About me
I am the Director of the Institute of AI and Language Science and a Full Tenured Professor of Computational Linguistics at Tongji University. My research investigates how language and contextual meaning are represented and processed in human and artificial intelligence, bridging computational linguistics, cognitive science, and AI.
My work centers on three interconnected areas: computational language and human cognition, language intelligence in AI, and humanβAI cognitive alignment. I combine computational modeling and large language models with behavioral and cognitive-neuroscience methods, including eye-tracking, EEG, and fMRI, to study language comprehension and representation across humans and machines. A particular focus is the development of interpretable computational measures that connect linguistic structure and context with machine representations and human behavioral and neural dynamics.
More broadly, I aim to develop a cognitively grounded science of language and AI that uses insights from human language and cognition to understand, evaluate, and improve artificial intelligence.
π¬ Research Focus
My research investigates how language and contextual meaning are represented and processed in human and artificial intelligence. It spans three core themesβcomputational language and human cognition, language intelligence in AI, and humanβAI cognitive alignmentβwith complementary work in speech, computational discourse, and responsible AI.
Computational Language & Human Cognition
Developing interpretable computational models of language comprehension, prediction, semantic integration, and discourse processing, using behavioral, eye-tracking, EEG, and fMRI evidence.
Language Intelligence in AI
Investigating the linguistic, pragmatic, and reasoning capabilities of large language models, including their internal representations, contextual generalization, and mechanisms of language understanding.
HumanβAI Cognitive Alignment
Comparing human behavioral and neural dynamics with representations and computations in artificial models to identify where human and machine language processing converge and diverge.
Speech & Multimodal Language Processing
Studying spoken-language processing through computational modeling, self-supervised speech representations, prosody, and multimodal signals, with a focus on links between linguistic structure and human cognition.
Computational Discourse & Digital Humanities
Developing computational approaches to discourse structure, coherence, information flow, language change, and large-scale text analysis across languages, genres, and historical corpora.
Responsible AI, Evaluation & Language Education
Evaluating AI systems for linguistic competence, cognitive and cultural biases, and human alignment, while translating computational language research into responsible applications in language education and digital scholarship.
π Academic Positions
π Journal Editorial Roles
π Impact & Recognition
My research has led to 30+ peer-reviewed journal publications in top-tier international venues including Cognition, Cognitive Science, Linguistics, Neural Networks, and PNAS, and 10 confernce papers in ACL and other AI top conferences, as well as 10+ publications in leading Chinese CSSCI journals such as δΈε½θ―ζ and ε½δ»£θ―θ¨ε¦. Many of my works have been reprinted in 人倧ε€ε°θ΅ζ and δΈε½η€ΎδΌη§ε¦ζζ. My research has been featured in MIT Technology Review.
π» Technical Expertise
Methods
Statistical modeling Β· GAMMs Β· Bayesian methods Β· Deep learning Β· LLM probing and representation analysis Β· Eye-tracking Β· EEG Β· fMRI Β· Speech self-supervised learning
Experimental Methods: Eye-tracking Β· EEG Β· fMRI Β· Online experiments
Languages: Chinese (native) Β· English (fluent) Β· German (intermediate) Β· Japanese (intermediate)
π Current Projects
My current projects examine language and contextual meaning across human and artificial intelligence, connecting computational modeling, cognitive neuroscience, large language models, and speech technologies:
Contextual meaning in human language comprehension: investigating how semantic relevance, contextual fit, and surprisal independently shape reading, speech processing, and neural dynamics using behavioral, eye-tracking, EEG, and fMRI data
Language intelligence and internal representations in LLMs: studying reasoning, pragmatics, contextual generalization, and internal representations through controlled evaluation, probing, and representation-level analysis
HumanβAI cognitive alignment: comparing human behavioral and neural responses with computational measures and model representations to identify shared and divergent mechanisms of language processing
Speech representations and linguistic structure: investigating how speech self-supervised learning models encode linguistic information, including the sparse localization and representation of various linguistic expressions
Affective and social language intelligence: examining how humans and language models represent emotion, figurative meaning, politeness, and other socially situated aspects of language across languages and contexts.
I'm always excited to discuss research collaborations, student supervision opportunities, or innovative applications of computational linguistics, AI and cognitive computation. Feel free to reach out at sharpksun at hotmail.com!