Wen-Yen Chang

Data Scientist / Machine Learning Engineer

  Taiwan Province, Taiwan

I’m interesting in algorithm design and using them applying on applications, such as computer vision and natural language processing. Specifically the machine learning and deep learning, I have three advanced research about computer vision in the conference, including ECCV, WACV, and CVGIP



2018 - 2020

National Tsing Hua University

M.Sc. in Electrical Engineering

2014 - 2018

National Chung Cheng University

B.Sc. in Electrical Engineering

Selected Experiences 

Machine Learning Research Consultant  •  Kaikutek Inc.

• Research: fast hand-gesture recognition and few-shot learning.
• Cloud Computing System: Server-less training platform by AWS.

五月 2020 - 九月 2020

DSP Summer Intern  •  MediaTek Inc.

• Tools Chains development: 5G NR field tried tools.
• DSP analysis: HRAM replay and analysis.

七月 2019 - 九月 2019


Enhance data selection efficiency with variational auto-encoder for object detection’s active learning  

Research directions: Active Learning, Unsupervised Learning, Object Detection | Tools: Pytorch              Master Thesis 

• It can save 70% labeled cost for achieving a usable model in diverse domains and rare events. 

• Uncertainty and diversity information are important for active learning.

• VAE can provide good representation for known the unlabeled data distribution in surveillance cameras 

ParkingLot Services: Car tracking and localization 

Research directions: Object Detection, Sensor Fusion | Tools: C++, Android, Tensorflow, Matlab                         Undergraduated Project 

• Getting more reliable tracking results, I use image motion to enhance image-based object detection model. 

• Eliminating GPS localization error, I use trajectory information to reduce the white noise by Kalman Filter. 

• Checking parking location for finding, I fuse GPS and image localization information by driving behavior matching

Awards and Honors 

  • Spring 2020, Phi Tau Phi Scholastic Honor: top 1 master student in the EE department of NTHU. 

  • Fall 2018, Appier Scholarship: for outstanding students in their research with top conference papers. 

  • Fall 2017, The High Distinction Award: the 1st prizes for undergraduated project of EE in CCU. 

  • Fall 2017, The Chair Award: the most potential product in undergraduated projects of EE in CCU. 

  • Fall 2014, 2015, 2016, Spring 2014, 2016, Academic Achievement Award: 
    top 3 students in a semester in CCU.



  • Deep Learning
  • Programming
  • Data Science
  • Computer Vision
  • Machine Learning
  • Active Learning
  • Reinforcement Learning
  • Object Detection
  • Image Segmentation
  • Domain Adaptation

  • English — 進階
  • Chinese — 母語或雙語
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