Yan-Ting Chen Email & Phone Number

英國 英格蘭 大倫敦

• Low Data Volume Machine Learning Project [Python/Pytorch/Pandas]:- Engaged in a research project to develop machine learning models for risk management in Specialty Insurance.- Improved prediction accuracy (AUC) of low-volume insurance claims by 5% via deploying deep learning model TabNet.

Taipei, Taiwan

• AI/ML Model Development [Python/TensorFlow/Keras]:- Led a team of 3 to develop a ML model via TensorFlow lite, utilising smartphone-detected signal information.- Contributed to POC (MVP) product and designed corresponding A/B testing experiments to estimate the efficacy.- Improved connectivity-recovered rate for smartphones by 30% via developing a patent-pending machine learning model.- Developed signal information parsing package to extract structural data from L1/L2 layer to accelerate data collectionsby reducing human workload by up to 40%.• Full Software Development life Cycle Experience in L3 (Wireless) Protocol Stack [C/C++]:- Developed features of Non-Access Stratum (L3 protocol stack) defined in 3GPP spec for modem devices independently.- Experience in design pattern, coding standards, code reviews, and source control management (Perforce).• Modem Issue Triage and Optimization [C/C++]:- Conducted modem performance analysis and system tuning to deliver a well-written and scalable codebase. • Firmware Verification [C/C++/Jenkins]:- Accelerated unit/regression testing for L3 protocol stack by 80% via GoogleTest framework- Developing and maintaining CI/CD pipelines for L3 protocol stack regression test vi CI tool Jenkins.

• Conducted research on network function virtualisation of 5G cellular network within the context of edge computing.• Delivered a presentation at the 2019 IEEE International Conference on Communications held in Shanghai, China.

台灣 Taipei City 台北

• Developed and maintained test automation system for streaming websites with open-source project Selenium.• Contributed to the open-source project, Calabash, by addressing a threading issue.

- Implemented approximation algorithm to evaluate network routing efficiency- Studied multicast traffic problems for software-defined networks, assisted to develop a polynomial algorithm that minimized bandwidth consumption

Master Of Science - Ms, Data Science And Machine Learning, Distinction

Elected modules: Reinforcement Learning (Google Deepmind) | Information Retrieval and Data Mining Graphical Models (Probabilistic.

Master'S Degree, Computer Science In Engineering, Cgpa 4.09

Bachelor'S Degree, Science In Electrical Engineering, Cgpa 3.73