Prof. ZHANG, Jiji
Position
Professor

E-mail
[javascript protected email address]

Tel
3411-7292

Room
CEC 1004

ORCID
0000-0003-0684-2084

Full CV

Qualification & Experience
Teaching
Research
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.

SelectedResearch 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.