Trustworthiness of Machine-Learning-Based Systems
TrustML facilitates development of trustworthy machine-learning-based systems, i.e., systems that are reliable, secure, explainable, and ethical. The cluster will examine trust-related requirements in several life-critical domains, including medicine and aerospace, and will investigate solutions for building trustworthy systems that professionals and the general public can reliably adopt.
Campus
Vancouver
Affiliated UBC Faculty & Postdocs
| Name | Role | Research Interests |
|---|---|---|
| Ford, Cristie | Faculty (G+PS eligible/member) | Law and legal practice; Law; Regulation; Social, Economical and Political Impacts of Innovations; Laws, Standards and Regulation Impacts; Administrative Law; Ideological, Political, Economical and Social Environments of Social Transformations; Financial innovation and fintech; financial regulation; Legal innovation and law tech; regulation & governance theory; securities regulation; the legal profession; Innovation and the law |
| Lee, Gene | Faculty (G+PS eligible/member) | Economics and business administration; Management information systems; Applied Machine Learning; Business Analytics; Computer Science and Statistics; Cybersecurity; Information Systems; Mobile Ecosystem; Social Media Analysis; Text Mining |
| Rubin, Julia | Faculty (G+PS eligible/member) | Computer engineering; Programming languages and software engineering; Computer Systems; software engineering; Software quality, security, and robustness; program analysis; Adversarial robustness, explainability, and interpretability of ML-based systems; Mobile and cloud software |