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Dr Joerg Simon Wicker

Research | Current

My main research area is machine learning and its application to bioinformatics, cheminformatics, computational sustainability, and privacy. My approach to research is to use interesting and challenging questions in other research areas and develop new machine learning methods that address them to potentially advance not only the field of machine learning, but also the area it is applied to. In my career, I worked on diverse machine learning topics including autoencoders, Boolean matrix decomposition, inductive databases, multi-label classification, privacy-preserving data mining, adversarial learning, and time series analysis. For more details, check the web page of the Machine Learning Group and my private web page.

Areas of expertise

  • Machine Learning
  • Data Mining
  • Computational Sustainability
  • Data Science
  • Cheminformatics
  • Adversarial Learning
  • Bioinformatics
  • Privacy

Selected publications and creative works (Research Outputs)

  • Roeslin, S., Ma, T. M. Q. M., Chigullapally, P. C., Wicker, J. S. W., & Wotherspoon, L. M. W. (2020). Feature Engineering for a Seismic Loss Prediction Model using Machine Learning, Christchurch Experience. 17WCEE 17th World Conference on Earthquake Engineering Sendai, Japan. Related URL.
    Other University of Auckland co-authors: Samuel Roeslin, Pavan Chigullapally, Liam Wotherspoon
  • Roeslin, S., Ma, Q., Juárez-Garcia H, Gómez-Bernal A, Wicker, J., & Wotherspoon, L. (2020). A machine learning damage prediction model for the 2017 Puebla-Morelos, Mexico, earthquake. Earthquake Spectra10.1177/8755293020936714
    Other University of Auckland co-authors: Samuel Roeslin, Liam Wotherspoon
  • Roeslin, S., Ma, Q., Wicker, J., & Wotherspoon, L. (2020). Data Integration for the Development of a Seismic Loss Prediction Model for Residential Buildings in New Zealand. Communications in Computer and Information Science. 10.1007/978-3-030-43887-6_8
    Other University of Auckland co-authors: Samuel Roeslin, Liam Wotherspoon
  • Wicker, J., Hua, Y. C., Rebello, R., & Pfahringer, B. (2019). XOR-based Boolean Matrix Decomposition. In J. Wang, K. Shim, X. Wu (Eds.) 2019 IEEE International Conference on Data Mining (ICDM), 638-647. Beijing, China. 10.1109/ICDM.2019.00074
  • Jonauskaite, D., Wicker, J., Mohr, C., Dael, N., Havelka, J., Papadatou-Pastou, M., ... Oberfeld, D. (2019). A machine learning approach to quantifying the specificity of color-emotion associations and their cultural differences. Royal Society Open Science, 6 (9), 190741-190741. 10.1098/rsos.190741
  • Williams, J., Stönner C, EdtBauer, A., Derstorff, B., Bourtsoukidis, E., Klüpfel T, ... Kramer, S. (2019). What can we learn from the air chemistry of crowds?. In A. Hansel, J. Dunkl (Eds.) 8th International Conference on Proton Transfer Reaction Mass Spectrometry and its Applications, 121-123. Innsbruck, Austria: Innsbruck University Press. Related URL.
  • Stönner C, Edtbauer, A., Derstroff, B., Bourtsoukidis, E., Klüpfel T, Wicker, J., & Williams, J. (2018). Proof of concept study: Testing human volatile organic compounds as tools for age classification of films. PloS one, 13 (10)10.1371/journal.pone.0203044
  • Wicker, J., & Kramer, S. (2017). The best privacy defense is a good privacy offense: Obfuscating a search engine user's profile. Data Mining and Knowledge Discovery, 31 (5), 1419-1443. 10.1007/s10618-017-0524-z

Identifiers

Contact details

Office hours

Mon 2pm-3pm

Primary office location

SCIENCE CENTRE 303 - Bldg 303
Level 5, Room 526
38 PRINCES ST
AUCKLAND 1010
New Zealand

Web links