CakeResume 找人才
最轻量、快速的招募方案，数百家企业的选择
搜寻简历，主动联系求职者，提升招募效率。
- 浏览所有搜寻结果
- 每日可无限次数开启陌生对话
- 搜尋僅開放付費企業檢視的简历
- 检视使用者信箱 & 电话
职场能力评价定义
李岳倫
ML Engineer, Data Engineer
Yilan County, Taiwan
2 年以上使用 python 和 VB.NET 編程開發的 ML 和系統實踐經驗。
我喜歡觀察和發現有價值數據的所有細節，這些數據可以讓我的代碼世界充滿好奇心和熱情。
我希望所有的數據科學技術都能幫助我探索更多
可能性並解決我們的生活問題。
這就是我來這裡的原因，我相信我的能力，可以成為一個講故事的人，可以解釋所有有價值的數據，並為我的團隊和團隊提供最佳解決方案。
linkedin.com/in/李-岳倫-bb0b44221
工作經歷
六月 2020 - 三月 2023
京元電子股份有限公司
高級工程師/
ML Engineer
資料科學:
●數據收集與準備：使用可靠的數據來源（如Stat. Canada）收集所需資料，並使用Python (Pandas, Numpy, etc.) 與SQL進行數據準備。
●數據清洗與完整化：使用Python (scikit-learn) 對缺失數據執行imputation以及對數據標準化。
●數據匯整：使用Python套件把數據整合並匯入SQL伺服器。
●分析數據：使用深度學習技術找出數據背後的意義並預測未來可能的結果。
●數據分類：使用機器學習技術 (DNN, K-mean) 為數據做分類。
●人工智慧應用:使用機器學習技術 (DNN,決策樹, K-mean) 為數據分類/分群，影像辨識(YOLO)
最佳化演算法:
●工程報表系統維護。
●採用適當最佳演算法改善產線生產效能。
#Python #MS SQL #軟體程式設計 #Machine Learning #資料庫程式設計 #軟體工程系統開發
學歷
國立台中科技大學
資訊管理所
2017 - 2019
國立勤益科技大學
資訊工程
2013 - 2017
技能
數據分析程式
- Python
- R
- TensorFlow(Keras)
資料庫設計
- SQL
- MySQL
- MongoDB
- MSSQL
- Oracle SQL
技能
- Machine Learning
- K-means
- 決策樹(Decision Tree)
- 最近鄰居分類(KNN)
- 支持向量機(SVM)
- 貝葉斯分類器
- Deep Learning
- DNN
- RNN
- LSTM
- YOLO
技能
- 系統開發
- VB
- C
- C#
- 伺服器
- Apache
- Flask
專案成就
生產效率異常分析
專案目標:
規劃及開發產線效率異常分類模型，以解決生產期間所產生的問題，大致如下四題。
1.良率異常。
2.RunTime不足。
3.人員作業Loss。
4.標準工時設定不符。
應用技術:
1.採用分類演算法用於資料分析。
2.異常偵測。
3.規畫系統後端之API開發及介接。
成果:
1.以視覺化呈現系統開發之分析結果，回傳至管理系統之統計分析儀表板。
2.有效即時偵測產線異常之問題。
生產配件組合最佳化
專案目標:
從現有可用配件中搭配當前最佳配件組合
應用技術:
1.利用Xgboost建立預測良率模型
2.改良後的基因演算法搭配最佳配件組合
3.規畫系統後端之API開發及介接。
成果:
1.以視覺化呈現系統開發之分析結果，回傳至管理系統之推薦儀表板。
2.有效即時推薦配件組合。
3.提升生產效率 OEE 3.33 %
4.降低First Yield Gap 0.47 %
配捲出貨最佳化
專案目標 :
從現有可調配的貨批中，依照客戶訂定組合條件，找尋最佳配捲組合，減少人工組合時間，加速出貨速度。
應用技術:
採用最佳化演算法(隨機貪婪演算法、退火演算法)用於找尋最適組合。
成果:
1.有效即時推薦配捲組合
2.減少配捲人員20+分鐘搭配時間
3.加快出貨流程，提升客戶滿意度
挑K出貨最佳化
2021/1~2021/2
專案目標 :
從現有可晶圓中，依照出貨條件搭配當前最佳出貨組合，減少決策時間，加速出貨流程。
應用技術:
1.採用最佳化演算法(SSO演算法)用於找尋最適組合
成果:
1.有效即時推薦出貨組合
2.減少配捲人員10+分鐘搭配時間
3.加快出貨流程
YUEH LUN LI
ML Engineer, Data Engineer
Yilan County, Taiwan
2+ years of practical experience in ML & system-developed with python and VB.NET programming.
I like to observe and discover all the detail of the valuable data that can fill my code world with curiosity and enthusiasm.
I expect all the data science techniques can help me explore more
possibilities and soulate our life problems.
That's why I'm here, I believe in my ability and can become a storyteller who can explain all the valuable data and provide the best solution for my group and my team.
Skills
Programming Languages
- Python
- R
- SQL
- C/C++
- C#
- VB.NET
Deployment
- Server: Apache
- Backend API: Flask
Data Analysis
- Data Analysis: Statistics / Time Series Analysis
- Data Science: scikit-learn / Keras / Optimization
Data Engineering
- Data Processing: Pandas / MongoDB Aggregation
- Database:
- MySQL
- MongoDB
- MSSQL
- Oracle SQL
- EDA
EXPERIENCE
Jun 2020 - Mar 2023
KYEC Electronics Co., Ltd.
Senior Engineer
Data Science:
- Data collection and preparation: Use reliable data sources (such as Stat. Canada) to collect the required data, and use Python (Pandas, Numpy, etc.) and SQL for data preparation.
- Data cleaning and completeness: use Python (scikit-learn) to perform imputation on missing data and normalize data.
- Data integration: use Python package to integrate and import data into SQL server.
- Analyze data: Use machine learning techniques and experimental design analysis to find meaning behind the data and predict possible future outcomes.
- Data classification: Use machine learning techniques (DNN, K-mean) to classify data.
- Artificial intelligence applications: use machine learning techniques (DNN, decision tree, K-mean) for data classification/grouping, image recognition (YOLO)
- Optimization algorithm: Improve the production efficiency of the production line by using the appropriate optimal algorithm.
Other:
- Maintenance of engineering report system.
#Python #MS SQL #software programming #Machine Learning #database programming #software engineering system development #Oracle SQL
EDUCATION
2017 - 2019
NATIONAL TAICHUNG UNIVERSITY OF SCIENCE AND TECHNOLOGY
Graduated from Institute of Information Management
Research
- Online Social Network
- Emotional Contagion
- Text Mining
- Reactions
- Social Networking Site
2013 - 2017
National Chin-Yi University of Technology
B.S. in Department of Computer Science and Information Engineering
PROJECTS
Anomaly analysis of production efficiency
Plan and develop a product line efficiency anomaly classification model to solve problems during product, roughly as follows.
1. The yield is abnormal.
2. Insufficient RunTime.
3. Personnel work Loss.
4. The standard working hours are not set up.
Application Technology:
1. The classification algorithm is used for data analysis.
2. Anomaly detection.
3. API development and interface of the planning system backend.
Results:
1. Visually present the analysis results of system development, and send them back to the statistical analysis dashboard of the management system.
2. Effective and real-time detection of abnormal production line problems.
Optimizing the combination of production parts
According to our design the new rules, there can help us to combinate the available accessories with the other best accessories, there can effectively improve the product line.
Application Technology:
1. Use Xgboost to build a predictive yield model
2. Improve genetic algorithm with the best combination accessories
3. response for plan backend development and API development
Results:
1. As the visually present analysis system of the results, it will send this information to the recommended dashboard of the management system.
2. It can be effectively and instantly recommend accessories combination
3. Improve production efficiency OEE 3.33 %
4. Reduce First Yield Gap 0.47%
Optimum delivery of reels
In the light of the client's demand to adjust conditions, and immediately reduce factory' people spend time to adjust the existing batches.
Application Technology:
It's used to find the optimal combination by optimization algorithm (random greedy algorithm, annealing algorithm)
Results:
1. Effectively and instantly recommend the matching volume
2. Reduce the matching time by 20+ minutes
3. Speed up the delivery process and improve customer satisfaction
Pick K shipment optimization
In the light of the wafers shipping conditions combinate the best matching which reduces people's decisions, and speeds up the shipping process.
Application Technology:
1. Use the optimization algorithm (SSO algorithm) to find the best combination
Results:
1. Effective and real-time recommend shipping matching
2. Reduce the matching time by 10+ minutes
3. Speed up the shipping process
李岳倫
ML Engineer, Data Engineer
Yilan County, Taiwan
2 年以上使用 python 和 VB.NET 編程開發的 ML 和系統實踐經驗。
我喜歡觀察和發現有價值數據的所有細節，這些數據可以讓我的代碼世界充滿好奇心和熱情。
我希望所有的數據科學技術都能幫助我探索更多
可能性並解決我們的生活問題。
這就是我來這裡的原因，我相信我的能力，可以成為一個講故事的人，可以解釋所有有價值的數據，並為我的團隊和團隊提供最佳解決方案。
linkedin.com/in/李-岳倫-bb0b44221
工作經歷
六月 2020 - 三月 2023
京元電子股份有限公司
高級工程師/
ML Engineer
資料科學:
●數據收集與準備：使用可靠的數據來源（如Stat. Canada）收集所需資料，並使用Python (Pandas, Numpy, etc.) 與SQL進行數據準備。
●數據清洗與完整化：使用Python (scikit-learn) 對缺失數據執行imputation以及對數據標準化。
●數據匯整：使用Python套件把數據整合並匯入SQL伺服器。
●分析數據：使用深度學習技術找出數據背後的意義並預測未來可能的結果。
●數據分類：使用機器學習技術 (DNN, K-mean) 為數據做分類。
●人工智慧應用:使用機器學習技術 (DNN,決策樹, K-mean) 為數據分類/分群，影像辨識(YOLO)
最佳化演算法:
●工程報表系統維護。
●採用適當最佳演算法改善產線生產效能。
#Python #MS SQL #軟體程式設計 #Machine Learning #資料庫程式設計 #軟體工程系統開發
學歷
國立台中科技大學
資訊管理所
2017 - 2019
國立勤益科技大學
資訊工程
2013 - 2017
技能
數據分析程式
- Python
- R
- TensorFlow(Keras)
資料庫設計
- SQL
- MySQL
- MongoDB
- MSSQL
- Oracle SQL
技能
- Machine Learning
- K-means
- 決策樹(Decision Tree)
- 最近鄰居分類(KNN)
- 支持向量機(SVM)
- 貝葉斯分類器
- Deep Learning
- DNN
- RNN
- LSTM
- YOLO
技能
- 系統開發
- VB
- C
- C#
- 伺服器
- Apache
- Flask
專案成就
生產效率異常分析
專案目標:
規劃及開發產線效率異常分類模型，以解決生產期間所產生的問題，大致如下四題。
1.良率異常。
2.RunTime不足。
3.人員作業Loss。
4.標準工時設定不符。
應用技術:
1.採用分類演算法用於資料分析。
2.異常偵測。
3.規畫系統後端之API開發及介接。
成果:
1.以視覺化呈現系統開發之分析結果，回傳至管理系統之統計分析儀表板。
2.有效即時偵測產線異常之問題。
生產配件組合最佳化
專案目標:
從現有可用配件中搭配當前最佳配件組合
應用技術:
1.利用Xgboost建立預測良率模型
2.改良後的基因演算法搭配最佳配件組合
3.規畫系統後端之API開發及介接。
成果:
1.以視覺化呈現系統開發之分析結果，回傳至管理系統之推薦儀表板。
2.有效即時推薦配件組合。
3.提升生產效率 OEE 3.33 %
4.降低First Yield Gap 0.47 %
配捲出貨最佳化
專案目標 :
從現有可調配的貨批中，依照客戶訂定組合條件，找尋最佳配捲組合，減少人工組合時間，加速出貨速度。
應用技術:
採用最佳化演算法(隨機貪婪演算法、退火演算法)用於找尋最適組合。
成果:
1.有效即時推薦配捲組合
2.減少配捲人員20+分鐘搭配時間
3.加快出貨流程，提升客戶滿意度
挑K出貨最佳化
2021/1~2021/2
專案目標 :
從現有可晶圓中，依照出貨條件搭配當前最佳出貨組合，減少決策時間，加速出貨流程。
應用技術:
1.採用最佳化演算法(SSO演算法)用於找尋最適組合
成果:
1.有效即時推薦出貨組合
2.減少配捲人員10+分鐘搭配時間
3.加快出貨流程
YUEH LUN LI
ML Engineer, Data Engineer
Yilan County, Taiwan
2+ years of practical experience in ML & system-developed with python and VB.NET programming.
I like to observe and discover all the detail of the valuable data that can fill my code world with curiosity and enthusiasm.
I expect all the data science techniques can help me explore more
possibilities and soulate our life problems.
That's why I'm here, I believe in my ability and can become a storyteller who can explain all the valuable data and provide the best solution for my group and my team.
Skills
Programming Languages
- Python
- R
- SQL
- C/C++
- C#
- VB.NET
Deployment
- Server: Apache
- Backend API: Flask
Data Analysis
- Data Analysis: Statistics / Time Series Analysis
- Data Science: scikit-learn / Keras / Optimization
Data Engineering
- Data Processing: Pandas / MongoDB Aggregation
- Database:
- MySQL
- MongoDB
- MSSQL
- Oracle SQL
- EDA
EXPERIENCE
Jun 2020 - Mar 2023
KYEC Electronics Co., Ltd.
Senior Engineer
Data Science:
- Data collection and preparation: Use reliable data sources (such as Stat. Canada) to collect the required data, and use Python (Pandas, Numpy, etc.) and SQL for data preparation.
- Data cleaning and completeness: use Python (scikit-learn) to perform imputation on missing data and normalize data.
- Data integration: use Python package to integrate and import data into SQL server.
- Analyze data: Use machine learning techniques and experimental design analysis to find meaning behind the data and predict possible future outcomes.
- Data classification: Use machine learning techniques (DNN, K-mean) to classify data.
- Artificial intelligence applications: use machine learning techniques (DNN, decision tree, K-mean) for data classification/grouping, image recognition (YOLO)
- Optimization algorithm: Improve the production efficiency of the production line by using the appropriate optimal algorithm.
Other:
- Maintenance of engineering report system.
#Python #MS SQL #software programming #Machine Learning #database programming #software engineering system development #Oracle SQL
EDUCATION
2017 - 2019
NATIONAL TAICHUNG UNIVERSITY OF SCIENCE AND TECHNOLOGY
Graduated from Institute of Information Management
Research
- Online Social Network
- Emotional Contagion
- Text Mining
- Reactions
- Social Networking Site
2013 - 2017
National Chin-Yi University of Technology
B.S. in Department of Computer Science and Information Engineering
PROJECTS
Anomaly analysis of production efficiency
Plan and develop a product line efficiency anomaly classification model to solve problems during product, roughly as follows.
1. The yield is abnormal.
2. Insufficient RunTime.
3. Personnel work Loss.
4. The standard working hours are not set up.
Application Technology:
1. The classification algorithm is used for data analysis.
2. Anomaly detection.
3. API development and interface of the planning system backend.
Results:
1. Visually present the analysis results of system development, and send them back to the statistical analysis dashboard of the management system.
2. Effective and real-time detection of abnormal production line problems.
Optimizing the combination of production parts
According to our design the new rules, there can help us to combinate the available accessories with the other best accessories, there can effectively improve the product line.
Application Technology:
1. Use Xgboost to build a predictive yield model
2. Improve genetic algorithm with the best combination accessories
3. response for plan backend development and API development
Results:
1. As the visually present analysis system of the results, it will send this information to the recommended dashboard of the management system.
2. It can be effectively and instantly recommend accessories combination
3. Improve production efficiency OEE 3.33 %
4. Reduce First Yield Gap 0.47%
Optimum delivery of reels
In the light of the client's demand to adjust conditions, and immediately reduce factory' people spend time to adjust the existing batches.
Application Technology:
It's used to find the optimal combination by optimization algorithm (random greedy algorithm, annealing algorithm)
Results:
1. Effectively and instantly recommend the matching volume
2. Reduce the matching time by 20+ minutes
3. Speed up the delivery process and improve customer satisfaction
Pick K shipment optimization
In the light of the wafers shipping conditions combinate the best matching which reduces people's decisions, and speeds up the shipping process.
Application Technology:
1. Use the optimization algorithm (SSO algorithm) to find the best combination
Results:
1. Effective and real-time recommend shipping matching
2. Reduce the matching time by 10+ minutes
3. Speed up the shipping process