The Ozcan Research Group.UCLA

Ensemble learning and feature engineering significantly improve the inference and image classification performance of diffractive optical networks https://www.nature.com/articles/s41377-020-00446-w Light: Science & Applications #DeepLearningOptics #DiffractiveOpticalNetworks

Terahertz pulse shaping using diffractive surfaces https://nature.com/articles/s41467-020-20268-z Nature Communications UCLA Engineering #deeplearning #diffractivenetworks #opticalnetworks

The technology behind Pictor Labs: https://bioengineeringcommunity.nature.com//from-cutting-e #DeepLearningPathology #VirtualStaining #Histology

Demonstrated "An Automated, Cost-Effective Optical System for Accelerated Antimicrobial Susceptibility Testing using Deep Learning". In collaboration with Omai Garner & Dino Di Carlo, first author: Calvin Brown, published in ACS Photonics: https://doi.org/10.1021/acsphotonics.0c00841

"Early detection and classification of live bacteria using time-lapse coherent imaging and deep learning". We demonstrated a limit-of-detection of 1 colony forming unit/bacterium per 1 Liter of water sample in <9 hours of total test time. There is no EPA-approved nucleic acid-based method for detecting coliform bacteria in water samples as they are not capable of differentiating between live and dead microorganisms. That is why culture-based methods are needed. Published at Light: Science & Applications https://www.nature.com/articles/s41377-020-00358-9

Misalignment resilient diffractive optical networks: https://www.degruyter.com//article-10.1515-nanoph-2020-029

Automated screening of sickle cells using a smartphone-based microscope and deep learning: https://lnkd.in/gx-iNKC https://lnkd.in/g33Y8r8

Our lab will be presenting ten different talks in the upcoming OSA CLEO virtual conference, which is "free" to register and attend through Zoom: https://www.cleoconference.org/home/ Presenters: Tairan Liu, Jingxi Li, Calvin Brown, Yi Luo, Artem Goncharov, Kevin deHaan, Yichen Wu, Deniz Mengu

"Deep learning-enabled point-of-care sensing using multiplexed paper-based sensors" published in npj Digital Medicine: https://rdcu.be/b304T

Digital synthesis of histological stains using multiplexed virtual staining of label-free tissue: https://rdcu.be/b3Za6

All the professional societies should postpone or cancel their upcoming conferences. Please follow the great leadership of APS Physics: cancelled their March Meeting because of the coronavirus. Conferences host some of the most frequent flyers in the world, who will inevitably spread the disease widely. It is time to act: SPIE, the international society for optics and photonics, IEEE, The Optical Society, American Chemical Society and all the other professional societies. https://march.aps.org/

Broadband diffractive neural networks: https://newsroom.ucla.edu//optical-system-recognize-object

"Holographic detection of nanoparticles using acoustically actuated nanolenses" published in Nature Communications: https://www.nature.com/articles/s41467-019-13802-1.pdf

Measurement of Serum Phosphate Levels Using a Mobile Sensor: https://pubs.rsc.org//co/articlelanding/2020/AN/C9AN02215E

We have created a paper-based point of care diagnostic test for early-stage Lyme disease using deep learning and a multiplexed vertical flow assay - in collaboration with Dino Di Carlo, Omai Garner and Paul Arnaboldi. First authors: Hyou-Arm Joung and Zachary Ballard. Published in ACS Nano, funded by the the Steven and Alexandra Cohen Foundation: https://www.acs.org//paper-based-test-could-diagnose-lyme-

Broadband diffractive neural networks simultaneously process a continuum of wavelengths to all-optically perform a specific task learned using deep learning: https://www.nature.com/articles/s41377-019-0223-1

Deep-Learning-Based Image Reconstruction and Enhancement in Optical Microscopy - published in Proceedings of IEEE: https://ieeexplore.ieee.org/document/8901171 IEEE

Deep learning-based virtual refocusing of fluorescence images also works with an engineered point spread function (PSF), such as the double-helix PSF, which can increase the depth-of-field in super-resolution microscopy. Read more @Nature Methods: https://lnkd.in/gPrKyeK