Bio


I study systems and computational neurobiology under the guidance of Profs. Karl Deisseroth and David Sussillo.

Honors & Awards


  • Asan Foundation Biomedial Science Scholarship, Asan Foundation (2018)
  • KAIST Creativity and Challenge Award, KAIST (2018)
  • Samsung HumanTech Paper Award (Silver), Samsung Electronics (2017)
  • Talent Award of Korea, Republic of Korea Government (2015)
  • SPIE Optics and Photonics Education Scholarship, SPIE (2014)
  • KAIST Presidential Fellowship, KAIST (2013)

Education & Certifications


  • B.S., KAIST, Physics and Mathematics (2018)

Stanford Advisors


All Publications


  • Cycle-consistent deep learning approach to coherent noise reduction in optical diffraction tomography OPTICS EXPRESS Choi, G., Ryu, D., Jo, Y., Kim, Y., Park, W., Min, H., Park, Y. 2019; 27 (4): 4927–43
  • Intensiometric biosensors visualize the activity of multiple small GTPases in vivo NATURE COMMUNICATIONS Kim, J., Lee, S., Jung, K., Oh, W., Kim, N., Son, S., Jo, Y., Kwon, H., Heo, W. 2019; 10: 211

    Abstract

    Ras and Rho small GTPases are critical for numerous cellular processes including cell division, migration, and intercellular communication. Despite extensive efforts to visualize the spatiotemporal activity of these proteins, achieving the sensitivity and dynamic range necessary for in vivo application has been challenging. Here, we present highly sensitive intensiometric small GTPase biosensors visualizing the activity of multiple small GTPases in single cells in vivo. Red-shifted sensors combined with blue light-controllable optogenetic modules achieved simultaneous monitoring and manipulation of protein activities in a highly spatiotemporal manner. Our biosensors revealed spatial dynamics of Cdc42 and Ras activities upon structural plasticity of single dendritic spines, as well as a broad range of subcellular Ras activities in the brains of freely behaving mice. Thus, these intensiometric small GTPase sensors enable the spatiotemporal dissection of complex protein signaling networks in live animals.

    View details for DOI 10.1038/s41467-018-08217-3

    View details for Web of Science ID 000455595400034

    View details for PubMedID 30643148

    View details for PubMedCentralID PMC6331645

  • Quantitative Phase Imaging and Artificial Intelligence: A Review IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS Jo, Y., Cho, H., Lee, S., Choi, G., Kim, G., Min, H., Park, Y. 2019; 25 (1)
  • Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells BIOSENSORS & BIOELECTRONICS Kim, G., Jo, Y., Cho, H., Min, H., Park, Y. 2019; 123: 69–76

    Abstract

    We present a rapid and label-free method for hematologic screening for diseases and syndromes, utilizing quantitative phase imaging (QPI) and machine learning. We aim to establish an efficient blood examination framework that does not suffer from the drawbacks of conventional blood assays, which are incapable of profiling single cells or require labeling procedures. Our method involves the synergistic employment of QPI and machine learning. The high-dimensional refractive index information arising from the QPI-based profiling of single red blood cells is processed to screen for diseases and syndromes using machine learning, which can utilize high-dimensional data beyond the human level. Accurate screening for iron-deficiency anemia, reticulocytosis, hereditary spherocytosis, and diabetes mellitus is demonstrated (>98% accuracy) using the proposed method. Furthermore, we highlight the synergy between QPI and machine learning in the proposed method by analyzing the performance of the method.

    View details for DOI 10.1016/j.bios.2018.09.068

    View details for Web of Science ID 000450376600010

    View details for PubMedID 30321758

  • Deep-Learning-Based Label-Free Segmentation of Cell Nuclei in Time-Lapse Refractive Index Tomograms IEEE ACCESS Lee, J., Kim, H., Cho, H., Jo, Y., Song, Y., Ahn, D., Lee, K., Park, Y., Ye, S. 2019; 7: 83449–60
  • Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning. Journal of visualized experiments : JoVE Yoon, J., Jo, Y., Kim, Y. S., Yu, Y., Park, J., Lee, S., Park, W. S., Park, Y. 2018

    Abstract

    We describe here a protocol for the label-free identification of lymphocyte subtypes using quantitative phase imaging and machine learning. Identification of lymphocyte subtypes is important for the study of immunology as well as diagnosis and treatment of various diseases. Currently, standard methods for classifying lymphocyte types rely on labeling specific membrane proteins via antigen-antibody reactions. However, these labeling techniques carry the potential risks of altering cellular functions. The protocol described here overcomes these challenges by exploiting intrinsic optical contrasts measured by 3D quantitative phase imaging and a machine learning algorithm. Measurement of 3D refractive index (RI) tomograms of lymphocytes provides quantitative information about 3D morphology and phenotypes of individual cells. The biophysical parameters extracted from the measured 3D RI tomograms are then quantitatively analyzed with a machine learning algorithm, enabling label-free identification of lymphocyte types at a single-cell level. We measure the 3D RI tomograms of B, CD4+ T, and CD8+ T lymphocytes and identified their cell types with over 80% accuracy. In this protocol, we describe the detailed steps for lymphocyte isolation, 3D quantitative phase imaging, and machine learning for identifying lymphocyte types.

    View details for PubMedID 30507910

  • Holographic deep learning for rapid optical screening of anthrax spores SCIENCE ADVANCES Jo, Y., Park, S., Jung, J., Yoon, J., Joo, H., Kim, M., Kang, S., Choi, M., Lee, S., Park, Y. 2017; 3 (8): e1700606

    Abstract

    Establishing early warning systems for anthrax attacks is crucial in biodefense. Despite numerous studies for decades, the limited sensitivity of conventional biochemical methods essentially requires preprocessing steps and thus has limitations to be used in realistic settings of biological warfare. We present an optical method for rapid and label-free screening of Bacillus anthracis spores through the synergistic application of holographic microscopy and deep learning. A deep convolutional neural network is designed to classify holographic images of unlabeled living cells. After training, the network outperforms previous techniques in all accuracy measures, achieving single-spore sensitivity and subgenus specificity. The unique "representation learning" capability of deep learning enables direct training from raw images instead of manually extracted features. The method automatically recognizes key biological traits encoded in the images and exploits them as fingerprints. This remarkable learning ability makes the proposed method readily applicable to classifying various single cells in addition to B. anthracis, as demonstrated for the diagnosis of Listeria monocytogenes, without any modification. We believe that our strategy will make holographic microscopy more accessible to medical doctors and biomedical scientists for easy, rapid, and accurate point-of-care diagnosis of pathogens.

    View details for DOI 10.1126/sciadv.1700606

    View details for Web of Science ID 000411589900007

    View details for PubMedID 28798957

    View details for PubMedCentralID PMC5544395

  • Identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning SCIENTIFIC REPORTS Yoon, J., Jo, Y., Kim, M., Kim, K., Lee, S., Kang, S., Park, Y. 2017; 7: 6654

    Abstract

    Identification of lymphocyte cell types are crucial for understanding their pathophysiological roles in human diseases. Current methods for discriminating lymphocyte cell types primarily rely on labelling techniques with magnetic beads or fluorescence agents, which take time and have costs for sample preparation and may also have a potential risk of altering cellular functions. Here, we present the identification of non-activated lymphocyte cell types at the single-cell level using refractive index (RI) tomography and machine learning. From the measurements of three-dimensional RI maps of individual lymphocytes, the morphological and biochemical properties of the cells are quantitatively retrieved. To construct cell type classification models, various statistical classification algorithms are compared, and the k-NN (k = 4) algorithm was selected. The algorithm combines multiple quantitative characteristics of the lymphocyte to construct the cell type classifiers. After optimizing the feature sets via cross-validation, the trained classifiers enable identification of three lymphocyte cell types (B, CD4+ T, and CD8+ T cells) with high sensitivity and specificity. The present method, which combines RI tomography and machine learning for the first time to our knowledge, could be a versatile tool for investigating the pathophysiological roles of lymphocytes in various diseases including cancers, autoimmune diseases, and virus infections.

    View details for DOI 10.1038/s41598-017-06311-y

    View details for Web of Science ID 000406366000012

    View details for PubMedID 28751719

    View details for PubMedCentralID PMC5532204

  • Collaborative effects of wavefront shaping and optical clearing agent in optical coherence tomography JOURNAL OF BIOMEDICAL OPTICS Yu, H., Lee, P., Jo, Y., Lee, K., Tuchin, V. V., Jeong, Y., Park, Y. 2016; 21 (12): 121510

    Abstract

    We demonstrate that simultaneous application of optical clearing agents (OCAs) and complex wavefront shaping in optical coherence tomography (OCT) can provide significant enhancement of penetration depth and imaging quality. OCA reduces optical inhomogeneity of a highly scattering sample, and the wavefront shaping of illumination light controls multiple scattering, resulting in an enhancement of the penetration depth and signal-to-noise ratio. A tissue phantom study shows that concurrent applications of OCA and wavefront shaping successfully operate in OCT imaging. The penetration depth enhancement is further demonstrated for

    View details for DOI 10.1117/1.JBO.21.12.121510

    View details for Web of Science ID 000392914600012

    View details for PubMedID 27792807

  • Label-free identification of individual bacteria using Fourier transform light scattering OPTICS EXPRESS Jo, Y., Jung, J., Kim, M., Park, H., Kang, S., Park, Y. 2015; 23 (12): 15792–805

    Abstract

    Rapid identification of bacterial species is crucial in medicine and food hygiene. In order to achieve rapid and label-free identification of bacterial species at the single bacterium level, we propose and experimentally demonstrate an optical method based on Fourier transform light scattering (FTLS) measurements and statistical classification. For individual rod-shaped bacteria belonging to four bacterial species (Listeria monocytogenes, Escherichia coli, Lactobacillus casei, and Bacillus subtilis), two-dimensional angle-resolved light scattering maps are precisely measured using FTLS technique. The scattering maps are then systematically analyzed, employing statistical classification in order to extract the unique fingerprint patterns for each species, so that a new unidentified bacterium can be identified by a single light scattering measurement. The single-bacterial and label-free nature of our method suggests wide applicability for rapid point-of-care bacterial diagnosis.

    View details for DOI 10.1364/OE.23.015792

    View details for Web of Science ID 000356902500066

    View details for PubMedID 26193558

  • Angle-resolved light scattering of individual rod-shaped bacteria based on Fourier transform light scattering SCIENTIFIC REPORTS Jo, Y., Jung, J., Lee, J., Shin, D., Park, H., Nam, K., Park, J., Park, Y. 2014; 4: 5090

    Abstract

    Two-dimensional angle-resolved light scattering maps of individual rod-shaped bacteria are measured at the single-cell level. Using quantitative phase imaging and Fourier transform light scattering techniques, the light scattering patterns of individual bacteria in four rod-shaped species (Bacillus subtilis, Lactobacillus casei, Synechococcus elongatus, and Escherichia coli) are measured with unprecedented sensitivity in a broad angular range from -70° to 70°. The measured light scattering patterns are analyzed along the two principal axes of rod-shaped bacteria in order to systematically investigate the species-specific characteristics of anisotropic light scattering. In addition, the cellular dry mass of individual bacteria is calculated and used to demonstrate that the cell-to-cell variations in light scattering within bacterial species is related to the cellular dry mass and growth.

    View details for DOI 10.1038/srep05090

    View details for Web of Science ID 000336425400002

    View details for PubMedID 24867385

    View details for PubMedCentralID PMC4035574

  • Quantitative Phase Imaging Techniques for the Study of Cell Pathophysiology: From Principles to Applications SENSORS Lee, K., Kim, K., Jung, J., Heo, J., Cho, S., Lee, S., Chang, G., Jo, Y., Park, H., Park, Y. 2013; 13 (4): 4170–91

    Abstract

    A cellular-level study of the pathophysiology is crucial for understanding the mechanisms behind human diseases. Recent advances in quantitative phase imaging (QPI) techniques show promises for the cellular-level understanding of the pathophysiology of diseases. To provide important insight on how the QPI techniques potentially improve the study of cell pathophysiology, here we present the principles of QPI and highlight some of the recent applications of QPI ranging from cell homeostasis to infectious diseases and cancer.

    View details for DOI 10.3390/s130404170

    View details for Web of Science ID 000318036400013

    View details for PubMedID 23539026

    View details for PubMedCentralID PMC3673078