Gaussian Processes for Advancing Understanding of Planetary Magnetism with Spacecraft Observations
A research team, comprised of Dr. Abigail Azari with Drs. Catherine Johnson and Lindsey Heagy from the Department of Earth, Ocean, and Atmospheric Sciences, and Dr. Frank Wood from the Department of Computer Science, has been awarded the Post-Doctoral Matching Fund from the UBC Data Science Institute. This project aims to expand current development of Gaussian process-based estimations of the solar wind, focused on Mercury, Venus, and Mars, and develop integration of Gaussian processes into simulation-based inference, specifically focused on planetary magnetism.
Anytime-Valid PAC-Bayes for Industrial Applications
A research team led by Drs. Danica Sutherland (Computer Science) and Trevor Campbell (Statistics) were awarded postdoctoral funding from the UBC Data Science Institute. The team aims to provide a suite of tools which will allow companies to make optimal use of their data while providing rigorous, statistically principled and continously monitorable generalization guarantees on their deployed AI models.
Innovative deep-learning based program for cervical cancer screening
The Data Science Institute is pleased to announce a research team led by Drs. Xiaoxiao Li (Electrical and Computer Engineering) and Gang Wang (Pathology and Laboratory Medicine) has been awarded the Postdoctoral Matching Fund from the DSI. This project aims to develop an end-to-end automatic deep learning-based cervical cancer screening pipeline that requires less labelling and addresses challenges in multi-institutional learning.
Technology pipeline for the development of Machine-Learned Interatomic Potentials
With the postdoctoral funding award from the Data Science Institute, Dr. Joerg Gsponer (Biochemistry and Molecular Biology) aims to establish and benchmark a technology pipeline for the development of Machine-Learned Interatomic Potentials (MLIPs) for Intrinsically Disordered Proteins (IDPs), thereby establishing a pathway to close a huge methodology gap that currently prevents significant progress in many areas of biochemistry and biomedicine.