Data Science & Composite Materials Manufacturing; NSERC Allicance
A two-year NSERC Alliance grant was recently awarded to a consortium of two University of British Columbia research clusters--the Composite Research Network and Data Science Institute--for their project "Data Science & Composite Materials Manufacturing". This project aims to apply breakthroughs in data science, machine learning, and probabilistic programming to innovate and accelerate manufacturing and design of composite materials, specifically for the aerospace sector.
Visual analytics support for the HEiDi virtual physician COVID-19 deployment
Drs. Tamara Munzner and Kendall Ho were award DSI funding for their project, "Visual Analytics Support for the HEiDi Virtual PHysician COVID-19 Deployment." This project will leverage advances in data visualization and analytics to optimze the delivery of telehealth care to patients stricken with COVID-19. The outcomes will help health system experts to gain a holistic snapshot of the current care system and expedite analysis and decision-making.
The use of methods of deep learning for the virtual screening for COVID19 therapeutic candidates
The DSI is excited to announce funding support to Drs. Artem Cherkasov and Faraz Hach from the Vancouver Prostate Centre for their COVID-19 therapeutic discovery project. This project seeks to use deep learning (DL) techniques to identify potential therapeutic agents to target one of the main proteins (Mpro/3CLpro) that enable the SARS-CoV-2 virus to replicate. A description of the project follows below.
Personalized risk assessment in pediatric kidney transplantation using metabolomics data
Drs. Cohen-Freue (Statistics) and Blydt-Hansen (Pediatrics) were awarded funding for their project on applying statistical models (i.e., classification) to identify a metabolomic signature for personalized risk assessment in pediatric patients receiving a transplated kidney. The funds from the DSI Postdoctoral Matching Fund Program will enable the team to apply and extend various machine learning methods to build a metabolomics classifer to predict long-term outcome (i.e., allograft survival).
A deep learning approach to analyzing retinal imaging for medical diagnosis and prediction
The DSI is excited to annouce that Drs. Ipek Oruc and Ozgur Yilmaz are awarded funding for their project on deep learning models for analyzing retinal images for medicial diagnosis and prediction. The funds from the DSI Postdoctoral Matching Fund Program will allow this research team to extend their work with deep neural networks to make predictions (i.e., classification) on various medical disorders ranging from neurological diseases to cancer using retinal images integrated with electronic medical records.