Samuel J. Yang

Samuel J. Yang joined Google AI in 2016. Prior to that, he completed a Ph.D. in Electrical Engineering at Stanford University, where his research in the labs of Karl Deisseroth and Gordon Wetzstein focused on computational imaging and display, the co-design and optimization of optics hardware and data processing alogrithms. He was supported by a NSF Graduate Research Fellowship and a NDSEG Graduate Fellowship.

About

About

I received a B.S. in Electrical Engineering from Caltech, studying engineering physics and optics in Changhuei Yang's lab.

In 2013, I received a M.S. in Electrical Engineering from Stanford, studying machine learning, image processing and computer vision.

In 2015, at Google Research, I applied deep learning methods to images as a Software Engineering Intern.

In 2014, at Google [x], I worked with optical physicists to design and implement imaging instrumentation hardware as an intern.

In 2013, at Pelican Imaging, I explored computational photography applications as a research intern.

I volunteer for Science Olympiad after learning to program and building some robots myself a long time ago. I also enjoy photography, and was a teaching assistant for Stanford's Digital Photography class.

I have also been involved with several interesting team efforts as well, including designing and building an autonomous robot (second place at Robogames 2009), developing a functional license-plate-reading iPhone app during a 24-hour hackathon, designing and constructing a net-zero solar-powered smart home for the Solar Decathlon competition, and participating in and winning the $10,000 first place prize in a early-stage technology commercialization plan competition.

Contact: samuely (at) alumni (dot) stanford (dot) edu

News

April 2018: In Silico Labeling is out in Cell.

March 2018: Updated with blog post and 4 recent publications.

March 2016: I presented this work at Focus on Microscopy 2016.

February 21, 2016: Added two computer vision/machine learning projects, real-time tail/eye tracking for zebrafish virtual reality and depth-assisted portrait perspective correction.

February 15, 2016: Our multifiber recording paper is out in Nature Methods, with software released on GitHub.

December 2015: Our light sheet microscopy paper is out at Cell.

December 2015: My paper is out at Optics express.

October 2015: I presented this poster at SFN 2015. I also contributed to work in this poster.

August 2015: Our adaptive spectral projector was presented at SIGGRAPH Asia 2015.

Publications

    I am also on Google Scholar, ResearchGate and GitHub.

  1. Christiansen, E. M., Yang, S. J., Ando, D. M., Javaherian, A., Skibinski, G., Lipnick, S., Mount, S., O'Neil, A., Shah, K., Lee, A. K., Goyal, P., Fedus, W., Poplin, R., Esteva, A., Berndl, M., Rubin, L. L., Nelson, P., & Finkbeiner, S. (2018). In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images. Cell. [ link | blog post | in Wired | from NIH | from Gladstone ]
  2. Yang, S. J., Berndl, M., Ando, D. M., Barch, M., Narayanaswamy, A., Christiansen, E., Hoyer, S., Roat, C., Hung, J., Rueden, C. T., Shankar, A., Finkbeiner, S., & and Nelson, P. (2018). Assessing microscope image focus quality with deep learning. BMC Bioinformatics, 19(1). [ PDF | link | blog post ]
  3. Tabak, G., Fan, M., Yang, S. J., Hoyer, S., & Davis, G.. (2017). Correcting Nuisance Variation using Wasserstein Distance. arXiv. [ PDF | link ]
  4. Allen, W.E., Kauvar, I.V., Chen, M.Z., Richman, E.B., Yang, S. J., Chan, K., Gradinaru, V., Deverman, B.E., Luo, L., & Deisseroth, K. (2017). Global Representations of Goal-Directed Behavior in Distinct Cell Types of Mouse Neocortex. Neuron, 94(4). [ PDF | link ]
  5. Grosenick, L.M., Broxton, M., Kim, C.K., Liston, C., Poole, B., Yang, S., Andalman, A.S., Scharff, E., Cohen, N., Yizhar, O., Ramakrishnan, C., Ganguli, S., Suppes, P., Levoy, M., & Deisseroth, K. (2017). Identification Of Cellular-Activity Dynamics Across Large Tissue Volumes In The Mammalian Brain. bioRxiv, 94(4). [ PDF | link ]
  6. Kim, C.*, Yang, S.*, Pichamoorthy, N., Young, N., Kauvar, I., Jennings, J., Lerner, T., Berndt, A., Lee, S.Y., Ramakrishnan, C., Davidson, T., Inoue, M., Bito, H., & Deisseroth, K. (2016). Simultaneous fast measurement of circuit dynamics at multiple sites across the mammalian brain. Nature Methods, 13(4). *co-first authors [ PDF | supplement | link | software ]
  7. Tomer, R., Lovett-Barron, M., Kauvar, I., Andalman, A., Burns, V.M., Sankaran, S., Grosenick, L., Broxton, M., Yang, S. & Deisseroth, K. (2015). SPED Light Sheet Microscopy: Fast Mapping of Biological System Structure and Function. Cell, 163(7), 0092-8674. [ PDF | link ]
  8. Yang, S., Allen, W., Kauvar, I., Andalman, A., Young, N., Kim, C., Marshel, J., Wetzstein, G., & Deisseroth, K. (2015). Extended field-of-view and increased-signal 3D holographic illumination with time-division multiplexing. Optics express, 23(25), 32573-32581. [ PDF | link ]
  9. Kauvar, I., Yang, S., Shi, L., McDowall, I., & Wetzstein, G. (2015). Adaptive Color Display via Perceptually-driven Factored Spectral Projection. ACM SIGGRAPH Asia (Transactions on Graphics). [ PDF | link ]
  10. Cohen, N., Yang, S., Andalman, A., Broxton, M., Grosenick, L., Deisseroth, K., Horowitz, M., & Levoy, M. (2014). Enhancing the performance of the light field microscope using wavefront coding. Optics express, 22(20), 24817-24839. [ PDF | link ]
  11. Broxton, M., Grosenick, L., Yang, S., Cohen, N., Andalman, A., Deisseroth, K., & Levoy, M. (2013). Wave optics theory and 3-D deconvolution for the light field microscope. Optics express, 21(21), 25418-25439. [ PDF | link ]
  12. Lee, S. A., Leitao, R., Zheng, G., Yang, S., Rodriguez, A., & Yang, C. (2011). Color capable sub-pixel resolving optofluidic microscope and its application to blood cell imaging for malaria diagnosis. PloS one, 6(10), e26127. [ PDF | link ]
  13. Zheng, G.*, Lee, S. A.*, Yang, S.*, & Yang, C. (2010). Sub-pixel resolving optofluidic microscope for on-chip cell imaging. Lab on a Chip, 10(22), 3125-3129. *co-first authors [ PDF | link ]

Unpublished work includes depth-assisted perspective correction for portrait photography, holographic illumination for all-optical neurophysiology, the application of light field microscopy to 3D calcium imaging, and a robust real time zebrafish high speed tail tracking approach (computer vision and machine learning in OpenCV/Matlab) for zebrafish virtual reality.