Email: hayato.ikoma(at)10xgenomics(dot)com
Github: https://github.com/hayatoikoma
I am working on computational imaging and image analysis problems related to in-situ multiomics imaging technologies at 10x Genomics and am a former Ph.D. student of Stanford Computational Imaging Group at Department of Electrical Engineering, Stanford University. For my Ph.D. research, I was focusing on the development of computational imaging techniques for fluorescence optical microscopy, and I am broadly interested in signal processing, machine learning and optimization. Particularly, most of my Ph.D. works focused on the end-to-end optimization of optical imaging systems, which involves machine learning and micro fabrication of an optical element. I also served as a teaching assistant for EE267: Virtual Reality for five years and have written the template of homework in JavaScript with Three.js.
Before coming to Stanford University, I worked on new computational imaging techniques for fluorescence optical microscopy and a space telescope at MIT Media Lab and Centre de Mathématiques et Leurs Applications at École Normal Supérieure de Cachan (CMLA, ENS Cachan) in France. I also worked on an image processing algorithm for head-mount displays at the Google's Daydream team and a learning-based computational photography at the Google's Perception team as an intern. My work at Google contributed to Cinematic Photos of Google Photos.
Ph.D., Electrical Enginnering, Stanford University, Sept 2015 - June 2021
Project: Computational Imaging for Fluorescence Optical Microscopy
Supervised by Gordon Wetzstein
M.S., Applied Mathematics, Ecole normale supérieure de Cachan, July 2015
Program: M2 MVA Mathématiques/Vision/Apprentissage (Mathematics/Commputer Vision/Machine Learning)
Project: Phase Diversity: Estimation of an Optical Wavefront from Landscape Images
Supervised by Jean-Michel Morel
M.S., Media Arts and Sciences, Massachusetts Institute of Technology, June 2014
Project: Attenuation-corrected Fluorescence Spectra Unmixing for Spectroscopy and Microscopy
Supervised by Ramesh Raskar
MIT Media Lab
M.S., Biophysics, Kyoto University, March 2012
Project: Analysis of Dynamics of Actin Bundles in Fish Epidermal Keratocytes
Supervised by Yoshinori Fujiyoshi
B.E., Materials Engineering, University of Tokyo, March 2010
Project: Electrical Property Measurement of Germanium Crystal Based on Rapid Melt Growth
Supervised by Akira Toriumi, Koji Kita, Kosuke Nagashio
Jacques Hadamard Mathematics Foundation (9/2014 - 6/2015)
Funai Overseas Scholarship (9/2012 - 5/2014)
Iwadare Scholarhip (4/2011 - 3/2012)
Google Inc., June 2019 - December 2019
Research intern / Student researcher
Project: Development of Monocular Depth Estimation Model for Computational Photography
Google Inc., June 2016 - September 2016
Software engineering intern
Project: Development of Efficient Image Processing Algorithms for Head-mount Displays
Teaching assistant: EE267: Virtual Reality (Sprint 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021)
This class was previously taught in C++/OpenGL in 2016, and we revised all homework to use JavaScript/WebGL as a rendering platform for our DIY head-mount displays. For this transition, I was responsible for the re-implementation of all homework in Javascript with Three.js. (Its source code is available upon request.)
Ikoma, H., Kudo, T., Peng, Y., Broxton, M. and Wetzstein, G., “Deep Learning Multi-shot 3D Localization Microscopy Using Hybrid Optical–electronic Computing,” Optics Letters, 2021. [website] [paper]
Arguello, H., Pinilla, S., Peng, Y., Ikoma, H.,Bacca, J. and Wetzstein, G., “Shift-variant Color-coded Diffractive Spectral Imaging System,” Optica, 8, 1424-1434, 2021. [website] [paper]
Ikoma, H., Nguyen, C., Metzler, C., Peng, Y., and Wetzstein, G., “Depth from Defocus with Learned Optics for Imaging and Occlusion-aware Depth Estimation,” 2021 IEEE International Conference on Computational Photography (ICCP), 2021, pp. 1-12 [website] [paper]
Wetzstein, G., Ikoma, H., Metzler, C., and Peng, Y., “Deep Optics: Learning Cameras and Optical Computing Systems,” 2020 54th Asilomar Conference on Signals, Systems, and Computers, 2020, pp. 1313-1315, [paper]
Baek, S., Ikoma, H., Jeon, D., Li, Y., Heidrich, W., Wetzstein, G., Kim, Mi., “End-to-End Hyperspectral-Depth Imaging with Learned Diffractive Optics,” arXiv:200900463, ICCV 2021 [paper]
Dun, X., Ikoma, H., Wetzstein, G., Wang, Z., Cheng, X., and Peng, Y., “Learned rotationally symmetric diffractive achromat for full-spectrum computational imaging,” Optica 7, 913-922, 2020. [website] [paper]
Metzler, C., Ikoma, H., Peng, Y., and Wetzstein, G. “Deep Optics for Single-shot High-dynamic-range Imaging,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. [website] [paper]
Ikoma, H., Broxton, M., Kudo, T., and Wetzstein, G. “A convex 3D deconvolution algorithm for low photon count fluorescence imaging.” Scientific Reports, 11489, 2018. [website] [paper] [software]
Ikoma, H., Heshmat, B, Wetzstein, G, and Raskar, R. “Attenuation-corrected fluorescence spectra unmixing for spectroscopy and microscopy.” Optics Express, 22(16):19469–19483, 2014. [paper]
Goda, M. Ohata, M., Ikoma, H., Fujiyoshi, Y., Sugimoto, M., and Fujii, R. “Integumental reddish-violet coloration owing to novel dichromatic chromatophores in the teleost fish, Pseudochromis diadema.” Pigment Cell & Melanoma Research, 24(4):614–617, 2012. [paper]
Ikoma, H., Peng, Y., Broxton, B., and Wetzstein, G. “Snapshot multi-PSF 3D single-molecule localization microscopy using deep learning,” in Imaging and Applied Optics Congress, OSA Technical Digest, paper CW3B.3., 2020 [link]
Heshmat, B., Ikoma, H., Lee, I. H., Rastogi, K., and Raskar, R. “Computational hair quality categorization in lower magnifications,” in Proceeding of SPIE 9333, Biomedical Applications of Light Scattering IX, 93330Z, 2015 [link]
Ikoma, H., Heshmat, B., Wetzstein, G., and Raskar, R. “Nonlinear fluorescence spectral unmixing” in CLEO, OSA Technical Digest, JTh2A.9., 2014 [link]
Kadambi, A., Ikoma, H., Lin, X., Wetzstein, G., and Raskar, R. “Subsurface Enhancement through Sparse Representations of Multispectral Direct/Global Decomposition.” in Imaging and Applied Optics, OSA Technical Digest, CTh1B.4., 2013 [link]
Ikoma, H., Broxton, M., Kudo, T., and Wetzstein, G., “A convex 3D deconvolution algorithm for low photon count fluorescence imaging,” Olympus Corporation, July 2018
Ikoma, H., Konrad, R., Padmanaban, N., and Molner, K., “Build Your Own VR Display: An Introduction to VR Display Systems for Hobbyists and Educators,” Electronic Imaging Short Courses, January 2018, January 2019
Wetzstein, G., Konrad, R., Padmanaban, N., and Ikoma, H., “Build Your Own VR Display: An Introduction to VR Display Systems for Hobbyists and Educators,” SIGGRAPH, August 2017 [Link]
Ikoma, H., Delvit, J., Latry, C., Thiebaut, C., and Morel, J.“Phase Diversity: Estimation of an Optical Wavefront from Landscape Images,” Le site du Centre national d’études spatiales (CNES), 2015
Ikoma, H., Delvit, J., Latry, C., Thiebaut, C., and Morel, J.“Phase Diversity: Estimation of an Optical Wavefront from Landscape Images,” Applied Inverse Problems conference, May 2015
Python, JavaScript, Julia, C++, MATLAB
image processing, computer vision, convolutional neural net, computational imaging/photography, optics, grayscale photolithography, etc