Dr. Lingpeng Kong is an Assistant Professor in the Department of Computer Science at the University of Hong Kong (HKU), affiliated with the School of Computing and Data Science. He obtained his PhD from Carnegie Mellon University in 2017, co-advised by Noah Smith and Chris Dyer. Previously, he was a research scientist at Google DeepMind (2017–2020). His research focuses on natural language processing (NLP), machine learning, and deep learning, particularly in structured prediction and representation learning. He co-directs the HKU NLP Lab. Notable contributions include work on syntactic parsing, neural architecture design, and lifelong learning. He has received an Outstanding Paper Award at EACL 2017. Dr. Kong teaches courses such as Natural Language Processing (COMP3361/COMP7607) and Machine Learning (COMP3314), and has advised multiple research projects at HKU.
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.
Lucia Binotti is a Professor of Spanish and Digital Humanities at the University of North Carolina at Chapel Hill (UNC-CH), with dual affiliations in the Department of Romance Studies and interdisciplinary Digital Humanities initiatives. She serves as Graduate Advisor for Hispanic Linguistics and Director of the Honors Study Abroad Program in Rome. Her work bridges traditional philology with cutting-edge digital scholarship, emphasizing immersive learning experiences through virtual reality (VR) and augmented reality (AR) technologies. Binotti holds a Ph.D. from UC Santa Barbara (1990), an MA (1985), and a BA (1984) in Romance Philology from the Università degli Studi di Pisa. Her research focuses on linguistic identity, cultural heritage preservation, and innovative educational platforms like Amirabilia—the world’s first experiential learning platform. Notable projects include nXimena (healthcare training for Latinx abuse survivors) and Entiendelo (a linguistic/cultural navigation app for Latinx communities). Her scholarly interests span Early Modern Spanish literature, digital humanities applications, and the intersection of technology with social justice. She has directed grants totaling over $500K for projects involving AR/VR integration in education, including collaborations with the UNC Health Science Library and NCSU’s DELTA initiative. Recent awards include the John K. Walsh Award (2010) and Digital Innovation Fellow status (2014). Grants & Leadership: CFE-Lenovo Technology Grants (2018–2019) for AR/VR expansion iLRN 2020 International Co-Chair XR Faculty Learning Community Leader (UNC) Teaching: Courses span from introductory Hispanic literature to advanced topics like medieval intellectual history and digital humanities methodologies. Binotti’s work emphasizes ‘place, presence, and perspective’ in Cyber-Humanities education, particularly addressing marginalized communities through technology-driven solutions.
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.
Kevyn Collins-Thompson is an Associate Professor at the University of Michigan with joint appointments in the School of Information and College of Engineering. As Academic Director of the Master of Applied Data Science program, his research bridges information retrieval, machine learning, and educational technology. Research focuses on: Readability prediction and text difficulty assessment Search as learning frameworks Adaptive educational systems Risk-aware information retrieval Human-centered AI for education Publications demonstrate innovation in educational technology, including gaze-tracking systems for learning optimization and conversational AI for technical reading support. Recent work explores large language models for educational applications.
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.
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.
Julian Jara-Ettinger is an Associate Professor of Psychology and Computer Science at Yale University. He holds a Ph.D. from MIT (2016). His research focuses on understanding the cognitive and computational mechanisms underlying human social behavior, including fairness, linguistic communication, gesture, moral reasoning, and pedagogy. He employs interdisciplinary methods such as computational modeling, eye-tracking, cross-cultural studies, and developmental research to bridge psychology and artificial intelligence. Key research areas include the development of social cognition in children, the integration of theory of mind with communication, and the application of cognitive science principles to build socially intelligent machines. His work emphasizes how humans infer others' knowledge, intentions, and desires, with implications for AI safety and ethical systems design. Publications span topics like epistemic inference, moral judgments, and the computational foundations of social interaction. His lab's research often intersects with evolutionary simulations, neural modeling, and cultural psychology. No scientific awards are explicitly mentioned in the provided text. Collaborations involve cross-disciplinary teams addressing challenges in developmental science, AI ethics, and cognitive robotics. His work has practical applications in educational strategies, social policy, and human-AI collaboration frameworks.
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'.
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.
Prof. Dennis Komm is an Associate Professor at ETH Zurich's Department of Computer Science, leading the group for Algorithms and Didactics. He chairs the Center for Computer Science Education (ABZ) and serves on committees such as the Swiss Maturity Board (Schweizerische Maturitätskommission) and the STEM Commission of the Swiss Academies. Previously, he held roles at RWTH Aachen University (Master's, 2008), ETH Zurich (PhD, 2012), University of Zurich (external lecturer, 2014–2020), and PH Graubünden (including department head and professor of 'Fachdidaktik Informatik'). Education: He completed a Master's in Computer Science at RWTH Aachen (2008), a PhD at ETH Zurich (2012), and studies in Information Technology at Queensland University of Technology (2006). His academic journey includes visiting roles at King's College, Stanford, and Comenius University. He has taught extensively across institutions, emphasizing Python and LOGO-based approaches for beginners. Research focuses on algorithm design, approximation algorithms, reoptimization, and advice complexity in theoretical CS. His work in education explores computational thinking, programming pedagogy (especially for K–12), and interdisciplinary approaches (e.g., robotics in math). Recent trends in his articles highlight advancements in online algorithms, optimization under dynamic conditions, and initiatives to integrate CS into Swiss school curricula sustainably. He actively promotes CS education through platforms like WebTigerPython and collaborates on projects such as CyberQuest and MINTerlink. His outreach includes organizing conferences (e.g., STIU 2025) and workshops on programming and cybersecurity for teachers and students. Despite no listed scientific awards, his contributions to education and theoretical CS are recognized through editorial roles in journals like Informatics in Education and contributions to the TigerJython Group. Grant-related advising includes co-supervising doctoral theses on robotics, USOs, and programming didactics. He advocates for equitable educational opportunities via the Passerelle exam and the Swiss Beaver Competition. His team's work spans teacher training, didactic certifications, and bridging university-school collaborations through initiatives like MINTerlink. Labs and teams: Head of ABZ (ETH's CS education center), collaborator with the Computational Robotics Lab, and part of the TigerJython Group. He also co-organizes the Colloquium on Mathematics, Computer Science, and Education with ETH's Mathematics Department.
Tim Van de Cruys is a Senior Lecturer at the Faculty of Arts, KU Leuven, serving as Head of the Centre for Computational Linguistics (CCL). He maintains significant affiliations with LECTIO (KU Leuven Institute for the Study of the Transmission of Texts, Ideas and Images), Leuven.AI (KU Leuven Institute for Artificial Intelligence), and LILI (KU Leuven Interdisciplinary Language Institute). His work bridges computational linguistics, artificial intelligence, and humanities research with practical applications across multiple disciplines. Dr. Van de Cruys specializes in computational semantics and creative language generation, with particular expertise in applying NLP techniques to historical and classical texts. His research spans multiple domains including: Natural Language Processing for ancient languages (Latin, Ancient Greek) Computational approaches to lexical and compositional semantics Large language models and their applications in humanities research Creative language generation and human-AI collaboration Named entity recognition and disambiguation in historical contexts Non-autoregressive modeling for sequential generation tasks His recent publications demonstrate a strong focus on applying cutting-edge NLP techniques to humanities challenges, particularly in processing ancient languages. He frequently employs transformer models to address named entity recognition, word sense discrimination, and semantic analysis in low-resource language contexts. His work consistently bridges formal linguistic theory with practical computational applications, creating valuable tools for digital humanities scholars. As promotor and co-promotor on numerous research projects extending through 2029, Dr. Van de Cruys supervises PhD students working at the AI-humanities intersection. His current major projects include "Living Corpora" (exploring human-AI collaboration in digital humanities), "Stochastic processes and non-autoregressive models for sequential generation," and "NIKAW" (exploring knowledge networks from classical antiquity). These projects demonstrate his commitment to advancing both theoretical understanding and practical applications of computational linguistics. He teaches various courses including Computational Linguistics, Scripting Languages, Programming for Humanities, Computational Creativity, and AI for Humanities, training students to work at this critical interdisciplinary crossroads. His leadership of the Centre for Computational Linguistics positions him at the forefront of computational linguistics research in Belgium, where he continues to expand the boundaries of what's possible at the intersection of language, computation, and humanistic inquiry.
David Mimno is an Associate Professor and Chair of the Department of Information Science at Cornell University. He holds a PhD from the University of Massachusetts Amherst and previously worked at the Perseus Project and Princeton University. His research focuses on computational social science, natural language processing, and historical text analysis. Mimno is known for developing the MALLET toolkit, a widely used Java-based platform for machine learning in text processing. He teaches courses such as INFO 4940: How LLMs Work and INFO 6150/CS 6788: Advanced Topic Modeling. His work has been supported by the Sloan Foundation, NEH, and NSF. Mimno advises PhD students in Information Science and Computer Science, emphasizing interdisciplinary research at the intersection of computing and humanities/social sciences. He also contributes to initiatives like AI for Humanists, making large language models accessible for text-as-data research. Bachelor’s degree: Not explicitly stated in text PhD: University of Massachusetts Amherst Research Interests: Mimno explores large language models, topic modeling, cross-lingual semantics, and ethical AI applications in humanities and legal domains. His recent work addresses data curation practices for language models, LLM memorization of poetry, and generative AI’s societal impacts. He co-authored reports on generative AI in academic research and education. Grants & Collaborations: His projects include the Text as Data (TADA) conference and collaborations on generative AI law workshops. Mimno’s MALLET toolkit supports document classification, clustering, and topic modeling, with applications in cultural analytics and computational historiography.
Jens Kreitewolf is a Faculty Lecturer in the Departments of Psychology and Mathematics and Statistics at McGill University. He teaches courses in statistics, research methodology, and psychophysics. His research focuses on auditory cognition, speech comprehension, and the neural mechanisms underlying voice perception. Dr. Kreitewolf holds a Ph.D. (Dr. rer. nat.) from Humboldt University of Berlin and completed postdoctoral fellowships at BRAMS and the University of Lübeck. His work combines experimental psychology, neuroimaging, and psychophysics to explore auditory processing challenges in adverse listening conditions. Key interests include how familiarity with a talker’s voice aids comprehension and the impact of hearing impairment on speech perception. Education: M.Sc. in Psychology (Ruhr University Bochum, 2009); Ph.D. in Psychology (Humboldt University of Berlin, 2014). Research Interests: Auditory scene analysis and speech-in-noise processing Voice recognition and familiarity effects Neural correlates of perceptual decision-making Circadian rhythms and perceptual sensitivity Cognitive neuroscience of auditory attention Publications highlight contributions to understanding: Risk factors for depression symptom progression Self-concept clarity in romantic evaluations Neurobiological mechanisms of working memory vulnerability Vestibular symptoms in migraine patients His interdisciplinary approach bridges psychology, statistics, and neuroscience, with applications to clinical populations and sensory processing disorders.
Liangming Pan is an Assistant Professor at the University of Arizona's College of Information Science. His research focuses on building trustworthy large language models (LLMs) with an emphasis on logical reasoning, truthfulness, and safety. He holds a PhD in Computer Science from the National University of Singapore (2022), a Master's from Tsinghua University, and a Bachelor's from Beihang University. Education : PhD in Computer Science, National University of Singapore (2022) Master of Engineering in Computer Science, Tsinghua University (2017) Bachelor of Engineering in Computer Science, Beihang University (2014) Research Interests : Dr. Pan's work centers on enhancing LLMs' reliability through: Logical reasoning mechanisms to ensure faithful deductions Truthfulness verification to combat misinformation Safety protocols to mitigate societal harm Key Contributions : Developed TART, an open-source framework for explainable table-based reasoning Created benchmarks like SCITAB and FactCheck-Bench for evaluating LLMs Advanced techniques for knowledge editing and causal reasoning Awards : Best Paper Runner-Up at NeurIPS Table Representation Workshop (2024) Area Chair Award for Question Answering (IJCNLP-AACL 2023) Service & Outreach : He serves as an Area Chair for EMNLP (2024), COLING (2025), and ACL (2024). He has delivered invited talks at Tsinghua University, Peking University, and other institutions.
