Projects per year
Personal profile
Biography
Daniel Schmidt is a Senior Lecturer in Data Science at the Faculty of Information Technology, Monash University, Melbourne, Australia.
Since completing his PhD in information theoretic inference of linear time series models he has spent 10 years working primarily in the area of Bayesian inference, with specific interest in applications to epidemiological problems, particular the area of statistical genomics.
His specific research interests include:
- Bayesian inference of high dimensional regression models, particularly linear and generalized linear models. He has, along with Dr. Enes Makalic, written a highly efficient toolbox supporting state-of-the-art Bayesian shrinkage priors for high dimensional regression models (available here);
- Information theoretic statistics, particularly the application of information theory to statistical inference through the Minimum Message/Description Length principles;
- Statistical genomics, risk prediction and variant discovery, particularly in the areas of cancer genomics.
He is interested in using mammography and machine learning techniques to improve risk prediction and the stratification of women by their future risk of breast cancer, with the aim of assisting the creation of personalised screening programmes.
Recent pre-prints:
- Adaptive Bayesian Shrinkage Estimation Using Log-Scale Shrinkage Priors, which describes a new class of shrinkage prior distributions for regression coefficients that can adapt to the sparsity characteristics of the underlying regression coefficients. It also provides simple bounds on behaviour of most existing Bayesian shrinkage priors.
- A Minimum Message Length Criterion for Robust Linear Regression, which develops a simple, finite sample criterion for model selection in linear models with heavy tailed error distributions using the Minimum Message Length principle. Interestingly, the penalty term can be shown to be related to the signal-to-noise ratio of the fitted model.
External Links:
- His personal github page is https://github.com/dfschmidt80.
- The BayesReg package for efficient, high dimensional Bayesian penalized regression can be downloaded from here.
- His google scholar page is here.
Monash teaching commitment
Daniel Schmidt has developed, and acted as Chief Examiner and Lecturer, for the following units at the Faculty of Information Technology:
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
Education/Academic qualification
Computer Science, Doctor of Philosophy, Monash University
Award Date: 16 Oct 2008
Computer Science, Bachelor of Digital Systems (Honours), Monash University
Award Date: 15 Mar 2003
External positions
Senior Research Fellow (Adjunct), University of Melbourne
1 Mar 2018 → …
Research area keywords
- Bayesian Inference
- Information Theory
- Minimum Message Length
- Minimum Description Length
- Statistical and Data Analysis
- Shrinkage Estimation
- Statistical genomics
- Cancer genomics
- Mammography
Collaborations and top research areas from the last five years
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Efficient and effective methods: Efficient and effective methods for classifying massive time series data
Webb, G. (Primary Chief Investigator (PCI)), Schmidt, D. (Chief Investigator (CI)) & Keogh, E. (Partner Investigator (PI))
11/03/24 → 10/03/27
Project: Research
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Quantum Information Technology: Quantum Information Technology: Industry Readiness & Applications
Steinfeld, R. (Primary Chief Investigator (PCI)), simmons, M. (Chief Investigator (CI)), Usman, M. (Chief Investigator (CI)), Phung, D. (Chief Investigator (CI)), Tack, G. (Chief Investigator (CI)), Pas, E. (Chief Investigator (CI)), Sakzad, A. (Associate Investigator (AI)), Cui, S. (Associate Investigator (AI)), Aleti, A. (Associate Investigator (AI)), Garcia De La Banda Garcia, M. (Associate Investigator (AI)), Nakashima, P. (Associate Investigator (AI)), Schmidt, D. (Associate Investigator (AI)), Esgin, M. (Associate Investigator (AI)) & Fay, J. (Associate Investigator (AI))
1/01/23 → 31/12/27
Project: Research
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SETAR-Tree: Global model-based forecasting with Trees and Threshold Autoregressive Models
Schmidt, D. (Primary Chief Investigator (PCI)), Koo, B. (Chief Investigator (CI)) & Hyndman, R. (Chief Investigator (CI))
21/10/22 → 31/12/23
Project: Research
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Rethinking the Data-driven Discovery of Rare Phenomena
Boley, M. (Primary Chief Investigator (PCI)), Buntine, W. (Partner Investigator (PI)), Schmidt, D. (Chief Investigator (CI)), Kuhlmann, L. (Chief Investigator (CI)) & Scheffler, M. (Partner Investigator (PI))
29/07/21 → 28/11/24
Project: Research
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Tokopedia : TokoNOW Forecasting Project and Review-based Recommendation Systems development-Tokopedia
Bergmeir, C. (Primary Chief Investigator (PCI)), Schmidt, D. (Chief Investigator (CI)), Saputra, R. (Chief Investigator (CI)), Wilson, C. (Chief Investigator (CI)), Taniar, D. (Chief Investigator (CI)) & Lee, V. (Chief Investigator (CI))
27/07/21 → 19/09/22
Project: Research
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Time series adversarial attacks: an investigation of smooth perturbations and defense approaches
Pialla, G., Ismail Fawaz, H., Devanne, M., Weber, J., Idoumghar, L., Muller, P. A., Bergmeir, C., Schmidt, D. F., Webb, G. I. & Forestier, G., Jan 2025, In: International Journal of Data Science and Analytics. 19, p. 129–139 11 p.Research output: Contribution to journal › Article › Research › peer-review
Open AccessFile1 Citation (Scopus) -
Breast and bowel cancers diagnosed in people ‘too young to have cancer’: A blueprint for research using family and twin studies
Hopper, J. L., Li, S., MacInnis, R. J., Dowty, J. G., Nguyen, T. L., Bui, M., Dite, G. S., Esser, V. F. C., Ye, Z., Makalic, E., Schmidt, D. F., Goudey, B., Alpen, K., Kapuscinski, M., Win, A. K., Dugué, P. A., Milne, R. L., Jayasekara, H., Brooks, J. D. & Malta, S. & 11 others, , Dec 2024, In: Genetic Epidemiology. 48, 8, p. 433-447 15 p.Research output: Contribution to journal › Article › Research › peer-review
Open Access1 Citation (Scopus) -
Computing marginal and conditional divergences between decomposable models with applications in quantum computing and earth observation
Lee, L. K., Webb, G. I., Schmidt, D. F. & Piatkowski, N., 22 Aug 2024, In: Knowledge and Information Systems. 66, p. 7527–7556 30 p.Research output: Contribution to journal › Article › Research › peer-review
Open AccessFile -
Forecasting events in multidimensional electroencephalographic brain data: Application to epileptic seizure prediction.
Liu, Y., Soto-Breceda, A., Cook, M. J., Karoly, P., Grayden, D. B., Kuhlmann, L., Freestone, D. R. & Schmidt, D., 2024, FUSION 2024 - 27th International Conference on Information Fusion. Marcenaro, L. & Rago, C. (eds.). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 8 p.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
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Genetic and environmental causes of variation in an automated breast cancer risk factor based on mammographic textures
Ye, Z., Dite, G. S., Nguyen, T. L., MacInnis, R. J., Schmidt, D. F., Makalic, E., Al-Qershi, O. M., Nguyen-Dumont, T., Goudey, B., Stone, J., Dowty, J. G., Giles, G. G., Southey, M. C., Hopper, J. L. & Li, S., 1 Feb 2024, In: Cancer Epidemiology, Biomarkers and Prevention. 33, 2, p. 306-313 8 p.Research output: Contribution to journal › Article › Research › peer-review
1 Citation (Scopus)