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.
Supervisor's projects
| Project name Sort descending | Type |
|---|---|
| Bayesian Poisson regression with global-local shrinkage priors | Honours and Masters project |
| Inductive inference with Minimum Message Length | Honours and Masters project |
| Minimum Message Length | Research project |
| MML decision trees for survival analysis | Honours and Masters project |
| Using AI and machine learning to improve polygenic risk prediction of disease | Honours and Masters project |
| Using AI and machine learning to improve polygenic risk prediction of disease | Research project |
Co-supervising
| Project name Sort descending | Type | Primary supervisor |
|---|---|---|
| Generating explanations that involve uncertainty | Research project | Ingrid Zukerman |