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進階搜尋
On
4 到 6 年
6 到 10 年
10 到 15 年
15 年以上
Software engineer
Avatar of Patrick Hsu.
Avatar of Patrick Hsu.
Algorithm Research & Development @適着三維科技股份有限公司 TG3D Studio Inc.
2021 ~ 現在
Software Engineer
一個月內
Patrick Hsu AI Research & Development As a seasoned AI engineer with six years of experience, I specialize in computer vision, 3D body model reconstruction, generative AI, and possessing some knowledge in natural language processing (NLP). | New Taipei City, [email protected] Work Experience (6 years) Algorithm Research & Design• TG3D Studio MayPresent A skilled engineer specialized in computer vision and generative AI with experience in developing and training AI models for digital fashion applications. Body AI: Virtual Try On Integrated cutting-edge technologies such as Stable Diffusion, ControlNet, and Prompt Engineering to create a sophisticated system for
Python
AI & Machine Learning
Image Processing
就職中
正在積極求職中
全職 / 對遠端工作有興趣
4 到 6 年
國立台灣大學
生物產業機電工程所

最輕量、快速的招募方案,數百家企業的選擇

搜尋履歷,主動聯繫求職者,提升招募效率。

  • 瀏覽所有搜尋結果
  • 每日可無限次數開啟陌生對話
  • 搜尋僅開放付費企業檢視的履歷
  • 檢視使用者信箱 & 電話
搜尋技巧
1
嘗試搜尋最精準的關鍵字組合
資深 後端 php laravel
如果結果不夠多,再逐一刪除較不重要的關鍵字
2
將須完全符合的字詞放在雙引號中
"社群行銷"
3
在不想搜尋到的字詞前面加上減號,如果想濾掉中文字,需搭配雙引號使用 (-"人資")
UI designer -UX
免費方案僅能搜尋公開履歷。
升級至進階方案,即可瀏覽所有搜尋結果(包含數萬筆覽僅在 CakeResume 平台上公開的履歷)。

職場能力評價定義

專業技能
該領域中具備哪些專業能力(例如熟悉 SEO 操作,且會使用相關工具)。
問題解決能力
能洞察、分析問題,並擬定方案有效解決問題。
變通能力
遇到突發事件能冷靜應對,並隨時調整專案、客戶、技術的相對優先序。
溝通能力
有效傳達個人想法,且願意傾聽他人意見並給予反饋。
時間管理能力
了解工作項目的優先順序,有效運用時間,準時完成工作內容。
團隊合作能力
具有向心力與團隊責任感,願意傾聽他人意見並主動溝通協調。
領導力
專注於團隊發展,有效引領團隊採取行動,達成共同目標。
超過一年
Computer Vision Reseacher
國立中央大學
2020 ~ 現在
Hsinchu, 新竹市台灣
專業背景
目前狀態
就學中
求職階段
專業
軟體工程師, Python 開發人員, 機器學習工程師
產業
軟體, 人工智慧 / 機器學習, 機器人科學
工作年資
小於 1 年
管理經歷
技能
Artificial Intelligence
Deep Learning with PyTorch
Deep Learning with Tensorflow
Computer Vision
Python
Git
C++
Web
語言能力
English
專業
German
進階
Chinese
母語或雙語
求職偏好
希望獲得的職位
Software Engineer
預期工作模式
全職
期望的工作地點
台灣台北市, 台灣新北市, 台灣新竹市新竹, 台灣桃園
遠端工作意願
對遠端工作有興趣
接案服務
學歷
學校
國立清華大學
主修科系
資訊應用研究所
列印

林佳縈 Chia Ying Lin

Computer Vision Reseacher

  Computer Vision Lab, NTHU


With 4-year solid training in computer science fundamentals, 2-year independent research experience in computer vision, and diverse hands-on development experiences, I always pursue excellence, crave new challenges, and am open to all possibilities.  

  [email protected]

https://github.com/lykasbongbongbong

linkedin.com/in/lykas-chia-ying-lin

  0937-464-176

Skills

Languages


  • Python (familiar)
  • C++, C
  • JAVA

Deep Learning Frameworks


  • PyTorch (familiar)
  • Tensorflow

CV & DL Libraries


  • OpenCV
  • Keras

Development Tools


  • Git/Github
  • RESTFul APIs

Web Development


  • HTML, CSS, JS
  • Python Flask
  • PHP, Laravel
  • MySQL, NoSQL(mongoDB)
  • AWS

Others


  • English TOEFL iBT 97
  • English TOEIC 935 
  • German B2

Education


Master's Studies, Computer Vision Lab, NTHU

Dept. Information Systems and Application, Sep 2020~Now

  • Anomaly detection and segmentation for smart manufacturing as primary research target

Bachelor Degree, WASN Lab, NCU

Dept. Computer Science, Sep 2016 ~ June 2020

Exchange Student, Hochschule München, Germany

Dept. Informatik (Computer Science), Sep 2019 ~ Feb 2020

Experiences

August 2021

Attendee,

Machine Learning Summer School, NTU

August 2021

Backend Developer (Anomaly Detection Demo Website),

CVLab, FUTEX 2021

  • Use Python Flask, MySQL for RestFul APIs development
  • Optimized backend system with Python threading to support sudden massive traffic

June 2018 ~ July 2019, 1y1m

Full-Stack Web Developer (NCU Internship Web)

Career Center NCU

  • Reconstruct website with Laravel Framework
  • Optimize backend and database to handle larger amounts of access
  • Over 30% of users received intern opportunities via this site 
  • Website Link: https://ncuinternship.careercenter.ncu.edu.tw/

Master Thesis: 

SABDN: Self-Attention Based Deviation Network for few-shot anomaly detection and segmentation (Under Review ECCV 2022)



Contributions

  • Combine self-attention mechanism with feature extraction CNN network and anomaly synthesis mechanism for anomaly scoring to achieve outstanding anomaly detection accuracy under few-shot setting
  • Reach SOTA performance on benchmark dataset MVTecAD dataset and BTAD dataset with over 90 percent reduction in training data requirements

Side Projects




GlueGAN: a generative model based on SuperGlue structure

  • Extend MagicLeap’s SuperGlue end-to-end GNN concept for object synthesis to solve synthetic object image generation problem
  • 10% accuracy improvement compared to initial GAN backbone 

Let's play GAN with flows and friends!

  • Generate synthetic object images with multi-label conditions with conditional GAN manner
  • Human faces generation by conditional normalizing flow

2048: by Temporal Difference Learning Approach (RL)

  • Construct TD-learning algorithm and design own n-tuple network to solve 2048 game
  • Reach 98% 2048-tile win rate in 1000 games

The LunarLander: under Deep Q-Network and DDPG manner (RL)

  • Implement DQN and DDPG network to solve LunarLander game problem
  • Thoroughly understand Deep Q-learning, actor-critic mechanism

Real-Time car detection and counting system

  • Retrain YOLOv3 with our own hatchback dataset
  • Reach average accuracy: 99% on both day-light and evening scenarios
履歷
個人檔案

林佳縈 Chia Ying Lin

Computer Vision Reseacher

  Computer Vision Lab, NTHU


With 4-year solid training in computer science fundamentals, 2-year independent research experience in computer vision, and diverse hands-on development experiences, I always pursue excellence, crave new challenges, and am open to all possibilities.  

  [email protected]

https://github.com/lykasbongbongbong

linkedin.com/in/lykas-chia-ying-lin

  0937-464-176

Skills

Languages


  • Python (familiar)
  • C++, C
  • JAVA

Deep Learning Frameworks


  • PyTorch (familiar)
  • Tensorflow

CV & DL Libraries


  • OpenCV
  • Keras

Development Tools


  • Git/Github
  • RESTFul APIs

Web Development


  • HTML, CSS, JS
  • Python Flask
  • PHP, Laravel
  • MySQL, NoSQL(mongoDB)
  • AWS

Others


  • English TOEFL iBT 97
  • English TOEIC 935 
  • German B2

Education


Master's Studies, Computer Vision Lab, NTHU

Dept. Information Systems and Application, Sep 2020~Now

  • Anomaly detection and segmentation for smart manufacturing as primary research target

Bachelor Degree, WASN Lab, NCU

Dept. Computer Science, Sep 2016 ~ June 2020

Exchange Student, Hochschule München, Germany

Dept. Informatik (Computer Science), Sep 2019 ~ Feb 2020

Experiences

August 2021

Attendee,

Machine Learning Summer School, NTU

August 2021

Backend Developer (Anomaly Detection Demo Website),

CVLab, FUTEX 2021

  • Use Python Flask, MySQL for RestFul APIs development
  • Optimized backend system with Python threading to support sudden massive traffic

June 2018 ~ July 2019, 1y1m

Full-Stack Web Developer (NCU Internship Web)

Career Center NCU

  • Reconstruct website with Laravel Framework
  • Optimize backend and database to handle larger amounts of access
  • Over 30% of users received intern opportunities via this site 
  • Website Link: https://ncuinternship.careercenter.ncu.edu.tw/

Master Thesis: 

SABDN: Self-Attention Based Deviation Network for few-shot anomaly detection and segmentation (Under Review ECCV 2022)



Contributions

  • Combine self-attention mechanism with feature extraction CNN network and anomaly synthesis mechanism for anomaly scoring to achieve outstanding anomaly detection accuracy under few-shot setting
  • Reach SOTA performance on benchmark dataset MVTecAD dataset and BTAD dataset with over 90 percent reduction in training data requirements

Side Projects




GlueGAN: a generative model based on SuperGlue structure

  • Extend MagicLeap’s SuperGlue end-to-end GNN concept for object synthesis to solve synthetic object image generation problem
  • 10% accuracy improvement compared to initial GAN backbone 

Let's play GAN with flows and friends!

  • Generate synthetic object images with multi-label conditions with conditional GAN manner
  • Human faces generation by conditional normalizing flow

2048: by Temporal Difference Learning Approach (RL)

  • Construct TD-learning algorithm and design own n-tuple network to solve 2048 game
  • Reach 98% 2048-tile win rate in 1000 games

The LunarLander: under Deep Q-Network and DDPG manner (RL)

  • Implement DQN and DDPG network to solve LunarLander game problem
  • Thoroughly understand Deep Q-learning, actor-critic mechanism

Real-Time car detection and counting system

  • Retrain YOLOv3 with our own hatchback dataset
  • Reach average accuracy: 99% on both day-light and evening scenarios