Senior Research Scientist

Office: 208

Spemannstr. 38

72076 Tübingen

Spemannstr. 38

72076 Tübingen

+49 7071 601-563

+49 7071 601 552

**(I joined the department of philosophy of Carnegie Mellon University as an assistant professor in 2015.)**

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.

- 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."

- fundamental
**characterization**of causal information in observational data, and refinement of concepts related to causality - precise notion of “model
**complexity**” for causal inference - 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**

- 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)

- Organizational activities
- 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 Technologie 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
- 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

55 results
(BibTeX)

**On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection**
*Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016)*, pages: 825-834, (Editors: Ihler, A. and Janzing, D.), AUAI Press, 2016, plenary presentation (conference)

**Learning Causal Interaction Network of Multivariate Hawkes Processes**
*Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016)*, 2016, poster presentation (conference)

**Causal discovery and inference: concepts and recent methodological advances**
*Applied Informatics*, 3(3):1-28, 2016 (article)

**Domain Adaptation with Conditional Transferable Components**
*Proceedings of the 33nd International Conference on Machine Learning (ICML 2016)*, 48, pages: 2839-2848, JMLR Workshop and Conference Proceedings, (Editors: Balcan, M.-F. and Weinberger, K. Q.), 2016 (conference)

**Special Issue on Causal Discovery and Inference**
*ACM Transactions on Intelligent Systems and Technology (TIST)*, 7(2), January 2016, (Guest Editors) (misc)

**Model Selection for Gaussian Mixture Models**
*Statistica Sinica*, 2016 (article) To be published

**On estimation of functional causal models: General results and application to post-nonlinear causal model**
*ACM Transactions on Intelligent Systems and Technologies*, 7(2), January 2016 (article)

**Recent Methodological Advances in Causal Discovery and Inference**
In *15th Conference on Theoretical Aspects of Rationality and Knowledge*, pages: 23-35, (Editors: Ramanujam, R.), TARK, 2015 (inproceedings)

**Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components**
In *Proceedings of the 32nd International Conference on Machine Learning*, 37, pages: 1917–1925, JMLR Workshop and Conference Proceedings, (Editors: F. Bach and D. Blei), JMLR, ICML, 2015 (inproceedings)

**Discovering Temporal Causal Relations from Subsampled Data**
In *Proceedings of the 32nd International Conference on Machine Learning*, 37, pages: 1898–1906, JMLR Workshop and Conference Proceedings, (Editors: F. Bach and D. Blei), JMLR, ICML, 2015 (inproceedings)

**Distinguishing Cause from Effect Based on Exogeneity**
In *Fifteenth Conference on Theoretical Aspects of Rationality and Knowledge*, pages: 261-271, (Editors: Ramanujam, R.), TARK, 2015 (inproceedings)

**Identification of Time-Dependent Causal Model: A Gaussian Process Treatment**
In *24th International Joint Conference on Artificial Intelligence, Machine Learning Track*, pages: 3561-3568, (Editors: Yang, Q. and Wooldridge, M.), AAAI Press, Palo Alto, California USA, IJCAI15, 2015 (inproceedings)

**Multi-Source Domain Adaptation: A Causal View**
In *Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence*, pages: 3150-3157, AAAI Press, AAAI, 2015 (inproceedings)

**Likelihood and Consilience: On Forster’s Counterexamples to the Likelihood Theory of Evidence**
*Philosophy of Science, Supplementary Volume 2015*, 82(5):930-940, 2015 (article)

**A Permutation-Based Kernel Conditional Independence Test**
In *Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI2014)*, pages: 132-141, (Editors: Nevin L. Zhang and Jin Tian), AUAI Press Corvallis, Oregon, UAI2014, 2014 (inproceedings)

**Causal discovery via reproducing kernel Hilbert space embeddings**
*Neural Computation*, 26(7):1484-1517, 2014 (article)

**Single-Source Domain Adaptation with Target and Conditional Shift**
In *Regularization, Optimization, Kernels, and Support Vector Machines*, pages: 427-456, 19, Chapman & Hall/CRC Machine Learning & Pattern Recognition, (Editors: Suykens, J. A. K., Signoretto, M. and Argyriou, A.), Chapman and Hall/CRC, Boca Raton, USA, 2014 (inbook)

**Nonlinear Causal Discovery for High Dimensional Data: A Kernelized Trace Method**
In *13th International Conference on Data Mining*, pages: 1003-1008, (Editors: H. Xiong, G. Karypis, B. M. Thuraisingham, D. J. Cook and X. Wu), IEEE Computer Society, ICDM, 2013 (inproceedings)

**Domain adaptation under Target and Conditional Shift**
In *Proceedings of the 30th International Conference on Machine Learning, W&CP 28 (3)*, pages: 819–827, (Editors: S Dasgupta and D McAllester), JMLR, ICML, 2013 (inproceedings)

**On estimation of functional causal models: Post-nonlinear causal model as an example**
In *First IEEE ICDM workshop on causal discovery *, 2013, Held in conjunction with the 12th IEEE International Conference on Data Mining (ICDM 2013) (inproceedings)