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.
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
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):
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SDG 3 Good Health and Well-being
Collaborations and top research areas from the last five years
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Saving Endangered Australian Species Through Reproductive Hormone Analysis
Martin, L. (Primary Chief Investigator (PCI)), Johnston, S. T. (Chief Investigator (CI)), Hoffmann, P. (Chief Investigator (CI)), Fanson, K. (Chief Investigator (CI)), Makalic, E. (Chief Investigator (CI)) & Renfree, M. B. (Chief Investigator (CI))
5/01/26 → 4/01/30
Project: Research
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Leveraging AI-driven genomics to accelerate precision medicine for brain cancer
Nguyen, T. (Primary Chief Investigator (PCI)), Makalic, E. (Chief Investigator (CI)), Rodriguez, M. L. (Chief Investigator (CI)), Fettke, H. (Chief Investigator (CI)), Alpen, K. (Chief Investigator (CI)), Moore, J. (Chief Investigator (CI)), Koh, E.-S. (Chief Investigator (CI)), Ho, G.-Y. (Chief Investigator (CI)), Amukotuwa, S. (Associate Investigator (AI)) & Cardinal, C. (Associate Investigator (AI))
1/01/26 → 31/12/28
Project: Research
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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. (Primary Chief Investigator (PCI)), Southey, M. (Chief Investigator (CI)), Frazer, H. (Chief Investigator (CI)), Emery, J. D. (Chief Investigator (CI)), Makalic, E. (Chief Investigator (CI)), Reintals, M. (Chief Investigator (CI)), Petrie, D. (Chief Investigator (CI)), Stone, J. L. (Chief Investigator (CI)), Thompson, E. W. (Chief Investigator (CI)), Macinnis, R. (Chief Investigator (CI)), Bickerstaffe, A. (Associate Investigator (AI)), Dite, G. (Associate Investigator (AI)), Boyle, D. (Associate Investigator (AI)), Jenkins, M. A. (Associate Investigator (AI)), Sung, J. (Associate Investigator (AI)), Bondell, H. (Associate Investigator (AI)), Winship, I. M. (Associate Investigator (AI)), Ingman, W. (Associate Investigator (AI)), Britt, K. (Associate Investigator (AI)) & Lee, D. (Associate Investigator (AI))
NHMRC - National Health and Medical Research Council (Australia)
1/10/21 → 31/10/27
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. (Primary Chief Investigator (PCI)), Schmidt, D. (Chief Investigator (CI)), Nickson, C. A. (Chief Investigator (CI)), Makalic, E. (Chief Investigator (CI)), Nguyen, L. (Chief Investigator (CI)), Mann, G. B. (Chief Investigator (CI)), Frazer, H. (Chief Investigator (CI)), Dugue, P.-A. (Chief Investigator (CI)), Dite, G. S. (Chief Investigator (CI)) & Evans, J. (Chief Investigator (CI))
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. (Primary Chief Investigator (PCI)), Schmidt, D. (Chief Investigator (CI)), Makalic, E. (Chief Investigator (CI)), Apicella, C. (Chief Investigator (CI)), Keogh, L. A. (Chief Investigator (CI)), Frazer, H. (Chief Investigator (CI)), Dugué, P.-A. (Chief Investigator (CI)), Highnam, R. (Chief Investigator (CI)), Nguyen, L. (Chief Investigator (CI)) & Evans, J. (Chief Investigator (CI))
1/01/17 → 3/06/23
Project: Research
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Evaluation of agreement between common clustering strategies for DNA methylation-based subtyping of breast tumours
Zarean, E., Li, S., Wong, E. M., Makalic, E., Milne, R. L., Giles, G. G., McLean, C., Southey, M. C. & Dugué, P. A., 2025, In: Epigenomics. 17, 2, p. 105-114 10 p.Research output: Contribution to journal › Article › Research › peer-review
2 Link opens in a new tab Citations (Scopus) -
Assessing Pancreatic Fat and Its Correlation with Liver Fat in Suspected MASLD Patients Using Advanced Deep Learning Techniques from MRI Images
Cherrie Fung, H. C., Meneses, J. P., Surendran, N., Kutaiba, N., George, Y., Makalic, E. & Uribe, S., 2 Dec 2024, In: Applied Sciences. 14, 24, 15 p., 11924.Research output: Contribution to journal › Article › Research › peer-review
Open Access -
Associations between pathological features and risk of metachronous colorectal cancer
Zhang, Y., Win, A. K., Makalic, E., Buchanan, D. D., Pai, R. K., Phipps, A. I., Rosty, C., Boussioutas, A., Karahalios, A. & Jenkins, M. A., 15 Sept 2024, In: International Journal of Cancer. 155, 6, p. 1023-1032 10 p.Research output: Contribution to journal › Article › Research › peer-review
Open Access4 Link opens in a new tab Citations (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 Access2 Link opens in a new tab Citations (Scopus) -
Causation and familial confounding as explanations for the associations of polygenic risk scores with breast cancer: Evidence from innovative ICE FALCON and ICE CRISTAL analyses
Li, S., Dite, G. S., MacInnis, R. J., Bui, M., Nguyen, T. L., Esser, V. F. C., Ye, Z., Dowty, J. G., Makalic, E., Sung, J., Giles, G. G., Southey, M. C. & Hopper, J. L., Dec 2024, In: Genetic Epidemiology. 48, 8, p. 401-413 13 p.Research output: Contribution to journal › Article › Research › peer-review
Open Access2 Link opens in a new tab Citations (Scopus)