BingMin (Ben) Wang

I have four years' experience in machine learning/deep learning and about 2 years' experience in dealing with medical images. Have good communication and coordination capabilities, can withstand the pressure of work, willing to accept challenging work. Enjoy bringing what I have learned into real-world applications.

Location: New-Taipei City,TW
E-mail: [email protected]


  • Design of state-of-the-art deep neural networks to solve imaging problems (2D/3D), sequential/time series and tabular data
    • object detection such as Faster R-CNN, SSD and YOLO, image segmentation such as U-Net
    • attention model such as Transformer
    • numerical optimization
  • Familiar with traditional machine learning models and workflow of a machine learning project 
  • Test and evaluate algorithms to prove robustness
  • Have great communication and planning skills and profound experience in cooperating with experts in other fields
  • Good skills in paper reading and presentation

Work Experience

HTC Healthcare (DeepQ),2018 年 9 月 - 至今

Senior Deep Learning Engineer, Deep Learning Apps

  • Nodule Detection: Developed the 3D detection model (Faster R-CNN, U-Net like backbone) from scratch and use Focal loss and adding hard negative example gradually to deal with the large class imbalance
  • Intracranial Hemorrhage Classification: Developed the ICH classification model (Efficient-Net) combined with transformer encoder to consider sequential information and deployed to hospital PACS system with heatmap visualization
  • Ischemic stroke segmentation: Develop the segmentation model to segment core/penumbra zone which can be used in deciding treatment in stroke patient. 
  • Facial Landmark Detection: Developed the real-time face detection model(SSD: Single Shot MultiBox Detector) from scratch and deployed to mobile web browser(onnxjs, tfjs)
  • Fingertip Detection: Improved performance(~5 AP) of the real-time YOLO-like model running on mobile device(tflite) by a novel data augmentation


RSNA Intracranial Hemorrhage Detection (Kaggle), ranked top 3% (silver)

  • Developed an algorithm to detect acute intracranial hemorrhage and its subtypes
  • Multi-label Classification

APTOS 2019 Blindness Detection (Kaggle), ranked top 4% (silver) 

  • Developed a classifier that output the severity of diabetic retinopathy given the retina images
  • Ordinal Classification

KDD CUP 2017 - Task2, ranked top 2.3%

  • For every 20-minute time window, predict the entry and exit traffic volumes at tollgates


Stand ML Group - CheXpert

  • Developed a convolutional neural networks that output the probability of 14 observations given the available frontal and lateral radiographs

Credit Scoring (Sinopac), Jul. 2017 to Jul. 2018

  • Developed a classifier to predict a company will default or not, and extract the readable rules which are verified by the experts


National Cheng Kung University (NCKU), Sep. 2016 - Jun. 2018

Master of Electrical Engineering

  • Thesis: Exploring neural network hyper-parameters on small datasets and hand-crafted features: take credit scoring as an example
  • GPA 4.15

National Cheng Kung University (NCKU), Sep. 2012 - Jun. 2016

Bachelor of Electrical Engineering

  • Independent Study: Transmitter Front-End Circuit Architecture

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