Professor Ann E. Nicholson was Dean in the Faculty of Information Technology 2020-2025. Other past leadership roles including Deputy Dean (Research) 2018-2020, (Acting) Deputy Dean 2016-2017, and Associate Dean (Education) 2014-2016.

After completing her BSc (Hons) and MSc in Computer Sciene at the University of Melbourne, in 1988 Professor Nicholson was awarded a Rhodes scholarship to Oxford, where she  did her doctorate in the Robotics Research Group. After completing a post-doc at Brown University, she returned to Australia to take up a lecturing position position at Monash in 1994. Prof. Nicholson specialises in the broad area of Artificial Intelligence, a  sub-discipline of computer science.  She is a leading international researcher in the specialised area of Bayesian networks, now the dominant technology for probabilistic causal modelling in intelligent systems. She has published more than 100 peer reviewed papers, co-authored the well-received book "Bayesian Artificial Intelligence", and attracted more than $12M in research funding. She has applied Bayesian Network technology to problem-solving in many domains including meteorology, epidemiology, medicine, education and environmental science. Examples include the use of BNs in biosecurity risk assessment, predicting the impact of conservation actions on threats and habitats of threatened species, fog forecasting and decision support for clinical cardiovascular risk assessment.

• Victorian Rhodes Scholarship Selection Committee, Honorary Secretary (2019-2025). In 2026, this role has move to Prof Nathan Grills, U. of Melbourne. 

While having no curent teaching commitments, Professor Ann Nicholson has experience as the Chief Examiner and/or lecturer for the following units in the Faculty of IT:

• FIT1010 Introduction to software engineering
• FIT1016/2044 Advanced project level 1 & 2
• FIT1049/2003 IT professional practice

Ann specialises in Bayesian network modelling and is an expert in dynamic Bayesian networks, planning under uncertainty, user modelling, Bayesian inference methods, and knowledge engineering with Bayesian networks.