Note: Kun Zhang has transitioned from the institute (alumni).
(I joined Carnegie Mellon University as an assistant professor in the philosophy department in 2015, and I am also affiliated to the machine learning department.)
There has been a long history of debate on causality in philosophy, statistics, economics, and related fields. I have been concerned with this classic question--how can we discover causal information from purely observed data (i.e., perform causal inference)? How such causal information can facilitate solving other problems such as modeling, prediction, and control, is also interesting to me.
My research consists of three main lines.
- First, I have focused on developing practical computational methods for causal inference, to produce more reliable causal information.
- Secondly, to better understand causality and derive more universal methods for causal inference, I also work on finding fundamental and testable principles that help discover causality from data.
- Thirdly, latent variable modeling is closely related to causality, and it has been interesting me for over eight years. Developing more general yet identifiable latent variable models would benefit the causality field, as well as the machine learning and signal processing communities.
Since machine learning plays a key role in data analysis as well as causal inference, I am also very interested in this field.
What's New:
- The workshop "Causal modeling & machine learning" will take place in Beijing, China, in June 2014.
- We are editing the ACM Transactions on Intelligent Systems and Technologies (ACM TIST) special issue on causal discovery and inference; see the call for papers here. Submission deadline: 14 March 2014.
- The workshop "Causality: Perspectives from different disciplines" took place in August, 2013.
- Slides and poster for a recent paper "Domain adaptation under target and conditional shift."
Ongoing projects:
- fundamental characterization of causal information in observational data, and refinement of concepts related to causality
- precise notion of “model complexity” for causal inference
- machine learning beyond the i.i.d. setting
- unified/universal approach for causal inference
- domain-specific causal inference (in finance, brain signal analysis, etc.)
- causal understanding of machine learning tasks
- practical causal inference system for large-scale problems
- domain adaptation
- big data analytics: a causal perspective
- computational finance
Research Interests
- Causal discovery: Theory and applications
- developing advanced and practical computational methods for causal inference
- finding fundamental and testable principles to characterize causality
- latent variable modeling
- Statistical machine learning and applications
- kernel methods, Gaussian processes, domain adaptation, mixture models, model selection, independent component analysis, sparse coding
- Computational finance
- Neuroscience (especially MEG and EEG data analysis)
Academic Service
- Organizational activities
- Co-organizer of the Munich Workshop on Causal Inference and Information Theory (MCI), May 23-24, 2016 (with Negar Kiyavash and Gerhard Kramer)
- Co-organizer of the 2016 ACM SIGKDD Workshop of Causal Discovery (With Jiuyong Li, Elias Bareinboim, and Lin Liu), 2016
- Guest editor of the Journal of Data Science and Analytics Special Issue on Causal Discovery (with Jiuyong Li, Elias Bareinboim, and Lin Liu), 2016
- Organizer of ICML'14 workshop "Causal modeling and machine learning" (with Bernhard Schölkopf, Elias Bareinboim, and Jiji Zhang), June, 2014
- Guest editor of ACM Transactions on Intelligent Systems and Technology special issue on Causality (with Jiuyong Li, Elias Bareinboim, Bernhard Schölkopf, and Judea Pearl)
- Organizer of workshop "Causality: Perspectives from different disciplines" (with Bernhard Schölkopf and Jiji Zhang), Vals, Switzerland, August 5-8, 2013
- Co-organizer of the First IEEE ICDM Workshop on Causal Discovery (CD 2013), Dallas, Texas, USA, December 8, 2013
- Co-organizer of workshop “Networks -- Processes and causality”, Menorca, Spain, September, 2012
- Publicity chair of AISTATS 2012 (15th International Conference on Artificial Intelligence and Statistics)
- Reviewer for journals
- Annals of Statistics; Journal of Machine Learning Research; Annals of Applied Statistics; Journal of the American Statistical Association; Neural Computation; Machine Learning; IEEE Transactions on Pattern Analysis and Machine Intelligence; IEEE Transactions on Neural Networks; IEEE Transactions on Signal Processing; Neural Networks; IEEE Transactions on Knowledge and Data Engineering; Quantitative Finance; Neurocomputing; IEEE Signal Processing Letters; Frontiers of Computer Science; International Journal of Imaging Systems and Technology; Circuits, Systems & Signal Processing; International Review of Economics and Finance
- Program committee member for international conferences
- 2017: AISTATS (SPC), IJCAI (SPC), AAAI...
- 2016: AISTATS (SPC), AAAI, KDD, ICML, IJCAI (SPC), NIPS (area chair), UAI (SPC)...
- 2015: AISTATS, KDD, UAI, NIPS, IJCAI, ECML-PKDD, AMBN;
- 2014: AISTATS (SPC), UAI, NIPS, WSDM, KDD (both research & industry tracks),ACML, iKDD CoDS;
- 2013: UAI, NIPS, AISTATS, SDM, KDD, IJCAI, IJCNN, ASE/IEEE Big Data;
- 2012: UAI, AISTATS, MLSP, WSDM, SDM;
- 2011: UAI, NIPS, KDD, IJCNN, ICONIP;
- 2010: UAI, NIPS, ICA/LVA, SDM, ACML, ICPR;
- 2009: NIPS, ACML, ICONIP