Yongli Ren is a Senior Lecturer at the Computer Science and Information Technology, School of Science, at RMIT University in Melbourne, Australia. He has won the “Alfred Deakin Medal for Doctoral Thesis” 2013 at Deakin University, and the “best paper” award at the IEEE/ACM ASONAM 2012 Conference. He has a PhD degree in Information Technology from Deakin University, Australia. His research interest lies in: Personalisation, Recommender System, Collaborative Filtering, Web Mining, and Log Analysis.
Personalisation
Recommender system
Collaborative Filtering
A project done in collaboration with Arup Melbourne
May 18/2020.
An ARC Linkage Project with Scentre group (Westfield)
Jun 14/1818.
Understanding the Predictability of User Demographics from Cyber-Physical-Social Behaviours in Indoor Retail Spaces
Jun 14/1818.
This project will deploy machine learning to human behaviour and building operational data obtained through Activity Based Working (ABW), aimed at providing insights towards optimising and personalising ABW. The outcome is a cloud-based integrated platform, with tailored ABW apps, for capturing and analysing occupancy behaviour and building performance data.
Sep 18/2020.
Cortana Intelligence Institute is a co-funded initiative between Microsoft Research, Cortana Research and RMIT University, which will drive the next-generation of capabilities for Microsoft’s digital assistant, Cortana.
Jan 05/2020.
This research aims to develop a framework to recognise and anticipate unforeseen emerging situations, such as schedule changes, incidents, and disruptions. The project will address a significant knowledge gap by capturing and modelling unpredictability in human mobility and work routines. The outcome will be a situation recognition framework that can be applied at the individual, social group, and urban level, and at multiple locations and time scales. This should provide users with timely notifications and recommendations to resume their activities and routines. The expected benefits will be far-ranging and adaptable to many domains, from personal smart assistants to trip planning and emergency services. - ARC Discovery Project DP190101485
Jan 15/2222.
The modeling of social and mobility networks continues to gain importance in a variety of fields ranging from epidemiology social group and community detection, to user movement and behavior understanding.
Jan 15/2020.
© 2021 Flora Salim - CRUISE Research Group.
