Biografie

2006 -

Researcher Intelligent Data Analysis (IDA) in Geophysics/Seismology, University of Potsdam, Germany.

2002 - 2006

Ph.D.-Student Intelligent Data Analysis, Utrecht University (Inst. of Comp.Sci., Research Group "Algorithmic Data Analysis"), The Netherlands.

1998 - 2002

B.Sc./M.Sc.-Student Cognitive Artificial Intelligence, Utrecht University, The Netherlands.

1994 - 1996

B.Sc.-Student Mathematics/Computer Science, Aarhus University, Denmark.

Forschung

I am the PI of the DFG funded Intelligent Data Analysis in Seismology: Bridging the Gap Between Data Generation and Data Comprehension. Also, I am working/participating in the PROGRESS research cluster on automatic data analysis. Until recently I was working in the EC project NERIES on data mining and data reduction in seismology using IDA techniques (e.g. machine learning, data mining, etc.). I engage in various teaching and supervision activities.

Moreover, I am associated (contractual via UP Transfer GmbH) with United Nations CTBTO (Comprehensive nuclear Test Ban Treaty Organization) in the realm of automatic analysis of seismic data using IDA methods.

Research Interests

A non-exhaustive and ever evolving list:

• Intelligent Data Analysis (IDA): Machine Learning, Data mining, Computational statistics
• Graphical Models (GM): Bayesian networks, Learning GMs
• Applications of IDA in seismology: Analysis/classification of seismic data, Ground motion models, Warning systems, Nuclear test-ban verification

Lehre

• Intelligent Data analysis (IDA) - winter 2007/2008: Gaining insight into the underlying data generation processes, pattern recognition, classification, prediction, decision making/support, etc.
• IDA and Inversion Theory - winter 2008/2009: Finding (or reinforcing) the link between IDA techniques and geophysical inversion theory, with emphasis on graphical models.

Publikationen

Peer Reviewed Papers (in both machine learning/AI and geo communities)

Vogel, K., Riggelsen, C., Merz, B., Kreibich, H., Scherbaum, F., 2012. Flood Damage and Influencing Factors: A Bayesian Network Perspective. Submitted.

Gianniotis, N., Riggelsen, C., Kühn, N., Scherbaum, F., 2012. Autoencoding Ground Motion Data for Visualisation. Submitted.

Riggelsen, C., Ohrnberger, M., 2012. A Machine Learning Approach for Improving the Detection Capabilities at CTBTO/IMS 3C Seismic Stations. Submitted, Recent Advances in Nuclear Explosion Minotoring Vol. 2 - Pure and Applied Geophysics, PAGEOPH.

Hermkes, M., Kühn, N., Riggelsen, C., 2012. Learning Task Relatedness via Dirichlet Process Priors for Linear Regression Models. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN'12.

Vogel, K., Riggelsen, C., Kühn, N., Scherbaum, F., 2012. Graphical Models as Surrogates for Complex Ground Motion Models. 11th International Conference on Artificial Intelligence and Soft Computing, ICAISC'12.

Blaser, L., Ohrnberger, M., Riggelsen, C., Babeyko, A., Scherbaum, F., 2011. Bayesian Networks for Tsunami Early Warning. Geophysical Journal International, GJI.

Riggelsen, C., Gianniotis, N., Scherbaum, F., 2011. Learning Aggregations of Ground-Motion Models Using Data. 11th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP'11.

Kühn, N., Riggelsen, C., Scherbaum, F., 2011. On the Use of Bayesian Networks for Probabilistic Seismic Hazard Analysis. 11th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP'11.

Scherbaum, F., Kühn, N., Ohrnberger, M., Riggelsen, C., Gianniotis, N., 2011. Quantification of Epistemic Uncertainties in Probabilistic Seismic Hazard Analysis. 11th International Conference onApplications of Statistics and Probability in Civil Engineering, ICASP'11.

Kühn, N., Scherbaum, F., Riggelsen, C., Allen, T., 2011. A Bayesian Hierachical Global Ground-Motion Model to Take into Account Regional Difference. Submitted, Bulletin of the Seismological Society of America, BSSA.

Kühn, N., Riggelsen, C. and Scherbaum, F., Allen, T., 2011. A Bayesian Ground-Motion Model with Correlation of Ground Motion Intensity Parameters. Submitted, Bulletin of the Seismological Society of America, BSSA.

Kühn, N., Riggelsen, C. and Scherbaum, F., 2011. Modeling the Joint Probability of Earthquake, Site and Ground-motion Parameters using Bayesian Networks. Bulletin of the Seismological Society of America, BSSA.

Kühn, N., Riggelsen, C. and Scherbaum, F., 2009. Facilitating Probabilistic Seismic Hazard Analysis Using Bayesian Networks. Seventh Annual Workshop on Bayes Applications (in conjunction with UAI/COLT/ICML 2009).

Delavaud, E., Scherbaum, F., Kühn, N. and Riggelsen, C., 2009. Informationtheoretic Selection of Ground-motion Prediction Equations for Seismic Hazard Analysis: An Applicability Study using California Data. Bulletin of the Seismological Society of America, BSSA.

Blaser, L., Ohrnberger, M., Riggelsen, C. and Scherbaum, F., 2009. Bayesian Belief Network for Tsunami Warning Decision Support. European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU'09.

Scherbaum, F., Delavaud, E. and Riggelsen, C., 2009. Model Selection in Seismic Hazard Analysis: An Information-Theoretic Perspective. Bulletin of the Seismological Society of America, BSSA.

Kühn, N., Scherbaum, F. and Riggelsen, C., 2009. Deriving Empirical Ground-Motion Models: Balancing Data Constraints and Physical Assumptions to Optimize Prediction Capability. Bulletin of the Seismological Society of America, BSSA.

Riggelsen, C., 2008. Learning Bayesian Networks: A MAP Criterion for Joint Selection of Model Structure and Parameter. IEEE International Conference on Data Mining, ICDM'08.

Köhler, A., Ohrnberger, M., Riggelsen, C. and Scherbaum, F., 2008. Unsupervised Feature Selection for Pattern Search in Seismic Time Series. Journal of Machine Learning Research: Workshop and Conference Proc., JMLR.

Köhler, A., Ohrnberger, M., Riggelsen, C. and Scherbaum, F., 2008. Feature Selection For Pattern Discovery in Seismic Wavefields. Feature Selection in Data Mining and Knowledge Discovery. FSDM'08.

Riggelsen, C., Ohrnberger, M. and Scherbaum, F., 2007. Dynamic Bayesian Networks for Real-Time Classification of Seismic Signals. Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD/ECML'07.

Riggelsen, C., 2006. Learning Bayesian Networks from Incomplete Data: An Efficient Method for Generating Approximate Predictive Distributions. SIAM International Conference on Data Mining, SDM'06.

Riggelsen, C., 2006. Learning Parameters of Bayesian Networks from Incomplete Data via Importance Sampling. International Journal on Approximate Reasoning, IJAR.

Riggelsen, C. and Feelders, A. J., 2005. Learning Bayesian Network Models from Incomplete Data using Importance Sampling. Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics, AISTATS'05.

Riggelsen, C., 2005. MCMC Learning of Bayesian Network Models by Markov Blanket Decomposititon. European Conference on Machine Learning, PKDD/ECML'05.

Riggelsen, C., 2004. Bayesian Network Learning from Incomplete Data via Monte Carlo Samples. Workshop on Probabilistic Graphical Models 2004, PGM'04.

Other Publications and Contributions

Various abstract-, talk- and poster contributions at geophysics and computer science conferences and workshops (PKDD, EGU, AGU, ESC, SSA, etc) are not listed.

Riggelsen, C., 2008. Approximation Methods For Efficient Learning of Bayesian Networks. Monograph Series: Frontiers in Artificial Intelligence and Applications, Vol. 168. IOS Press. ISBN 978-1-58603-821-2.

Riggelsen, C., 2006. Approximation Methods for Efficient Learning of Bayesian Networks. PhD-Thesis, Universiteit Utrecht, Gildeprint Press. Prom./coprom.: Prof. Dr. A.P.J.M. Siebes/Dr. A.J. Feelders. ISBN 978-90-393-4289-3.

Riggelsen, C., 2002. Induction of Bayesian Networks with a priori Domain Knowledge. Master's-Thesis, Universiteit Utrecht.

Riggelsen, C., 2002. Induction of Bayesian Networks in Conjunction with a priori Domain Knowledge. BNVKI Newsletter.