A significant bottleneck to innovation and discovery—in biology, health care, earth sciences, astronomy and beyond—is research teams’ capacity to transform massive data sets into meaningful insights. Building that capacity demands specialized knowledge in data methods, and building interdisciplinary teams that combine domain-specific and data science expertise.
The DSI provides a hub for UBC researchers who require more than standard analyses to participate in interdisciplinary teams that both push the forefront of data science and accelerate their own research. We offer expertise in assessing, cleaning and integrating data, modelling data, and help in interpreting data. To help UBC researchers tackle major challenges in data-intensive research, the DSI builds a supportive training and mentoring environment for post-doctoral fellows.
List of Funded Projects:
- Personalized risk assessment in pediatric kidney transplantation using metabolomics data
- A deep learning approach to analyzing retinal imaging for medical diagnosis and prediction
- Using machine learning models for understanding the role of the non-coding genome in brain development and autism
- Quantifying individual differences from complex datasets in developmental psychology
- Using contact networks, administrative, and linked genomic data to understand tuberculosis transmission in BC
- Leveraging eye-tracking data to improve reliable detection of Alzheimer’s Disease and related patient’s states
- Knowledge Graphs – Mining, Cleaning and Maintenance
- Computer vision and machine learning techniques for video and facial understanding
- Leveraging more accurate and flexible discourse structures in question-answering and summarization
- Large-scale Bayesian modelling of drug resistance and evolution in human cancers at single-cell resolution
- Automated diagnosis and prognostication of severity in COPD via deep learning frameworks using multi-modal data
- User modeling and adaptive support for MOOCS
- Using text analysis for chronic disease management
- Application of deep learning approaches in modelling cheminformatics data and discovery of novel therapeutic agents for prostate cancer
- Data science over graphs, streams, and sequences: From the analysis of fake news to prediction and intervention
- A platform for interactive, collaborative, and repeatable genomic analysis
- From heuristics to guarantees: the mathematical foundations of algorithms for data science
- Modeling multiple types of "omics" data to understand the biology of human exposure to pollution and allergens
DSI Postdoctoral Fellow Matching Fund
The DSI is providing seed or matched funding for postdoctoral fellowships to help champion early-stage, interdisciplinary data science projects. These high-calibre trainees will be the driving force to acquire preliminary data and results necessary to apply for larger grant and industry funding. The DSI PDF fund will contribute up to $40,000 for one year to selected projects. It is expected that DSI PDFs will spend up to 2 days a week working in the DSI lab and interacting with other fellows. If you're interested in learning more about this opportunity, contact the DSI Scientific Director.
PHIX-DSI Postdoctoral Fellowship
The Pacific Health Innovation eXchange (PHIX) is a joint initiative by the Vancouver Coastal Health (VCH), Vancouver Coastal Health Research Institute (VCHRI), University of British Columbia (UBC) and VGH & UBC Hospital Foundation, working to accelerate health care breakthroughs in British Columbia. PHIX is creating a collaborative ecosystem that will implement new innovations, technology, system change and treatment options. Data analytics is a key area crucial to the success of this mission. As such, PHIX is partnering with the Data Science Institute (DSI) at UBC to support the PHIX-DSI Postdoctoral Fellow Fund. This fund enables teams of VCHRI researchers and data science researchers at UBC to recruit and fund exceptional, young scientists to lead multidisciplinary projects that will drive the development and implementation of new tools for harnessing the potential of large and disparate health data sets. For more details, please contact the DSI Scientific Director.