Скачать или смотреть Machine Learning Based Design Automation of Microfluidic Flow Focusing Droplet Generators

Presented By: Ali Lashkaripour


Speaker Biography: Ali Lashkaripour holds a PhD in Biomedical Engineering from Boston University, and is an incoming post-doctoral fellow at Stanford University. His research focuses on design automation of continuous flow microfluidic droplet-based devices using data-driven approaches such as machine learning and rapid prototyping methods including micro-milling.


Webinar: Machine Learning Based Design Automation of Microfluidic Flow-Focusing Droplet Generators


Webinar Abstract: Droplet generators are at the heart of many microfluidic devices developed for life science applications but are difficult to tailor to each specific application. The high fabrication costs, complex fluid dynamics, and incomplete understanding of multi-phase flows make engineering droplet-based platforms an iterative and resource-intensive process. Large-scale experimental datasets and machine learning algorithms are employed to enable performance prediction and design automation of flow-focusing devices with high accuracy. The developed capabilities are captured in a software tool that converts high-level performance specifications to a device that delivers the desired droplet diameter and generation rate. This tool effectively eliminates the need for resource-intensive design iterations to achieve functional droplet generators.


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