Ed Hirata

Last call! This is a great opportunity for those interested in learning and applying SOTA ML/DL models to microscopy data with the leading experts in the field.

Personally super excited to be a lead TA along with the amazing @afoix.bsky.social and @edhirata.bsky.social!

Hope to see you there :)

WaveOrder is a powerful new #microscopy tool from Biohub that helps researchers reconstruct messy microscope data into clean, quantitative maps. It’s grounded in physics & it’s already changing how we see biology.

📚 Preprint https://bit.ly/4jgDJLB
🔬WaveOrder: https://czi.co/4pMwGgb

We moved the AI@MBL course "Deep Learning for Microscopy Image Analysis" to HHMI Janelia (@hhmijanelia.bsky.social).

Join us for two weeks of intense lectures, exercise, and hands-on project work!

Course dates: June 4-18 2026
Application by: January 15 2026

www.janelia.org/you-janelia/...

Deep Learning for Microscopy Image Analysis Topics The following will be covered extensively during lectures, exercises, and project work: Image denoising and restoration (fully supervised and self-supervised) Image translation (e.g.,

📢 Apply now for the first DL@Janelia Bootcamp (June 4–18, 2026)! 2 weeks of hands-on deep learning for microscopy: train models on your own data, Python needed (no ML experience), housing + meals included, no registration fee

📍 Janelia Research Campus, VA
🗓️ Apply by Jan 15, 2026
🔗 shorturl.at/j4sgY

📢 Apply now for the first DL@Janelia Bootcamp (June 4–18, 2026)! 2 weeks of hands-on deep learning for microscopy: train models on your own data, Python needed (no ML experience), housing + meals included, no registration fee

📍 Janelia Research Campus, VA
🗓️ Apply by Jan 15, 2026
🔗 shorturl.at/j4sgY

We’ve upgraded ShapeEmbed 🎉 ShapeEmbedLite decodes latent codes via an MLP to guarantee valid EDMs, making it lighter and ideal for small microscopy datasets or limited compute. Hear more at my BIC workshop talk or poster at #ICCV2025! Try it out at github.com/uhlmanngroup...

🤝 And collaborators: @adrianjacobo.bsky.social, Akilandeswari Balasubramanian, Tiger Lao, @edhirata.bsky.social @mattersoflight.bsky.social , @lammerdinglab.bsky.social Richa Agrawal, Alexandre X. Falcao @alexandredizeux.bsky.social Andrew Sweet and @aganders3.bsky.social @cziscience.bsky.social

Finally, a fun collaboration published in Optica:

opg.optica.org/optica/fullt...

Fun times at the @mblscience.bsky.social , playing with optical toys with @edhirata.bsky.social and others :)

DynaCLR: Contrastive Learning of Cellular Dynamics with Temporal Regularization We report DynaCLR, a self-supervised method for embedding cell and organelle Dynamics via Contrastive Learning of Representations of time-lapse images. DynaCLR integrates single-cell tracking and time...

Excited to share an update on #DynaCLR—a self-supervised method to learn dynamic cell & organelle embeddings from time-lapse microscopy using contrastive learning.
DynaCLR combines single-cell tracking & time-aware sampling for robust representations.

arxiv.org/abs/2410.11281

Grateful to our incredible collaborators! This work wouldn’t have been possible without the deep interdisciplinary collaboration across CZ Biohub teams.
These fast iterations and bold ideas are only possible because of you! 🙏 #CZBiohub @czbiohub.bsky.social

#science #CellBiology #AI #microscopy

We provide a:
- PyTorch code for training & inference: github.com/mehta-lab/Vi...
- A GUI to visualize trajectories in real and embedding space
- Tools for efficient human-in-the-loop annotation

DynaCLR’s temporally consistent embeddings enable in silico synchronization of asynchronous dynamics like infection & mitosis, along with organelle responses, enabling pseudotime estimation across multiple imaging channels. 5/⏳

We introduce a strategy to accelerate label-free cell state analysis: cross-modal knowledge distillation transfers labels from easy-to-annotate fluorescence sensors to unlabeled phase images, unlocking multi-state measurements from label-free data. 4/🧑‍🏫

DynaCLR embeddings enable efficient classification of cell states, including mitosis and viral infection, with a few thousand point annotations of cell state, which speeds up measurement of complex cell state dynamics. 3/🦠

DynaCLR learns embeddings from 3D multi-channel movies and enables:
1) cell state classification
2) fluorescence-to-label-free label transfer
3) alignment of dynamics.

This update adds knowledge distillation and temporal alignment for even richer analysis. 2/🔬

DynaCLR: Contrastive Learning of Cellular Dynamics with Temporal Regularization We report DynaCLR, a self-supervised method for embedding cell and organelle Dynamics via Contrastive Learning of Representations of time-lapse images. DynaCLR integrates single-cell tracking and time...

Excited to share an update on #DynaCLR—a self-supervised method to learn dynamic cell & organelle embeddings from time-lapse microscopy using contrastive learning.
DynaCLR combines single-cell tracking & time-aware sampling for robust representations.

arxiv.org/abs/2410.11281

#sciencejobs #imageprocessing #bioimage #zarr #xarray @czbiohub.bsky.social

#bioimaging #opensource #zarr #imageanalysis #sciencejobs #python… | Eduardo Hirata-Miyasaki We also have an exciting opportunity for recent BS/MS graduates! We're looking for a software engineer excited to build and deploy high-performance tools for…

www.linkedin.com/posts/edhira...

Software Engineer, Computational Microscopy Platform (Biohub SF) San Francisco, California, United States

We're looking for a software engineer (recent grad BS/MS) excited to build and deploy high-performance tools for dynamic imaging of cells, tissues, and organs 🐟🦠🔬 — think terabytes of image data, cell phenotyping, and high-throughput microscopy.
job-boards.greenhouse.io/chanzuckerbe...