Corson N. Areshenkoff

I'm a doctoral candidate in the Memory, Action, and Perception Lab at the Centre for Neuroscience Studies at Queen's University. My research focuses on motor learning, and on the contributions of different learning systems (e.g. reinforcement learning, cognitive strategies) to motor control. Lately, my work uses fMRI to characterize brain networks underlying individual differences in motor learning. More generally, I’ve been experimenting with geometric methods for studying brain networks.

My background is in statistics and mathematics, and I tend to enjoy any research problem involving interesting or unusual analyses. I also enjoy complaining about statistics I don’t like. Most of my complaining is done on my webpage.

Repositories

I've been making increasing efforts to open-source my workflow by packaging and releasing code I use repeatedly. If the code is universal enough, then this is usually in the form of an R package. For specific research projects, I'm trying to get into the habit of organizing the entire pipeline into an R package or a set of jupyter notebooks, along with example data if the entire dataset cannot be released publically.

My repositories are organized mostly by abusing Github organizations.

areshenk-rpackages

Contains R packages for procedures that I use repeatedly in my work. Of these packages, the most complete is spdm, a library implenting operations on the space of symmetric, positive-definite matrices, which are used repeatedly in differentio-geometric approaches to the study of fMRI functional connectivity.

areshenk-opendata

Contains data or code for replicating published work.

Posts

• Spurious dynamics in functional brain networks
• Calibrating “surprise” in high dimensions
• More spurious modular structure in functional brain networks
• Spurious modular structure in functional brain networks
• In a room sit three textbooks…