Charles Yang is a Professor of Linguistics and Computer Science at the University of Pennsylvania , where he also directs the Cognitive Science Program. His research integrates computational models with studies of language acquisition, processing, and evolution. Education: Ph.D. in Computer Science, MIT, 2000 Yang's work spans language acquisition , computational linguistics, and the evolution of cognition. He has authored The Price of Linguistic Productivity (2016), which received the Leonard Bloomfield Award from the LSA. Recent publications focus on large language models as cognitive models, the Chinese aspectual system , and statistical approaches to linguistic patterns. His 15 most recent articles (2025-2021) demonstrate a trajectory from computational models of language change to machine translation and multiword expression analysis . Yang has received significant funding from the National Science Foundation and the Guggenheim Foundation . He co-directs the Integrated Language Science and Technology group with John Trueswell and mentors students in linguistics, computer science, and psychology.
Prof. Martin Boeker is a Professor of Medical Informatics at the Technical University of Munich (TUM), affiliated with the TUM School of Medicine and Health. His work focuses on advancing healthcare through AI-driven solutions, interoperability frameworks, and precision medicine initiatives. Key projects include the German Medical Text Corpus (GeMTeX) and the MIRACUM DIFUTURE Alignment Hub. Expertise: Medical Informatics, AI in Healthcare, Federated Learning, Health Data Integration Key Contributions: FHIR-based systems, clinical decision support, patient-centered outcomes research Leadership: Director of the Institute for AI and Informatics in Medicine at TUM Hospital Right of the Isar Research emphasizes bridging clinical practice and data science through projects like modular health crawlers, automated guideline adherence monitoring, and cross-institutional medical NLP solutions. His work spans oncology informatics, rare disease management, and pandemic response data ecosystems. Recent articles highlight innovations in digital twins for precision oncology, federated analysis in oncology, and German-language medical NLP challenges. He collaborates internationally on EHR standardization and healthcare interoperability, contributing to the Medical Informatics Initiative (MII) and pandemic evidence ecosystems. Grants and collaborations involve the German Federal Ministry of Education and Research, European initiatives, and industry partnerships. Educational efforts focus on training future medical informatics professionals through MII competency programs.
Dr. Ewan Jones is Associate Professor in Nineteenth-Century Literature at the University of Cambridge's Faculty of English and a Fellow at Downing College, where he serves as Director of Studies for Part II. He completed his BA, MPhil, and PhD at King's College, Cambridge, and was previously Thole Research Fellow at Trinity Hall. His educational background includes comprehensive training in English literature at Cambridge, with specialization in nineteenth-century literary studies. His academic journey reflects deep engagement with both traditional literary scholarship and innovative computational approaches to textual analysis. Dr. Jones's research concentrates on poetics, embodied cognition, and computational approaches to intellectual history. His work fundamentally questions how we understand 'close reading' not as interpretation but as a specific cognitive practice. He explores how aesthetic phenomenology can illuminate historical reading communities, develops pedagogical routines to de-habituate standard attention modes, and seeks constructive engagement between computational analysis and traditional reading practices. His scholarship bridges literary studies with cognitive science, demonstrating how rhythm, attention, and cognition intersect in literary form. His research trajectory shows increasing focus on attentional practices in reading, with recent work examining group attention practices and rhythm in Victorian poetry. The computational dimension of his work has grown steadily, moving from individual textual analysis to large-scale corpus studies through projects like the Concept Lab. Fulbright Scholar Leverhulme Research Fellowship (2021/22) on 'Close Reading as Attentional Practice' Residential fellow at the Swedish Collegium of Advanced Study Visiting fellow at Ludwig Maximilian Universität Visiting fellow at Pomona College Dr. Jones supervises doctoral work on diverse topics including 'radical conservatism' in Victorian poetry, queer theory and historical poetics, Wagner reception in Anglophone poetry, morphogenetic conceptions of form, and metre and exact measurement. His interdisciplinary collaborations include work with Phyllis Weliver on digitizing Tennyson manuscripts and with Peter de Bolla on computational concept analysis projects that have secured significant research funding. He has developed innovative teaching approaches that integrate cognitive science with literary analysis. He co-founded and participates in the Concept Lab, an interdisciplinary project developing open-source computational resources for historical dataset analysis. This initiative has fostered collaborations across humanities and computer science disciplines, creating tools that bridge traditional literary scholarship with digital methods. His work with the Princeton Prosody Archive demonstrates his commitment to building scholarly infrastructure for rhythm and meter studies.
Professor Eric Atwell is a Professor of Artificial Intelligence for Language at the University of Leeds' School of Computer Science, part of the Faculty of Engineering and Physical Sciences. He holds additional roles as a LITE Fellow at the Leeds Institute for Teaching Excellence (40% part-time), Turing Fellow at the Alan Turing Institute, and member of the Leeds Institute for Data Analytics (LIDA) and Language at Leeds (LATL). His research focuses on AI applications in corpus linguistics, text analytics, and computational analysis of religious and medical texts, with a strong emphasis on Arabic and Islamic studies. He leads the AI4L research group and has supervised over 60 research students and fellows, many of whom have pursued careers in academia, tech, and AI-driven fields. Education: PhD in Corpus Linguistics and Language Learning (University of Leeds, 2008), BA (First Class) in Computing and Linguistics (Lancaster University, 1981) Grants: Includes EPSRC-funded projects like Natural Language Processing with Arabic and Islamic Studies (£337K, 2013-2015) and EDUBOTS chatbots for education (£94K, 2019-2022) Teaching: Leads modules in Data Mining, Text Analytics, and AI across multiple programs, including online Masters and PhDs. Known for innovative teaching approaches and student support, receiving positive feedback for engagement and course design. Research Interests: AI applied to corpus linguistics, Quranic text analysis, Arabic NLP, chatbots for education, and decolonizing curricula. Notable projects include Quranic semantic search tools, Hadith corpus analysis, and AI-driven fact-checking systems. Publications: Over 277 publications, with recent work focusing on Quranic QA systems, Arabic dialect identification, and generative AI applications in education. His research is widely cited, earning recognition from ResearchGate for high readership. Awards: Recognized for his most-read research items on ResearchGate (June 2021). Gallup StrengthsFinder highlights his top traits as Learner, Achiever, Ideation, Intellection, and Maximizer. Labs/Teams: Leads the AI4L research group and collaborates with international institutions including SUSTECH Sudan, King Saud University, and SWJT University (China). Active in research networks like LIDA and LATL.
George H. Chen is an Associate Professor at Carnegie Mellon University , with dual affiliations in the Heinz College of Information Systems and Public Policy and the Machine Learning Department . His research focuses on trustworthy machine learning methods for temporal reasoning , particularly in health applications such as time-to-event prediction (survival analysis) and electronic health records analysis . He has extensive experience in nonparametric methods requiring minimal data assumptions. Educational Background PhD in Electrical Engineering and Computer Science, MIT (2015) SM in Electrical Engineering and Computer Science, MIT (2012) BS in Electrical Engineering and Computer Sciences & Engineering Mathematics and Statistics, UC Berkeley (2010) His work spans survival analysis , deep learning , and time series modeling , with applications in neurological prognostication , medical adherence , and health equity . He has developed self-contained educational resources including a 2024 monograph on deep survival analysis and tutorials at CHIL and SIGMETRICS. His 2025 course 95-865: Unstructured Data Analytics focuses on practical unstructured data analysis techniques. Notable projects include advising the AgriTech startup CoolCrop , which provides cold storage and market forecasts for Indian farmers serving 9,000+ farmers across 7 states. His Google Scholar publications reveal a strong focus on temporal modeling in healthcare, with recent advancements in neural survival analysis and fairness-aware temporal prediction.
Colin J Akerman is Professor of Neuroscience and Group Leader in the Department of Pharmacology at the University of Oxford, concurrently serving as Corange Fellow and Medical Tutor at Corpus Christi College. His research investigates fundamental mechanisms of synaptic circuit formation and plasticity, with direct implications for epilepsy, dementia, and schizophrenia through multidisciplinary approaches integrating electrophysiology, optical imaging, and computational modeling. His primary research interests encompass Synaptic Plasticity, Neural Circuit Formation, and Excitatory-Inhibitory Balance, with specific focus on neuronal progenitor influences on connectivity, chloride dynamics in inhibitory transmission, and learning mechanisms in disease contexts. The lab employs custom-built equipment and molecular tools to probe synaptic function across in vivo , in vitro , and in silico platforms, emphasizing how activity-dependent processes shape neural networks during development and disease. Recent publications (2023-2025) reveal strong thematic convergence on intracellular chloride regulation in sleep-wake cycles, cortical circuit assembly from embryonic progenitors, and innovative optical tools for neural monitoring. This work bridges molecular neuroscience with systems-level understanding of synaptic plasticity, particularly regarding ionic mechanisms in epilepsy and sleep homeostasis. No scientific awards or fellowships are explicitly documented in the source materials. Professor Akerman currently mentors four PhD students (Vourvoukelis, Selfe, Wang, Gemayel) and multiple postdoctoral researchers, having previously trained scientists now leading independent groups in Toronto, Edinburgh, Cape Town, Oxford, and London. His research is funded by the European Research Council, Innovative Medicines Initiative, and Wellcome Trust, supporting investigations into synaptic mechanisms underlying neurological disorders. The Akerman Group, established in 2008, operates as an integrative neuroscience hub within Oxford's Pharmacology Department. The 10-member team combines expertise in patch-clamp electrophysiology, optogenetics, multiphoton imaging, and computational modeling, with current projects spanning neuronal progenitor biology, inhibitory synaptic plasticity, and learning rule implementation in neural networks. The lab emphasizes technical innovation, regularly developing custom instrumentation and molecular tools for neural observation and manipulation.
Emily M. Bender is the Thomas L. and Margo G. Wyckoff Endowed Professor in the Department of Linguistics at the University of Washington. She also holds adjunct appointments in the School of Computer Science and Engineering and the Information School. Her research spans multilingual grammar engineering, computational linguistics, societal impacts of language technology, and sociolinguistic variation. She directs the Computational Linguistics Laboratory (The Treehouse) and leads the CLMS program. Bender is a Fellow of the AAAS (2022) and previously served as Howard and Frances Nostrand Endowed Professor (2019–2022). She has authored influential textbooks on NLP fundamentals and pioneered work on data statements to mitigate bias in NLP systems. Her work integrates linguistic theory with computational methods, emphasizing ethical AI and language documentation. Education: PhD in Linguistics from Stanford University (advisor: Ivan A. Sag), AB in Linguistics from UC Berkeley, with studies at Tohoku University. Past roles include NAACL Executive Board Chair (2016–2017) and current roles in the Association for Computational Linguistics leadership. Her Erdős number is 4. Research focuses on the LinGO Grammar Matrix, automatic grammar inference from interlinear glossed text (AGGREGATION project), and societal implications of NLP technologies like large language models. She co-leads the RAISE initiative and contributes to labs like the Tech Policy Lab and Value Sensitive Design Lab. Over 30 advisees have completed PhD and MS degrees under her mentorship. Teaching includes courses on syntax for NLP, societal impacts of language tech, and computational linguistics. Her 2020 ACL paper on form-meaning distinctions in NLP has been influential in ethical discussions. Current projects include The AI CON (2025) on combating tech hype.
Brita Wårvik is a Professor in the Department of Languages at Åbo Akademi University, affiliated with the Faculty of Humanities, Psychology and Theology. She holds a Doctor of Philosophy (Phil. Dr.) from the same institution, awarded in 2013, and has been actively contributing to linguistic research for decades. Doctor of Philosophy (Phil. Dr.), Åbo Akademi University, 2013 Her research centers on discourse analysis, pragmatics, and historical English linguistics, with a particular focus on Early Modern and Medieval English texts. She investigates discourse interaction, textual structures, and pragmatic conservatism in religious prose, often employing corpus-based and textual analysis methods. Her work bridges micro and macro perspectives in discourse, exploring how semantic units contribute to broader text worlds. Analysis of her recent publications reveals a consistent trajectory in historical discourse studies, with increasing emphasis on corpus linguistics, manuscript variation, and the evolution of discourse markers. Her editorial work, such as the 2024 volume Structures in Discourse , highlights her leadership in synthesizing interdisciplinary perspectives on language use. Scientific Awards: Teacher of the Year (2001) ÅAU Student Union's Rose (2007) Brita Wårvik actively supervises and mentors academic visitors and collaborators, as seen in her hosting of scholars like Carmen Lee and Lieselotte Anderwald. She is the coordinator of the ongoing Project ICLE (International Corpus of Learner English) , securing research continuity and international collaboration. Her involvement in organizing workshops, such as the ICLE+30 FI-subcorpus Workshop in 2024, underscores her role in fostering academic exchange. In terms of research infrastructure, she is deeply embedded in the ICLE network, contributing to the development of learner language corpora. Her activities reflect strong international collaborations, particularly in the fields of historical linguistics and discourse studies, with a visible presence in both research and academic community-building.
Cecilia O. Alm is a Professor in the Department of Psychology within the College of Liberal Arts at Rochester Institute of Technology (RIT), where she serves as the Artificial Intelligence Program Director. She holds multiple leadership roles including Director of the Center for Human-aware AI and Director of the Computational Linguistics and Speech Processing Lab (CLaSP). Her institutional affiliations span the School of Information, Ph.D. Programs in Cognitive Science and Computing and Information Sciences, Department of Computer Science, and MS in Data Science program. Dr. Alm earned her Ph.D. from the University of Illinois at Urbana-Champaign. Her research focuses on human-centered artificial intelligence with particular emphasis on linguistic and multimodal sensing, affective computing, and natural language processing. She investigates how AI systems can better understand and respond to human communication through multimodal dialogue processing, with applications in accessibility, education, and healthcare. Her recent publications demonstrate a strong trend toward developing inclusive AI systems, particularly through projects addressing Deaf community needs (MULTICOLLAB-ASL), subtle emotion recognition (FUSE corpus), and bias mitigation in NLP. The work consistently integrates multimodal data streams (speech, gaze, gesture) to create more responsive human-AI interaction frameworks. Current research directions emphasize diversity in AI education, visual prosody in sign languages, and human-in-the-loop AI development. Dr. Alm leads several significant NSF-funded initiatives including the AWARE-AI program, IRES AI-PROWIL international research experience, and collaborative projects with Gallaudet University focused on Deaf scientist-centered AI research. She has secured over $2.5 million in external funding for her work on human-aware AI systems. She directs the CLaSP lab which provides research opportunities for PhD, MS, and undergraduate students, with graduates employed at major technology companies including Amazon, Apple, Microsoft, and Facebook. The lab focuses on real-world AI applications in accessibility, human-robot interaction, and multimodal communication systems.
Ian Ewart is an Associate Professor at the University of Reading's School of the Built Environment, serving as Head of Construction and Engineering Management and Research Group Lead for Organisation, People and Technology. He chairs the Research Ethics Committee since 2016 and supervises undergraduate/postgraduate dissertations. His academic journey spans engineering and anthropology: DPhil Social and Cultural Anthropology, University of Oxford, St Hugh's College (2007-2012) MSc Material Anthropology and Museum Ethnography, University of Oxford, St Hugh's College (2006-2007) BA (Hons) Archaeology and Anthropology, University of Oxford, Harris Manchester College (2003-2006) Diploma in Management Studies, University of the West of England (1990-1994) BEng (Hons) Mechanical Engineering, Staffordshire University (1983-1987) Ewart's research integrates ethnographic methods with digital technology studies, examining human-technology interactions in construction and domestic settings. His work bridges engineering practice and social anthropology, focusing on skill transmission, sustainable design, and multisensory experiences in virtual environments. Publications from 2025-2013 reveal a dominant trajectory in digital twins for socio-ecological sustainability, VR-based occupant behavior prediction, and HBIM for heritage conservation. The corpus demonstrates consistent cross-disciplinary innovation, merging archaeological reconstructions with healthcare applications while maintaining anthropological rigor. Key recognition: ESRC Future Research Leader fellowship (2013) for Designing Healthy Homes project He supervises PhD candidates like Afolabi Dania (Nigerian sustainable construction) and Joanna Hull (Heritage BIM), leveraging ESRC funding for ethnography-VR health studies. His grants emphasize participatory design and real-world impact assessment in built environments. Leaders the Organisation, People and Technology research group, developing multisensory Roman town reconstructions with sound/smell integration to advance archaeological and architectural experience modeling.
Mohammad T. Alhawary is Professor of Arabic Linguistics and Second Language Acquisition at the University of Michigan's Middle East Studies department within the College of Literature, Science, and the Arts. He serves as Director of both the MA Program in Arabic for Professional Purposes (APP) and the MA Program in Teaching Arabic as a Foreign Language (TAFL). His educational background includes a Ph.D. from Georgetown University (1999). Prior to joining the University of Michigan, he contributed to developing Arabic and Middle Eastern Studies programs at various US institutions. Professor Alhawary's research spans both theoretical and applied Arabic linguistics, with particular focus on second language acquisition processes. His work examines how factors like age, input quality, output practice, and first language transfer affect Arabic language learning. He has made significant contributions to understanding Arabic language pedagogy, curriculum design, proficiency testing, and the application of technology in language learning. His research also extends to bilingualism, multilingualism, language impairment, and Arabic medieval grammatical traditions. His publications reflect a strong trajectory in Arabic linguistics research, with recent works focusing on practical language teaching applications, reading comprehension mechanisms, code-switching patterns in digital communication, and multilingual acquisition processes. The 2023 publication 'Teaching Arabic as a Foreign Language' represents his latest contribution to language pedagogy methodology. 2019 AATA Book Award for 'Arabic Second Language Learning and Effects of Input, Transfer, and Typology' As an academic leader, Professor Alhawary serves as Executive Director of the American Association of Teachers of Arabic and edits both the Journal of Arabic Linguistics Tradition and Al-'Arabiyya journal. He continues to develop empirical research on Arabic second language acquisition to inform teaching practices both in America and globally.
Naoki Yoshinaga is a tenured Associate Professor at the Institute of Industrial Science, The University of Tokyo, with extensive experience in natural language processing and computational linguistics. He has held academic positions since 2008 and currently leads research on pragmatic NLP models and multilingual systems. PhD in Computer Science, The University of Tokyo (2005-2008) MSc in Information Science (2000-2002) BSc in Information Science (1996-2000) His research focuses on mechanistic interpretability in NLP models, multilingual/multimodal NLP , and efficient model design using trie structures and conjunctive features. He also investigates knowledge acquisition from social data and evaluation metrics for language generation . Recent publications include work on neuron empirical gradient analysis (ACL-25), multilingual knowledge representation (EACL-24), and compact embedding methods (CoNLL-24). His research has been funded by multiple grants, including the University of Tokyo Excellent Young Researcher program and JSPS fellowships. Committee Special Award, Association for NLP (2023) JSAI SIG Research Award (2022) Best Interactive Award, DEIM Forum (2019, 2016) He developed widely-adopted NLP tools like pecco (fast classification library), RenTAL (LTAG-to-HPSG grammar converter), and J.DepP (Japanese dependency parser). His lab emphasizes strong equivalence in formalism comparisons and pragmatic model design .
Brendan T. O'Connor is an Associate Professor at the College of Information and Computer Sciences, University of Massachusetts Amherst, where he directs the SLANG Lab and serves as Associate Director of the Computational Social Science Institute. His research bridges statistical machine learning and natural language processing with social science applications, particularly using text data from news and social media to understand societal patterns. His work focuses on developing text analysis methods to answer social science questions in domains like political science and sociolinguistics. Current collaborative projects include combating misinformation, analyzing bias in news coverage, and developing tools for clinical discourse assessment using large language models. O'Connor's publications demonstrate a consistent focus on computational social science, with recent work exploring multilingual analysis, legal discourse patterns, and sociolinguistic variation. His methodological contributions span coreference resolution, event extraction, and argument mining. At UMass, he contributes to multiple research centers including the Computational Social Science Institute, UMass NLP group, and Centers for Data Science and Intelligent Information Retrieval. He teaches graduate seminars in natural language processing and maintains active collaborations across disciplines.
Xuezhe Ma is an Assistant Professor in the Department of Computer Science at the University of Southern California's Viterbi School of Engineering. Previously, he was a Ph.D. student at Carnegie Mellon University's Language Technologies Institute, where he worked under the supervision of Professor Eduard Hovy. His academic journey includes a Master's degree from Shanghai Jiao Tong University's Center for Brain-like Computing and Machine Intelligence and a Bachelor's degree in Computer Science from the same institution. Ph.D. in Computer Science, Carnegie Mellon University (completed ~2020) M.S. in Brain-like Computing, Shanghai Jiao Tong University B.S. in Computer Science, Shanghai Jiao Tong University Dr. Ma's research spans multiple areas at the intersection of Natural Language Processing and Machine Learning, with particular focus on structured prediction, syntactic and semantic parsing, machine translation, language generation, and deep generative models. His recent work has expanded into vision-language models, large language model architectures, and applications across computer vision tasks. His research combines theoretical foundations with practical implementations, as evidenced by his development of tools like NeuroNLP2 and MaxParser. His publication record shows a clear trajectory from foundational NLP work during his PhD (including papers on dependency parsing and sequence labeling) to more recent contributions in generative models and large language systems. The 15 most recent publications reveal a strong focus on addressing fundamental challenges in generative modeling, context handling, and multimodal integration, with applications spanning literary translation, medical imaging, and news diffusion analysis. AI2 Outstanding Intern Award (2018) Dr. Ma has secured research funding supporting his work in generative models and language technologies, with projects focusing on improving the efficiency and capabilities of large language models. His research group at USC is actively working on next-generation language understanding and generation systems, with particular emphasis on context-aware modeling and multimodal integration. He has established collaborations with industry partners including the Allen Institute for AI and has contributed to open-source projects like Texar. At USC, Dr. Ma leads research in the Information Sciences Institute, directing projects on efficient large language model architectures and multimodal reasoning systems. His lab focuses on developing novel approaches to context handling, model efficiency, and multimodal integration, with applications across diverse domains including healthcare, literary analysis, and news media.
Suzanne Stevenson is a Professor in the Department of Computer Science at the University of Toronto, affiliated with the Cognitive Science Research Community (CoRC). She holds a BS in Computer Science and Linguistics from William & Mary and MS/PhD in Computer Science from the University of Maryland. Before joining UofT in 2000, she was faculty at Rutgers University with joint appointments in Computer Science and Cognitive Science. Her research focuses on computational cognitive models of language acquisition and processing, integrating insights from linguistics, psycholinguistics, and machine learning. Key areas include semantic/syntactic learning from text, probabilistic computational models of word learning, and cross-situational learning. Notable awards include the NSERC University Faculty Award (2000) and NSF CAREER Award (1997). Her work bridges computational linguistics and cognitive science, emphasizing multidisciplinary approaches. Recent publications (2014–2018) explore topics like probabilistic perspective models in language production, bilingual word associations, and semantic search algorithms. She advises on computational linguistics and cognitive modeling, with grants supporting investigations into language acquisition dynamics and semantic networks. Active in teaching, she previously offered courses on computational linguistics and the computational lexicon. Her lab’s research themes include child language acquisition, ambiguity resolution, and computational modeling of linguistic phenomena.
