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Senior Manager, Solution Architect
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My passion is in the area of applying machine learning algorithms to solve real world problems. Prior to NVIDIA, I am a machine learning engineer at Foxconn, took the lead of the analytical team, developed various kinds of predictive modeling projects centered around manufacturing processes, including defect inspection and predictive maintenance, etc. During Ph.D. program, I worked as a visiting scholar at Los Alamos National Laboratory's Bio Science Team for 2 years, working on Human and Environmental Microbiome Projects.
National Cheng Kung University
Ph.D., Information Management
January 1, 2007 – January 1, 2015
National Cheng Kung University
Master, Information Management
January 1, 2005 – January 1, 2007
National Cheng Kung University
Bachelor, Industrial Management Science
January 1, 2002 – January 1, 2005
NVIDIA 台灣
Senior Manager, Solution Architect
December 1, 2024 – Present
NVIDIA
Senior Data Scientist
February 1, 2017 – December 1, 2024
台北
Foxconn
Senior Software Engineer (Machine Learning Engineer)
August 1, 2015 – February 1, 2017
China
Viking Products, Inc.
Business Consultant - AP Region
August 1, 2012 – August 1, 2015
Taiwan
Tokyo Institute of Technology
Visiting Student
July 1, 2011 – August 1, 2011
Japan
RoyalCert International Registrars
Auditor
January 1, 2011 – August 1, 2015
Taiwan
Los Alamos National Laboratory
Graduate Research Assistant
October 1, 2009 – June 1, 2010
Los Alamos National Laboratory
Intern student
October 1, 2008 – October 1, 2009
National Cheng Kung University
Research Assistant (PhD student)
September 1, 2007 – February 1, 2015
Taiwan
Gerogia International Certification
Senior Engineer
January 1, 2006 – August 1, 2015
Taiwan
Naive Bayesian classifiers with multinomial models for rRNA taxonomy asignment
September 1, 2013 – Present
Naïve Bayesain classifiers are a popular tool for classifying gene sequence in metagenomics. The Ribosomal Database Project launched by MSU, developed the RDP classifier, which firstly utilized Naïve Bayesain classifier to classify 16S rRNA reads. The RDP classifier employs a binomial approach to estimate the occurrence probabilities of 8-mer nucleotides from training data. The Laplace’s estimate and the m-estimate approach are two general approaches to avoid having a zero value for a specific probability estimate. These two approaches are both special cases of assuming noninformative Dirichlet priors, and that nonin-formative generalized Dirichlet priors is an even better choice for the naïve Bayesian classifier. Since the computation of the expected values of the variables in a high-dimensional generalized Dirichlet ran-dom vector can be time-consuming, Wong established several properties of the generalized Dirichelt distribution to resolve such problem for classifying document data. In this research project, we found out that a multinomial Naïve Bayesain classifiers will provide a better classification accuracy for both bacterial 16S and fungal 28S sequence reads. Furthermore, through utilizing appropriate priors for gene sequences, e.g Dirichlet and generalized Dirichlet priors, the classification accuracy will be further improved. The source codes of the program was implemented by C++ code.
Fungal ITS classifier
January 1, 2011 – Present
In this project, we compared the classification accuracy of two sections of the fungal Internal Transcribed Spacer (ITS) region, individually and combined, and the 5′ section (about 600 bp) of the large-subunit rRNA, using a naïve Bayesian Classifier and BLASTN. A hand-curated ITS-LSU training set of 1091 sequences and a larger training set of 8967 ITS region sequences were used.
Fungal LSU classifier
January 1, 2010 – Present
Taxonomic and phylogenetic fingerprinting based on sequence analysis of gene fragments from the large subunit rRNA gene (LSU) or the internal transcribed spacer (ITS) region is becoming an integral part of fungal classification. The lack of an accurate and robust classification tool, trained by a validated sequence database for taxonomic placement of fungal LSU genes, is a severe limitation in taxonomic analysis of fungal isolates or large datasets obtained from environmental surveys. Using a hand-curated set of 8506 fungal LSU gene fragments, we determined the performance characteristics of a naïve Bayesian classifier across multiple taxonomic levels and compared the classifier performance to a sequence similarity-based (BLASTN) approach.
Human saliva microbiome project
January 1, 2009 – Present
In this project, we analyse adult saliva microbiomes with 19 caries-active and 26 healthy human hosts. The 16S rRNA gene PCR amplicons were analyzed via pyrosequencing on 454 Life Sciences Genome Sequencer FLX Titanium (GS-Titanium; 454 Life Sciences, Branford, CT, USA). My main task within this project is taxonomic analysis with RDP classifier and BLAST, and also comparison analysis between two population host.
AI電腦視覺於工業瑕疵檢測之應用
NVIDIA
June 24, 2026 – Present
DLI Platform Course for Instructors
NVIDIA
June 24, 2026 – Present
Deep Learning for Industrial Inspection
NVIDIA
June 24, 2026 – Present
Data or Specimens Only Research
CITI Program
June 24, 2026 – Present
運用 RAPIDS 加速資料科學的基本原理
NVIDIA Deep Learning Institute
June 24, 2026 – Present
Fundamentals of Accelerated Data Science with RAPIDS
NVIDIA Deep Learning Institute
June 24, 2026 – Present
edX Verified Certificate for Big Data Analysis with Apache Spark
edX
June 24, 2026 – Present
Tackling the Challenges of Big Data
MIT Professional Education
June 24, 2026 – Present
Big Data Xseries
edX
June 24, 2026 – Present
edX Verified Certificate for Scalable Machine Learning
edX
June 24, 2026 – Present
edX Verified Certificate for Introduction to Big Data with Apache Spark
edX
June 24, 2026 – Present
R Programming
Coursera Course Certificates
June 24, 2026 – Present
The Data Scientist’s Toolbox
Coursera Course Certificates
June 24, 2026 – Present
Process Mining: Data science in Action
Coursera Course Certificates
June 24, 2026 – Present
Cultural Fit Analysis
The candidate has a strong academic background and significant experience in both research institutions (Los Alamos National Laboratory, Tokyo Institute of Technology) and industry (NVIDIA, Foxconn). The projects are highly specialized in bioinformatics and machine learning, which aligns with data-intensive roles. However, the target role is 'Data Analyst', which might be a step down from 'Senior Data Scientist' or 'Senior Manager, Solution Architect' roles, potentially indicating a mismatch in career trajectory or expectations. The diversity of roles, from auditor to business consultant, suggests adaptability, but the core technical focus has been consistent in data science/ML.
Soft Skills & Operational Fit
The candidate's extensive experience in research and senior roles suggests strong problem-solving, analytical thinking, and potentially leadership skills. The project descriptions indicate a methodical approach to complex data challenges. However, without specific psychometric test results or interview data, it's difficult to fully assess work attitude, stress handling, and team collaboration.