Rafael Calvo is Professor at the University of Sydney, ARC Future Fellow and Director of the Wellbeing Technologies Lab.
He worked at the Language Technology Institute in Carnegie Mellon University, Universidad Nacional de Rosario (Argentina) and on sabbaticals at the University of Cambridge and the University of Memphis. Rafael also has worked as an Internet consultant for projects in the US, Australia, Brasil, and Argentina. He is the recipient of 5 teaching awards for his work on learning technologies, and the author of two books and many publications in the fields of learning technologies, affective computing and computational intelligence.
Rafael is Editor of the Oxford Handbook of Affective Computing and co-author of “Positive Computing” (MIT Press) with Dorian Peters. Rafael has also been Associate Editor of the IEEE Transactions on Affective Computing, IEEE Transactions on Learning Technologies and the Journal of Medical Internet Research Human Factors (JMIR-HF).
Experience
–present
Professor, University of Sydney
2015–present
Future Fellow, Australian Research Council
Education
2000
Universidad Nacional de Rosario, PhD
Grants and Contracts
2017
ARC Industrial Transformation Research Hub for Digital Enhanced Living
Role:
CI
Funding Source:
Australian Research Council
2017
Get A Move On NetworkPlus
Role:
AI
Funding Source:
UK Engineering and Physical Sciences Research Council (EPSRC)
2015
Well@Work. Wellbeing in the Workplace
Role:
CI
Funding Source:
Beyondblue
2013
Linking virtual clinic and wellbeing centre
Role:
CI
Funding Source:
Young and Well CRC
2013
Learning Environments Across Disciplines (LEADS): Supporting Technology Rich Learning across Disciplines
Role:
CI
Funding Source:
Social Sciences and Humanities Research Council of Canada (SCHRCA)
2012
Comprehensive support for collaborative writing: visualising argument, text and process structures
Role:
CI
Funding Source:
ARC - DP
2011
Measuring the impact of feedback on the writing process and learning outcomes
Role:
CI
Funding Source:
Google
2006
Using machine Learning and automated document analysis methods to support English composition training