CakeResume 找人才
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
職場能力評價定義
[email protected]
0961 100 735, Hsinchu, Taiwan
Work Experience
June 2021 — August 2021
Software Engineering Internship at HTC
- Responsible for writing automated tests using Robot Framework, a test automation framework based on Selenium.
- Worked on a UI test for the VIVE UK home page that verifies texts are correct, images can load properly, links and buttons lead to a working page, and forms are able to submit.
- Worked on an API test for the VIVE UK Accessories page that verifies the product prices displayed are the correct prices received from the API.
Education
September 2018 — Present, expected to graduate in June 2022.
BS, Computer Science, National Tsing Hua University
Current GPA - 3.97
Relevant Skills and Courses:
— Python, C++, C, SQL — Data Structures and Algorithms
— Keras, Pytorch, Scikit-learn — Git Version Control
— Data Science — Database Systems
Projects
3D Tic-Tac-Toe with Monte Carlo Tree Search
Applied the Monte Carlo Tree Search with Upper Confidence Bound applied for Trees on a 4 by 4 by 4 Tic-Tac-Toe state space to create an agent that calculates the next optimal move which resulted in a more than 100%-win rate when faced against a Random Agent.
Image Classifier with ResNet
Developed an image classifier in python using the fast.ai library with training data taken from Bing image search by fine-tuning the ResNet model, which resulted in a prediction accuracy higher than 90%.
Tic-Tac-Toe agent with Reinforcement Learning
Designed a Tic-Tac-Toe agent by learning state values from over 50,000 iterations of playing against itself and applying the Monte Carlo algorithm, which resulted in an unbeatable agent.
COVID-19 30-day Mortality Prediction from CXR
Built a CNN model by using Keras in the Tensorflow library with three hidden layers, which predicts a 30-day mortality from CXR which had a 0.75 F1 score.
Pathfinding Visualizer
Designed and built a program with python to visualize pathfinding in a randomly generated maze using Kruskal's Algorithm, Bellman-Ford and A* Search algorithms.
[email protected]
0961 100 735, Hsinchu, Taiwan
Work Experience
June 2021 — August 2021
Software Engineering Internship at HTC
- Responsible for writing automated tests using Robot Framework, a test automation framework based on Selenium.
- Worked on a UI test for the VIVE UK home page that verifies texts are correct, images can load properly, links and buttons lead to a working page, and forms are able to submit.
- Worked on an API test for the VIVE UK Accessories page that verifies the product prices displayed are the correct prices received from the API.
Education
September 2018 — Present, expected to graduate in June 2022.
BS, Computer Science, National Tsing Hua University
Current GPA - 3.97
Relevant Skills and Courses:
— Python, C++, C, SQL — Data Structures and Algorithms
— Keras, Pytorch, Scikit-learn — Git Version Control
— Data Science — Database Systems
Projects
3D Tic-Tac-Toe with Monte Carlo Tree Search
Applied the Monte Carlo Tree Search with Upper Confidence Bound applied for Trees on a 4 by 4 by 4 Tic-Tac-Toe state space to create an agent that calculates the next optimal move which resulted in a more than 100%-win rate when faced against a Random Agent.
Image Classifier with ResNet
Developed an image classifier in python using the fast.ai library with training data taken from Bing image search by fine-tuning the ResNet model, which resulted in a prediction accuracy higher than 90%.
Tic-Tac-Toe agent with Reinforcement Learning
Designed a Tic-Tac-Toe agent by learning state values from over 50,000 iterations of playing against itself and applying the Monte Carlo algorithm, which resulted in an unbeatable agent.
COVID-19 30-day Mortality Prediction from CXR
Built a CNN model by using Keras in the Tensorflow library with three hidden layers, which predicts a 30-day mortality from CXR which had a 0.75 F1 score.
Pathfinding Visualizer
Designed and built a program with python to visualize pathfinding in a randomly generated maze using Kruskal's Algorithm, Bellman-Ford and A* Search algorithms.