Peggy L.
About Peggy L.
Peggy L. is a Software Engineer with extensive experience in application development and accessibility features. She holds a Master's degree in Information System and Application and has worked at ASUS for nine years, contributing to various projects including face recognition systems and app enhancements.
Work at ASUS
Peggy L. has been employed at ASUS as a Software Engineer since 2015. Over her nine years in this role, she has contributed to various projects, including the development of accessibility features for the ZenUI Launcher app, enhancing usability for Talkback users. She played a key role in developing the AppLock AAR for ASUS Apps, supporting its integration on Android N 7.0 and 7.1.1, which included features such as twinApps and deep shortcut. Additionally, she implemented object recognition features for ASUS AiCam on platforms like Qualcomm and Movidius NCS, and developed a face recognition system for over 6000 employees using FaceNet models.
Education and Expertise
Peggy L. holds a Master's degree in Information System and Application from National Tsing Hua University, which she completed from 2013 to 2015. Prior to that, she earned a Bachelor's degree in Information Management and Technology Management from National Taiwan University of Science and Technology, studying from 2009 to 2013. She also participated in an International Exchange Student Program at Hong Kong Baptist University in 2012, where she studied Computer Science for 11 months.
Internship Experience
Peggy L. gained early professional experience through internships. In 2010, she interned in the Information Department at Apacer for one month. Later, in 2014, she completed a one-month internship at Yahoo in Taiwan. These internships provided her with foundational skills in software engineering and exposure to the tech industry.
Projects and Contributions
Throughout her career, Peggy L. has been involved in significant projects that showcase her technical skills. She collaborated with the ZenUI Launcher team to develop a wallpaper style detection model, which involved collecting and labeling over 4000 wallpapers and text styles. Her work on accessibility features and object recognition systems reflects her commitment to enhancing user experience and leveraging advanced technologies in software development.