CakeResume Talent Search

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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
1ヶ月以内
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
面接の用意ができています
フルタイム / リモートワークに興味あり
4〜6年
國立台灣大學
生物產業機電工程所

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1年以上
Computer Vision Reseacher
國立中央大學
2020 ~ 現在
Hsinchu, 新竹市台灣
Professional Background
現在の状況
就学中
求人検索の進捗
Professions
Software Engineer, Python Developer, Machine Learning Engineer
Fields of Employment
ソフトウェア, 人工知能/機械学習, ロボティクス
職務経験
1年未満
Management
なし
スキル
Artificial Intelligence
Deep Learning with PyTorch
Deep Learning with Tensorflow
Computer Vision
Python
Git
C++
Web
言語
English
ビジネスレベル
German
流暢
Chinese
ネイティブまたはバイリンガル
Job search preferences
希望のポジション
Software Engineer
求人タイプ
フルタイム
希望の勤務地
台灣台北市, 台灣新北市, 台灣新竹市新竹, 台灣桃園
リモートワーク
リモートワークに興味あり
Freelance
いいえ。
学歴
学校
國立清華大學
専攻
資訊應用研究所
印刷

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
Resume
プロフィール

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