic derivations, linearization, ellipsis phenomena (gapping, stripping), and the empirical adequacy of formal syntactic theories. Selected recent contributions include analyses of Principle B derivation, prosodic spell-out mechanisms, and theta-role theory. He maintains an active publication schedule with over three decades of work in top journals like Linguistic Inquiry and Nordlyd , addressing topics ranging from adjunct islands to multidominant movement theories. His research combines theoretical innovation with experimental plausibility, often challenging traditional assumptions in generative syntax. Teaching responsibilities include both undergraduate and graduate syntax courses, with supplementary materials like Transformational Grammar lecture notes and a comprehensive bib file for researchers. Office hours are scheduled via his online calendar with a preference for in-person meetings.
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.
Mark Baker is a Professor of Linguistics and Cognitive Science at Rutgers University. His work focuses on comparative syntax, linguistic universals, semantic roles, and the study of Amerindian and African languages. He holds the title of Distinguished Professor and maintains an office in Scott Hall. As of 2021, his office hours were by appointment due to sabbatical leave. His research interests include syntactic diversity, its limits, and patterns across languages. Notable themes in his work involve logophoric pronouns, switch-reference mechanisms, and the relationship between case systems and agreement structures. He has contributed to discussions on language typology, formal syntax, and the cognitive foundations of linguistic structures. Baker teaches advanced syntax courses and has published extensively on topics ranging from Panoan languages to theoretical models in generative grammar. His recent articles explore syntactic variation, morphosyntactic alignment, and interdisciplinary connections between linguistics and cognitive science. He serves on editorial boards and has reviewed for journals such as Journal of East Asian Linguistics and Yearbook of Corpus Linguistics and Pragmatics . His work bridges formal linguistic theory with empirical cross-linguistic analysis, emphasizing the interplay between universal grammatical principles and lineage-specific traits.
Mohit Iyyer is an Associate Professor in Computer Science at the University of Maryland, College Park (UMD), affiliated with the Manning College of Information and Computer Sciences (CICS) and the CLIP lab. Previously, he held roles at UMass Amherst, AI2, and completed his PhD at the University of Maryland. His research focuses on advancing NLP and machine learning, particularly in long-context LLMs, factuality evaluation, and human-LLM collaboration. Education : PhD, Computer Science, University of Maryland, College Park (2017) MS, Computer Science, University of Maryland, College Park (2014) BS, Computer Science, Washington University (2012) Research Interests : His work emphasizes improving instruction-following in LLMs, evaluating long-form text, detecting AI-generated content, and enhancing LLM robustness. He actively explores multilingual LLMs, literary translation, and creative writing support through AI. Key Contributions : Developed influential frameworks like ELMo and QuAC, and introduced benchmarks such as BEARCUBS and BooookScore. His work on watermarking (PostMark) and adversarial attacks (OverThink) addresses security and reliability in AI systems. Awards & Recognition : NSF CAREER Award (2021) Samsung AI Researcher of the Year (2022) Best Paper Awards at NAACL 2018 and CCS 2023 Advising & Grants : Advises a dynamic research group and has received grants for projects like multilingual QA and long-context LLM evaluation. Collaborates with industry partners like AI2 and DeepMind. Labs & Teams : Leads the CLIP lab at UMD, focusing on foundational NLP research and applications in literature, dialogue systems, and computational storytelling.
Maj Schian Nielsen is a Senior Research Librarian at the University Library of the University of Agder. Her work focuses on multilingualism, crosslinguistic awareness, and German language pedagogy. She is affiliated with the research groups 'Media and Communication Studies' and 'Multilingualism in Society and Education (MUSE).' University of Agder Research Groups: Media and Communication Studies, MUSE Her research explores how multilingual awareness can enhance grammar instruction in German third-language (L3) teacher education programs across Denmark and Norway. Recent publications analyze curriculum structures, educational materials, and the integration of generative AI tools like ChatGPT in multilingual education contexts. Scientific output trends reveal a focus on: Cross-linguistic pedagogy L3 German acquisition Grammar teaching methodologies AI applications in language learning Teacher training for multilingual classrooms Systemic Functional Linguistics (SFL) frameworks She has not been publicly recognized with scientific awards listed in the available data.
Gregory Ward is Professor of Linguistics, Gender & Sexuality Studies, and Philosophy at Northwestern University. His work bridges pragmatic theory, information structure, and intonational meaning, with a focus on reference/anaphora. He has taught courses like Pragmatics (LING 372) and Language & Gender (GSS 234), receiving the E. LeRoy Hall Award for Excellence in Teaching (2012). BA in Comparative Literature and Linguistics (1978, UC Berkeley) PhD in Linguistics (1985, University of Pennsylvania) His research explores how contextual meaning shapes linguistic structures, including demonstratives, implicatures, and noncanonical word order. He has contributed to experimental pragmatics, syntax-discourse interactions, and the semantics-pragmatics boundary, co-authoring key works like Information Status and Noncanonical Word Order in English (1998). Recent publications analyze deferred reference, event anaphora, and functional compositionality. These works span subfields such as pragmatic theory, syntactic variation, and discourse processing, reflecting his interdisciplinary approach. Scientific awards include: E. LeRoy Hall Award for Excellence in Teaching (2012) He served as Secretary-Treasurer of the Linguistic Society of America (2004-2007), co-PI on NIH and NSF grants, and Fellow at the Center for Advanced Study in the Behavioral Sciences (2004-05).
Tianyi Zhang is a Tenure-Track Assistant Professor in the Department of Computer Science at Purdue University, part of the College of Science. He leads the Human-Centered Software Systems Lab, focusing on AI-driven systems that synergize human expertise with machine intelligence to enhance programming productivity and software reliability. Prior to Purdue, he was a Postdoctoral Fellow at Harvard University under Dr. Elena Glassman and earned his Ph.D. from UCLA (2019) and B.Sc. from Huazhong University of Science and Technology (2013). Education: Ph.D. in Computer Science, University of California, Los Angeles (2019) Bachelor's in Computer Science, Huazhong University of Science and Technology (2013) Research Interests: His work spans Software Engineering, Human-Computer Interaction, and AI. Key areas include program synthesis, interactive debugging tools, autonomous driving system testing, and mitigating biases in AI models. He develops systems like Interpretable Program Synthesis and SQLucid to bridge human and machine intelligence. Recent Trends in Publications: Recent work emphasizes human-in-the-loop AI, including mixed-initiative systems for data wrangling (Dango), interactive program repair, and bias analysis in text representations (STILE). He also explores challenges in autonomous driving testing and LLM-based code generation errors. Awards & Grants: NSF Career Award (2024) Amazon Research Award Showalter Trust Research Award for pre-diabetes research $1.5M NSF grant for software supply chain security Best Paper Honorable Mentions at CHI and VAHC Advising & Teams: Supervises 12+ PhD/Master's students and 30+ research interns. Notable advisees include Bonan Kou (API misuse studies) and Yuan Tian (text-to-SQL systems). Collaborates with Harvard Medical School on healthcare data analysis. Labs & Initiatives: Directs Purdue's Human-Centered Software Systems Lab. Co-founded the Societal Impact Fellows program. Active in open-source projects like Examplore for API usage visualization and JShrink for Java debloating.
Mark Steedman is Professor of Cognitive Science at the University of Edinburgh's School of Informatics, with adjunct appointment at University of Pennsylvania. His research spans computational linguistics, AI, and cognitive science, focusing on Combinatory Categorial Grammar (CCG) and its applications. His research examines: Combinatory Categorial Grammar parsing and semantics Language model capabilities and limitations Cross-linguistic semantic inference Brain modeling of language processing Recent publications analyze hallucination sources in large language models, cross-linguistic entailment graphs, and brain-computer parallels in structure-building. He develops computational models integrating symbolic and distributional approaches to semantics. Honors include ACL Lifetime Achievement Award (2018) and George E. Davis Medal (2001). He serves on editorial boards of major linguistics journals and has authored influential books including 'The Syntactic Process' and 'Taking Scope'.
Prof. Dr. Pia Knoeferle is a leading academic at Humboldt-Universität zu Berlin , where she serves as a Professor in the Department of German Language and Linguistics . She is a principal investigator in the Collaborative Research Center (CRC 1412) focused on register phenomena and leads the Reaction Time, Eye-tracking, and EEG Laboratories . Her research spans psycholinguistics , cognitive neuroscience , and computational modeling of language , with a central interest in how real-time language comprehension interacts with social context , formality-register congruence , and morphosyntactic processing . Appointments: Professor at Humboldt-Universität (2024), CRC 1412 member Methodologies: Eye-tracking, EEG, Visual World Paradigm, ERP Her work investigates lifespan language processing (children, adults, older adults) through contextual cue integration , including emotional , spatial , and social context such as gender cues , eye gaze , and facial expressions . Key questions include: How do pragmatic and contextual factors modulate lexical and grammatical processing ? What representations underlie register sensitivity in spoken and written comprehension ? Recent articles (2022-2024) focus on register congruence effects in German sentence processing, age-related differences in formality-register anticipation , and interactions between register and morphosyntactic knowledge . Using eye-tracking and visual world paradigms , her team examines incremental integration of socially-situated context with verb-argument relations and grammatical constraints . Findings suggest subtle late-stage register effects and interference between pragmatic and syntactic processing . Her lab collaborates with researchers like Katja Maquate , Valentina Nicole Pescuma , and Camilo Ronderos , contributing to the Frame text of the Second Phase Proposal for CRC 1412 (2020) and subsequent reviews in Frontiers in Psychology (2023). The work emphasizes complementary methods to model register variability across languages , modalities , and cultural contexts .
