Projects per year
Personal profile
Biography
Enes Makalic is a Professor of Machine Learning at the Department of Data Science and AI, Faculty of Information Technology, Monash University, Melbourne, Australia. He has a lifelong interest in theoretical computer science and a mission to enable global impact through quality teaching and research that emphasizes collaborative, inter-disciplinary partnerships. Since completing his PhD in machine learning, he has spent over 15 years working in Bayesian inference, information theoretic statistics and digital health.
Research interests
His research interests include:
- Theoretical and applied Bayesian statistics, including model selection and parameter estimation of ultra-high dimensional statistical models;
- The minimum message length principles of inductive inference, and applications of information theoretic statistics to epidemiology;
- Image processing and risk prediction, focusing on digital mammography and breast cancer; and
- Statistical genetics and genomic prediction models for rare, polygenic diseases and traits.
Enes is also an active member of the academic community, serving as a reviewer and program committee member for numerous journals and conferences.
Monash teaching commitment
Enes has developed and coordinated subjects and short courses in the areas of computer science, biostatistics, survival analysis, and machine learning. He enjoys mentoring students, and has supervised Honours, Masters and PhD students to completion.
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
Machine Learning, PhD, Minimum message length inference of artificial neural networks, MONASH UNIVERSITY
Award Date: 19 Jun 2007
Computer Science, Bachelor of Computer Science (Honours), MONASH UNIVERSITY
Award Date: 31 Dec 2002
Research area keywords
- Statistics
- Machine Learning
- Data Science
- Digital Health
- Statistical genomics
- Information Theory
Collaborations and top research areas from the last five years
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Centre of Research Excellence in Precision Public Health Approaches to Breast Cancer Screening, Early Detection and Mortality Reduction
Hopper, J. L., Southey, M., Frazer, H., Emery, J. D., Makalic, E., Reintals, M., Petrie, D., Stone, J. L., Thompson, E. W., Macinnis, R., Bickerstaffe, A., Dite, G., Boyle, D., Jenkins, M. A., Sung, J., Bondell, H., Winship, I. M., Ingman, W., Britt, K. & Lee, D.
National Health and Medical Research Council (NHMRC) (Australia)
1/10/21 → 30/09/26
Project: Research
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Improved and automated measures of breast cancer risk based on digital mammography and family history data collected by BreastScreen that will enable tailored screening for breast cancer
Hopper, J. L., Schmidt, D., Nickson, C. A., Makalic, E., Nguyen, L., Mann, G. B., Frazer, H., Dugue, P., Dite, G. S. & Evans, J.
1/01/19 → 31/12/21
Project: Research
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Development of automated measures from mammograms that predict masking and risk and pilot implementation into a clinical service
Hopper, J. L., Schmidt, D., Makalic, E., Apicella, C., Keogh, L. A., Frazer, H., Dugué, P., Highnam, R., Nguyen, L. & Evans, J.
1/01/17 → 3/06/23
Project: Research
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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) -
Minimum message length inference of the Weibull distribution with complete and censored data
Makalic, E. & Schmidt, D. F., 2024, AI 2023: Advances in Artificial Intelligence - 36th Australasian Joint Conference on Artificial Intelligence, AI 2023 Brisbane, QLD, Australia, November 28 – December 1, 2023 Proceedings, Part I. Liu, T., Webb, G., Yue, L. & Wang, D. (eds.). Singapore Singapore: Springer, p. 291-303 13 p. (Lecture Notes in Computer Science; vol. 14471).Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
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Outcomes of possible and probable rheumatic fever: A cohort study using northern Australian register data, 2013–2019
Goddard, L., Kaestli, M., Makalic, E. & Ralph, A. P., 3 Jan 2024, In: PLOS Global Public Health. 4, 1, 16 p., e0002064.Research output: Contribution to journal › Article › Research › peer-review
Open AccessFile -
Australian genome-wide association study confirms higher female risk for adult glioma associated with variants in the region of CCDC26
Alpen, K., Vajdic, C. M., MacInnis, R. J., Milne, R. L., Koh, E. S., Hovey, E., Harrup, R., Bruinsma, F., Nguyen, T. L., Li, S., Joseph, D., Benke, G., Dugue, P. A., Southey, M. C., Giles, G. G., Rosenthal, M., Drummond, K. J., Nowak, A. K., Hopper, J. L., Kapuscinski, M., & 1 others , Jul 2023, In: Neuro-Oncology. 25, 7, p. 1355-1365 11 p.Research output: Contribution to journal › Article › Research › peer-review
Open Access6 Citations (Scopus) -
Causal relationships between breast cancer risk factors based on mammographic features
Ye, Z., Nguyen, T. L., Dite, G. S., MacInnis, R. J., Schmidt, D. F., Makalic, E., Al-Qershi, O. M., Bui, M., Esser, V. F. C., Dowty, J. G., Trinh, H. N., Evans, C. F., Tan, M., Sung, J., Jenkins, M. A., Giles, G. G., Southey, M. C., Hopper, J. L. & Li, S., 25 Oct 2023, In: Breast Cancer Research. 25, 1, 127.Research output: Contribution to journal › Article › Research › peer-review
Open AccessFile4 Citations (Scopus)