Klaus Ackermann

Senior Lecturer (Assistant Professor)

Department of Econometrics and Business Statistics

Monash University (Melbourne, Australia)

Research Interests

Machine Learning, Causal Inference, Applied Econometrics, Alternative Data

Contact

klaus.ackermann@monash.edu

Please do not hesitate to contact me for consultation and individualised expert advice for insights into large or complicated datasets for your organisation.


Klaus Ackermann is a Senior Lecturer (Assistant Professor) in the Department of Econometrics and Business Statistics at Monash University in Melbourne, Australia. His research interests are in the areas of Machine Learning, Causal Inference, Applied Econometrics, and Alternative Data.

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. He pursued a postdoctoral fellowship at the Center for Data Science and Public Policy at the University of Chicago.

Klaus is a founding member of Monash 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.

Klaus is also the co-founder and one of the directors of KASPR Datahaus Pty. Ltd. and co-founder of the IP Observatory.


Publications

Causal Inference and Machine Learning methods

Grecov, P., Prasanna, A.N., Ackermann, K., Campbell, S., Scott, D., Lubman, D.I. and Bergmeir, C., 2022. Probabilistic Causal Effect Estimation With Global Neural Network Forecasting Models. IEEE Transactions on Neural Networks and Learning Systems. [Published Version]

  • We propose an estimation procedure for estimating distributional treatment effects in a synthetic control style setting. Rather than estimating the average effect only, we estimate the full distributional treatment effect for causal inference in the distributional tails.

Grecov, P., Bandara, K., Bergmeir, C., Ackermann, K., Campbell, S., Scott, D. and Lubman, D., 2021, May. Causal Inference Using Global Forecasting Models for Counterfactual Prediction. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 282-294). Springer, Cham. [Published Version]

  • We propose a new causal inference method similar to the synthetic control methods. Instead of estimation of a weight matrix, we make use of time-series properties and forecast the average treatment effect.


Economics

Ackermann, K., Churchill, S.A. and Smyth, R., 2021. Mobile phone coverage and violent conflict. Journal of Economic Behavior & Organization, 188, pp.269-287. [Published Version]

  • We examine the effects of mobile phone coverage on violent conflicts in Africa. We find that mobile phones expansion increase non-state based conflict, but the effect is mostly driven by the economic inequality that arises of the economic growth due to mobile coverage.


Insights from challenging data-sets

Spencer, L., Fernando, J., Akbaridoust, F., Ackermann, K. and Nosrati, R., 2022. Ensembled Deep Learning for the Classification of Human Sperm Head Morphology. Advanced Intelligent Systems, p.2200111. [Published Version]

  • In vitro fertilisation (IVF) requires the manual selection of a sperm cell. In this work we present a machine learning based system to aid the selection process.

Ackermann, K., Walsh, J., De Unánue, A., Naveed, H., Navarrete Rivera, A., Lee, S.J., Bennett, J., Defoe, M., Cody, C., Haynes, L. and Ghani, R., 2018, July. Deploying machine learning models for public policy: A framework. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 15-22). [Published Version]

  • We present a framework how to deploy machine learning model as a system to be integrated in the operational workflow in public and private institutions.

Helsby, J., Carton, S., Joseph, K., Mahmud, A., Park, Y., Navarrete, A., Ackermann, K., Walsh, J., Haynes, L., Cody, C. and Patterson, M.E., 2018. Early intervention systems: Predicting adverse interactions between police and the public. Criminal justice policy review, 29(2), pp.190-209. [Published Version]

  • We investigate how machine learning can be use to help with stress interventions of police officers compared to a rule based strike system.

Ackermann, K., Blancas Reyes, E., He, S., Anderson Keller, T., Van Der Boor, P., Khan, R., ... & González, J. C. (2016, August). Designing policy recommendations to reduce home abandonment in Mexico. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 13-20). [Published Version]

  • We build a model for risk assessment for low income households at risk of abandoning their mortgage and consequently their home.

Ackermann, K., & Angus, S. D. (2014). A resource efficient big data analysis method for the social sciences: the case of global IP activity. International Conference on Computational Science, Procedia Computer Science, 29, 2360-2369. [Published Version]

  • We present a method on how to repurpose a large data-set on internet measurements for insights creation regarding human behaviour.




Media coverage

Klaus Ackermann was part of this documentary in August 2022 regards to world wide internet measurements. [IMDB]

ABC News (April 2020) - Coronavirus affecting internet speeds, as COVID-19 puts pressure on the network [ABC]

MIT Technology Review (January 2017) - The Trillion Internet Observations Showing How Global Sleep Patterns Are Changing [MIT Tech Review]

Get in touch at klaus.ackermann@monash.edu