Willmer R. Quinones R.

Willmer R. Quinones R.

Postdoctoral Research Fellow at UNIST LAIT

About

I am a Postdoctoral Research Associate at UNIST, working with Jaejun Yoo in the Lab of Advanced Imaging Technology (LAIT). I received my Ph.D. from the Graduate School of Data Science at KAIST, where I was advised by Mun Yong Yi as part of KIRC (Knowledge Innovation Research Center). My research interests are in developing and applying deep learning-based systems to assist and improve the fields of healthcare, visual recognition, and computational imaging.

Education

  • Ph.D., Graduate School of Data Science, KAIST, South Korea Mar 2020 - Feb 2025
  • M.S., Knowledge Service Engineering, KAIST, South Korea Mar 2018 - Feb 2020
  • B.S., Electronic and Communication Engineering, INTEC, Dominican Republic Feb 2011 - Aug 2014

Experience

  • Postdoctoral Research Associate, LAIT, UNIST, South Korea Oct 2025 - Present
  • PhD Researcher, KIRC, KAIST, South Korea Mar 2020 - Feb 2025
  • SCADA Engineer, Empresa de Transmisión Eléctrica Dominicana (ETED), Dominican Republic Mar 2015 - Feb 2018
    • Engineer in the SCADA (Supervisory Control And Data Acquisition) department at the Centro de Control de Energía.

Publications

  • Quinones, W. R., Ashraf, M., & Yi, M. Y. (2021, December). Impact of Patch Extraction Variables on Histopathological Imagery Classification Using Convolution Neural Networks. In 2021 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 1176-1181). IEEE.

  • Ashraf, M., Quinones, W. R., Kim, M., Ko, Y. S., & Yi, M. Y. (2022). A loss-based patch label denoising method for improving whole-slide image analysis using a convolutional neural network. Scientific reports, 12(1), 1392.

  • Ko, Y. S., Choi, Y. M., Kim, M., Park, Y., Ashraf, M., Quiñones Robles, W. R., … & Yi, M. Y. (2022). Improving quality control in the routine practice for histopathological interpretation of gastrointestinal endoscopic biopsies using artificial intelligence. Plos one, 17(12), e0278542.

  • Kim, M., Quiñones Robles, W. R., Ko, Y. S., Wong, B., Lee, S., & Yi, M. Y. (2024). A predicted-loss based active learning approach for robust cancer pathology image analysis in the workplace. BMC Medical Imaging, 24(1), 5.

  • Noree, S., Quiñones Robles, W. R., Ko, Y. S., & Yi, M. Y. (2025). Leveraging commonality across multiple tissue slices for enhanced whole slide image classification using graph convolutional networks. BMC Medical Imaging, 25(1), 11.

  • Kim, J. W., Wong, B., Fu, H., Quiñones Robles, W. R., Ko, Y. S., & Yi, M. Y. (2025). MicroMIL: Graph-Based Multiple Instance Learning for Context-Aware Diagnosis with Microscopic Images. In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI).

Preprints / Under Review

  • Quiñones Robles, W. R., Noree, S., Ko, Y. S., & Yi, M. Y. (2024). Artificial Feature Maps using Fractals: A New Data Augmentation Strategy for Deep Learning-based Whole-Slide Image Analysis. Under Review.

  • Quiñones Robles, W. R., Noree, S., Ko, Y. S., Wong, B., Kim, J., & Yi, M. Y. (2025). Leveraging Spatial Context for Positive Pair Sampling in Histopathology Image Representation Learning. arXiv preprint.