林鼎智

Fresh graduate of M.S. of Statistics in National Tsing Hua University, and have 2+ years' experience in data science and statistical data analysis, including spatiotemporal statistical modeling, machine learning, CNN-based methods for computer vision, and other statistical methods for numerical and categorical data analysis.

  Taiwan Province, Taiwan    https://davidlinn89222.github.io/        

學歷

National Tsing Hua University

Statistics - data science program  •  2020 - 2022

National Taipei University

Economics  •  2015 - 2020

工作經歷

AI Software Engineer Intern

Chimes AI  •  十二月 2021 - 四月 2022

・Maintained and tested 10+ supervised/unsupervised learning algorithms in an autoML platform
・Implemented anomaly detection (iForest/LOF) and process optimization (elastic-net regression + LM-BFGS)
・Surveyed relevant papers, performed feasibility studies and clarified the technical details for a PoC/project

Data Analyst Intern

Wistron NeWeb Corporation  •  七月 2021 - 八月 2021

・Found potential factors that cause product anomalies during the manufacturing process
・Constructed the pipeline of streaming and prototype of analyzing data to generate consistent results
・Processed data from Oracle and Hadoop databases via SQL and Spark (2GB+ per user)
・Developed a R-and-Shiny based web application with dashboards for other analysts, and saved 70%+ of analysis time

Research Assistant

National Taiwan University of Science and Technology, ME.  •  十一月 2020 - 十二月 2020

・Based on sensor imagery, calculate the designated proportions for various colors and further derive the volume of interest within a confined tubular space
・Built a web application by using RESTful API with Flask, Apache HTTP server, and deployed it on AWS EC2

Research Assistant

National Taipei University, ECON.  •  三月 2020 - 九月 2020

・Conducted research about machine learning and causal inference
・Re-implemented Double/Debiased Machine Learning for Treatment and Structural Parameters

Capstone Project

Legal Aid Foundation  •  一月 2020 - 七月 2020

・Identified significant factors affecting the geographical distribution of legal aid cases
・Improved the Foundation’s policy on prioritizing key areas and marked outlier regions
・Utilized Bayesian hierarchical model with spatial structure, modeled by conditional autoregressive model
・Organized tasks for the technical team, comprised of four students, and cross-discipline communication with LAF
・Publication: Using Spatial Statistics to Explore the Distribution and Influential Factors of Legal Aid cases, D.C. Lin, et al. (2021), Journal of Chinese Statistical Association, 59(4), 226-254

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