Find Academic Supervisors

Emily M. BenderView profile

Professor

• Multilingual Grammar Engineering
• Linguistics in NLP/Computation in Linguistics
• Societal Impacts of Language Technology
• +3 more

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.

Tim Van de CruysView profile

Senior Lecturer

• Computational Linguistics
• Natural Language Processing
• Computational Semantics
• +5 more

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 MimnoView profile

Associate Professor

• Natural Language Processing
• Machine Learning
• Computational Social Science
• +4 more

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.

Liangming PanView profile

Assistant Professor

• Natural Language Processing
• Large Language Models
• Machine Learning
• +3 more

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.

• Computational Linguistics
• Cognitive Science
• Semantic Analysis
• +3 more

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.

• Computational Linguistics
• Computer Science
• Machine Learning
• +1 more

Noah A. Smith is an Adjunct Professor of Computer Science and Engineering at the University of Washington. His work focuses on computational linguistics, machine learning, and natural language processing. He holds a Ph.D. in Computer Science from Johns Hopkins University (2006). His research explores ethical AI applications, multimodal systems, and foundational aspects of language models. Key research areas include: Ethical considerations in NLP, such as detecting rights abuses through text analysis Efficient decoding and alignment strategies for large language models Large-scale evaluation frameworks for multitask and multimodal generation Understanding pretraining dynamics and data composition effects Recent work emphasizes transparency in language models (e.g., tracing outputs to training data) and improving alignment through human feedback. He has contributed to open-source projects like OLMo and Dolma, advancing reproducibility in NLP research. No awards explicitly listed in provided texts. No specific advising or grant details available, though extensive publication output indicates active research involvement.

Jing JiangView profile

Professor

• Natural Language Processing
• Machine Learning
• Data Mining
• +4 more

Roles and Affiliations: Full Professor at the School of Computing and Information Systems (SCIS), Singapore Management University (SMU). Research Advisor to Xiaosen Zheng and Kankan Zhou. Serves as Action Editor for Transactions of the Association for Computational Linguistics (TACL) , Program Co-Chair of EMNLP 2019, and Editorial Board Member of Computational Linguistics (2015-2017). Education: PhD in Computer Science, University of Illinois at Urbana-Champaign (2008) B.S. and M.S. in Computer Science, Stanford University Research Focus: Specializes in natural language processing (NLP), text mining, machine learning, and data mining. Current interests include question answering, social media content analysis, and combating misinformation. Explores topics like counterfactual syntax for cross-lingual understanding, interventional training for robust NLU, and bias detection in vision-language models. Publications: Over 100+ peer-reviewed papers across top conferences (ACL, EMNLP, NAACL) and journals. Recent work emphasizes multimodal analysis, hate speech detection in memes, and robustness improvements for large language models. Key themes include cross-lingual systems, knowledge base question answering, and misinformation mitigation. Grants & Advising: Supervises PhD/Master’s students in cutting-edge NLP research. Leads projects on model memorization studies, hate meme classification, and interventional training frameworks. Active in organizing conferences and editorial roles. Teaching: Teaches courses in software foundations and programming fundamentals, bridging theory and practical NLP applications.

Shiqing MaView profile

Assistant Professor

• Security and Privacy
• Trustworthy Machine Learning
• Software Engineering
• +3 more

Shiqing Ma is an Assistant Professor in the Manning College of Information and Computer Sciences at the University of Massachusetts Amherst. Previously, he held a faculty position at Rutgers University from 2019 to 2023. He earned his Ph.D. in Computer Science from Purdue University (2019) and B.E. from Shanghai Jiao Tong University (2013). His research focuses on secure, intelligent, and transparent computing systems, particularly at the intersection of security, AI, and software systems. Key areas include integrating machine learning into software systems, ensuring algorithmic security through program analysis, and developing novel system architectures. Professor Ma's work has been recognized with prestigious awards, including the NSF CAREER Award (2023), and distinguished paper awards at USENIX Security (2017) and NDSS (2016). He actively contributes to the academic community through editorial roles and program committees in security, privacy, and software engineering. His research explores topics like backdoor attacks, AI safety, and bias mitigation in large language models. Recent articles emphasize defense mechanisms against adversarial attacks, watermarking techniques, and automated debugging systems for machine learning pipelines.

• Software Engineering
• Graph Transformation Systems
• Model-Based Development
• +5 more

Reiko Heckel is a Professor of Software Engineering at the University of Leicester, serving as Director of Postgraduate Teaching for Computing degrees and Data Analytics Lead at the Leicester Innovation Hub. She previously held academic roles at the Technical Universities of Dresden and Berlin before joining Leicester in 2004. Her research focuses on graph transformation systems, model-based development, stochastic modeling, and formal methods in software engineering. She earned her PhD (Dr.-Ing.) in Computer Science from TU Berlin in 1998. Her research interests span software engineering pedagogy, formal specification techniques, and applications of graph grammars in system modeling. Recent work explores stochastic graph transformations for social networks, transparency engineering in AI systems, and blockchain-based smart contract frameworks. Her contributions bridge theoretical foundations with practical applications in cybersecurity, data integration, and human-centric systems design. Key contributions include advancements in automated test case generation via graph transformations, visual contracts for software reverse engineering, and formal methods for complex system analysis. Her work frequently intersects with industry through collaborations via the Leicester Innovation Hub, emphasizing data analytics and technology transfer. Education: MSc Computer Science, Technical University of Dresden PhD (Dr.-Ing.), Computer Science, TU Berlin (1998) Leadership Roles: Head of Department (2014-2018) Director of Postgraduate Teaching (Ongoing) Research Themes: Model-Based Development Stochastic Systems Analysis Graph Neural Networks Trustworthy AI Her publications reflect a focus on formal methods, with recent trends in applying graph transformation techniques to social network modeling, blockchain smart contracts, and educational pedagogy.

Martin VolkView profile

Professor

• Computational Linguistics
• Machine Translation
• Natural Language Processing
• +5 more

Martin Volk is a Full Professor of Computational Linguistics at the University of Zurich, with a dual affiliation to the Department of Informatics since 2019. He holds a PhD from the University of Koblenz and has held academic positions at institutions including Stockholm University (part-time from 2008-2011), Zurich University of Applied Sciences, and the University of Georgia. His research focuses on grammar engineering, machine translation evaluation, multilingual text analysis, and cross-language information retrieval. Education : Born in Cochem, Germany Studied Computer Science and Computational Linguistics at EWH University, Koblenz Master's in Artificial Intelligence at the University of Georgia (Fulbright Scholar) PhD in Computational Linguistics from the University of Koblenz Research Interests : His work emphasizes data-driven NLP methods, including corpus-based approaches, parsing technologies, and the application of machine learning to historical and multilingual texts. Key focuses include: Machine translation systems and evaluation frameworks Grammar testing environments (e.g., GTU) OCR and digitization of historical documents (e.g., Gothic script) Development of parallel corpora for linguistic research Projects : SMULTRON: Multilingual parallel treebank project Bullinger Digital: Historical document digitization initiative Text+Berg: Digital Humanities project for alpine textual heritage EU-funded MuchMore (cross-language medical IR) Grants & Collaborations : Recipient of grants from the Swiss National Science Foundation, EU projects, and industry partnerships (e.g., Siemens, Xerox). His work integrates academic and industrial perspectives in NLP tool development. Labs & Teams : Leads research teams in the Institute of Computational Linguistics at UZH, focusing on projects like the Zurich Parallel Corpus Collection and MODERN (modeling discourse for MT).

• Bilingualism
• Cognitive Aging
• Executive Functioning
• +7 more

Ellen Bialystok is a prominent academic in the fields of psycholinguistics and cognitive neuroscience, focusing on bilingualism and its effects on aging and executive functioning. She has contributed extensively to interdisciplinary research through publications, editorships, and theoretical analyses. Her research explores how bilingualism modifies cognitive control, working memory, and neuroplasticity, particularly in aging populations. Key themes include cognitive reserve, language representation, and neural mechanisms underlying bilingual processing. The articles highlight a consistent focus on bilingualism's impact on cognitive aging, executive function, and lexical access, with methodologies spanning behavioral studies, ERP analysis, and theoretical frameworks. Keywords include psycholinguistics, neuroscience, and cognitive psychology.

Michela IppolitoView profile

Associate Professor

• Formal Semantics
• Pragmatics
• Linguistics
• +6 more

Michela Ippolito is an Associate Professor in the Department of Linguistics at the University of Toronto. She holds a PhD from the Massachusetts Institute of Technology (2002) and an MPhil from the University of Oxford (1997). Her research focuses on formal semantics and pragmatics, with expertise in modality, counterfactuals, focus, presuppositions, implicatures, tense, aspect, and Romance linguistics. She also explores crosslinguistic semantics and gestures. Education: PhD in Linguistics, MIT (2002), Dissertation: The Time of Possibilities: Truth and Felicity of Subjunctive Conditionals MPhil in General Linguistics, Oxford (1997), Thesis: The Semantics of Tense and Aspect in English and Italian BA in Philosophy, University of Milan (1994) Research interests span formal semantics, pragmatics, and crosslinguistic phenomena. Recent work includes semantic analysis of clitic pronouns and theoretical debates on embedded implicatures. Her publications address subjunctive conditionals, counterfactual presuppositions, and focus-sensitive particles. Awards 2019 Faculty Research Fellow, Jackman Humanities Institute Teaching includes courses like Semantic Theory and Introduction to Semantics . Her employment history includes roles at Boston University (2004–2006) and UCSC (2003–2004). She has also held visiting positions and research roles in Germany.

Julie Anne LegateView profile

Professor

• Syntactic Theory
• Morphosyntax
• Syntax-Semantics Interface
• +8 more

Julie Anne Legate is Professor and Chair of Linguistics at the University of Pennsylvania's Department of Linguistics. She earned her PhD from the Massachusetts Institute of Technology in 2002. Legate co-directs the Penn Syntax Lab and served as Editor-in-Chief of Natural Language & Linguistic Theory for 10 years. Her research focuses on syntactic theory, morphosyntax, and syntax-semantics interfaces, with specialization in endangered/understudied languages. Key interests include passives, impersonals, causatives, null subjects, case theory, and locality domains. She maintains secondary research interests in language acquisition. Legate's publications demonstrate extensive work on voice systems (especially passives), ergativity, case theory, recursion, and cross-linguistic analysis of languages including Acehnese, Warlpiri, and Lithuanian. Her research frequently combines theoretical innovation with empirical fieldwork. Awards include the LSA’s Best Paper in Language Award (2020) for On Passives of Passives . She has advised multiple PhD students including Faruk Akkuş (2021), Milena Šereikaitė (2020), Ava Irani (2019), Helen Jeoung (2018), and Einar Freyr Sigurðsson (2017). Current projects include: syntactic islands (with Charles Yang), Mandarin bridge verbs (with Jiayi Lu and Charles Yang), null subjects in Brazilian Portuguese (with Gesoel Mendes), and passive agent binding.

• Computational Linguistics
• Natural Language Processing (NLP)
• Dependency Parsing
• +5 more

Joakim Nivre is a Professor at Uppsala University's Department of Linguistics and Philology. He is a leading researcher in computational linguistics, with a focus on dependency parsing, Universal Dependencies (UD) framework development, and multilingual NLP applications. His recent work explores LLMs in climate change discourse analysis, pharmacovigilance explainability, and historical text processing. Key research areas: Dependency parsing theory, Universal Dependencies standardization, LLM evaluation Collaborations: SweSAT-1.0 benchmark development, ClimateEval project, PARSEME integration His 2025-2023 publications demonstrate expertise in explainable AI for healthcare, synthetic data generation for idioms, and multilingual benchmark design. Notably, he co-developed SweSAT-1.0 to evaluate Swedish LLMs and contributed to typology-informed UD revisions. Despite extensive work in NLP, no scientific awards are mentioned in available texts.

Sherry Tongshuang WuView profile

Assistant Professor

• Human-Computer Interaction
• Natural Language Processing
• Artificial Intelligence
• +6 more

Sherry Tongshuang Wu is an Assistant Professor at Carnegie Mellon University's School of Computer Science, with primary appointments in the Human-Computer Interaction Institute (HCII) and secondary affiliation with the Language Technology Institute (LTI) . Trained at the University of Washington under Jeffrey Heer and Dan Weld, she bridges HCI and NLP to study human interactions with AI systems across diverse user groups. Her educational background includes a Ph.D. (2016-22) and M.S. (2016-18) in Computer Science and Engineering from the University of Washington, and a B.Eng. (2012-16) from Hong Kong University of Science and Technology. Industry experience includes research internships at Google Brain, Microsoft Research, and Apple. Wu's research focuses on three interconnected pillars: Real-world AI Evaluation (developing frameworks like SPHERE for systematic assessment), Task-specific AI Test & Distill (optimizing general-purpose models for specific use cases), and Human-AI Task Delegation (designing optimal collaboration between humans and AI). Her work emphasizes practical deployment, user-specific net gains, and error recovery mechanisms. Analysis of her 15 most recent publications reveals a strong trend toward evaluation frameworks (35%), human-AI collaboration systems (30%), and specialized model distillation (25%), with growing emphasis on educational applications (10%). Key methodological themes include checklist-based evaluation, perspective-aware retrieval, and structural analysis of AI outputs. Google Academic Research Award (2024) Amazon Research Awards (2024) AIED 2024 Best Paper Award ACL 2020 Best Paper Award Rising Stars in EECS Workshop (2020) Wu actively mentors 19 students across PhD, Master's, and undergraduate levels, with notable projects including synthetic data generation, LLM literacy tools, and retrieval system optimization. She leads significant grant-funded work through Amazon Research Awards and Google Academic Research Awards, focusing on deployable model generation and human-AI collaboration frameworks. Her lab develops practical tools like Promp2Model and SPHERE that bridge theoretical research with industry applications.