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Lau Felix
Research Engineer at Apple with expertise in machine learning and artificial intelligence.
Felix Lau is a research engineer with extensive experience in machine learning and artificial intelligence, particularly in the medical imaging field. He worked as a Senior Machine Learning Scientist at Arterys from April 2016 to August 2019, a period of 3 years and 5 months.1
During his time at Arterys, Felix made significant contributions to the development of AI-powered medical imaging technologies:
Key Achievements at Arterys
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CardioAI Development: Felix led the development of CardioAI, a U-Net-based fully convolutional network (FCN) that segments ventricular structures in cardiac MRI scans. This AI model achieved radiologist-level accuracy and became the first FDA-cleared cloud-based deep learning clinical product. CardioAI is used by clinicians in various prestigious medical institutions and can save up to 25 minutes of their time per case.1
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ScarGAN Project: He spearheaded the development of ScarGAN, an innovative synthetic data generation technique. This method uses chained Generative Adversarial Networks (GANs) to simulate pathological tissues in normal patients' MRI LGE scans, reducing the need for collecting data on rare diseases.1
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LungAI Co-development: Felix also contributed to the development of LungAI, an algorithm for lung nodule detection and segmentation using U-Net and ResNet architectures.1
Career Progression
After his role at Arterys, Felix Lau moved on to other prominent positions in the AI field:
- Research Engineer at Scale AI (September 2019 - September 2021)
- Research Engineer at Apple (October 2021 - Present), where he works in the SIML - Human and Object Understanding team1
Education
Felix Lau holds dual bachelor's degrees from The Hong Kong University of Science and Technology:
- Bachelor's Degree in Business Administration (First class honours)
- Bachelor's Degree in Computer Science (First class honours)
Both degrees were completed between 2008 and 2012.1
Felix Lau's career demonstrates a strong focus on applying machine learning and AI technologies to solve complex problems, particularly in the medical imaging domain. His work has contributed to significant advancements in automated clinical workflows and AI-assisted medical diagnostics.
