Shiau, Yi Luen

  Taipei City, Taiwan

“You can’t connect the dots looking forward; you can only connect them looking backwards. So you have to trust that the dots will somehow connect in your future.”

    

Skills


  • C
  • C++
  • Python
  • SIMetrix/SIMPLIS
  • Tensorflow/Keras
  • PyTorch
  • Excel/PowerPoint
  • Matlab

     


  • MySQL/PostgreSQL
  • R/SQL
  • Git/Git-hub 
  • Amazon Web Services
  • Flask/Django
  • Heroku
  • English — Advanced (TOEIC 785)

Education


Semiconductor Industry Bootcamp

National Yang Ming Chiao Tung University

Learning

1. Expertise in solid-state electronics or circuit systems 

2. Design, simulate and analyze data of solid-state electronic or circuit systems

3. Use SIMetrix/SIMPLIS and Python to analyze and simulate electronics, circuits, electromagnetism

4. Theory of semiconductor manufacturing technology, semiconductor physics and devices

Nov 2021 - Feb 2022

Data Structures and Algorithms

A course taught by Professor Hsuan-Tien Lin, Department of Computer Science and Information Engineering, National Taiwan University. 

Use C++ as the language to learn Array, Linked List, Stack, Queue, Container, Tree, Heap, and discuss how the programming process uses two resources, compute and memory, appropriately and efficiently.

Nov 2021 - Current






Machine Learning 2021

A course taught by Professor Hung-Yi LEE, Department of Electrical Engineering, National Taiwan University. 

Solidly explain the theory of the latest and most classic models of machine learning and deep learning. Implement a model according to the homework of each class, and use different knowledge to improve the accuracy and robustness of the model.

Jun 2021 - Dec 2021

AI Applications Engineer

Wistron TibaMe and Institute for Information Industry

300 hours of intensive training by professional teachers from industries, learning python, wxformbuilder, Git, and Docker. 

Also, SQL, HTML, Crawler, Chatbox, Cloud Platform (AWS), and Flask/Django.

Learning the theory and implementation of machine learning and deep learning. 

An eight-person team collaborates to do a project and present it to guests from different companies. 

Jun 2020 - Oct 2020

Python for Data Science and Machine Learning Bootcamp

Using Python for Data Science and Machine Learning

Using Spark for Big Data Analysis

Implement Machine Learning Algorithms

Learn to use NumPy, Pandas, Matplotlib, Seaborn, Plotly, and SciKit-Learn

K-Means Clustering, Logistic Regression, Linear Regression, Random Forest and Decision Trees, Natural Language Processing and Spam Filters, Neural Networks, Support Vector Machines 

May 2020 - Jul 2020

HarvardX's Professional Certificate in Data Science

Use R language, and learn statistics such as probability, inference, and modelling.

Apply them in real situations. Learn to use the tidyverse, including using ggplot2 for data visualization, and using dplyr for data wrangling.

Unix/Linux, git/GitHub, RStudio, and Implement the ML algorithm.

Apr 2020 - Jul 2020

Fu Jen Catholic University

Physics

2015 - 2019




Projects


WHERE GOURMET

Use Line Chatbox and Google Maps to randomly recommend nearby food. Let users get inspiration about what to eat through simple operations. 

AIoT PeopleFlow Analysis Platform 

Train CNN model, and use Jetson Nano and lenses to implement a real-time PeopleFlow analysis platform, use algorithms to calculate the number of people entering the venue and face recognition to analyze gender and age information; then use PostgreSQL & Amazon RDS database to operate the data, and use AWS EC2 and Django make a real-time Dashboard web platform. 

Sensors of Semiconductor Manufacturing with Logistic Regression

A semiconductor manufacturing process is normally under constant surveillance via the monitoring of signals/variables collected from sensors, but they are not all equally valued.

Use machine learning to build a classifier to predict the Pass/Fail yield of a particular process and obtain key feature vectors that decide the prediction, in other words, affects the product to pass the QA test.

Capacitively Coupled Amplifier

The principle of the capacitively coupled amplifier and its frequency response bode plot.

Simulate it with SIMETRIX and Python.

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