Research

At the Affective Data Science Lab, we integrate programming, experimental methods, statistics, and machine learning to study emotion and its related disorders. As an interdisciplinary team, we aim to develop innovative techniques and technologies for recognizing, predicting, and expressing emotions.

Research themes

Our work focuses on three key research themes, each contributing to the study of emotion.

Data provisioning

We focus on curating, collecting, and managing large-scale datasets that capture emotional expressions, physiological responses, and related behavioral data. Our priority is to ensure that the data we use is reliable, representative, and ethically sourced (e.g., RAVDESS). You can learn more about these resources on our Datasets page.

Research tools

Based in the Computer Science department at Ontario Tech, our students often have strong software development skills and are eager to build portfolios for future employers. At the Affective Data Science Lab, we are developing the next generation of research tools for both psychology and computing. Students gain hands-on experience with industry-standard tools and practices (e.g., Git, PyPI, documentation, automated test cases), which help them build a robust portfolio for potential employers. More information on these tools can be found on our Tools page.

Affective Data Science

This unifying theme focuses on analyzing datasets using advanced tools to generate new insights into emotion. Some of our previous findings include:

• Head movements of speakers encode emotional information [pdf]
• Patients with Parkinson's disease exhibit impaired facial mimicry [pdf]
• Body sway reflect leadership in joint music performance [pdf]

Funding

Our research is generously supported by numerous funding organizations: