Research
How do people sample audiovisual information made noisy by aging and by everyday environments?
Where attention goes when sight and sound get noisy: eye tracking and EEG studies of faces, listening and attention, in younger and older adults.
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Individual differences and aging in face perception
Where younger and older adults look when they identify faces, how fixation location, emotion and face masks change what they see, and why people differ.
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Aging, hearing loss and hearing aids
How hearing loss and hearing aids shape everyday listening: remote microphones in group conversations, tinnitus management, and age-related changes in speech processing.
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Listening effort and attention in noisy, everyday settings
Eye movements, head movements and neural speech tracking as markers of how hard people work to listen, and when their attention drifts away.
Psychometrics & data science
How do we measure human behaviour reliably, in the lab and in everyday life, when the recordings are noisy?
Bringing noisy recordings into focus: measuring eyes, movement and attention with lab-grade and everyday devices, and turning them into numbers you can trust.
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Gaze + EEG
Modelling how people sample audiovisual information made noisy by aging and by everyday environments, using eye tracking and EEG together.
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Wi-Fi sensing
Two ESP32 boards read Wi-Fi channel state information to pick up breathing-related movement in a room — no camera, nothing worn.
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R&D Research Analyst, Sonova Group
Applied hearing-aid research for Unitron, Phonak and Sonova: co-authored two white papers and contributed to two registered clinical trials (NCT05292534, NCT05521308).
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Signal processing for EEG and eye tracking
Pipelines for naturalistic audiovisual behavioural data, including ocular and head markers of listening effort from mobile eye-tracking glasses.
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Statistical & Bayesian modelling
Mixed-effects models, psychometric functions, signal detection theory, drift-diffusion models and foveated ideal-observer analysis, in Python, R and MATLAB.
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Teaching statistics
Course instructor at the University of Toronto: Introductory Statistics (180 students, in person) and Advanced Statistics (190 students, online).
Scientific translation
How can I best explain my findings to the people who would benefit from them, and help them find ways to act on them?
Science in plain sight: videos, games and tools anyone can try.
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YouTube channel
Science explained for everyone. First videos on the way.
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Dementia risk check
Nearly half of dementia cases are linked to 14 things people can change. See which apply to you, from the 2024 Lancet Commission.
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Two faces in one picture
Mix two faces into one picture, then walk away and watch them swap. A game about how your eyes see big shapes and tiny details.
More games — on the horizon.
AI / ML
How can AI and machine learning models help us understand human vision, and put that understanding to work in healthcare and advertising?
A human point of view on AI: models that quantify what people see, and people who evaluate the models.
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Perceptual experience with computer vision
Quantifying what people see and attend to with computer vision models.
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Remote health screening
Research toward remote health technology — webcams and Wi-Fi as everyday measurement tools, not diagnosis.
ML/AI for marketing, finance and business — in my sights next.
Toolkit
Which tools let us measure behaviour in the lab, model it, and take it into everyday life?
The lenses behind the work: lab-grade measurement, the models that read it, and everyday devices that take it outside the lab.
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Psychometric tools
EEG, eye tracking and behavioural testing — psychophysics, online experiments (jsPsych, with a virtual chinrest to calibrate screen size and viewing distance) and daily experience sampling (EMA).
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ML/AI & computational tools
Computer vision (DeepGaze, MediaPipe), foveation filters and foveated ideal-observer analysis, Bayesian models, eye-movement HMMs, and LLMs that explain what vision models decode.
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Naturalistic tools
Remote cameras for eye tracking, Wi-Fi for body movement, AirPods for head movement, and mobile eye-tracking glasses.
Fun projects
How can everyday sensors and gamification help people understand and improve their own health and habits?
Side projects from my peripheral vision: playful uses of computer vision and sensors in daily life.
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flamingo
How old is your balance? A webcam one-leg balance test, read against published age norms. Comments welcome.
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Whack-to-Snap
Slap your MacBook to take a screenshot.