Benjamin D. Singer, PhD
Director of Scientific Computing
Princeton Neuroscience Institute
I work on neuroimaging analysis methods such as cortical alignment, realtime fMRI correlation and classification, surface-based analysis and
visualization, and model-based neural networks in multiple voxel pattern analysis. In addition to
implementing novel methods, I work to speed up and streamline
neuroimaging analysis algorithms via low-level optimizations and
parallelization. The latter is achieved with the help of
computer clusters containing hundreds of processors that I help to acquire, write software for, and maintain.
Currently working on the Princeton FCMA toolbox for high-performance computing clusters
Neuroimaging Analysis Methods
Function-based cortical alignment
- Sabuncu MR, Singer BD, Conroy BR, Ramadge PJ, Haxby JV. (2010). Function-based inter-subject alignment of
cortical anatomy. Cerebral Cortex, 20, 130-140. pubmed. demo
- Conroy, BR, Singer, BD, Haxby, JV, and Ramadge, PJ. (2009). FMRI-based inter-subject cortical
alignment using functional connectivity. Advances in Neural Information Processing Systems. 22, 378-386. pdf.
- Conroy BR, Singer BD, Ramadge PJ, Haxby JV. (2008). Inter-subject functional connectivity alignment. Annual Meeting of the Organization of Human Brain Mapping, Melbourne, Australia. poster pdf.
- Sabuncu MR, Singer BD, Bryan RE, Ramadge PJ, Haxby JV. (2006). Function-based inter-subject alignment of cortical anatomy. Annual Meeting of the Organization of Human Brain Mapping, Florence, Italy. poster pdf.
- Arcaro MJ, McMains SA, Singer BD, Kastner S. (2009). Retinotopic organization of human ventral visual cortex. Journal of Neuroscience, 29, 10638-10652. jneurosci.org.
Work related to the Princeton MVPA toolbox for Matlab
- Detre G, Polyn SM, Moore CD, Natu VS, Singer BD, Cohen JD, Haxby JV, Norman KA. (2006). The Multi-Voxel Pattern Analysis (MVPA) toolbox. Annual Meeting of the Organization of Human Brain Mapping, Florence, Italy. poster pdf.
- Polyn S, Detre G, Takerkart S, Natu V, Benharrosh M, Singer B, Cohen J, Haxby J, Norman K. (2005). A Matlab-based toolbox to facilitate multi-voxel pattern classification of fMRI data. Annual Meeting of the Organization of Human Brain Mapping, Toronto, Canada. poster pdf.
At the Center for Visual Science, University of Rochester, I did
psychophysics, electrophysiology, and real-time retinal imaging software as a research associate, following
doctoral work in color vision psychophysics at UCI Cognitive Science and
undergraduate work on the development of perception in infants Cornell Psychology.
Adaptive optics for retinal imaging and supernormal vision:
- Singer B. (2006). Adaptive optics software for vision research. In Porter J, Awwal A, Lin J, Queener H, Thorn K. (Eds). Adaptive Optics for Vision Science: Principles, Practices, Design, and Applications. John Wiley, Sons, Inc, 139-153. google books.
- Chen L, Kruger P, Hofer H, Singer B, Williams DR. (2006). Accommodation with higher-order monochromatic aberrations corrected with adaptive optics. Journal of the Optical Society of America A, 23, 1-8. pdf.
- Chen L, Singer B, Guirao A, Porter J, Williams DR. (2005). Image metrics for predicting subjective image quality. Optometry and Vision Science, 82, 358-369. pdf.
- Hofer H, Singer B, Williams DR. (2005). Different sensations from cones with the same photopigment. Journal of Vision, 444-454. pdf
- Artal P, Chen L, Fernandez EJ, Singer B, Manzanera S, Williams DR. (2004). Neural compensation for the eye's optical aberrations. Journal of Vision, 281-287. pdf.
- Doble, N, Yoon GY, Chen L, Bierden P, Singer B, Olivier S, Williams DR. (2002). Use of a microelectromechanical mirror for adaptive optics in the human eye. Optics Letters, 27, 1537-1539. pdf.
- Hofer H, Chen L, Yoon GY, Singer B, Yamauchi Y, Williams DR. (2001). Improvement in retinal image quality with dynamic correction of the eye's aberrations. Optics Express, 8, 631-643. pdf.
- Hofer H, Artal P, Singer B, Aragon JL, Williams DR. (2001). Dynamics of the eye's wave aberration, Journal of the Optical Society of America A, 18, 497-506. pdf
Human color vision:
- D'Zmura M, Singer B. (1999). Contrast gain control. In Sharpe LT, Gegenfurtner KR. (Eds.) Color Vision: From Genes to Perception. Cambridge: Cambridge University Press, 369-385. google books.
- D'Zmura M, Singer B. (1996). The spatial pooling of contrast in contrast gain control. Journal of the Optical Society of America A, 13, 2135-2140. pdf.
- D'Zmura M, Iverson G, Singer B. (1995). Probabilistic color constancy. In Luce RD, D'Zmura M, Hoffman DD, Iverson G, Romney K. (Eds.), Geometric Representations of Perceptual Phenomena. Mahwah, NJ: Lawrence Erlbaum Associates, 187-202. google books.
- Singer B, D'Zmura M. (1995). Contrast gain control. A bilinear model for chromatic selectivity. Journal of the Optical Society of America A, 12, 667-685. pdf.
- Singer B, D'Zmura M. (1994). Color contrast induction. Vision Research, 34, 3111-3126. pdf.
Past. A theme throughout my life has been fooling with computers and programming. My main contribution to the above
work was (and still is) implementing the algorithms underlying research questions in software (algorithm
development, stimulus presentation, device control/communication, parallelization/optimization for multiple
processors, and data analysis.) I started as a BASIC and Pascal programmer in my teens, writing apps to graph
data in my dad's lab, then writing 2D and 3D graphics apps in Matlab and C in grad school for my thesis work, C++
realtime adaptive optics software running on Macs as a research associate.
Present. Currently I use a wide variety depending on the task, but mostly Matlab, shell scripts, the occasional MPI and
GPU programming. Over the last 8 years or so I've delved deep into clusters, job scheduling, and internals of
Linux -- from drivers to shell scripts. Luckily most labs here have Macs as desktop machines, and being a Mac
enthusiast that means an opportunity for more interfaces in Cocoa + Objective-C.
Future. I'm working towards the day
when scientists here can run analyses that use hundreds of cores on our cluster, and/or the variety of cores in
their desktop-- heck, via the Cloud if they want-- without needing to know how that power has been brought to
bear, without ever needing to open Terminal.app. We want scientists doing science, not tool-making (unless,
like me, they enjoy that part!). There is a very compelling story here, that has worked its way through physics,
biology, and several fields. Neuroscience will get its big breakthroughs via massive, parallel, flexible,
data-soaked computing power. So when compute-intensive
work is quick and easy, work such as correlating and classifying massive amounts of fMRI data-- a whole new
level of discovery can take place. If you are getting meaningful visual feedback from large scale data analysis
in only seconds, rather than via hundreds of stdout text file logs from day-long batch jobs-- that you had to
launch only after learning a suite of esoteric
site-specific Terminal commands (no finger pointing here, I probably wrote those commands)-- my hunch is that this will be a turn for the exceedingly better. Takes lots of work, but it will be worth it, for everyone involved.
I've managed to keep myself programming by-- for better or worse-- avoiding management and by not delegating the fun stuff!
- An archive of old (mostly pre-Mac OS X) Mac software I made is here.