AARMS Collaborative Research Group (CRG)
Real-world problems are constantly challenging us to invent new statistical methods. Our group members are actively involved in methodological research and have numerous multidisciplinary collaborations tied to real-world problems. All the members have active interdisciplinary research programs funded externally by individual and/or partnership grants, are active in HQP training (undergraduate/graduate/PDF), and are publishing high quality papers. For the past decade, the group members have been solving problems involving complex dependent data, big data, and statistical learning, in important application areas such as environmental sciences, manufacturing quality control, public health, medical science research and bioinformatics. There have been active and successful collaborations among the members, modelling longitudinal, spatial, and clustered dependent data; testing trend in time series data; data mining; and bioinformatics/biostatistics.
The primary objective of the CRG is to develop a collaborative research program to share resources and coordinate activities in order to
- address emerging statistical learning methods and computing issues motivated by multidisciplinary collaborations related to big data with complex dependency structure.
- develop and distribute novel statistical software, popularizing our research tools in both statistics and application areas.
- encourage collaboration, the exchange of ideas and joint supervision of HQP at all levels (undergraduate, graduate, PDF) among universities and in partnership with external organizations.
Paul Cabilio (Acadia)
Hugh Chipman (Acadia)
Hong Gu (Dalhousie)
Tariqul Hasan (UNB)
Wenjiang Jiang (Yunnan Normal)
Toby Kenney (Dalhousie)
Renjun Ma (UNB)
Jianan Peng (Acadia)
Gary Sneddon (MSVU)
Connie Stewart (UNB)
Guohua Yan (UNB)
Ying Zhang (Acadia)
Statistical Learning and Health Data Analytics Workshop
On October 15, 2017 University of New Brunswick Fredericton (UNBF) hosted an AARMS Workshop on Statistical Learning and Health Data Analytics, as a post-event of the Annual Conference for Science Atlantic Mathematics, Statistics and Computer Science.
We had a great turn out! Special thanks to our speakers for their interesting talks. Also a big thank you to our sponsors and everyone who attended. We hope to see you all at our next workshop (more information to come later).
The workshop was organized by an AARMS Collaborative Research Group (CRG), Statistical Learning for Dependent Data under the Administration of Ying Zhang. The Science Atlantic AARMS Workshop promoted the statistical research in the region through numerous talks by researchers in the field of statistical learning and health data analytics.
A detailed workshop program can be found here: Workshop Program
Please contact Ying Zhang , Acadia University for any questions or comments.
We are grateful for the support of Science Atlantic, the Atlantic Association for Research in the Mathematical Sciences, Canadian Statistical Sciences Institute Health Science Collaborating Centres, and UNB Fredericton.