[email protected]
0961 100 735, Hsinchu, Taiwan
June 2021 — August 2021
September 2018 — Present, expected to graduate in June 2022.
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
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.
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%.
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.
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.
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
June 2021 — August 2021
September 2018 — Present, expected to graduate in June 2022.
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
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.
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%.
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.
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.
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.