The line-up for the CANSSI Showcase 2022 on November 25 has been announced, and the list of speakers and presenters at the online event is impressive.
The keynote lecture will be presented by Bin Yu from the University of California, Berkeley. Her talk on “Veridical Data Science with a Case Study to Seek Genetic Drivers of a Heart Disease” will consider the “dangers” inherent in the use of artificial intelligence in data science as a result of the human judgement calls that are “ubiquitous at every step of a data science life cycle.”
She will introduce a framework based on three core principles—predictability, computability, and stability (PCS)—to “maximally mitigate” those dangers, and will illustrate the usefulness of PCS in the development of iterative random forests (iRF) for predictable and stable non-linear interaction discovery.
Finally, she will present the use of iRF and UK Biobank data to recommend gene-gene interaction targets for knock-down experiments in the pursuit of genetic drivers of a heart disease called hypertrophic cardiomyopathy (HCM).
A discussion on “Current Innovations in Statistics and Data Science in Environmental Statistics” will feature a panel of distinguished participants: Charmaine Dean (University of Waterloo), Johanna Neslehova (McGill University), Will Welch (University of British Columbia), and Xuebin Zhang (Environment and Climate Change Canada). They will consider cutting-edge statistical methodology for environmental statistics and the role of research collaborations in the development of these innovations.
The day will also include presentations by members of CANSSI’s Collaborative Research Teams and Distinguished Postdoctoral Fellowships program, as well as lightning talks and posters by graduate students from across Canada.
Visit the event page for more information and to register.
