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.



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


June 2023: Added Venugopalan et al. 2023.

September 2022: Added Yang et al. 2022 at Interspeech 2022.

March 2022: Added Schiff et al. 2022.

January 2022: Added Cooke et al. 2021.

September 2021: Added Yang et al. 2021 and associated press release.

July 2021: See blog post on applying speech enhancement for cochlear implants.

July 2021: Added Wright & Yang 2021.

2020: Added Schiff et al., 2020 and Tabak et al., 2020.

2019: Added Yang et al., 2019 and Andalman et al., 2019.

2018: In Silico Labeling is out in Cell. Updated with blog post and 4 recent publications.

2016: I presented this work at Focus on Microscopy 2016. Added two computer vision/machine learning projects, real-time tail/eye tracking for zebrafish virtual reality and depth-assisted portrait perspective correction. Our multifiber recording paper is out in Nature Methods, with software released on GitHub.

2015: Our light sheet microscopy paper is out at Cell. My paper is out at Optics express. I presented this poster at SFN 2015. I also contributed to work in this poster. Our adaptive spectral projector was presented at SIGGRAPH Asia 2015.


    I am also on Google Scholar, ResearchGate and GitHub.

  1. Venugopalan, S., Tobin, J., Yang, S. J., Seaver, K., Cave, J.N.R., Jiang, P.P., Zeghidour, N., Heywood, R., Green, J., Brenner, M.P. (2023). Speech Intelligibility Classifiers From 550K Disordered Speech Samples. ICASSP 2023. [ PDF ]
  2. Yang, S. J., Wisdom, S., Gnegy, Chet., Lyon, R.F., Savla, Sagar. (2022). Listening with Googlears: Low-Latency Neural Multiframe Beamforming and Equalization for Hearing Aids. Interspeech 2022. [ PDF | Clarity workshop 2021 version link ]
  3. Schiff, L.*, Migliori, B.*, Chen, Y.*, Carter, D.*, Bonilla, C., Hall, J., Fan, M., Tam, E., Ahadi, S., Fischbacher, B., Geraschenko, A., Hunter, C. J., Venugopalan, S., DesMarteau, S., Narayanaswamy, A., Jacob, S., Armstrong, Z., Ferrarotto, P., Williams, B., Buckley-Herd, G., Hazard, J., Goldberg, J., Coram, M., Otto, R., Baltz, E. A., Andres-Martin, L., Pritchard, O., Duren-Lubanski, A., Daigavane, A. Reggio, K., NYSCF Global Stem Cell Array Team, Nelson, P.C., Frumkin, M., Solomon, S.L., Bauer, L., Aiyar, R. S., Schwarzbach, E., Noggle, S. A., Monsma, Jr., F. J., Paull, D., Berndl, M.**, Yang, S. J.**, Johannesson, B.** (2022). Integrating deep learning and unbiased automated high-content screening to identify complex disease signatures in human fibroblasts. Nature Communications. [ press release | PDF | link | biorxiv ]
  4. Cooke, C.L., Kong, F., Chaware, A., Zhou, K.C., Kim, K., Xu, R., Ando, D.M., Yang, S. J., Konda, P.C., Horstmeyer, R. (2021). Physics-Enhanced Machine Learning for Virtual Fluorescence Microscopy. International Conference on Computer Vision ICCV2021. [ PDF | link ]
  5. Yang, L., Haber, J. A., Armstrong, Z., Yang, S. J., Kan, K., Zhou, L., Richter, M. H., Roat, C., Wagner, N., Coram, M., Berndl, M., Riley, P., Gregoire, J. M. (2021). Discovery of complex oxides via automated experiments and data science. PNAS. [ PDF | link | press release ]
  6. Wright, Carrie. Yang, S. J. (2021). Deep learning for automated focus quality detection in wafer inspection. SPIE Optical Metrology. [ link ]
  7. Tabak, G., Fan, M., Yang, S. J., Hoyer, S., & Davis, G.. (2020). Correcting nuisance variation using Wasserstein distance. PeerJ. [ PDF | link | old arXiv version ]
  8. Venugopalan, S.*, Narayanaswamy, A.*, Yang, S.*, Geraschenko, A., Lipnick, S., Makhortova, N. R., Hawrot, J., Marques, C., Pereira, J., Brenner, M., Rubin, L., Wainger, B., Berndl, M. (2019). It's easy to fool yourself: Case studies on identifying bias and confounding in bio-medical datasets. Neurips 2019 LMRL workshop, extended abstract. [ PDF | link ]
  9. 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 ]
  10. 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 ]
  11. 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 ]
  12. 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 ]
  13. 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 ]
  14. 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 ]
  15. 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 ]
  16. 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 ]
  17. 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 ]
  18. 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 ]
  19. 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 ]
  20. 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 ]
  21. 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 ]
  22. 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 graduate 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.