-A creative person with steady motivation and keen to learn new things. My cross-disciplinary background gives me insights and a passion to reflect from different perspectives. I have got many certificates from multiple online platforms and will keep updating my personal learning schedule.
- I embrace an open-source technical philosophy: the most efficient way to develop new technology is to share, discuss ideas with people and have brainstorms together.
- Cross-disciplinary Background: bachelor's degree in Material Science and Engineering, Master's degree in Philosophy of Mind and Cognition.
- International Conference experience: Oral presentation at The Science of Consciousness (TSC 2017). Poster presentation at Consciousness Research Network (CoRN 2017). Multiple international certifications related to machine learning, artificial intelligence, algorithms and data structures
Summary of Skills: Python (pandas, numpy, sklearn, keras, pytorch, selenium), C programming, SQL, Linux, AI/ML model testing, feature engineering, AI theory research
Languages: native Mandarin speaker, fluent in English (TOEIC 860,TOEFL 103), knowledge of Japanese (JLPT N3)
Desired Position: Data/AI scientist, AI researcher
Career objective: Focus on AI theoretical research, AI application in different domains (medicine, finance)
Future Plan: Practice AI in the financial industry (including Fintech), continue my research on AI theory
Websites:
github:
https://github.com/rayln-matrix
hackerrank:
https://www.hackerrank.com/wbwithpiano?hr_r=1
codewars:
https://www.codewars.com/users/hb_codewars/completed
三月 2021 - Now
任職於計算藥物設計部門,協助整理藥物蛋白資料(包含自動化網路下載資料)、編寫演算法、測試ML模型(現已改成合約包案,2022/11持續合作中)
Data Scientist at Computational Drug Design Department, Biodelight Biotech Inc. (2021/03-):
Collecting protein structure data from pdb database (Protein Data Bank)
Analyzing, redesigning and implementing algorithms (c programming, centos7 Linux shell script)
Testing feature sets and machine learning models (Using Python pandas, sklearn, numpy)
11月 2021 - Now
Customized financial investment APP (2021/11 -)
1.Collecting financial and economic data from the internet (historical stock prices, exchange rate, gold/silver/oil price, interest rate, GDP, unemployment rate, etc.)
2. Data cleaning (using pandas) and visualization (using plotly)
3. Implement a simple customized APP for interactively plotting screening data. (Using dash)
4. Add an AI feature to the APP: using historical data as training data to train different models for searching and narrowing the range of correlations between data. (Using pandas, numpy, sklearn, keras, pytorch)
三月 2020 - 三月 2021
任職於台北榮總醫學研究部,主要工作內容:
整理醫療數據: 將非結構化資料整理成結構化資料(從大量病例報告中汲取特徵並整理成結構化資料)
Linux系統管理: 更新系統、使用者管理、資料庫管理
AI模型測試: 使用logistic regression, SVM, random forest, CNN, 3D-CNN等模型對資料數據進行監督式學習-分類辨識並評估分析優劣 特徵工程: 測試不同維度數據資料、不同萃取法(SVD、PCA)的組合對模型訓練與預測的影響
Research Assistant at Taipei Veterans General Hospital Department of Medical Research (2020/03-2021/03):
Using Python, Regex to extract meaning and create structured data from unstructured big data (clinical reports)
Linux system administration (CentOS 7)
Feature engineering (pandas, numpy, sklearn), statistical analyzing and machine learning model (logistic regression, SVM, random forest, CNN, 3D-CNN) (keras, pytorch, sklearn, seaborn) testing on fMRI data
2016 - 2020
2007 - 2013
2004 - 2007
-A creative person with steady motivation and keen to learn new things. My cross-disciplinary background gives me insights and a passion to reflect from different perspectives. I have got many certificates from multiple online platforms and will keep updating my personal learning schedule.
- I embrace an open-source technical philosophy: the most efficient way to develop new technology is to share, discuss ideas with people and have brainstorms together.
- Cross-disciplinary Background: bachelor's degree in Material Science and Engineering, Master's degree in Philosophy of Mind and Cognition.
- International Conference experience: Oral presentation at The Science of Consciousness (TSC 2017). Poster presentation at Consciousness Research Network (CoRN 2017). Multiple international certifications related to machine learning, artificial intelligence, algorithms and data structures
Summary of Skills: Python (pandas, numpy, sklearn, keras, pytorch, selenium), C programming, SQL, Linux, AI/ML model testing, feature engineering, AI theory research
Languages: native Mandarin speaker, fluent in English (TOEIC 860,TOEFL 103), knowledge of Japanese (JLPT N3)
Desired Position: Data/AI scientist, AI researcher
Career objective: Focus on AI theoretical research, AI application in different domains (medicine, finance)
Future Plan: Practice AI in the financial industry (including Fintech), continue my research on AI theory
Websites:
github:
https://github.com/rayln-matrix
hackerrank:
https://www.hackerrank.com/wbwithpiano?hr_r=1
codewars:
https://www.codewars.com/users/hb_codewars/completed
三月 2021 - Now
任職於計算藥物設計部門,協助整理藥物蛋白資料(包含自動化網路下載資料)、編寫演算法、測試ML模型(現已改成合約包案,2022/11持續合作中)
Data Scientist at Computational Drug Design Department, Biodelight Biotech Inc. (2021/03-):
Collecting protein structure data from pdb database (Protein Data Bank)
Analyzing, redesigning and implementing algorithms (c programming, centos7 Linux shell script)
Testing feature sets and machine learning models (Using Python pandas, sklearn, numpy)
11月 2021 - Now
Customized financial investment APP (2021/11 -)
1.Collecting financial and economic data from the internet (historical stock prices, exchange rate, gold/silver/oil price, interest rate, GDP, unemployment rate, etc.)
2. Data cleaning (using pandas) and visualization (using plotly)
3. Implement a simple customized APP for interactively plotting screening data. (Using dash)
4. Add an AI feature to the APP: using historical data as training data to train different models for searching and narrowing the range of correlations between data. (Using pandas, numpy, sklearn, keras, pytorch)
三月 2020 - 三月 2021
任職於台北榮總醫學研究部,主要工作內容:
整理醫療數據: 將非結構化資料整理成結構化資料(從大量病例報告中汲取特徵並整理成結構化資料)
Linux系統管理: 更新系統、使用者管理、資料庫管理
AI模型測試: 使用logistic regression, SVM, random forest, CNN, 3D-CNN等模型對資料數據進行監督式學習-分類辨識並評估分析優劣 特徵工程: 測試不同維度數據資料、不同萃取法(SVD、PCA)的組合對模型訓練與預測的影響
Research Assistant at Taipei Veterans General Hospital Department of Medical Research (2020/03-2021/03):
Using Python, Regex to extract meaning and create structured data from unstructured big data (clinical reports)
Linux system administration (CentOS 7)
Feature engineering (pandas, numpy, sklearn), statistical analyzing and machine learning model (logistic regression, SVM, random forest, CNN, 3D-CNN) (keras, pytorch, sklearn, seaborn) testing on fMRI data
2016 - 2020
2007 - 2013
2004 - 2007