Research Projects

Drs. Janet Werker (Psychology) and Cristina Conati (Computer Science) have been awarded the DSI Postdoctoral Matching Fund for their project titled "Predicting Word Learning from Infants' Eye-Tracking Signals".

Summary

Drs. Mi Jung Park (Computer Science) and Young-Heon Kim (Math) have been awarded the DSI Postdoctoral Matching Fund for their project "Optimal Transport Enhances Data Privacy in Diffusion-based Foundation Models".

Summary

Drs. Joel E. Saylor (EOAS) and Michael Friedlander (Computer Science) have been awarded the DSI Postdoctoral Matching Fund for their project "Application of untrained machine learning analysis of multivariate sediment provenance data to critical metals exploration".

Summary

Drs. Wenying Liu (Materials Engineering) and Roger Beckie (Earth, Ocean and Atmospheric Sciences) are the latest recipients of the DSI Postdoctoral Matching Fund for their project "Prediction and management of the long-term environmental risk of mine waste rock piles via 5G-enabled instrumentation and monitoring".

Summary

The Data Science Institute is pleased to announce a research team led by Drs. Joss Ives (Physics & Astronomy) and Jackie Stewart (Chemistry) has been awarded the DSI Postdoctoral Matching Fund. The multi-institutional research applies critical research methodologies to identify systemic and structural barriers to achievement, and to identify relationships between the instructor-created classroom climate and students’ learning, sense of belonging and persistence in STEM.

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.

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.

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.

The UBC Data Science Institute is excited to fund a collaboration between Drs. Elodie Portales-Casamar (UBC Department of Pediatrics) and Giuseppe Carenini (UBC Computer Science) titled, "Robust, transferable and interpretable natural language processing of psychiatric clinical notes." The team includes postdoctoral fellow Dr. Ahmed Abura'ed, Dr. Ali Eslami (UBC Department of Psychiatry), and Ali Mussavi Rizi (PHSA).

Drs. Naomi Zimmerman (UBC Mechanical Engineering) and Amanda Giang (UBC Institute for Resources, Environment & Sustainability) were awarded funding for their pilot project “Optimal placement of low-cost air quality sensors in Metro Vancouver to better predict air quality exposure”. This project seeks to develop efficient models for monitoring and predicting air quality in urban centres by leveraging anonymized 5G mobile location data with weather and air quality surveys.

Drs. Christos Thrampoulidis and Mark Schmidt are teaming up to address unresolved challenges in the training of neural networks and its applications. With this postdoctoral funding award from the Data Science Institute, the team will combine their expertise in optimization and high-dimensional statistical learning theory to design more efficient training algorithms that are better suited for real-world use.

A team of UBC and SFU researchers led by Dr. Nadja Kunz was awarded DSI postdoctoral matching funds for their project that will examine the impacts of mining on the environment. Specifically, the team will build advanced statistical models using newly available data sets to better understand how mining is influencing hydrological variability, wildfires, and other environmental disturbances in Canada.

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.

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 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.

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).

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.

Developmental psychology fundamentally relies on the robust measurement of individual differences - capturing variables at an individual participant level - in order to formulate hypotheses about what children know, how their psychology changes over time, and to characterize the best predictors of their long-term educational, health, and psychological outcomes.