Flori Yellin joined Kitware’s Computer Vision Team and is located at our Virginia office.
Flori received her Bachelor’s in physics from Brandeis University and her Master’s in mechanical engineering from Johns Hopkins University. Her Master’s research focused on mathematical models of cellular level processes, such as how cells respond to electro-mechanical stimuli. During this time, Flori became interested in using computer vision algorithms to automate processing biological images.
She continued to pursue her Ph.D. at Johns Hopkins University under the guidance of Dr. René Vidal. She focused on machine learning algorithms to facilitate the development of point-of-care medical devices. Specifically, Flori’s Ph.D. research focused on developing unsupervised and weakly supervised machine learning algorithms for detecting, counting, and classifying blood cells in lens-free holographic images.
- F. Yellin, B. Béjar, B. Haeffele, E. Mathieu, C. Pick, S. Ray, and R. Vidal, "Joint Holographic Detection and Reconstruction," in Machine Learning in Medical Imaging. Springer International Publishing, 2019, pp. 664-672. [URL]
- F. Yellin, Y. Li, V. Sreenivasan, B. Farrell, M. Johny, D. Yue, and S. Sun, "Electromechanics and Volume Dynamics in Nonexcitable Tissue Cells," Biophysical Journal, vol. 114, no. 9, pp. 2231-2242, May 2018. [URL]
- F. Yellin, B. Haeffele, S. Roth, and R. Vidal, "Multi-cell Detection and Classification Using a Generative Convolutional Model," in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. [URL]
- F. Yellin, B. Haeffele, and R. Vidal, "Blood cell detection and counting in holographic lens-free imaging by convolutional sparse dictionary learning and coding," in 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 2017. [URL]