Samuel J. Yang

Samuel J. Yang joined Google Research 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

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

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

News

July 2019: Added Yang et al., 2019.

May 2019: Added Andalman et al., 2019.

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. Yang, S. J.*, Lipnick, S. L.*, Makhortova, N. R.*, Venugopalan, S.*, Fan, M.*, Armstrong, Z., Schlaeger, T. M., Deng, L., Chung, W. K., O'Callaghan, L., Geraschenko, A., Whye, D., Berndl, M., Hazard, J., Williams, B., Narayanaswamy, A., Ando, D. M., Nelson, P. & Rubin, L. L. (2019). Applying Deep Neural Network Analysis to High-Content Image-Based Assays. SLAS Discovery. [ PDF | link ]
  2. Andalman, A. S., Burns, V. M., Lovett-Barron, M., Broxton, M., Poole, B., Yang, S. J., Grosenick, L., Lerner, T. N., Chen, R., Benster, T., Mourrain, P., Levoy, M., Rajan, K. & Deisseroth, K. (2019). Neuronal dynamics regulating brain and behavioral state transitions. Cell. [ PDF | link ]
  3. 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. [ PDF | link | blog post | in Wired | from NIH | from Gladstone ]
  4. 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., & Nelson, P. (2018). Assessing microscope image focus quality with deep learning. BMC Bioinformatics, 19(1). [ PDF | link | blog post ]
  5. Tabak, G., Fan, M., Yang, S. J., Hoyer, S., & Davis, G.. (2017). Correcting Nuisance Variation using Wasserstein Distance. arXiv. [ PDF | link ]
  6. 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 ]
  7. 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 ]
  8. 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 ]
  9. 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 ]
  10. 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 ]
  11. 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 ]
  12. 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 ]
  13. 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 ]
  14. 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 ]
  15. 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 ]

Ph.D. Thesis: Coded Computational Illumination and Detection for Three-dimensional Fluorescence Microscopy [ PDF | summary ]

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.