School of Data Science, The Chinese University of Hong Kong (Shenzhen)
I am Chao Yang (杨超), a fourth-year Ph.D. student in Computer Science at the School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), where I am fortunate to be supervised by Prof. Shuang Li. I received my M.Sc. in Statistics with Data Science from The University of Edinburgh and my B.Sc. in Statistics from Shandong University, where I also completed a dual bachelor's degree in Finance.
Education
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The Chinese University of Hong Kong, ShenzhenSchool of Data Science
Ph.D. Student in Computer Science2022 - present -
The University of EdinburghM.Sc. in Statistics with Data Science2020 - 2022 -
Shandong UniversityB.S. in Statistics, Dual B.S. in Finance2016 - 2020
Experience
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JD.com’s TGT Internship ProgramResearch Intern on LLMs and AgentsJul. 2026 - present -
Peking Union Medical College HospitalResearch Intern on Rare Disease MDTJul. 2024 - Aug. 2024
Honors & Awards
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Duan Yong Ping Meritorious Research Award2025
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Mathematical Contest In Modeling – H-Mention2020
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The Research Scholarship of Shandong University2019
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The Outstanding Student Scholarship of Shandong University2018
News
Selected Publications (view all )
Forward-Chaining Temporal Point Process
Chao Yang, Wendi Ren, Shuang Li
The International Conference on Machine Learning (ICML) 2026
FC-TPP enables controllable and constraint-aware continuous-time event sequence generation by coupling temporal point processes with differentiable multi-hop logical reasoning in latent symbolic space.
Forward-Chaining Temporal Point Process
Chao Yang, Wendi Ren, Shuang Li
The International Conference on Machine Learning (ICML) 2026
FC-TPP enables controllable and constraint-aware continuous-time event sequence generation by coupling temporal point processes with differentiable multi-hop logical reasoning in latent symbolic space.
Branching Diffusion for Point Processes in Time and Space
Chao Yang, Wenjie Shen, Shuang Li
The International Conference on Machine Learning (ICML) 2026
We propose a non-autoregressive diffusion framework for temporal and spatio-temporal point processes derived from the Wasserstein–Fisher–Rao gradient flow, enabling principled generation via configuration-level denoising.
Branching Diffusion for Point Processes in Time and Space
Chao Yang, Wenjie Shen, Shuang Li
The International Conference on Machine Learning (ICML) 2026
We propose a non-autoregressive diffusion framework for temporal and spatio-temporal point processes derived from the Wasserstein–Fisher–Rao gradient flow, enabling principled generation via configuration-level denoising.
From Counts to Preferences: Preference-Driven Models for Spatio-Temporal Event Data
Chao Yang*, Yiling Kuang*, Shuang Li (* equal contribution)
The International Conference on Artificial Intelligence and Statistics (AISTATS) 2026
We introduce a preference-driven framework that models event distributions through a two-stage “consider–then–choose” process.
From Counts to Preferences: Preference-Driven Models for Spatio-Temporal Event Data
Chao Yang*, Yiling Kuang*, Shuang Li (* equal contribution)
The International Conference on Artificial Intelligence and Statistics (AISTATS) 2026
We introduce a preference-driven framework that models event distributions through a two-stage “consider–then–choose” process.
Evolving Minds: Logic-Informed Inference from Temporal Action Patterns
Chao Yang, Shuting Cui, Yang Yang, Shuang Li
The International Conference on Machine Learning (ICML) 2025
Models latent human mental states from irregular actions using a logic-informed TPP with variational EM, enabling accurate inference and prediction of behavior.
Evolving Minds: Logic-Informed Inference from Temporal Action Patterns
Chao Yang, Shuting Cui, Yang Yang, Shuang Li
The International Conference on Machine Learning (ICML) 2025
Models latent human mental states from irregular actions using a logic-informed TPP with variational EM, enabling accurate inference and prediction of behavior.
Flow-Based Delayed Hawkes Process
Chao Yang, Wendi Ren, Shuang Li
The Conference on Uncertainty in Artificial Intelligence (UAI) 2025
Enhances delayed Hawkes processes with normalizing flows to achieve greater flexibility and expressiveness while preserving interpretability, supported by theoretical guarantees and improved performance.
Flow-Based Delayed Hawkes Process
Chao Yang, Wendi Ren, Shuang Li
The Conference on Uncertainty in Artificial Intelligence (UAI) 2025
Enhances delayed Hawkes processes with normalizing flows to achieve greater flexibility and expressiveness while preserving interpretability, supported by theoretical guarantees and improved performance.