معرفی
Dr. Timothy D. Oleskiw is an Assistant Professor of Data Science, Machine Learning, and Artificial Intelligence at the University of Regina's Department of Computer Science. His research investigates the neural computations underlying object perception through computational models of visual cortex. He holds a PhD from the University of Washington and was previously a visiting scholar at NYU's Center for Neural Science.
Education:
- PhD Applied Mathematics, University of Washington
- MSc Computer Science, York University
- BSc Math & CS, University of Regina
Dr. Oleskiw's research bridges computational neuroscience and machine learning to understand how visual areas V2 and V4 integrate signals from primary visual cortex into object representations. Using generative models that learn local features from natural objects, his work overcomes limitations of artificial stimuli to investigate intermediate shape representations in the ventral pathway. Current projects develop hierarchical models of neural selectivity that account for both local and global shape features in primate cortex.
His publications employ advanced neural modeling techniques to explain feature selectivity in visual areas V1-V4, with recent work establishing foundational models of form selectivity. Article keywords consistently include neural computation, visual processing, and computational modeling.
