Congratulations to Radu Craiu, Gwendolyn Eadie, Sebastian Jaimungal, Dehan Kong, Jun Young Park, and Lei Sun for being awarded Data Sciences Institute Catalyst Grants to support cross-disciplinary data science research to solve complex and pressing problems.
“The global and research challenges we face today are increasingly complex. The DSI Catalyst Grant projects bring together collaborative research teams focused on the development of new data science methodology or the application of existing tools in innovative ways to address these challenges,” says Lisa Strug, director of the DSI. “We were floored by the cutting-edge advances proposed in the applications we received for our inaugural competition.”
Please have a look at the Catalyst Grant research projects our faculty will be involved in, ranging from improving data derived from single-cell sequencing to investigating stellar flares to risk-aware reinforcement learning for financial modeling:
Stellar Flares in Hiding: Discovering Flares in Stellar Time-Series Data with Hidden Markov Models
- Gwendolyn Eadie (Astronomy & Astrophysics, Faculty of Arts & Science, UofT); Radu Craiu (Statistical Sciences, Arts & Science, UofT)
Robust Risk-Aware Reinforcement Learning for Financial Modeling
- Sebastian Jaimungal (Statistical Sciences, Faculty of Arts & Science, UofT); John Hull (Joseph L. Rotman School of Management)
Removing unwanted variations from heterogeneous neuroimaging and genomic data
- Jun Young Park (Statistical Sciences, Faculty of Arts & Science, UofT); Laurent Briollais (Lunenfeld-Tanenbaum Research Institute); Michael Wilson (The Hospital for Sick Children)
Methods for genome-wide studies of variants with sex differences in genetic effect
- Lei Sun (Statistical Sciences, Arts & Science, UofT); Andrew Paterson (The Hospital for Sick Children)
Developing rigorous statistical methods for multimodal single-cell sequencing data analysis
- Project co-funded by Medicine by Design. Read the full story by Medicine by Design.
- Zhaolei Zhang (Donnelly Centre/ Molecular Genetics, Temerty Faculty of Medicine); Dehan Kong (Statistical Sciences, Faculty of Arts & Science, UofT); Dennis Kim (Princess Margaret Cancer Centre, UHN)
You can find a full list of all recipients here.
Congratulations to our faculty again and to all 2022 winners!
Data Sciences Institute Catalyst Grants support transformative data science research
February 10 | By Faculty of Arts & Science Staff
The Data Sciences Institute (DSI) at the University of Toronto is funding seventeen cross-disciplinary research teams focused on using the transformative nature of data sciences to solve complex and pressing problems.
“The global and research challenges we face today are increasingly complex. The DSI Catalyst Grant projects bring together collaborative research teams focused on the development of new data science methodology or the application of existing tools in innovative ways to address these challenges,” says Lisa Strug, director of the DSI. “We were floored by the cutting-edge advances proposed in the applications we received for our inaugural competition.”
Here we highlight a few of the inspiring funded proposals and research teams. The full list of recipients can be found below.
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Improving data derived from single-cell sequencing
Single-cell sequencing, the ability to look at cells at the individual level, has been revolutionary. However, the technology also poses challenges. For example, different types of sequencing approaches produce distinct data sets that don’t integrate with each other.
Professors Zhaolei Zhang (Donnelly Centre/Molecular Genetics, Temerty Faculty of Medicine), Dehan Kong (Statistical Sciences, Faculty of Arts & Science, UofT), and Dennis Kim (Princess Margaret Cancer Centre, UHN) received a Catalyst Grant for their project, Developing rigorous statistical methods for multimodal single-cell sequencing data analysis, which aims to tackle this problem. Their research is co-funded by Medicine by Design.
“We need robust and effective statistical tools to handle the data being measured from the thousands of cells and tens of thousands of genes in one experiment. Our framework will be able to integrate that data generated from different approaches in a very efficient and accurate way,” says Zhaolei Zhang.
Using datasets collected primarily from brain or blood cells, this multi-disciplinary research team aims to refine a method that can give researchers a more complete picture of single-cell data.