Computational & Quantitative Social Sciences

The DSI at the University of Toronto Scarborough, DSI@UTSC, is leading a tri-campus initiative to encourage research activity in Computational and Quantitative Social Sciences (CQSS) that includes community-building, seed funding, and training in CQSS.

The social sciences are undergoing a data sciences revolution spurred on by new statistical and algorithmic techniques, rapid advances in high-performance computing, and the proliferation of large, complex, and heterogeneous data structures (e.g., spatial, relational, temporal, and textual). These developments present exciting opportunities as well as new challenges for social scientists.

Join us for talks, workshops, and other events that explore the impact of the data sciences revolution on the social sciences across U of T and beyond.

News & Upcoming Events

New approaches to data:

With new data, novel algorithms, and increases in computing power, there is the potential to accelerate knowledge production in a wide range of areas.

Read the full story

CQSS ongoing events and opportunities

CQSS Speaker Series

World-renowned CQSS researchers

Social Science Methods Training

Coming Spring 2023
A series of workshops on methods with high potential to improve and advance research in the social sciences.

CQSS Seed Funding

Seed funding for cutting-edge, high-impact research in the area of computational and/or quantitative social science.

Blitz Workshops

A day of CQSS research.
Researchers present a lightning round of talks with opportunities for discussion and networking.

CQSS Speaker Series:

Prof. Dustin Duncan, Columbia University


Register today

CQSS Grant Recipients

Monica Alexander (Department of Statistical Sciences, Faculty of Arts and Science, University of Toronto): “Deficits of migration in the age of Covid-19: A new approach to studying changing migration patterns.”

Spike W. S. Lee (Joseph L. Rotman School of Management, University of Toronto): “What Do Fake News and Biased News Look Like? A Text-Analysis and Machine-Learning Approach.”

Zahra Shakeri (Institute of Health Policy, Management, and Evaluation, Dalla Lana School of Public Health, University of Toronto): “Social and Behavioural Determinants of Health in Prognostic Machine Learning Models for Patient Outcome Prediction.”