Tianqi Luan
About Tianqi Luan
Tianqi Luan is a Back End Developer at Nebo Agency, where he contributes to the company's significant annual revenues by creating impactful websites for high-profile clients. He has a diverse background in web development and education, with experience in various roles and a strong academic foundation in computer science and engineering.
Work at Nebo Agency
Tianqi Luan currently serves as a Back End Developer at Nebo Agency, a role he has held since 2023. In this position, he contributes to the agency's million-dollar annual revenues by designing, programming, and delivering high-impact websites for prestigious clients. His work focuses on backend development, ensuring robust functionality and performance of web applications.
Education and Expertise
Tianqi Luan holds a Master's degree in Computer Science from Georgia State University, which he completed from 2019 to 2022. He also earned a Master of Science in Chemistry from Western Washington University between 2017 and 2019. Additionally, he obtained a Bachelor of Engineering in Materials Science from Southern University of Science and Technology from 2013 to 2017. His technical expertise includes full-stack development with proficiency in LAMP, JavaScript, Angular, and Golang, as well as system administration skills with NGINX servers.
Professional Background
Before joining Nebo Agency, Tianqi Luan gained diverse experience in various roles. He worked as a Web Developer at Eyesore from 2022 to 2023 and as a Frontend Developer at Pure UX for two months in 2021. His earlier roles include serving as a Research Assistant at Georgia State University from 2021 to 2022 and as a Mathematics Tutor from 2019 to 2021. He also worked as a Lab Assistant at Western Washington University from 2017 to 2019 and completed an internship in electric engineering at Shenzhen Greatland Electrics INC in 2016.
Technical Projects and Contributions
Tianqi Luan has developed an automated analysis algorithm for acoustic data, utilizing Python and incorporating machine learning and deep learning techniques. This project focuses on defect classification and anomaly detection, showcasing his ability to apply advanced programming skills to real-world problems.