Researcher

Professor Flora Salim

My Expertise

Prof Salim brings with her a wealth of award-winning research experience, where she investigates the intersection of ubiquitous computing, machine learning, and data science, with specific interests in behaviour-modelling and data-efficient learning with multimodal sensor data.

Fields of Research (FoR)

Pervasive computing, Stream and sensor data, Machine learning, Deep learning, Artificial intelligence, Neural networks, Context learning, Semi- and unsupervised learning, Cyberphysical systems and internet of things, Data engineering and data science, Human-centred computing, Fairness, accountability, transparency, trust and ethics of computer systems

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Biography

Professor Flora Salim is the inaugural CISCO Chair of Digital Transport, UNSW Sydney. Her research sits in the cross-cutting areas of ubiquitous computing, machine learning, and data science, with specific interests on representation learning of spatio-temporal and mobility behaviours and data-efficient learning with multimodal sensor data. Her recent research focus includes self-supervised learning, machine learning for multimodal...view more

Professor Flora Salim is the inaugural CISCO Chair of Digital Transport, UNSW Sydney. Her research sits in the cross-cutting areas of ubiquitous computing, machine learning, and data science, with specific interests on representation learning of spatio-temporal and mobility behaviours and data-efficient learning with multimodal sensor data. Her recent research focus includes self-supervised learning, machine learning for multimodal time-series, explainable AI, fair machine learning, with specific applications on mobility data science and personalised recommender systems for smart cities/buildings/transport/energy, smart environments, and intelligent task assistants. Her research has been funded by Australian Research Council (ARC), Victorian Government, Microsoft Research US, Northrop Grumman Corporation US, Rheinmetall Defence Australia, Qatar National Priorities Research Program, IBM Research, Alexander von Humboldt Foundation, Bayer Foundation, several city councils, and many other industry and government partners/funders. She is the recipient of Women in AI Awards 2022 Australia New Zealand in the Defence and Intelligence category. She has received several fellowships in the past, including Humboldt Fellowship,  Humboldt-Bayer Fellowship, Veski Fellowship, and ARC Postdoctoral Industry (APDI) Fellowship. She obtained her PhD from Monash University in 2009.

She serves as a member of the Australian Research Council (ARC) College of Experts. She is an Associate Investigator of the ARC Centre of Excellence in Automated Decision Making and Society. She serves as a Steering Committee member of ACM UbiComp, Associate Editor of the PACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), Area Editor of Pervasive and Mobile Computing. She has served as a senior PC member of premier AI and data science conferences including AAAI, CIKM, WWW. She was the PC Co-Chair of UbiComp 2020 and PerCom 2018.

She holds an Honorary Professor appointment at RMIT University. Until recently, she was the co-Deputy Director of RMIT Centre for Information Discovery and Data Analytics. She was a Visiting Professor at University of Kassel, Germany, and University of Cambridge, England, in 2019. 

Personal website: florasalim.com ; Twitter: @flosalim ; LinkedIn


My Research Supervision


Supervision keywords


Areas of supervision

The R&D ecosystem through Prof Salim’s Cisco chair role brings in new opportunities for novel research in human behaviour representation learning at multiple scale, given the access to high quality, high volume of multimodal spatio-temporal and mobility datasets from Australian cities, and excellent opportunities to engage with industry and communities to translate the research into real-world contexts of use.

Available PhD Topics:

  • Representation learning of spatio-temporal and/or mobility data
  • Data-efficient learning with multimodal sensor data
  • Language models, graph models, hybrid models for time-series/spatio-temporal/mobility data
  • Edge AI / machine learning on the edge
  • Mobility data science
  • Graph embedding for spatio-temporal / POI recommendation
  • Human-centric behaviour learning with AI+IoT/wearables for applications in the future of transport/mobility and the associated areas

To apply, please submit the following documents to flora.salim@unsw.edu.au.

  • a cover letter (research statement), addressing the criteria and topics in the call.
  • a CV that includes any publications/awards and the contact details of 2 referees.
  • a copy of electronic academic transcripts.
  • a short brief or abstract of research proposal, to be developed further after successful interview.

My Engagement

A sample of news coverage and/or interviews

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