The UBC Data Science Institute is a Faculty of Science initiative designed to incubate and accelerate research, innovation and training in data-intensive science. Working across the university’s data science community and advanced computing teams, DSI equips researchers and external partners with the approaches, tools and expertise they need to fully leverage the potential of big data.

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DSI provides co-funding opportunities for data-science postdoctoral fellows.

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Get the latest updates on the DSI speaker series and other data science events.


DSI Seminar: Dr. Brett Beaulieu-Jones

Dr. Beaulieu-Jones will be speaking on December 16 about "Overcoming Obstacles for Practical Machine Learning in Health".

Winter Deep Learning School (Vancouver)

Winter Deep Learning School is coming to Vancouver December 2-6, 2019.

Data Science for Social Good 2019

September 9, 2019 | Final presentations for DSSG 2019.

Dr. Hyeju Jang awarded CIHR Health System Impact Fellowship

August 6, 2019 | Dr. Jang was awarded a CIHR Health System Impact Fellowship that will fund her work applying NLP and ML to enhance population health monitoring and evaluation.

New Cascadia Data Discovery Initiative accelerates health innovation

July 12, 2019 | UBC DSI partners with other research centres across Cascadia to build automatic metadata generation and recommendation tools for data discovery.

Using machine learning models for understanding the role of the non-coding genome in brain development and autism

June 11, 2019 | Drs. Mostafavi and Goldowitz were awarded DSI matching funds for their project applying machine learning to better understand neurodevelopmental disorders.

DSI Seminar: Dr. Yuval Shahar

May 7, 2019 | Dr. Yuval Shahar to speak at UBC on machine learning for supporting clinical decision-support systems on May 27, 2019.

Quantifying Individual Differences from Complex Datasets in Developmental Psychology

April 23, 2019 | Postdoctoral matching funds were awarded to a team of researcher (Odic, Conati, and Wu) for their work in extracting insights from heterogeneous data sets to better understand early childhood development.