BME PhD Preliminary Exam Announcement for Yilun Li (Fang Huang, advisor)

Everyone is invited to attend the public presentation beginning at 10:30AM.

Research Title: Cloud-SMLM: An Online Computation Platform for Seamless Super-Resolution Image Reconstruction

Thesis Committee Members:

FANG HUANG (chair)

DANIEL M. SUTER (member)

STEVEN T. WERELEY (member)

XIAOHUI SONG (member)

Date: August 1^st, 2023

Time: 10:30AM

Location: MJIS 2001 and Zoom

https://purdue-edu.zoom.us/j/91402648534

Abstract: Single-molecule localization microscopy (SMLM) leverages the detection and localization of individual fluorescently tagged molecules to visualize cellular and tissue samples at a nanometer resolution. The reconstruction of a super-resolution image via SMLM, however, involves intricate algorithms that may require hours to achieve a single high-quality 3D image. Additionally, the image generation process calls for specialized expertise and resources, such as knowledge of image processing and massively parallel computing. To address these challenges, we are developing an integrated cloud-based SMLM suite named "cloud-SMLM". This suite combines key algorithms from each stage of single molecule analysis, integrating with high levels of parallelization and modularization. Deployable on cloud-based computational services like Amazon Elastic Compute Cloud (Amazon EC2), cloud-SMLM interacts with its users through a dedicated, easy-to-use drag-and-drop webpage. This design eliminates the prerequisite for hardware such as GPU and reduces the knowledge barrier for users of single molecule nanoscopy. Accessible from any web-browser equipped device, it enables users to initiate and modify data reconstruction. Furthermore, it provides visualization and data mining tools for users to explore the reconstructed three-dimensional ultra-high-resolution cellular and tissue volumes. We expect that this project will democratize existing and new single molecule super-resolution imaging capacities by integrating them into computational pipelines for a wide range of biological model systems. We believe that the success of cloud-SMLM will represent a significant step towards unlocking the full potential of ultra-high resolution optical imaging in biological and biomedical research.