Contact
email: sdgh.ebrahimi@gmail.com
phone: 650-766-3687
Resume
I am interested in applying machine learning and quantitative analysis to solve engineering and scientific problems. During my PhD at stanford I used these techniques to understand information coding and communication in the mamalian brain the result of which was published in the Nature magazine. In addition to data science and machine learning , I am a profiecient software developer with a deep understanding of computer hardware.
Applied statistical analysis and machine learning methods on large scale neural activity recordings of mouse cortex to study communication between brain areas and stability of neural decoders over time. The results were published in the Nature journal
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Designed an embedded AI and developed the firmware for a wearable device to decode a patient’s hand movement intentions using AI and activate intended hand and finger movements using neuromuscular stimulation. Lead the software development for the wireless wearable smart stimulator that is controlled by a mobile app and drives wearable Neuro-Moscular Interfaces.
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Designed and assembled a compact brain imaging Virtual Reality (VR) environment for mice. Collected and analyzed the brain activity during a VR navigation through natural scenes while tracking the animal’s pupil movements using Deeplabcut. Using deep encoder-decoder networks, reconstructed the images that the mouse was looking at from its neural activity in the visual cortex. Conceived and led the project during post-doc and was awarded a Simons Foundation grant. A paper containing our initial results is under review for NeuroIPS 2022.
Designed a real-time clustering method to summarize large numbers of failure events on computer networks. The outcome of this project was patented by Oracle.
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1. Ebrahimi, S., Lecoq, J.A., Rumyantsev, O., Tasci,T. , Irimia, C., Zhang, Y. , Li,J., Ganguli, S. and Schnitzer, M.J., Emergent reliability in sensory cortical coding and inter-area communication. Nature (2022). https://doi.org/10.1038/s41586-022-04724-y
2. Ebrahimi, M.S. and Abadi, H.K., 2021. Study of residual networks for image recognition . In Intelligent Computing (pp. 754-763). Springer, Cham. (Cited by 30 )
3. Ebrahimi, M.S., Patti, R.H.R. and Garvey, D., Oracle International Corp, 2020. Using and updating topological relationships amongst a set of nodes in event clustering. U.S. Patent 10,678,610.
(Cited by 1)
4. Ebrahimi, M.S., Hills, G., Sabry, M.M., Shulaker, M.M., Wei, H., Wu, T.F., Mitra, S. and Wong, H.S.P., 2014, October.
Monolithic 3D integration advances and challenges: From technology to system levels
. In 2014 SOI-3D-Subthreshold Microelectronics Technology Unified Conference (S3S) (pp. 1-2). IEEE.
(Cited by 24)
5. Ebrahimi, M.S., Daraei, M.H., Behzadan, V., Khajooeizadeh, A., Behrostaghi, S.A. and Tajvidi, M., 2011, November. A novel utilization of cluster-tree wireless sensor networks for situation awareness in smart grids . In 2011 IEEE PES Innovative Smart Grid Technologies (pp. 1-5). IEEE. (Cited by 9)
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