# Ultrafast bioimaging

> **NIH NIH R35** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2024 · $455,805

## Abstract

Project Summary: This renewal application aims to support the PI in developing an intelligent ultrafast
bioimaging program that could open a new area of investigation and lead to a series of fundamental scientific
discoveries. Space and time, two critical physical dimensions, constitute the basis of modern metrology. In bio-
imaging, as recognized by the 2014 Nobel Prize in chemistry, there have been breathtaking advances in
improving spatial resolution, resulting in an impressive arsenal of nanoscopic tools that can break the diffraction
limit of light. Despite the importance, pursuing a high temporal resolution has only recently gained attention. The
motivation to develop methods for ultrafast imaging originates from the landscape shift of contemporary biology
from morphological explorations and phenotypic probing of organisms to seeking quantitative insights into
underlying mechanisms at molecular levels. The transient molecular events occur at a timescale varying from a
few nanoseconds that molecules glow in their "fluorescence lifetime" to tens of femtoseconds that molecules
take to vibrate. Therefore, ultrafast imaging is essential for observing and characterizing such dynamic events.
 With support from an ESI MIRA grant, we have established a highly productive research program and
developed several ultrafast imaging tools that showed great potential for biological studies. However, several
key challenges are still yet to be addressed, such as low light-detection sensitivity at high speed. To solve these
problems and further expand the realm of our program, I propose to bring in artificial intelligence and explore a
new research direction, intelligent ultrafast bioimaging. Our core idea is to treat an optical imaging system as an
encoder-decoder pair, where the imaging system optically encodes a scene on the sensor by physical
measurements; an electronic decoder then estimates the object's properties from the raw sensor data. Rather
than directly measuring the properties of a biological system, we will acquire encoded measurements through a
computational imaging scheme. We will also develop physics-informed machine-learning methods to seamlessly
integrate data with physics models in biological systems and codesign the machine-learning algorithms with
ultrafast imaging hardware in a holistic manner as an end-to-end optimization problem. We expect that such
synergy will lead to a new generation of ultrafast imagers and ultimately reshape many fields in biomedical
research.

## Key facts

- **NIH application ID:** 10765129
- **Project number:** 2R35GM128761-07
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Liang Gao
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $455,805
- **Award type:** 2
- **Project period:** 2018-08-01 → 2029-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10765129

## Citation

> US National Institutes of Health, RePORTER application 10765129, Ultrafast bioimaging (2R35GM128761-07). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10765129. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*