Professor Flora Salim
Honorary Professor
Details
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College: School of Computing Technologies
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Department: School of Computing Technologies
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Campus: City Campus Australia
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flora.salim@rmit.edu.au
Open to
- Media enquiries
- Masters Research or PhD student supervision
About
Flora Salim’s research is in the cross-cutting areas of human behaviour modelling, machine learning with multimodal time-series and spatio-temporal data, and AI on the edge (IoT, sensors, wearables, digital systems and infrastructures).
Flora Salim is a Professor in the School of Computing Technologies, the co-Deputy Director of RMIT Centre for Information Discovery and Data Analytics (CIDDA), and an Associate Investigator of ARC Centre of Excellence in Automated Decision Making and Society. Flora leads the Context Recognition and Urban Intelligence (CRUISE) group.
Flora's research interests include machine learning on stream and sensor data, behaviour modelling, time-series and spatio-temporal data mining, mobility data science, and ubiquitous computing. She has received more than $10M in research funding in the last 10 years. Her research has been funded by Australian Research Council (ARC), Victorian Government, Microsoft Research, Northrop Grumman Corporation US, Rheinmetall Defence Australia, Qatar National Priorities Research Program, IBM Research, Alexander von Humboldt Foundation, Bayer Foundation, city councils, and several other industry and local government partners.
She was a Humboldt-Bayer Fellow, Humboldt Fellow (experienced researcher), Victoria Fellow, the recipient of the RMIT Vice-Chancellor's Award for Research Excellence–Early Career Researcher 2016; the RMIT Award for Research Impact - Technology 2018; the RMIT School of Science HDR Supervision Excellence Award 2017; Australian Research Council (ARC) Postdoctoral Research Industry (APDI) Fellow (2012-2015), and IBM Smarter Planet Industry Skills Innovation Award (2010).
She serves as Associate Editor of the PACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), Area Editor of Pervasive and Mobile Computing, and a Steering Committee member of ACM UbiComp. She was a Visiting Professor at University of Kassel, Germany, and University of Cambridge, England, in 2019.
Industry experience:
Senior software engineer, mediaproxy and Xenon Systems
Awards:
2021 PACM IMWUT Distinguished Paper Award for n-Gage paper
2019 Humboldt-Bayer Fellowship, Bayer Foundation, Germany
2019 Humboldt Fellowship (Experienced Researcher), Alexander von Humboldt Stiftung, Germany
2019 RMIT Industry Media Star Award
2018 Victoria Fellowship, from veski, Victoria Government
2018 RMIT Award for Research Impact - Technology
2016 RMIT Vice-Chancellor’s Award for Research Excellence – Early Career Researcher
2012 Australian Research Council (ARC) Postdoctoral Fellowship (Industry)
2010 IBM Smarter Planet Industry Skills Innovation Award, IBM Research, USA
Flora Salim is a Professor in the School of Computing Technologies, the co-Deputy Director of RMIT Centre for Information Discovery and Data Analytics (CIDDA), and an Associate Investigator of ARC Centre of Excellence in Automated Decision Making and Society. Flora leads the Context Recognition and Urban Intelligence (CRUISE) group.
Flora's research interests include machine learning on stream and sensor data, behaviour modelling, time-series and spatio-temporal data mining, mobility data science, and ubiquitous computing. She has received more than $10M in research funding in the last 10 years. Her research has been funded by Australian Research Council (ARC), Victorian Government, Microsoft Research, Northrop Grumman Corporation US, Rheinmetall Defence Australia, Qatar National Priorities Research Program, IBM Research, Alexander von Humboldt Foundation, Bayer Foundation, city councils, and several other industry and local government partners.
She was a Humboldt-Bayer Fellow, Humboldt Fellow (experienced researcher), Victoria Fellow, the recipient of the RMIT Vice-Chancellor's Award for Research Excellence–Early Career Researcher 2016; the RMIT Award for Research Impact - Technology 2018; the RMIT School of Science HDR Supervision Excellence Award 2017; Australian Research Council (ARC) Postdoctoral Research Industry (APDI) Fellow (2012-2015), and IBM Smarter Planet Industry Skills Innovation Award (2010).
She serves as Associate Editor of the PACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), Area Editor of Pervasive and Mobile Computing, and a Steering Committee member of ACM UbiComp. She was a Visiting Professor at University of Kassel, Germany, and University of Cambridge, England, in 2019.
Industry experience:
Senior software engineer, mediaproxy and Xenon Systems
Awards:
2021 PACM IMWUT Distinguished Paper Award for n-Gage paper
2019 Humboldt-Bayer Fellowship, Bayer Foundation, Germany
2019 Humboldt Fellowship (Experienced Researcher), Alexander von Humboldt Stiftung, Germany
2019 RMIT Industry Media Star Award
2018 Victoria Fellowship, from veski, Victoria Government
2018 RMIT Award for Research Impact - Technology
2016 RMIT Vice-Chancellor’s Award for Research Excellence – Early Career Researcher
2012 Australian Research Council (ARC) Postdoctoral Fellowship (Industry)
2010 IBM Smarter Planet Industry Skills Innovation Award, IBM Research, USA
Academic positions
- Visiting Professor
- University of Kassel
- Kassel, Germany
- 2019 – 2019
- Visiting Professor
- University of Cambridge
- Cambridge, United Kingdom
- 2019 – 2019
Supervisor projects
- Bias and Fairness in ADM for time-series and sequential data
- 22 Nov 2021
- Systems for Automated Decision-Making
- 8 Nov 2021
Modelling Heterogeneous Time-series with Multi-resolution Sporadic Data
- 17 Oct 2019
- Optimization of Learning on Graphs and Spatio-temporal Data
- 30 Jul 2019
- Disentangled Representation Learning for Spatio-temporal Data
- 24 Jul 2019
- Learning from Multimodal Time-series Data with Minimal Supervision
- 20 Sep 2018
- Efficient and Flexible Visual Representation Learning
- 2 Jul 2018
- Human Behaviour Sensing and Profiling in the Wild
- 1 Mar 2018
- Modelling Dynamics of Urban Mobility for Predictive Surveillance of Crime
- 20 Jul 2015
- Evolutionary Multivariate Time Series Prediction
- 24 Jun 2015
- Situation Inference and Context Recognition for Intelligent Mobile sensing Applications
- 2 Mar 2015
- Building Utilisation Analytics: Human Occupancy Counting and Thermal Comfort Prediction with Ambient Sensing
- 25 Aug 2014
- Mining Human Mobility Patterns from Pervasive Spatial and Temporal Data
- 21 Jul 2014
- Learning spatiotemporal patterns for monitoring smart cities and infrastructure
- 21 Jul 2014
Teaching interests
Supervisor interest areas
Deep learning for time-series and sensor data
Large-scale/urban-scale event detection and forecasting with heterogeneous time-series for mobility and related behaviours (e.g. energy, public health)
Fair and explainable machine learning
Human behaviour, emotion, health, wellbeing sensing with wearables
Personalisation and mobile-based and POI recommender systems
Supervisor projects
Self-supervised learning for multimodal data
Forecasting with heterogeneous time-series
Representation learning with uncertainty
Disentanglement representation learning for spatio-temporal data
Spatio-temporal representation learning for situational awareness and traffic flow forecasting
Behaviour and emotion sensing with wearables
Graph and sequential embedding learning
Mobile information behaviour and intent modelling
Programs (https://www.rmit.edu.au/study-with-us/information-technology):
BP094 - Bachelor of Computer Science
MC271 - Master of Artificial Intelligence
MC267 - Master of Data Science
Deep learning for time-series and sensor data
Large-scale/urban-scale event detection and forecasting with heterogeneous time-series for mobility and related behaviours (e.g. energy, public health)
Fair and explainable machine learning
Human behaviour, emotion, health, wellbeing sensing with wearables
Personalisation and mobile-based and POI recommender systems
Supervisor projects
Self-supervised learning for multimodal data
Forecasting with heterogeneous time-series
Representation learning with uncertainty
Disentanglement representation learning for spatio-temporal data
Spatio-temporal representation learning for situational awareness and traffic flow forecasting
Behaviour and emotion sensing with wearables
Graph and sequential embedding learning
Mobile information behaviour and intent modelling
Programs (https://www.rmit.edu.au/study-with-us/information-technology):
BP094 - Bachelor of Computer Science
MC271 - Master of Artificial Intelligence
MC267 - Master of Data Science
Research interests
Her expertise lies at the intersection of ubiquitous computing (on capturing and modelling human mobility and dynamic user behaviours) and data science and machine learning (particularly on time-series, spatiotemporal, and multimodal sensor data). She has many years of experience in analysing human behaviours in multiple contexts and developing key techniques to enable situational awareness for stakeholders and personalised intelligent assistance for the end users. She always take user-centric approaches of data capture and analysis in modelling and profiling human behaviours. Given the nexus of the two research areas, her research contributions on human behaviour (including mobility) modelling have been largely published in the pervasive/ubiquitous computing venues (including UbiComp/IMWUT, PerCom, Pervasive and Mobile Computing, and IEEE IoT Journal), and the contributions in machine learning with time-series, spatio-temporal, and trajectory data have been published in NeurIPS, WWW, KDD, WSDM, TKDE, PAKDD, and many more.
Research keywords:
Machine learning, Deep learning, Time-series, Sensor data, Spatio-temporal data, Ubiquitous computing, Mobility, Data science, Behaviour modelling, Human activity Recognition, Trajectory, Forecasting, Predictive analytics, Emotion sensing, Wearable computing, Trustworthy AI, Fairness in machine learning, Explainable AI
Research keywords:
Machine learning, Deep learning, Time-series, Sensor data, Spatio-temporal data, Ubiquitous computing, Mobility, Data science, Behaviour modelling, Human activity Recognition, Trajectory, Forecasting, Predictive analytics, Emotion sensing, Wearable computing, Trustworthy AI, Fairness in machine learning, Explainable AI
