Jonathan Yuelin Dong

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Interferometric imaging is an emerging technique for particle tracking and mass photometry. Mass or position are estimated from weak signals, coherently scattered from nanoparticles or single molecules, and interfered with a co-propagating reference. In th ...

Optical projection tomography (OPT) is a powerful tool for three-dimensional (3D) imaging of mesoscopic samples. While it is able to achieve resolution of a few tens of microns over a sample volume of several cubic centimeters, the reconstructed images oft ...

Reservoir Computing (RC) has become popular in recent years thanks to its fast and efficient computational capabilities. Standard RC has been shown to be equivalent in the asymptotic limit to Recurrent Kernels, which helps in analyzing its expressive power ...

Objectives: Endometriosis is a common gynecologic condition characterized by the presence of endometrial-like tissue outside the uterus, often leading to pelvic pain and infertility. Diagnosis is frequently delayed, with prolonged diagnostic wandering that ...

Phase retrieval, a nonlinear problem prevalent in imaging applications, has been extensively studied using random models, some of which with i.i.d. sensing matrix components. While these models offer robust reconstruction guarantees, they are computational ...

Localization microscopy enables imaging with resolutions that surpass the conventional optical diffraction limit. Notably, the Maximally INFormative LUminescence eXcitation (MINFLUX) method achieves super-resolution by shaping the excitation point spread f ...

Optical projection tomography (OPT) is a powerful tool for biomedical studies. It achieves 3D visualization of mesoscopic biological samples with high spatial resolution using conventional tomographic-reconstruction algorithms. However, various artifacts d ...

Optical imaging through complex media, such as biological tissues or fog, is challenging due to light scattering. In the multiple scattering regime, wavefront shaping provides an effective method to retrieve information; it relies on measuring how the prop ...

Imaging at depth in opaque materials has long been a challenge. Recently, wavefront shaping has enabled notable advance for deep imaging. Nevertheless, most noninvasive wavefront-shaping methods require cameras, lack the sensitivity for deep imaging under ...

Reservoir Computing is a class of Recurrent Neural Networks with internal weights fixed at random. Stability relates to the sensitivity of the network state to perturbations. It is an important property in Reservoir Computing as it directly impacts perform ...

Optoacoustic (OA) imaging combined with reversibly photoswitchable proteins has emerged as a promising technology for the high-sensitivity and multiplexed imaging of cells in live tissues in preclinical research. Through carefully designed illumination sch ...

Reservoir Computing is a class of simple yet efficient Recurrent Neural Networks where internal weights are fixed at random and only a linear output layer is trained. In the large size limit, such random neural networks have a deep connection with kernel m ...

Positron emission tomography (PET) scanners use scintillation crystals to stop high-energy photons. The ensuing lower-energy photons are then detected via photomultipliers. We study the performance of a stack of monolithic siliconpixel detectors as an alte ...

In imaging, we retrieve information about a specimen by encoding it into a probe beam. We quantitatively analyze the Fisher information provided by phase and scattering microscopy techniques and discuss implications for microscope design. ...

Jonathan Yuelin Dong

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Fundamental bounds on the precision of iSCAT, COBRI and dark-field microscopy for 3D localization and mass photometry

Artifacts in optical projection tomography due to refractive-index mismatch: model and correction

Comparison of Reservoir Computing topologies using the Recurrent Kernel approach

The Hôtel-Dieu MRI Classification of Uterosacral Ligament Involvement in Endometriosis: A Pictorial Guide to Clinical Use

Structured Random Model for Fast and Robust Phase Retrieval

Point-spread-function engineering in MINFLUX: optimality of donut and half-moon excitation patterns

Mechanical artifacts in optical projection tomography: classification and automatic calibration

Physics-based neural network for non-invasive control of coherent light in scattering media

Single-pixel transmission matrix recovery via two-photon fluorescence

Asymptotic Stability in Reservoir Computing

Model-based temporal unmixing towards quantitative photo-switching optoacoustic tomography

Reservoir Computing meets Recurrent Kernels and Structured Transforms

A Silicon-Pixel Paradigm for PET

From information theory to novel imaging techniques