Research Centres

Interdisciplinary Collaborations

UBC is a hotbed of research in computational health and medicine, bioinformatics, and genomics. Our CS faculty engage in collaborative research with many other groups including the UBC Faculty of Medicine and the wider biomedical community (BC Cancer Agency; Genome BC; St. Paul's Hospital; and BC Children's Hospital).
UBC CS is a driving force behind many large-scale interdisciplinary initiatives, including Centre for Artificial Intelligence Decision-making and Action (CAIDA), Designing for People (DFP), and the Data Science Institute (DSI).
We have several joint appointments: Andy Roth bridges CS and Pathology, and Michael Friedlander links CS with Mathematics (and is Director of the UBC Institute of Applied Mathematics).

CAIDA: Centre for Artificial Intelligence Decision-making & Action

CAIDA is UBC's research hub for artificial intelligence, belonging to the Institute for Computing, Information and Cognitive Systems (ICICS). The expertise of its nearly 100 researchers, who span many academic units, ranges from theoretical to applied, enabled by technologies like machine learning and automated reasoning.

40 Members

DSI: Data Science Institute

The institute spans the Faculty of Science, equipping researchers and external partners in the data science community with the approaches, tools and expertise they need to fully leverage the potential of big data.

8 Members

DFP: Designing for People Cluster

The DFP cluster, which spans many academic units, addresses complex human-facing design problems by accelerating creativity at the nexus of social and technical problem-solving.

13 Members

MILD: Mathematics of Information, Learning and Data

MILD is a cross-departmental group working on rigorous mathematics related to machine learning and data. We aim to add theoretical foundations currently lacking in this domain across topics like differential privacy, algorithm design, information theory, learning theory, probability theory and mathematical statistics.

5 Members

AMLTN: Advanced Machine Learning Training Network

We aim to build a research training program in machine learning with value-added training opportunities beyond what traditional computer science programs provide, and linking students to a global network of world-leading researchers and institutions in the field. Our long-term goal is to support the growing digital tech ecosystems in western Canada and beyond.

12 Members