Skip to main content
Back

Prof. ZHANG, Jiji

Position
Professor
E-mail
[javascript protected email address]
Tel
3411-7292
Room
CEC 1004
Degree
  • B.A., Peking University, China
  • M.S., Carnegie Mellon University, USA
  • Ph.D., Carnegie Mellon University, USA
Work Experience:
  • Lingnan University, Hong Kong
  • California Institute of Technology, USA
Courses Taught:
  • GDAR1065 Critical Thinking
  • GDAR1067 Introduction to Western Philosophy
Teaching Areas:
  • Epistemology
  • Logic
  • Philosophy of Mind
  • Philosophy of Science
Research Areas:
  • Causation, Formal Epistemology, Philosophy of Science, Artificial Intelligence
Current Projects:
  • “Parsimony in Causal Inference: Epistemic Justifications and Methodological Implications”, Funded by the Research Grants Council of Hong Kong.
  • “Logical Investigations of Causal Models and Counterfactual Structures”, Funded by the Research Grants Council of Hong Kong.
Recent Publications:
  • Lin, H., and Zhang, J. (2020). “On Learning Causal Structures from Non-experimental Data without Any Faithfulness Assumption”. Proceedings of Machine Learning Research 117: 554-582.
  • Huang, B., Zhang, K., Zhang, J., et al. (2020). “Causal Discovery from Heterogeneous/Nonstationary Data”, Journal of Machine Learning Research, 21: 1-53.
  • Zhang, J., Seidenfeld, T., and Liu, H. (2019). “Subjective Causal Networks and Indeterminate Suppositional Credences”. Synthese, doi: 10.1007/s11229-019-02512-2.
  • Jaber, A., Zhang, J., and Bareinboim, E. (2019). "Identification of conditional causal effects under Markov equivalence", Proceedings of the 33rd Annual Conference on Neural Information Processing Systems.
  • Zhalama, Zhang, J., Eberhardt, F., et al. (2019). “ASP-based Discovery of Semi-Markovian Causal Models under Weaker Assumptions”, Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), 1488-1494.
Selected Outputs
  • Jaber, A., Zhang, J., and Bareinboim, E. (2019). “Causal Identification under Markov Equivalence: Completeness Results”, Proceedings of the 36th International Conference on Machine Learning, PMLR 97: 2981-2989.
  • Zhang, J., Liu, H., and Seidenfeld, T. (2018). “Agreeing to Disagree and Dilation”. International Journal of Approximate Reasoning, 150-162.
  • Zhalama, Zhang, J., and Mayer, W. (2017). “Weakening Faithfulness: Some Heuristic Causal Discovery Algorithms”, International Journal of Data Science and Analytics, 3(2): 93-104.
  • Zhang, J., and Spirtes, P. (2016). “The Three Faces of Faithfulness”, Synthese, 193(4): 1011-1027.
  • Zhang, J., and Zhang, K. (2015). “Likelihood and Consilience”, Philosophy of Science, 82(5): 930-940.
Selected Conference Presentations:
  • “Error Probabilities in Causal Discovery and Popper’s Two Criteria of Simplicity”, Workshop on Predictive Processing, Direction of Fit, and Causal Inference, Beijing, China, 2020.
  • “INUS and Occam’s Razors”, Workshop on Metaphilosophy and Philosophical Methodologies, Beijing, China, 2019.
  • “Causal Models with Metaphysical Dependencies”, National Philosophy of Science Meeting, Hangzhou, China, 2019.
  • “Causal Minimality in the Boolean Approach to Causal Inference”, The 16th Congress on Logic, Methodology, and Philosophy of Science and Technology, Prague, Czech, 2019.  
  • “A Characterization of Lewisian Causal Models”, The 16th Asian Logic Conference, Astana, Kazakhstan, 2019.
Selected Research Grants:
  • PI, “Parsimony in Causal Inference: Epistemic Justifications and Methodological Implications”, General Research Fund, Research Grants Council of Hong Kong, 01/01/2021 – 31/21/2022.
  • PI, “Logical Investigations of Causal Models and Counterfactual Structures”, General Research Fund, Research Grants Council of Hong Kong, 01/09/2018 – 31/12/2020.
  • PI, “Causation, Decision, and Imprecise Probabilities”, General Research Fund, Research Grants Council of Hong Kong, 01/01/2016 – 31/12/2017.
  • PI, “Philosophical Implications of Recent Advances in Causal Modeling”, General Research Fund, Research Grants Council of Hong Kong, 01/08/2013 – 31/01/2016.
  • PI, “A Constructive Examination of Standard Assumptions in Causal Discovery”, General Research Fund, Research Grants Council of Hong Kong, 01/01/2011 – 31/12/2012.