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.
Jianjing Kuang is an Associate Professor of Linguistics at the University of Pennsylvania, affiliated with the School of Arts and Sciences. As Director of the Penn Phonetics Laboratory, they lead research in phonetics, laboratory phonology, and tonal language studies. Kuang holds a Ph.D. from UCLA (2013) and specializes in the interplay between production and perception in speech, particularly focusing on tonal systems, prosody, and cross-linguistic fieldwork. Their work integrates behavioral experiments, corpus studies, and computational modeling to explore phonological contrasts, voice quality, and sound change. Key affiliations include MindCORE and the Center for East Asian Studies. Research interests include multidimensional cues in tone processing, glottal articulations, mapping production-perception relationships, and prosodic sentence processing. Kuang has conducted fieldwork on languages such as Yi, Q’anjob’al, and Mandarin. Their studies address topics like tonal splitting in Yi, cue-changing in Korean stops, and prosodic patterns in Mayan languages. Recent work explores voice quality’s role in pitch perception and prosodic boundary detection. Awards and grants are not explicitly listed, but Kuang’s extensive publications (over 50 peer-reviewed papers) reflect sustained academic impact. They advise graduate students in phonetics and phonology, with active collaborations in computational linguistics and speech technology. The Penn Phonetics Laboratory, under Kuang’s direction, emphasizes interdisciplinary research bridging experimental and theoretical phonetics.
Jiwon Yun is an Associate Professor in the Department of Linguistics at Stony Brook University. She holds a Ph.D. in Linguistics from Cornell University (2013) and a B.S.E. in Computer Science & Engineering from Seoul National University (cum laude with honors). Her research focuses on speech prosody, syntax-semantics interfaces, and computational modeling of sentence processing, with special attention to East Asian languages like Korean, Mandarin, and Japanese. Dr. Yun teaches undergraduate and graduate courses in computational linguistics, semantics, pragmatics, and experimental phonetics. Her work bridges theoretical linguistics and experimental methods, examining how prosody interacts with syntactic and semantic structures. Recent publications investigate prosodic disambiguation of syntactic ambiguities, computational models of relative clause processing, and intonation patterns in Korean and Mandarin. She has developed tools for automated speech corpus analysis and maintains a Hangul-Yale Romanization Converter for linguistic research. Her research has been published in journals like Humanities and Social Sciences Communications , Linguistic Inquiry , and Journal of East Asian Linguistics . Teaching responsibilities include courses such as LIN 335 (Computational Linguistics) and LIN 627 (Computational Semantics).
Takako Fujioka is an Associate Professor of Music at Stanford University, affiliated with the Center for Computer Research in Music and Acoustics (CCRMA). Her research focuses on the neural mechanisms underlying auditory perception, auditory-motor coupling, and music-supported therapy for neurorehabilitation. She holds a Ph.D. in Physiology from the Graduate University for Advanced Studies, Japan, and M.Sc./B.Eng. degrees in Electrical Engineering from Waseda University. Her work combines neurophysiological techniques such as MEG and EEG to study brain plasticity in development, aging, and stroke recovery. Notable contributions include investigating how music influences motor and cognitive recovery in stroke patients, as well as exploring the neural basis of musical perception through rhythmic synchronization and pitch discrimination studies. Supported by awards from the Canadian Institutes of Health Research during her postdoctoral work at the Rotman Research Institute, her research bridges clinical neuroscience and music cognition. Dr. Fujioka’s expertise spans auditory neuroscience, neurorehabilitation, and technology-assisted music therapy. She has pioneered studies on tactile mapping for cochlear implant users and networked music performance systems, emphasizing cross-modal perception and human-technology interaction. Her findings contribute to both theoretical understanding of auditory processing and practical applications in medical and educational settings. Awards: Canadian Institutes of Health Research Awards (postdoctoral phase) Labs/Teams: CCRMA, Stanford Music Perception Laboratory, Rotman Research Institute collaborations Key Themes: Neuroplasticity, Music-Mediated Rehabilitation, Auditory-Motor Integration, Multisensory Processing Her recent work examines aging-related changes in binaural hearing and the role of beta/gamma oscillations in rhythmic processing. She advocates for translational research that connects neural mechanisms with real-world therapeutic interventions.
Satoshi Tomioka is a Professor in the Department of Linguistics and Cognitive Science at the University of Delaware, affiliated with the College of Arts & Sciences. He holds a B.A. from International Christian University (Tokyo) and a Ph.D. from the University of Massachusetts Amherst. His expertise spans semantics, pragmatics, syntax, and prosody, with a focus on Japanese and comparative East Asian linguistics. Education: B.A. in Liberal Arts (1987), International Christian University; Ph.D. in Linguistics (1997), University of Massachusetts Amherst. Research interests include ellipsis, anaphora, wh-interrogatives, distributivity, and contrastiveness. He investigates how prosody interacts with syntax and semantics in Japanese, exploring topics like focus marking, intervention effects, and scalar implicatures. Recent work examines associative plurals and pragmatic disambiguation in embedded questions. Publications analyze theoretical and empirical questions in linguistic interfaces, such as Bare quotatives as embedded speech acts (2024) and Focus without pitch boost (2022). His work bridges formal semantics and experimental approaches, addressing challenges in cross-linguistic typology and cognitive aspects of language processing.
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.
Jonathan David Bobaljik is a leading linguist whose research spans formal syntax, phonology, and the documentation of endangered languages, particularly Itelmen. He is actively involved in multiple international research collaborations and regularly presents at major linguistic conferences across Europe and North America. His work is published in top journals such as Natural Language and Linguistic Theory and Language . His research interests lie at the intersection of theoretical and descriptive linguistics. He investigates universal patterns in morphology and syntax, including suppletion, vowel harmony, ergativity, and control structures, often using data from understudied languages like Itelmen. His work combines rigorous formal analysis with deep empirical grounding in language documentation. The recent publications reflect a strong trend toward integrating typological diversity with formal theory. Key areas include morphosyntactic universals (e.g., suppletion in pronouns), information structure in verb-final languages, and the phonology of ejective consonants. There is also a growing emphasis on sociolinguistic and community-based aspects, as seen in collaborative work on language revitalization and the social life of the Itelmen language. He has received recognition through numerous scholarly publications and invited talks, though specific awards are not listed in the provided text. His collaborative projects suggest active grant involvement, particularly in language documentation and cross-linguistic typology. Bobaljik plays a central role in the Itelmen documentation and revitalization project, contributing to the creation of a dictionary app, the publication of historical manuscripts, and public exhibits on Siberian Indigenous knowledge. He collaborates with a team including David Koester, Chikako Ono, Tatiana Degai, and Maria Pupynina.
Sanjit A. Seshia is the Cadence Founders Chair Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley . He is affiliated with the Group in Logic and the Methodology of Science and participates in centers like the Industrial Cyber-Physical Systems Center , Berkeley AI Research , and the Simons Institute for the Theory of Computing . Research interests include formal methods for automated verification and synthesis of dependable systems, with applications to cyber-physical systems , AI-based autonomy , and computer security . His work spans SMT solving, model counting, syntax-guided synthesis, and algorithmic improvisation, with tools like UCLID5 , VerifAI , and Scenic for verifying autonomous systems and educational platforms like CPSGrader . Students and collaborators include notable researchers such as Dorsa Sadigh (Stanford), Daniel Fremont (UC Santa Cruz), and Hazem Torfah (Chalmers). He has co-founded startups like Decyphir and 20ⁿ Labs based on his research.
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.
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.
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.
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.
