About me
I am the Director of the Institute of AI and Language Science and a Full Tenured Professor of Computational Linguistics in the School of Foreign Languages at Tongji University. I explore the fascinating intersection of computational linguistics, cognitive computation, and artificial intelligence. My research bridges the gap between human language processing and machine learning, integrating statistical modeling, deep learning, and cognitive-neuroscience experimental methods to understand the deep mechanisms of language understanding and processing in both humans and machines. I have developed a number of specialized large language models, as well as major databases and corpora, widely used in academia and industry. As global efforts accelerate in βAI + Educationβ, I am committed to applying data-driven approaches to advance language research toward big language science.
π¬ Research Focus
My work sits at the cutting edge of computational linguistics and cognitive AI, where I develop novel computational methods to understand human language processing and machine intelligence.
Cognitive Computation & Brain-inspired AI
Developing computational models that explain how humans process language, using eye-tracking, EEG, and fMRI data to understand the neural mechanisms of discourse comprehension.
Large Language Models & Reasoning
Evaluating and enhancing LLM reasoning capabilities, fine-tuning transformer-based models, and developing attention-aware computational metrics for multi-modal language processing.
Affective Computing
Understanding emotion and sentiment in human language through computational approaches, bridging cognitive and affective dimensions of language.
Advanced Statistical Analysis
Applying sophisticated statistical methods including GAMM, Bayesian modeling, and time-series analysis to linguistic and cognitive phenomena, as well as neuro data (EEG and fMRI).
Digital Humanities & Computational Text Analysis
Formal and computational models of discourse structure; computational measurement of coherence, cohesion, and information flow; discourse dependency and cross-framework conversion (RST, PDTB, dependency); multilingual discourse parsing; distant reading.
AI Methods, Ethics & Didactics
Efficient training and inference in LLMs; evaluations and benchmarking of AI models; cognitive-inspired reasoning in LLMs; critical assessment of AI biases, cultural tendencies, and societal impacts; development of DH-related curricula integrating AI literacy and ethical reflection.
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, 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
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
Iβm currently working on groundbreaking projects that combine cognitive neuroscience with artificial intelligence to understand how humans and machines process language:
- Large Language Model reasoning and evaluation frameworks
- Cognitive computation models for brain-inspired AI
- Advanced statistical approaches to linguistic and cognitive data using GAMM and Bayesian methods
- Cross-linguistic and multi-modal studies of affective computing
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!