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‧ Dissertation conducted research about Norm Mining. Researched the normative sentences and relationships in business contracts by using Nature Language Processing and Machine Learing techniques.
‧ Specialised Courses: Statistics for data analysis/ Artificial Intelligence/ Big Data Technologies/ Databases/ Data Mining/ Machine Learning/ Data Visualisation / Computer Programming for Data Scientists.

King’s College London, University of London

‧ Dissertation researched about Complexity-effective and Depth-adaptive alignment for Dual-camera HDR (High Dynamic Range) Synthesis
‧ Developed, simulated, and verified my thesis’s algorithms by using Matlab to analyze large amounts of image data.
‧ Implemented and demonstrated the alogrithm using C++ on an embedded system running Linux OS which includes TI OMAP4430 Processor and two Logitech real-time web cameras.

National Chung Cheng University

Data Scientist & Machine Learning Engineer @ Startup at CSIE of National Taiwan University & Ministry of Science and Technology

Hsiao Song-Wei I have over five years of experience as an UI/UX designer. By incorporating data analysis and user interview, I have assisted many b...

Hsiao Song-Wei

Startup at CSIE of National Taiwan University & Ministry of Science and Technology

Data Scientist & Machine Learning Engineer

‧ Proposed data-driven business solutions to perform Anomaly/Fraud detection application in FinTech project.
‧ Collaborated with top financial company to define Business Understanding and establish the success criteria ; then proposed Data Science project lifecycle based on customers’ pain points.
‧ Implemented Supervised and Unsupervised learning methods(Random Forest, XGBoots, GMM, IsolationForest) and
also built a deep learning-based anomaly detection model(Autoencoder) with TensorFlow, then use these models for
outlier detection with cloud-based infrastructure datasets to find abnormal behaviors in Client’s IT platforms.
‧ Constructed data pipelines(ETL) on Customer’s Financial datasets to prepare the needed data for building the ML model to solve Insurance Fraud Detection project.
‧ Developed ML pipeline, including Exploratory Data Analysis(EDA), Data Preparation(Data Transformations, Feature Engineering), Model training and Model evaluation(cross validation, ROC-AUC, etc.), will generate ML solutions to meet clients’ business requirements.
‧ Optimised ML solution with different features(feature selection, feature importance, feature engineering), algorithms, modeling techniques, and hyperparameter tuning with decent metrics to get the best performing ML model.
‧ Interpreted trained ML models with Explainable AI tools and reported models’ behavior to our customer ; then visualized the final result with all relevant data through a dashboard created by Data Visualization tools(Matplotlib, Seaborn, Tableau).
‧ Handled FinTech project with an imbalanced data set provided by our client, using different/various techniques (class weighting, proper/suitable evaluation metrics, oversampling) to improve the performance of the ML solutions.

‧ Operated EDA (Electronic Design Automation) tool to implement digital IC design Flow
‧ Developed Perl and UNIX/TCL scripts to make EDA tool operate more autonomously and efficiently.
 ‧ Created Perl and TCL scripts to analyze reports which was produced by EDA tool to make sure our chips were without any problems, then provided and delivered reports to our consumers.
‧As Project Leader to led company’s backend team.
‧As main contact window to communicate directly with our consumers to understand their needs, figure out problems from consumers and give prompt feedback.
‧Analyzed the reports to deliver useful documents to our consumers; cooperated closely with other colleagues to complete team projects.

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