萝莉乱伦

Kai-Wen Zhao has a diverse work experience in various roles related to computer vision, artificial intelligence, and machine learning. They are currently working as a Computer Vision Developer at Ubiquiti Inc. starting from 2022.

Prior to this, Kai-Wen worked as a Senior Software Engineer at 葉連科技 from October 2019 to December 2021. In this role, they led a team of 6 members and achieved significant accomplishments such as designing novel neural network architectures for face recognition NIST submission, improving lightweight face recognition models by reducing FNMR@1e-6 by 20%, and enhancing lightweight face detectors with speed-up and mAP improvement on mobile devices. They also participated in the ECCV 2020 GigaVision challenge and secured the 3rd place in the pedestrian and vehicle detection task among 250 registered teams.

Before joining 葉連科技, Kai-Wen worked as a Machine Learning Engineer at Viscovery from September 2018 to October 2019. They were responsible for developing machine learning models and algorithms in this role.

Prior to that, Kai-Wen worked as a Research Assistant at 資策會產業情報研究所(MIC) from March 2016 to August 2018. They also served as a Research Assistant at National Taiwan University from August 2015 to December 2015, and at Academia Sinica, Taiwan from August 2014 to August 2015. At Academia Sinica, their work focused on researching core algorithms and deep learning to improve the accuracy and performance of smart multimedia systems developed by the Multimedia Computing Laboratory (MCLab).

Kai-Wen Zhao pursued a Master of Science (M.S.) degree in Physics at National Taiwan University from 2016 to 2018. Prior to that, they completed a Bachelor of Science (B.S.) in Mechanical Engineering at National Cheng Kung University from 2009 to 2014. In addition to their formal education, they also obtained certifications in various fields. Kai-Wen completed the Deep Learning Specialization from Coursera in August 2018, the Computational Investing, Part I course from Coursera Course Certificates in September 2016, and the Financial Markets course from Coursera Course Certificates in August 2016.