- July 21, 2021:
- April 12, 2021:
- September 23, 2020:
- September 12, 2020:
- August 30, 2020:
Contact InfoEmail: mariakakis at cs dot toronto dot edu
I am an Assistant Professor in the Department of Computer Science at the University of Toronto and an Affiliate Scientist at Techna. My main research focus is on mobile health, but I also work in other areas of ubiquitous computing and human-computer interaction like mobile interaction and novel sensing.
Before joining the University of Toronto, I completed a joint-postdoctoral role under Larsson Omberg and Anind Dey, where I split my time between the Sage Bionetworks and the School of Computer Science and Engineering at the University of Washington. I completed my PhD at the University of Washington under the advising of Shwetak Patel and Jacob O. Wobbrock. I completed my undergraduate studies at Duke University with a double major in Electrical and Computer Engineering and Computer Science. During that time, I conducted research under Romit Roy Choudhury.
Graduate Recruiting (updated Nov. 2021)
I have a sizable cohort of graduate students at this time, so I will not be looking at MSc or PhD applications that are submitted during the 2021–2022 academic year.
Undergraduate Recruiting (updated Nov. 2021)
My research group is looking for undergraduates at the University of Toronto to take on the following roles. The descriptions listed with each position are intended to provide a rough idea of the skills and experience level that we are seeking, but everyone will be considered for all positions.
- Women's Health: Someone with interest or experience in conducting longitudinal user studies and interviewing participants; ideal for an early-stage undergraduate willing to invest time in a long-term project
- Women's Health: Someone with experience in generating visualizations and applying machine learning on data from surveys and sensors; ideal for a late-stage undergraduate familiar with data science
- Hand Motor Control Assessment: Someone with experience in deep learning and computer vision; ideal for a late-stage undergraduate who can work independently