Dr Andreas Wilhelm Kempa-Liehr

Dipl.-Phys., Dr. rer. nat.


I'm a data scientist with a strong background in nonlinear dynamics, stochastic time series analysis, and high performance computing. I like to apply computational analytics for automating micro decisions and deploying them as productive applications for businesses and research. 

2014-2016 Senior Data Scientist at Blue Yonder GmbH, Germany

2009-2014 Professional Analyst at EnBW Energie Baden-Württemberg AG, Germany

2004-2009 Head of Service Group Scientific Information Processing at Freiburg Materials Research Center, University of Freiburg, Germany

2004 Dissertation on "Dissipative Solitons in Reaction Diffusion Systems" at Institute of Applied Physics, University of Münster, Germany

Research | Current

Data Science for Engineering Applications

Time series feature engineering

Decision modelling in systems with state-dependent feature space dimensionality

Teaching | Current


Data Science (ENGSCI762)

Data Analysis (ENGSCI 311)

Computational Techniques (ENGSCI331)


Data Analysis (ENGSCI211, ENGSCI213)

Probability Theory (ENGSCI213)

Postgraduate supervision

Current Students:

Christina Lin - Healthcare Pathway Discovery, Conformance, and Enrichment (co-supervised with Michael O'Sullivan)

Hui Yie Teh - Sensor networks (co-supervision with Kevin I-Kai Wang)

Past Students:

Radhika Poduval (MMProf) - Admission and retail revenue forecasting (supported by DEXIBIT)

Neil Moraes (MMProf) - Predicting Imminent Changes of Fair Value for Nikkei 225 Futures Index on Basis of Order Book Data (supported by TRV Trading)


2008 - Apple Research and Technology Support Award

Committees/Professional groups/Services

Associate Member of Freiburg Materials Research Center (FMF)

Selected publications and creative works (Research Outputs)

As of 29 October 2020 there will be no automatic updating of 'selected publications and creative works' from Research Outputs. Please continue to keep your Research Outputs profile up to date.
  • Teh, H. Y., Kempa-Liehr, A. W., & Wang, K. I. K. (2020). Sensor data quality: a systematic review. Journal of Big Data, 7 (1).10.1186/s40537-020-0285-1
    Other University of Auckland co-authors: Kevin I-Kai Wang
  • Tang, Y., Blincoe, K., & Kempa-Liehr, A. W. (2020). Enriching feature engineering for short text samples by language time series analysis. EPJ DATA SCIENCE, 9 (1)10.1140/epjds/s13688-020-00244-9
    Other University of Auckland co-authors: Kelly Blincoe
  • Dempsey, D. E., Cronin, S. J., Mei, S., & Kempa-Liehr, A. W. (2020). Automatic precursor recognition and real-time forecasting of sudden explosive volcanic eruptions at Whakaari, New Zealand. Nature communications, 11 (1)10.1038/s41467-020-17375-2
  • Zarshenas, H., Ruddy, B. P., Kempa-Liehr, A. W., & Besier, T. F. (2020). Ankle torque forecasting using time-delayed neural networks. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. 10.1109/EMBC44109.2020.9175376
    Other University of Auckland co-authors: Bryan Ruddy, Homayoon Zarshenas, Thor Besier
  • Kempa-Liehr, A. W., Lin, C. Y.-C., Britten, R., Armstrong, D., Wallace, J., Mordaunt, D., & O'Sullivan M (2020). Healthcare pathway discovery and probabilistic machine learning. International journal of medical informatics, 13710.1016/j.ijmedinf.2020.104087
    Other University of Auckland co-authors: Michael O'Sullivan
  • Kempa-Liehr, A. W., Oram, J., Wong, A., Finch, M., & Besier, T. (2020). Feature Engineering Workflow for Activity Recognition from Synchronized Inertial Measurement Units. Communications in Computer and Information Science, 1180 CCIS, 223-231. 10.1007/978-981-15-3651-9_20
    Other University of Auckland co-authors: Thor Besier
  • Christ, M., Braun, N., Neuffer, J., & Kempa-Liehr, A. W. (2018). Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh – A Python package). Neurocomputing, 307, 72-77. 10.1016/j.neucom.2018.03.067
  • Christ, M., Krummich, J., & Kempa-Liehr, A. W. (2016). Integrating predictive analytics into complex event processing by using conditional density estimations. Paper presented at 2016 IEEE 20th International Enterprise Distributed Object Computing Workshop (EDOCW), Vienna, Austria. 5 September - 9 September 2016. Enterprise Distributed Object Computing Workshop (EDOCW), 2016 IEEE 20th International Proceedings. 10.1109/EDOCW.2016.7584363


Contact details

Primary office location

Level 3, Room 317
New Zealand

Web links