Projects per year
Klaus Ackermann is a Lecturer (US Assistant Professor) in the Department of Econometrics and Business Statistics at Monash University in Melbourne, Australia. His research interests are in the areas of Data Science, Machine Learning, Public Policy and Applied Econometrics.
He holds a PhD in Economics from Monash University and BSC and MSC in Business Informatics with major in Economics from the Technical University of Vienna. After his studies, he worked as a postdoctoral fellow at the Center for Data Science and Public Policy at the University of Chicago.
Klaus Ackermann is a founding member of SoDa Labs, an empirical research laboratory associated with Monash University's Department of Economics and Department of Econometrics in the Monash Business School. SoDa Labs applies new tools from data science, machine learning, and beyond to answer social science questions using alternative and big data.
My passion is in technology, economics, data and computational approaches to get exciting insights about human behaviour. Broadly speaking, my research sits under the headline: how does technological progress affect societies and vice versa? What behavioural patterns that were disguised previously can now be researched as people reveal their choices through the use of technology? How can we use collected data to improve operational outcomes of not for profit organizations by using Machine Learning ? There are social issues that need to be addressed on an individual level, but I believe it is possible to impact the world on an aggregate level by using data.
7/08/20 → 31/12/21
1/09/13 → 30/06/15
Ackermann, K., Walsh, J., De Unánue, A., Naveed, H., Rivera, A. N., Lee, S. J., Bennett, J., Defoe, M., Cody, C., Haynes, L. & Ghani, R., 19 Jul 2018, KDD' 18 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Broder, A. & Spiliopoulou, M. (eds.). New York NY USA: Association for Computing Machinery (ACM), p. 15-22 8 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
Ackermann, K., Walsh, J., Haynes, L., Cody, C., Patterson, M. E. & Ghani, R., 1 Mar 2018, In : Criminal Justice Policy Review. 29, 2, p. 190-209 20 p.
Research output: Contribution to journal › Article › Research › peer-review
Ackermann, K., Navarrete, A. & Ghani, R., 1 Oct 2017.
Research output: Contribution to conference › Poster
Ackermann, K., Reyes, E. B., He, S., Keller, T. A., Van Der Boor, P. & Khan, R., 13 Aug 2016, KDD'16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Aggarwal, C. & Smola, A. (eds.). New York NY USA: Association for Computing Machinery (ACM), p. 13-20 8 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other › peer-review
A resource efficient big data analysis method for the social sciences: The case of global IP activityAckermann, K. & Angus, S. D., 2014, 2014 International Conference on Computational Science (ICCS 2014). Abramson, D., Lees, M., Krzhizhanovskaya, V. V., Dongarra, J. & Sloot, P. M. A. (eds.). Amsterdam Netherlands: Elsevier, p. 2360-2369 10 p. (Procedia Computer Science).
Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-reviewOpen AccessFile