Research Interests
- Large-scale image alignment for EM and confocal microscopy
- Erasure coding for efficient storage of imaging datasets
Since entering the high-performance computing (HPC) field in 1995, I have been involved with a variety of research projects that span both data analysis and model simulation. Initially, I worked on a system for realtime fMRI, a proof-of-concept that one could take raw data from an MRI scanner and stream it through a parallel image-processing pipeline to immediately visualize the locations in a subject’s brain that were activated by various stimuli. More recently, I have developed algorithms for image alignment, and have applied those to several large datasets of electron microscopy images that are being used to reveal the connectome of neural circuitry present in various organisms. I have also been involved in system architecture design, and played a major role in the design of the interconnect for PSC’s first Bridges supercomputer, which was a $9.65-million NSF-funded resource that was in production from 2016-2021. More recently, I have been involved in architecting the NIH-funded Brain Image Library.
Selected publications:
1. Benninger K, Hood G, Simmel D, Tuite L, Wetzel A, Ropelewski A, Watkins S, Watson A, Bruchez M. (2020). Cyberinfrastructure of a Multi-Petabyte Microscopy Resource for Neuroscience Research. PEARC’20: Practice and Experience in Advanced Research Computing, p. 1-7. doi: 10.1145/3311790.3396653.
2. Lee WA, Bonin V, Reed M, Graham BJ, Hood G, Glattfelder K, Reid RC. (2016). Anatomy and function of an excitatory network in the visual cortex. Nature, Vol. 532, 370-374. PMCID: PMC4844839
3. Bock, D.D., Lee, W.A., Kerlin, A.M., Andermann, M.L, Hood, G., Wetzel, A.W., Yurgenson, S., Soucy, E.R., Kim, H.S., & Reid, R.C. (2011). Network anatomy and in vivo physiology of visual cortical neurons. Nature, Vol. 471, 177-182. PMCID: PMC3095821
4. Goddard, N.H., Hood, G., Cohen, J.D., Eddy, W.F., Genovese, C.R., Noll, D.C. and Nystrom, L.E. (1997). Online Analysis of Functional MRI Datasets on Parallel Platforms, J. Supercomputing, 11:295-318.