The Ozcan Research Group.UCLA

03/02/2022

Very excited & honored to receive the 2022 Joseph Fraunhofer Award and Robert M. Burley Prize from Optica. This recognition goes to my students, postdocs and collaborators that I was fortunate to work with:

The Joseph Fraunhofer Award/Robert M. Burley Prize recognizes significant research accomplishments in the field of optical engineering.

11/18/2021

Demonstrated "Biopsy-free in vivo virtual histology of skin using deep learning" in collaboration with Philip Scumpia, Yair Rivenson, Gene Rubinstein, first authors: Jingxi Li, Jason Garfinkel https://www.nature.com/articles/s41377-021-00674-8

An invasive biopsy followed by histological staining is the benchmark for pathological diagnosis of skin tumors. The process is cumbersome and time-consuming, often leading to unnecessary biopsies and scars. Emerging noninvasive optical technologies such as reflectance confocal microscopy (RCM) can....

06/29/2021

Machine learning and computation-enabled intelligent sensor design @ Nature Machine Intelligence: https://rdcu.be/cnlwu

Traditional sensing techniques apply computational analysis at the output of the sensor hardware to separate signal from noise. A new, more holistic and potentially more powerful approach proposed in this Perspective is designing intelligent sensor systems that ‘lock-in’ to optimal sensing of da...

02/11/2021

In collaboration with Philip Tinnefeld, Guillermo Acuna, Qingshan Wei and Birka Lalkens: "Addressable nanoantennas with cleared hotspots for single-molecule detection on a portable smartphone microscope" published in Nature Communications https://www.nature.com/articles/s41467-021-21238-9

Single-molecule fluorescence currently requires specialized imaging equipment due to the low signal of a single emitter. Here the authors introduce NanoAntennas with Cleared HOtSpots (NACHOS) to boost the signal sufficient for detection of a single emitter by a smartphone, opening the door to point-...

02/05/2021

"Neural Network-Based On-Chip Spectroscopy Using a Scalable Plasmonic Encoder" published in ACS Nano:
https://pubs.acs.org/doi/10.1021/acsnano.1c00079
ACS Publications

01/22/2021

Single-shot autofocusing of microscopy images using deep learning ACS Photonics
https://pubs.acs.org/doi/10.1021/acsphotonics.0c01774

Autofocusing is a critical step for high-quality microscopic imaging of specimens, especially for measurements that extend over time covering large fields of view. Autofocusing is generally practiced using two main approaches. Hardware-based optical autofocusing methods rely on additional distance s...

01/11/2021

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

01/04/2021

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

12/25/2020

The technology behind Pictor Labs:
https://bioengineeringcommunity.nature.com/posts/from-cutting-edge-research-to-commercialization-transforming-histopathology-with-deep-learning-based-virtual-staining

By Aydogan Ozcan, Yair Rivenson, Kevin De Haan, Hongda Wang

07/15/2020

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

07/10/2020

"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

07/06/2020

Misalignment resilient diffractive optical networks:

AbstractAs an optical machine learning framework, Diffractive Deep Neural Networks (D2NN) take advantage of data-driven training methods used in deep learning to devise light–matter interaction in 3...