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1. 2年的管理職經驗。
2.超過10年的計算機視覺和深度學習經驗/client/server/android開發經驗。
2.使用python的編程經驗。
3.良好的paper閱讀能力和實作能力
4.熟悉計算機視覺，深度學習（CNN，Resnet，densnet，GAN），對象檢測（Yolo系列，RCNN系列），分割模型（UNet，DensUNet）。
5.熟悉半監督或無監督學習（pesudo labeling，Voxmorph模型）。
6.擁有Docker，Git，Jenkins DevOps的經驗。

2020年，我很幸運地在麻省理工學院-人工智能實驗室學習當訪問工程師六個月，研究新技術。

我是一個熱於挑戰，學習新技術的人，專案空閒之餘，也常常會去追最新的paper，技術文件，甚至是網路競賽，期待未來能將AI應用在更多地方。 

學歷: 台灣科技大學 – 資訊工程所碩士
經歷: 
    2010 緯創資通 - 軟體工程師
    2015 緯創資通 - ML/DL 影像處理工程師
    2019 緯創資通 - 技術經理 
    2020 麻省理工學院 - 計算機科學與人工智能實驗室(CSAIL)訪問工程師
競賽:     
    2016 經濟部工業局 – 掌握數據脈絡 Hackathon 競賽，數據行銷獎
    2017 Kaggle: The Nature Conservancy Fisheries Monitoring – bronze medal <6%
    2018 緯創資通金頭腦創業競賽第三名 - 寶寶守護者
    2021 AIGO AI種子教師二階段面試通過 - 王鈺強戰隊

1. Two years of management experience.

2. More than 10 years of computer vision and deep learning/software architecture development experience.
3. Programming experience using python.
4. Good paper reading ability and practical ability
5. Familiar with computer vision, deep learning (CNN, Resnet, densnet, GAN), object detection (Yolo series, RCNN series), segmentation models (UNet, DensUNet).
6. Experience in semi-supervised or unsupervised learning (pesudo labeling, Voxmorph model).
7. Experience with Docker, Git, Jenkins DevOps.

Education: 
National Taiwan University of Science and Technology - Master of Information Engineering

Experience: 
     2010 Wistron - Software Engineer
     2015 Wistron - ML/DL Image Processing Engineer
     2019 Wistron - Technical Manager
     2020 MIT - Computer Science and Artificial Intelligence Laboratory (CSAIL) Visiting Engineer

Competition:
     2016 Ministry of Economic Affairs Bureau of Industry – Mastering the Data Context Hackathon Competition, Data Marketing Award
     2017 Kaggle: The Nature Conservancy Fisheries Monitoring – bronze medal <6%
     2018 Wistron Capital Entrepreneurship Competition 3rd place - Baby Guardian

In 2020, I was fortunate to be a Visiting Engineer at Massachusetts Institute of Technology-Computer Science & Artificial Intelligence Laboratory for six months, to research new technologies.

I am a person who is eager to challenge and learn new technologies. In my spare time, I often follow the latest papers, technical documents, and even online competitions. I look forward to applying AI in more places in the future.

National Taiwan University of Science and Technology

Ming Chuan University

Technical Manager

林昭源 (Leo Lin)

緯創資通

資深經理

轉換為主管職，管理四個員工，原本的工作轉為規畫，任務指派，技術瀏覽，另外再包含:
1.  部屬績效管理。
2. 部屬職能發展規劃。
3. AI專利會議舉辦。
4. 構思MIT新技術應用。
5. MLOPS研究。

I switched to a managerial role, managing four employees, and the original job turned into planning, task assignment, technical survey, and also included:
1. Employee performance management.
2. Employee functional development planning.
3. Hold an AI patent conference.
4. Conceive the application of MIT new technology.
5. Research MLOPS

台灣新北市

我領導一個小組，並負責圖像處理和深度學習，以下是簡要介紹：
1. 高解析度圖像小物件技術，我們使用深度學習來識別高解析度圖像上的蟲卵：
- 該項目是某市府單位，用來快速篩檢/計數登革熱蟲卵。
- 對象檢測模型的微調/遷移學習。
- 工作內容包含影像模組與後端API，Docker的設計開發。
- 提升10倍效率(10分鐘 vs 1分鐘)
- 平均誤差率比Linker低15% (MAPE: 25% vs 10%)

2.快速篩選試劑上的圖像識別：
-該項目來自日本最大生產快速篩選試劑的客戶，客戶需要一台機器來自動識別陽性或陰性。
-設計了一種算法，可以識別嵌入式計算機上疾病檢測的結果是陽性還是陰性。
-由於每一台裝置LED色溫，Camera擺放位置都會不同，設計了在裝置上使用軟體校正影像的方法。

作為圖像處理/深度學習小組負責人，新功能開發設計，定期主持每周小組會議，並協調成員的任務。

I'm currently leading a group and working on image processing and deep learning, here's a brief introduction:
1. Detecting small objects in High-resolution images: we use deep learning to identify small eggs on High-resolution images:
-This project is from a government unit used to quickly screen/count dengue eggs.
- The work content includes the design and development of image modules and back-end APIs, as well as Docker.
- 10x increase in efficiency (10 minutes vs 1 minute)
- Average error rate 15% lower than Linker (MAPE: 15% vs 25%)

2. Image identification on a fast screening reagent: 
-This project comes from the first few Japanese customers who produce rapid screening reagents. The customer needs a machine to automatically identify positive or negative.
-Designed an algorithm that can identify whether the result of disease detection on the embedded computer is positive or negative.
-Because the LED color temperature and the placement of the Camera of each device will be different. A method of using software to correct the image on the device is designed.

As image processing/deep learning Group Leader, new features development, host the weekly group meeting regularly and trouble Shooting, coordinated member’s task.

Wistron

2020年有幸到MIT當Visiting Engineer半年，到人工智慧實驗室一起學習，研究新技術，也將最新的技術帶回公司應用在實際案子上。
我主要研究探討以下主題：
1. 無監督學習算法
- Voxel morph算法。
- 應用監督和無監督學習應用在工廠的瑕疵元件影像檢測上。

2. 錯誤標記資料處理探討
- 使用Confidenct Learning去除掉錯誤標記資料。
- 使用Co-teching強化訓練結果
- 此方法打敗了之前工廠的演算法的最低leak rate, leak rate從 1.2, 下降到0.7

3. 主持MIT與公司內部成員的會議。

In 2020, I was fortunate to be a Visiting Engineer at MIT for eight months, I focus on the following topics with MIT professor:
1. Unsupervised learning algorithm
-Voxel morph algorithm.
-Application of supervised and unsupervised learning in the image detection of defective components in the factory.

2. Discussion on the processing of incorrectly marked data
-Use Confidenct Learning to remove incorrectly marked data.
-Use Co-teching to reinforce training results
- This method defeated the previous algorithm, the lowest leak dropped from 1.2 to 0.7

3. Host meetings between MIT and internal members of the company.


MIT - Visiting Researcher 

在這段時間，參與了以下與圖像處理/深度學習相關的項目的經驗：

1.肝/腫瘤語義分割網絡：
-與醫院合作開發肝臟疾病識別系統（已在南港展覽館展出）
-使用3D語意分割網路識別出五種不同的疾病區域
-處理各種醫療影像格式(DICOM, NII)

2.以隊長身分組團參與過公司舉辦的金頭腦競賽，發想的寶寶攝影機得到第三名
-Raspberry pie上開發一個影像分類系統。
-學習到AIOT上的分工合作與創業的發想模式。

3.改進人臉識別系統的神經網絡。
-原來的團隊使用openface，在不做架構的大幅度調整下，使用fine-turning技術讓Network能fit較小群體的dataset

During this time, I participated in the following projects related to image processing/deep learning:

1. Liver/tumor semantic segmentation network:
- Cooperate with a hospital to develop a liver disease recognition system (has been exhibited in Nangang Exhibition Hall)
-Using a 3D semantic segmentation network to identify five different disease areas
-Processing various medical image formats (DICOM, NII)

2. As a team leader, I participated in the company's Golden Mind competition, and the baby camera project won the third place.
-Develop an image classification system on Raspberry pie.
-Learn the division of labor and cooperation on AIOT and the thinking model of start a business.

3. Improving the neural network for the face recognition system.
-The original team used openface, without major adjustments to the architecture, using fine-turning technology to allow the Network to fit smaller groups of datasets

I participated in the Golden Mind competition organized by the company as a team leader. The proposal of baby camera won the third place. I learned the AIOT division of labor and entrepreneurship mode.

AI/DL  Engineer

主要從事網站前後端開發工作，替公司的產品做出前後端的配套軟體，主要工作內容為:
1. 前後端系統軟體開發
2. Android軟體開發

Mainly engaged in front-end and back-end development of the website, making front-end and back-end supporting software for the company's products, the main works are:
1. Front-end and back-end software development
2. Android software development

林昭源 (Leo Lin)’s posts