Klaus Ackermann

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 Artifical Intelligence, 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 synthetic control methods. Instead of estimating a weight matrix, we use time-series properties and forecast the average treatment effect.

Economics

Lin, M., Churchill, S. A., & Ackermann, K. (2024). The fattening speed: Understanding the impact of internet speed on obesity, and the mediating role of sedentary behaviour. Economics & Human Biology, 101439. [Published Version] 

• We examine the effect of high-speed internet access on obesity, finding that faster internet increases BMI and the probability of obesity, with sedentary behaviour and inactivity serving as mediating mechanisms, particularly among young adults and males.

Ackermann, K., Awaworyi Churchill, S., & Munyanyi, M. E. (2024). Internet and Gambling: Insights from Australia’s NBN Rollout. Journal of Gambling Studies, 1-25.  [Published Version] 

• Our analysis of Australia's National Broadband Network rollout reveals that while high-speed internet is linked to increased online gambling, it is also associated with a significant decline in venue-based gambling, suggesting that faster internet access may reduce overall gambling behaviour through changes in gambling modalities

Ackermann, K., Churchill, S.A. and Smyth, R., 2024. Estimating the relationship between ethnic inequality, conflict and voter turnout in Africa using geocoded data. World Development, 180, p.106644. [Published Version] 

• We examine the relationship between ethnic inequality, conflict, and voter turnout in Africa, finding that higher ethnic inequality increases conflict, which in turn reduces voter turnout, with this effect being more pronounced in highly populated areas.

Ackermann, K., Churchill, S.A. and Smyth, R., 2023. High-speed internet access and energy poverty. Energy economics, 127, p.107111. [Published Version] 

• We examine the effects of high-speed internet access on energy poverty in Australia. We find that the expansion of NBN access increases the probability of energy poverty, primarily driven by the decline in social capital resulting from greater internet access.

Hewamalage, H., Ackermann, K. and Bergmeir, C., 2023. Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practices. Data Mining and Knowledge Discovery.  [Published Version] 

• This is a tutorial-style paper on how to choose the evaluation metrics for your forecasting problem. It also emphasises the point of evaluating economic and financial time series.

Ackermann, K., Awaworyi Churchill, S. and Smyth, R., 2023. Broadband internet and cognitive functioning. Economic Record, 99(327), pp.536-563. [Published Version] [Best Paper Award of the Journal in 2023] 

• We examine the effects of high-speed internet access on cognitive functioning in Australia, finding that it causes a decline in crystallised intelligence. The effects are mediated by social capital and moderated by age and gender. 

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 phone expansion increases non-state-based conflict, but the effect is mostly driven by the economic inequality that arises from the economic growth due to mobile coverage.

Artificial Intelligence in Medicine

Shahali, S., Murshed, M., Spencer, L., Tunc, O., Pisarevski, L., Conceicao, J., McLachlan, R., O’Bryan, M.K., Ackermann, K., Zander‐Fox, D. and Neild, A., 2024. Morphology Classification of Live Unstained Human Sperm Using Ensemble Deep Learning. Advanced Intelligent Systems, p.2400141. [Published Version] 

• We present a method to select live human unstained sperm cells ready for IVF in a clinical setting using an AI-based System.

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.

Artificial Intelligence in Public Policy

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 for repurposing a large data set on internet measurements to create insights regarding human behaviour.

Media coverage

Get in touch at klaus.ackermann@monash.edu