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Automatic Fish Detection and Tracking in Underwater Videos
09/2017 - 08/2018 
Advisor: Dr. Jung-Hua Wang

· Re-trained the Faster-RCNN-Inception-V2-COCO model from Google's TensorFlow Object Detection API and employed OpenCV with Python to detect and track fish in complex, changeable underwater environments from recorded video clips

· Collected and labeled fish pictures manually to create a custom training dataset

· Calculated the FPS to evaluate the model performance 

˙ https://yijing-sie.github.io/#project

水下影像之魚體即時偵測及追蹤

Deep Learning Algorithm Engineer | Carnegie Mellon University MS ECE '22

謝宜靜

On Detecting Rare Patterns 							     	      08/2018 - 05/2019
Advisor: Dr. Christino Tamon

• Implemented from scratch the model and replicated the experiments in Finite Sample Complexity of Rare Pattern Anomaly Detection
• Analyzed and identified the model weaknesses, creating an improved model to accommodate imbalanced datasets and outperform other popular outlier detection algorithms, such as GMM, PPCA, LSA, LOF, one−class SVM, and Isolation Forest on categorical datasets, such as shuttle, wine-quality, ann-thyroid, mammography, yeast, and covertype
• Calculated AUC scores to evaluate the model performance    


On Detecting Rare Patterns

Designed and constructed 3D printed parts along with ramps 1.4 controller board powering an Arduino board mega 2560, two 42-step motors, and a servo to create a mini programmable robot arm that can draw and write enabled by G-code programs

Drawbot Scara 

https://yijing-sie.github.io/

I am a deep learning algorithm engineer, with a master's degree in Electrical and Computer Engineering from Carnegie Mellon University and a bachelor's degree in Electrical Engineering from National Taiwan Ocean University. 

I conducted two machine learning independent research projects in college, the first about Object Detection and the second about Anomaly Detection; During my time at CMU, I further deepened and broadened my knowledge and skills in machine learning and deep learning via coursework and projects.

I am looking to expand my network, so please feel free to connect with me or message me with any inquiries!

Programming Language: Python, C, Java, C++, Haskell, MATLAB 
Operating Systems: Linux, Microsoft Windows
Scientific Libraries: PyTorch, TensorFlow, NumPy, SciPy, Pandas, Scikit-Learn, FastAPI, OpenCV, JavaFx, PySpark
Technical Tools: Docker, Git, bash, Android Studio

At CMU, I deepened and broadened my knowledge and skills in machine learning with coursework.

In the summer of 2021, I became a research assistant, assisting in one of Dr. Kun Zhang’s research projects about the causal representation of multimodal data. 

• Studied state-of-the-art research work in causal representation and multi-tasking learning and summarized findings
• Collaborated with other students to study a theoretical model for the causal representation of multimodal
 data
• Scheduled and attended regular meetings with professors

Carnegie Mellon University

大一期間，我專心致至，以課業為主而成為了系上第一。

在課業最繁重的大二階段，為了學習更多課堂外的知識，除了盡全力並成功地繼續維持系排第一的優異成績外，我也擔任了為期一年的助教。同時，我也加入系上王榮華主任的實驗室，做機器學習相關的專題。

大三那年，為了本身的興趣和理想，我離開了臺灣到美國深造成為了Clarkson University 的 交換學生。

回國後，大四的我開始申請國外研究所，並很榮幸的拿到了: Columbia University - Computer Science, Carnegie Mellon University - Electrical and Computer Engineering 和 University of Maryland - Machine Learning 的研究所秋季入學錄取通知。

國立臺灣海洋大學

大三那年，為了學到更多機器學習相關的知識，滿足我對其的熱情與求知慾望，在教授的推薦下，我便隻身飛往相距約9000公里遠的美國到Clarkson University做一年的交換學生。
在Clarkson University就讀時，我與當時一位Computer Science的教授做異常偵測在機器學習上應用的研究專題 (請參閱作品集) 並透過修課強化了程式語言及機器學習理論的能力。

Clarkson University

Carnegie Mellon University's College of Engineering

Research Assistant

I assisted in one of Dr. Kun Zhang’s research projects about the causal representation of multimodal data. 

• Studied state-of-the-art research work in causal representation and multi-tasking learning and summarized findings
• Collaborated with other students to study a theoretical model for the causal representation of multimodal
 data
• Scheduled and attended regular meetings with professors

Overviewed by Dr. Kun Zhang and Dr. Yuejie Chi

https://github.com/yijing-sie/Multimodal-Data-and-Causal-Representation-Learning

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