Shih-Wen Tsou

- With more than 5 years of experience in Data Analysis, Machine Learning and Deep Learning, familiar with Modeling, Data Analysis, Image Processing, Machine Learning, and Deep Learning.

  Taipei City, Taiwan      

Work Experience

Research Assistant  •  Lab. for Cloud Dynamics and Modeling, NTU

Sep. 2021 - Present

1. Developed a model to detect ship tracks in satellite images with U-net that saves 80% time.
2. Cooperated with multiple domain experts, including Atmospheric Science and Environmental Science, to explore machine learning techniques.
3. Developed a model to classify typhoon tracks with 96.5% accuracy rate.
4. Configured and managed a GPU-enforced workstation for the lab members to execute High Performance Computing (HPC) tasks.

Data Scientist  •  Vizuro 

五月 2019 - 八月 2021

1. Developed the end-to-end pipeline to detect Breast Cancer in 3D Breast MRI images, including data storage, data pre-processing, and detection model building. The project passed the pre-submission of FDA.
2. Deployed the Deep-learning Breast Cancer Detection model integrated into the hospital PACS system.
3. Developed the model to segment 3D breast MR images and deployed it to imageJ to speed up annotation to shorten the labeling time three times.
4. Cleaned and labeled the raw Dicom data of breast MRI images manually.
5. Managed the project timeline, updated work records every week and ensured the project direction is correct.

Education

Sep. 2016 - Jan. 2019

National Taiwan University, Taipei, Taiwan

Master of Science in Atmospheric Sciences

Sep. 2011 - Jun. 2016

National Taiwan University, Taipei, Taiwan

Master of Science in Atmospheric Sciences

Skills


  • Language :

    • Python, Matlab, Fortran

  • Machine Learning : 

    • Tree based (i.e. Random Forest, XGBoost)

    • Clustering (i.e. K-means, DBSCAN)

    • Dimensionality Reduction (i.e. PCA, t-SNE)

  • Deep Learning :

    • Object Detection (i.e. Fast/Faster/Mask R-CNN,)

    • Image Segmentation (i.e. U-Net)

    • Generative Adversarial Network (i.e., DCGAN, Cycle GAN)

    • Explainable AI (i.e. Saliency Map, CAM, LRP)

    • CNNs, RNNs, transformer

  • Database : 

    • PostgreSQL

  • Knowledge :

    • Satellite Image Processing, Medical Image Processing, Abnormal Detection

    • BlockChain, Quantitative Research

Side Projects


  • Lightning Prediction with Deep Learning and explain the model with physical methods.

  • Learning to generate the Manhattan building with Deep Convolutional GAN from OpenStreeMap building model.

  • Predicting short-term stock market price trends with Machine Learning.

  • Build an Investment Portfolio machine with a Rebalancing Strategy from scratch.

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