Martin Cole is a third-year PhD student in the Department of Biostatistics and Computational Biology at the University of Rochester, where he is currently researching statistical methods for the analysis of the human connectome. He holds a M.A. in Statistics at the University of Rochester, NY, and a BEng (Hons) Engineering degree (with a focus in Fluid Mechanics) at the Open University, England. His research interests include the development of statistical and computational methods to study high-dimensional data, Bayesian network analysis for the modelling of brain connectivity, and robust machine learning techniques for use alongside traditional statistical tools.

Publications

Consagra, W., Cole, M. and Zhang, Z., 2022. Analyzing Brain Structural Connectivity as Continuous Random Functions. arXiv preprint arXiv:2206.11191.

Chen, Q., Turnbull, A., Cole, M., Zhang, Z. and Lin, F.V., 2022. Enhancing cortical network-level participation coefficient as a potential mechanism for transfer in cognitive training in aMCI. NeuroImage, 254, p.119124.

Cole, M., Murray, K., St‐Onge, E., Risk, B., Zhong, J., Schifitto, G., Descoteaux, M. and Zhang, Z., 2021. Surface‐Based Connectivity Integration: An atlas‐free approach to jointly study functional and structural connectivity. Human Brain Mapping, 42(11), pp.3481-3499.

Wang, L., Lin, F.V., Cole, M. and Zhang, Z., 2021. Learning clique subgraphs in structural brain network classification with application to crystallized cognition. Neuroimage, 225, p.117493.

Poster Presentations

OHBM 2020 - Cole, M., Murray, K., St-Onge, E., Descoteaux, M., Zhong, J., Schifitto, G. and Zhang, Z., A Parcellation-Free Framework for Structural and Functional Connectivity Integration.

Awards

1st Place, UP-STAT Data Competition “Bioacoustic Analysis of a Madagascar Rainforest”: Issued by UP-STAT 2019 Conference Apr 2019