Alon Saguy
as7709@columbia.edu
Alon Saguy portrait

Alon Saguy

Postdoctoral Research Fellow

Columbia University · Zuckerman Institute

Email Google Scholar GitHub

I develop machine learning and signal processing methods for biomedical imaging and large-scale neuroscience, with a focus on robust inference from noisy, high-dimensional measurements.

Microscopy Neuroscience Signal Processing Machine Learning

About Me

I earned my BSc in Electrical Engineering from the Technion, followed by a PhD in Biomedical Engineering, and I am currently a Postdoctoral Research Fellow at Columbia University (Zuckerman Institute). My research interests span super-resolution microscopy, neuroscience, signal processing, and machine learning—with an emphasis on building practical, data-driven tools that extend what we can measure and infer in biology.

Current focus

  • Learning-based reconstruction and analysis for microscopy
  • Modeling and decoding neural activity from large-scale electrophysiology
  • Robust inference under noise, sparsity, and domain shift

Contact

Awards

  • 2024
    Fulbright Postdoctoral Fellowship
  • 2024
    Citation of Excellence (Jacobs) for outstanding PhD students
  • 2021–2023
    Excellent Tutor Award (consecutive years)

Research

Selected projects/papers with a brief overview.

One-click reconstruction teaser

One-click reconstruction in single-molecule localization microscopy

Parameter-aware deep learning for robust SMLM reconstruction with less manual tuning, aimed at improving generalization across imaging conditions.

Paper

DBlink: dynamic localization microscopy in super spatiotemporal resolution

A deep learning approach that reconstructs high spatiotemporal resolution videos from SMLM recordings, enabling visualization of cellular dynamics with improved temporal resolution.

Paper Article
Diffusion model microscopy teaser

This microtubule does not exist: super-resolution microscopy image generation by a diffusion model

Generative diffusion modeling for super-resolution microscopy images—exploring realism, diversity, and utility for simulation/augmentation.

Paper

Publications

Selected peer-reviewed publications and related proceedings/abstracts.

  1. Saguy, A., Xiao, D., Narayanasamy, K. K., Nakatani, Y., Saliba, N., Gagliano, G., Gustavsson, A.-K., Heilemann, M., Shechtman, Y. (2025). One-click reconstruction in single-molecule localization microscopy via experimental parameter-aware deep learning. npj Imaging, 3(1), 61.
  2. Saguy, A., Nahimov, T., Lehrman, M., Gómez-de-Mariscal, E., Hidalgo-Cenalmor, I., Alalouf, O., Balakrishnan, A., Heilemann, M., Henriques, R., Shechtman, Y. (2025). This Microtubule Does Not Exist: Super-Resolution Microscopy Image Generation by a Diffusion Model. Small Methods, 9(3), 2400672.
  3. Shalev Ezra, Y., Saguy, A., Levin, G., Weiss, L. E., Alalouf, O., Shechtman, Y. (2025). High-throughput DNA repair monitoring in Saccharomyces cerevisiae suggests SSB- and DSB-induced chromatin reconfiguration. Scientific Reports, 15(1), 32302.
  4. Hidalgo-Cenalmor, I., Pylvänäinen, J. W., G. Ferreira, M., Russell, C. T., Saguy, A., Arganda-Carreras, I., Shechtman, Y., Jacquemet, G., Henriques, R., et al. (2024). DL4MicEverywhere: deep learning for microscopy made flexible, shareable and reproducible. Nature Methods, 21(6), 925–927.
  5. Jang, S., Narayanasamy, K., Rahm, J., Saguy, A., Kompa, J., Dietz, M. S., Johnsson, K., Shechtman, Y., Heilemann, M. (2024). Neural network-assisted single-molecule localization microscopy with a weak-affinity protein tag. Biophysical Journal, 123(3), 463a. (meeting abstract)
  6. Saguy, A., Alalouf, O., Opatovski, N., Jang, S., Heilemann, M., Shechtman, Y. (2023). DBlink: dynamic localization microscopy in super spatiotemporal resolution via deep learning. Nature Methods, 20(12), 1939–1948.
  7. Jang, S., Narayanasamy, K., Saguy, A., Rahm, J., Kompa, J., Shechtman, Y., Hiblot, J., Johnsson, K., Heilemenn, M. (2023). Fast super-resolution single-molecule localization microscopy using exchangeable fluorescent probes. European Biophysics Journal (supplement), 52(Suppl 1), S59. (conference abstract)
  8. Allen, D., Weiss, L. E., Saguy, A., Rosenberg, M., Iancu, O., Matalon, O., Lee, C., Beider, K., Nagler, A., Shechtman, Y., et al. (2022). High-throughput imaging of CRISPR- and recombinant adeno-associated virus–induced DNA damage response in human hematopoietic stem and progenitor cells. The CRISPR Journal, 5(1), 80–94.
  9. Ferdman, B., Saguy, A., Xiao, D., Shechtman, Y. (2022). Diffractive optical system design by cascaded propagation. Optics Express, 30(15), 27509–27530.
  10. Saguy, A., Jünger, F., Peleg, A., Ferdman, B., Nehme, E., Rohrbach, A., Shechtman, Y. (2021). Deep-ROCS: from speckle patterns to superior-resolved images by deep learning in rotating coherent scattering microscopy. Optics Express, 29(15), 23877–23887.
  11. Saguy, A., Baldering, T. N., Weiss, L. E., Nehme, E., Karathanasis, C., Dietz, M. S., Heilemann, M., Shechtman, Y. (2021). Automated analysis of fluorescence kinetics in single-molecule localization microscopy data reveals protein stoichiometry. The Journal of Physical Chemistry B, 125(22), 5716–5721.

Talks

UCL Neuropixels Course (2025) — “Towards an Electrophysiological Atlas for Neuropixels recordings”

Video link + embedded player.

YouTube Course page

Single Molecule Localization Microscopy Symposium — Paris 2021

Video Link of my presentation about QAFKA, a quantitative localization microscopy method.

Webinar videos

Media