郭哲宇 Zhe-Yu Guo

  • 3 years experience in Machine Learning(ML) and software development.
  • Hands-on experience in AI, algorithms, and software solutions to realize consumer products target.    

AI Engineer / Machine Learning Engineer / Software Engineer

[email protected]


Skills

Machine Learning


  • Clustering: K-Means
  • Classification / Regression: SVM, KNN, Random Forest
  • Dimensionality reduction: PCA, MDS

Deep Learning


  • ANN, CNN, RNN, LSTM, GRU, GAN, AutoEncoder
  • Computer Vision: YOLO, SSD, VGG, ResNet
  • Natural Language Processing: Word2vec, BERT

Programming


  • Python
  • ML framework: Keras, Pytorch, Scikit-learn
  • Visualisation: Matplotlib, plotly
  • CV: OpenCV, Dlib
  • NLP: Gensim, NLTK, pytorch-transformers
  • Other: Numpy, Pandas

Database


  • MySQL
  • InfluxDB

Website development


  • Flask
  • Node.js
  • HTML, CSS, Bootstrap
  • JavaScript

Language


  • Chinese - native
  • English - TOEIC: 615

Work Experience

Associate Engineer

Industrial Technology Research Institute (ITRI)  •  Nov 2020 - Present

  • Build services with multiple machine learning algorithms and aggregation techniques.
  • Build web services to perform analysis and visualization.

Research Assistant

Yuntech Big Data Research Center  •  Sep 2018 - Jun 2020

  • In the case of industry-university cooperation, through various machine learning methods to help partner companies solve problems.

Intern

Hamastar Technology Co., Ltd.  •  Jul 2017 - Jul 2018

  • Develop Augmented Reality / Virtual Reality games through Unity
  • Develop the equipment needed for games through Arduino.

Education

Chiba University

18 May 2019 - 26 May 2019

  • Professional courses on topics related to robots.
  • 2019 short-term exchange plan


National Yunlin University of Science and Technology

Master's degree

Information Management  •  Sep 2018 ~ Jun 2020

  • Equipment manager of Big Data Research Center, responsible for the management of laboratory equipment and network.

National Kaohsiung University of Science and Technology

Bachelor's degree

Information Management  •  Sep 2014 ~ Jun 2018


Machine Learning Projects

Solar Photovoltaic power forecasting

  • Predicting the long-term and short-term generation of solar power plants can assist power plant managers in making decisions.
  • CNN, RNN, LSTM are used to extract weather features and predict future power generation. 
  • The clustering algorithm K-Means is used to segment the data of different weather to improve forecasting performance.
  • Related technologies: Keras, CNN, RNN, LSTM, Scikit-learn, K-Means

Pig location detection

  • When the sow is in heat, notify the pig farm for artificial insemination. When the boar passes by, you can confirm whether it is in estrus by the movement of the sow.
  • Read pre-trained models and use public pig image data sets on the Internet for transfer learning to predict sow location.
  • Related technologies: Tensorflow, Object Detection, YOLO, SSD, OpenCV

Disease classification

  • The patient's medical record is used to determine the type of disease the patient has.
  • After cutting the text description, use NLTK to perform pre-processing such as lemmatization and removing stop words. Then train the Word2vec model to convert the words into word vectors, and predict the disease category through LSTM, HAN.
  • Related technologies: Gensim, Word2vec, NLTK, Keras, LSTM, HAN

Signal compression

  • Compress the vibration signal of the device to reduce the required storage space, and confirm whether the device is abnormal by the vibration signal.
  • The Fourier transform is used to extract the signal period, and the AutoEncoder model is trained to extract features in the vibration signal.
  • Related technologies: Keras, AutoEncoder, CNN, Fast Fourier Transform

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