Supervisors in Information Technology, Monash University

Jianfei is a Professor at Faculty of IT, Monash University, where he currently serves as the Head for the Data Science & AI Department. His major research interests include computer vision, multimedia and visual computing. He has published more than 200 technical papers in international conferences and journals. He is a co-recipient of paper awards in ACCV, ICCM, IEEE ICIP and MMSP.

Andrew has over 20 years’ leadership experience in roles such as Associate Head of School (Learning), Associate Head of School (Curriculum) and Associate Head of School (Cloud Learning) at Deakin University. He also served as Deputy Academic Director (Learning Transformations) and Deputy Director (STEMed) at Swinburne University. Among his many contributions to education, Andrew invented the Task-Oriented Portfolio Assessment (TOPA) approach and the OnTrack system that applies TOPA used by over 230K people.

I am the Director of the Environmental Informatics Hub within the Faculty of Information Technology at Monash University, and a Professor of Artificial Intelligence in the Department of Data Science and AI. My expertise lies in decision AI, particularly in Markov Decision Processes (MDPs), Reinforcement Learning (RL), and Partially Observable Markov Decision Processes (POMDPs). I am interested in developing and applying new algorithms that support environmental protection and sustainability.

Muhammad Aamir Cheema is currently an ARC Future Fellow and Associate Professor at Monash University. He was a Lecturer at Monash from Nov-2013 to Dec-2015 and Senior Lecturer from 2016-2018. Prior to this, he was a Research Fellow (2011-2013) at School of Computer Science and Engineering at The University of New South Wales (UNSW), Australia. He obtained his Ph.D. degree (2008-2011) from UNSW, Australia. 

I am actively recruiting PhD candidates to undertake machine learning for biomedical imaging research.

The following funded research projects are open for applications - 

(i) Machine learning based MRI and PET image reconstruction for motion correction, fast imaging and quantitative imaging.

(ii) Deep learning development for mobile MRI scanners and their clinical utility validation.