معرفی
Enes Makalic is a Professor of Machine Learning at the Department of Data Science & AI, Faculty of Information Technology, Monash University, Melbourne, Australia. With over 15 years of experience in Bayesian inference, information theoretic statistics, and digital health, his academic career demonstrates significant interdisciplinary impact across computer science, statistics, and medical research.
- Faculty of Information Technology, Monash University (Current)
- Department of Data Science & AI, Monash University (Current)
Professor Makalic completed his PhD in Machine Learning at Monash University in 2007, following a Bachelor of Computer Science (Honours) from the same institution in 2002. His research expertise spans theoretical and applied statistics, with particular focus on model selection for ultra-high dimensional statistical models, minimum message length principles of inductive inference, and applications of information theoretic statistics to epidemiology and medical imaging.
His current research activities prominently feature image processing and risk prediction, with special emphasis on digital mammography and breast cancer, as well as statistical genetics and genomic prediction models for rare, polygenic diseases and traits. Professor Makalic has developed and coordinated subjects in computer science, biostatistics, survival analysis, and machine learning, and has supervised Honours, Masters, and PhD students to completion.
Analysis of his recent publication trends reveals a strong focus on breast cancer risk prediction through mammographic analysis, statistical genetics, and applications of minimum message length principles. His work integrates machine learning with medical imaging to develop automated measures for breast cancer risk assessment, with significant translational potential for clinical implementation.
- Active reviewer and program committee member for numerous journals and conferences
- Recipient of research funding from NHMRC and Cancer Council Victoria
Professor Makalic's research contributes to UN Sustainable Development Goals related to good health and well-being through his work in digital health and medical applications of machine learning. His projects demonstrate strong collaborative partnerships across disciplines, particularly with medical researchers focused on cancer prevention and early detection.
