Keywords
Biography
In 2022 Leo joined the Centre for Big Data Research in Health at the University of New South Wales as a research fellow, continuing his work on applied statistics and machine learning in healthcare. He aims to do meaningful work in bridging the gap between theory and practice. For more information on current activities and publications visit his LinkedIn or Researchgate.
Leo C.E. Huberts (1991) holds MSc and BSc degrees in Econometrics and a...view more
In 2022 Leo joined the Centre for Big Data Research in Health at the University of New South Wales as a research fellow, continuing his work on applied statistics and machine learning in healthcare. He aims to do meaningful work in bridging the gap between theory and practice. For more information on current activities and publications visit his LinkedIn or Researchgate.
Leo C.E. Huberts (1991) holds MSc and BSc degrees in Econometrics and a BSc in Natural and Social Sciences from the University of Amsterdam (UvA). During his studies, he worked at the financial department of the Stichting CPNB, was a strategy consultant for the Kleine Consultant Amsterdam (doing pro bono strategy projects for small and medium-sized companies), served on various committees, and was board member of the Analytics Academy (running pro bono data science projects for cultural and social institutions).
During his master, he was a manager for the Big Data Alliance and founded Delph, a data science consultancy and development firm with clients ranging from political parties to municipalities and construction companies. After two years with Delph, he decided to pursue a Ph.D. degree at the University of Amsterdam. His Ph.D. on statistical and predictive process monitoring finished in 2021, after which he became Assistant Professor in Business Analytics at the University of Amsterdam until joining UNSW in November 2022.
My Research Activities
Published studies:
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Huberts, L.C.E., Does, R. J. M. M., Ravesteijn, B., & Lokkerbol, J. (2021b). Predictive monitoring
using machine learning algorithms and a real-life example on schizophrenia [accepted for
publication]. Quality and Reliability Engineering International
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Huberts, L.C.E., Goedhart, R., & Does, R.J.M.M. (2021a). Improved control chart performance
using cautious parameter learning [submitted for publication]. Computers & Industrial
Engineering
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Huberts, L.C.E., Schoonhoven, M. & Does, R.J.M.M (2020). Monitoring student progress: A case
study to predict student success or failure. Early view in the Journal of Quality Technology
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Huberts, L.C.E., Schoonhoven, M., & Does, R.J.M.M. (2019). The effect of continuously updating
control chart limits on control chart performance. Quality and Reliability Engineering
International, 35(4), 1117-1128
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Huberts, L.C.E, Schoonhoven, M., Goedhart, R., Diko, M. D., & Does, R.J.M.M. (2018). The
performance of control charts for large non-normally distributed datasets. Quality and Reliability Engineering International, 34(6), 979-996
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Bun, M.J.G., & Huberts, L.C.E. (2018). The impact of higher fixed pay and lower bonuses on productivity. Journal of Labor Research, 39(1), 1-21
My Research Supervision
Areas of supervision
Applied statistics and machine learning in healthcare.