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Prof. Jiji ZHANG

Prof Jiji Zhang

Contact Information

    Office: CEC1004
    Telephone: 34117292
    Email: zhangjiji@hkbu.edu.hk

Professor, Department of Religion and Philosophy

  • ETHICAL AND THEORETICAL AI

Zhang Jiji received his PhD in Logic, Computation, and Methodology from the Department of Philosophy at Carnegie Mellon University in 2006, and taught previously at California Institute of Technology and Lingnan University, before joining the Department of Religion and Philosophy at Hong Kong Baptist University in 2021. His philosophical interests lie mainly in philosophy of science, formal epistemology, and logic. The interdisciplinary part of his research centers around the topic of causation, addressing both the epistemological and logical aspects of causal reasoning, and the statistical and computational aspects of causal modelling and discovery. His work has appeared in both premier journals in philosophy, such as Journal of Philosophical Logic, British Journal for the Philosophy of Science, Philosophy of Science, Synthese, etc., and in leading venues in computer science and statistics, such as Artificial Intelligence, Journal of Machine Learning Research, Statistical Science, as well as some top conference proceedings in the field of Artificial Intelligence. With the new opportunities provided by the Ethical and Theoretical AI lab, he aims to work with colleagues across and beyond the university to apply causal modelling tools to shed new light on some important issues with implications for AI ethics, including especially machine learning interpretability, algorithmic bias, and AI-powered personalized medicine.  

 

Project highlights:

 

  • "Parsimony in Causal Inference: Epistemic Justifications and Methodological Implications" (Supported by the RGC of Hong Kong)
  • "Logical Investigations of Causal Models and Counterfactual Structures" (Supported by the RGC of Hong Kong)  
  • "Causation, Decision, and Imprecise Probabilities" (Supported by the RGC of Hong Kong)
  • "Causal Discovery from Heterogeneous/Nonstationary Data" (Journal of Machine Learning Research 21 (2020) 1-53)

Fig 12

Fig 12: Visualization of estimated driving forces of changing causal modules for (a) smooth changes and (b) sudden changes. Left panel: blue lines show the recovered nonstationary components by KNV. Red lines are ground truth. Vertical black dashed lines indicate detected change points by Bayesian change point detection. Middle Panel: the largest ten eigenvalues of Gram matrix Mg . Right Panel: blue lines are recovered nonstationary components by linear time-dependent GP. Red lines are ground truth.

Fig 13

Fig 13: Recovered causal graph over 25 ROIs. The red circles denote that causal modules of corresponding brain areas changing over states.

 

 

Prof Jiji Zhang: The Convergence of Philosophy and Science

 

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