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Jiao Wang, Ph.D.

Technology Specialist

San Diego, CA
858-678-4390
[email protected]
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Jiao Wang, Ph.D. Photo

Background

Dr. Jiao Wang is a Technology Specialist in Fish & Richardson’s San Diego office. Dr. Wang focuses her practice on drafting and prosecuting patent applications directed to electrical engineering and computer-related technologies. Dr. Wang possesses a unique combination of technical expertise in machine learning, artificial intelligence, biomedical signal processing, medical devices, computer software technology and telecommunications.

Prior to joining the firm, Dr. Wang worked as individual contributor, team member, and manager in industries ranging from large companies to small startups. While working at GE Global Research Center, she conducted in-depth research in areas of medical imaging systems, statistical image reconstruction and data-intensive image processing. In one startup, Dr. Wang was a managing research scientist, responsible for developing deep learning-based computer vision solutions to medical image analysis and diagnosis problems, and involved in FDA and China FDA approval processes. While working at another previous role, Dr. Wang was a lead software engineer, responsible for developing and implementing machine learning based financial technology for digital identity verification and mobile check deposit.

Dr. Wang also has previous experience as a research fellow in the department of Electrical Engineering at University of Notre Dame, supported by GE Healthcare and its co-op programs, where she performed interdisciplinary research in the areas of electrical engineering, biomedical engineering and computer science. During her doctor’s study, Dr. Wang invented and developed an advanced Markov random field regularization algorithm for computed tomography (CT) image reconstruction. The advanced Markov random field regularization algorithm is capable of flexible spectral emphasis and improving spatial resolution in reconstructed CT images. Dr. Wang also developed an adapted non-convex optimization technique for efficient, reliable convergence. Dr. Wang holds 20 publications in refereed journals and conferences, and holds 4 U.S. patents/patent applications.

Education

Ph.D., University of Notre Dame 2012
Electrical Engineering


M.S., University of Notre Dame 2010
Electrical Engineering


M.S., Communication University of China 2007
Electrical Engineering


B.S., Huazhong University of Science and Technology 2005
Telecommunications


B.S., Huazhong University of Science and Technology 2005
Computer Science

Other Distinctions

Patents

US10140544B1 Enhanced Convolutional Neural Network for Image Segmentation.
US 8416914 Systems and Method of Iterative Image Reconstruction for Computed Tomography.
US16104449 Image segmentation and object detection using fully convolutional neural network.

Publications 

D. Yang, C. Bai, J. Hu, S. Lu, W. Shi, J. Wang, W. Li, H. Li, D. Gao, X. Zhong, C. A. Powell. “Artificial Intelligence vs. Lung-RADs for Lung Nodule Diagnosis in an Asian Population”, American Thoracic Society Conference, May 2019.

Yang, X., Dong, X., Wang, J., Li, W., Gu, Z., Gao, D., Zhong, N. and Guan, Y., 2019. Computed Tomography‐Based Radiomics Signature: A Potential Indicator of Epidermal Growth Factor Receptor Mutation in Pulmonary Adenocarcinoma Appearing as a Subsolid Nodule. The oncologist, pp.theoncologist-2018.

Y. Guan, X. Yang, J. He, J. Wang, W. Li, C. Liu, D. Gao. “Computed Tomography-Based Radiomics Signature for Differentiating Solitary Granulomatous Nodules from Solid Lung Adenocarcinoma.” Lung Cancer, Vol. 125, Pg 109-114, November 2018.

Y. Wang, B. Zheng, D. Gao, J. Wang, “Fully Convolutional Neural Networks for Prostate Segmentation and Cancer Detection Using Multi-Parametric Magnetic Resonance Images: An Initial Investigation”, International Conference on Pattern Recognition (ICPR), August 2018.

T. Zhao, D. Gao, J. Wang, Z. Yin, “Lung Segmentation in CT Images Using a Fully Convolutional Neural Network with Multi-Instance and Conditional Adversary Loss”, IEEE International Symposium on Biomedical Imaging (ISBI), April 2018.

D. Yang, C. A. Powell, C. Bai, J. Hu, S. Lu, W. Shi, N. Wang, P. Li, J. Wang, D. Gao, X. Zhong, “Deep Convolutional Neutral Networks Based Artificial Intelligence System for Pulmonary Nodule Detection and Diagnosis in United States and Chinese Dataset”, American Thoracic Society Conference, May 2018.

P. FitzGeral, P. Edic, H. Gao, Y. Jin, J. Wang, G. Wang, & B. De Man. “Quest for the ultimate cardiac CT scanner”. Medical physics, Sep 2017.

J. Wang, Y. Long, L. Fu, X. Rui, and B. De Man, “Sinogram Rebinning and Frequency Boosting for High Resolution Iterative CT Reconstruction with Focal Spot Wobbling”, Proceedings of SPIE Medical Imaging, March, 2014.

J. Wang, P. Fitzgerald, H. Gao, Y. Jin, G. Wang, and B. De Man, “Rotating and semi-stationary multi-beamline architecture study for cardiac CT imaging”, Proceedings of SPIE Medical Imaging, March, 2014.

P. FitzGerald, J Bennett, J Carr, PM Edic, D Entrikin, H Gao, M Iatrou, Y Jin, B Liu, G Wang, J Wang, “Cardiac CT: A system architecture study”, Journal of X-ray science and technology, 2016 Mar 1;24(1):43-65.

J. Wang, K. Sauer, J.-B. Thibault, Z. Yu, and C. Bouman, “Prediction Coefficient Estimation in Markov Random Fields for iterative X-ray CT reconstruction”, Proceedings of SPIE Medical Imaging, February, 2012.

L. Fu, J. Wang, X. Rui, J.-B. Thibault, and B. De Man, “Modeling and Estimation of Detector Response and Focal Spot Profile for High-Resolution Iterative CT Reconstruction”, Proceedings of IEEE Nuclear Science Symposium and Medical Imaging Conference, November, 2013, Seoul, Korea.

H. Gao, P. Fitzgerald, Y. Jin, J. Wang, P. Edic, and B. De Man, “Evaluation of Geometric Irradiation Efficiency for CT System Architecture”, RSNA 99th Scientific Assembly and Annual Meeting, December, 2013.

P. Fitzgerald, J. Bennett, J. Carr, P. Edic, D. Entrikin, H. Gao, M. Iatrou, Y. Jin, B. Liu, G. Wang, J. Wang, Z. Yin, H. Yu, K. Zeng, and B. De Man, “Selecting a Cardiac-Specific CT system Architecture”, AAPM 55th Annual Meeting & Exhibition, August, 2013.

J. Wang, K. Sauer, J.-B. Thibault, Z. Yu, and C. Bouman, “Prediction Coefficient Estimation in Markov Random Fields for iterative X-ray CT reconstruction”, Proceedings of SPIE Medical Imaging, February, 2012.

Z. Yu, J.-B. Thibault, J. Wang, C. Bouman, and K. Sauer, “Kinetic Model for Motion Compensation in Computed Tomography”, Proceedings of International Conference on Fully 3D Reconstruction in Radiology and Nuclear Medicine, July, 2011, Potsdam, Germany.

J. Wang, K. Sauer, J.-B. Thibault, Z. Yu, and C. Bouman, “Spectrally Focused Markov Random Field Image Modeling in 3D CT”, Proceedings of International Conference on Fully 3D Reconstruction in Radiology and Nuclear Medicine, July, 2011, Potsdam, Germany.

Z. Yu, J.-B. Thibault, J. Wang, C. Bouman, and K. Sauer, “Kinetic parameter reconstruction for motion compensation in transmission tomography”, Proceeding of IS&T/SPIE Electronic Imaging conference, pp. 78730T-78730T, 2011.

J. Wang, J.-B. Thibault, Z. Yu, K. Sauer, and C. Bouman, “Spectral Design in Markov Random Fields”, 30th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, July, 2010, Chamonix, France.

J. Wang, J.-B. Thibault, Z. Yu, K. Sauer, and C. Bouman, “System Modeling Studies in Iterative X-Ray CT Reconstruction”, Asilomar Conference on Signals, Systems, and Computers, October 2008.