Sharon Huang


Phone: (814) 863-7235

Office Address:
E317 Westgate Building

Title: Associate Professor


Research Areas:
Computer Science
Materials Science & Engineering
Petroleum & Natural Gas Engineering

Education Background:
• Tsinghua University, Beijing, China, Computer Science, BE, 1999
• Rutgers University, New Brunswick, NJ, Computer Science, MS, 2001
• Rutgers University, New Brunswick, NJ, Computer Science, PhD, 2006


Huang is an associate professor in information sciences and technology. She began at Penn State in 2018. Huang received her B.Eng. in computer science from Tsinghua University in China, and her M.S. and Ph.D. in computer science from Rutgers University. Her research interests are in the areas of image data analysis, computer vision, and machine learning, focusing on methods for object recognition, image segmentation, image synthesis, registration/ matching, tracking, skeletonization, computer-assisted diagnosis and intervention. Her broader interests include computer graphics, artificial intelligence, data visualization, data mining, biomedical informatics, and human-computer interaction.

Research Interests:

• Biomedical image data analysis
• Computer vision
• Machine learning
• Computer graphics and visualization
• Data mining

Memberships & Committees:

• Member of IEEE

Honors & Awards:

• Outstanding Reviewer Award, by Computers in Biology and Medicine (CBM) journal, 2015
• P.C. Rossin Assistant Professorship, Lehigh University, 2009
• Minority Junior Faculty Award for Career Enhancement, Christian R. and Mary F. Lindback Foundation, 2007


• IST 210: Organization of Data, Fall 2018
• IST 597: Machine Learning Methods in Biomedical Image Informatics, Spring 2019



Sharon Huang's Publications
Record 1 - 10 of 26 View All
Tawfik, M. S., A. S. Adishesha, Y. Hsi, P. Porswani, R. T. Johns, P. Shokouhi, X. Huang, and Z. Karpyn, (2022). Comparative Study of Traditional and Deep-Learning Denoising Approaches for Image-Based Petrophysical Characterization of Porous Media, Frontiers in Water, v. 3, [800369], doi: 10.3389/frwa.
Adishesha, A. S., D. J. Vanselow, P. L. Rivière, X. Huang, and K. C. Cheng, (2021). Zebrafish histotomography noise removal in projection and reconstruction domains, Proceedings - International Symposium on Biomedical Imaging, v. 2021-April, pp. 140-144,
Liu, H., T. J. Mu, and X. Huang, (2021). Detecting human—object interaction with multi-level pairwise feature network, Computational Visual Media, v. 7(2), pp. 229-239,
Purswani, P., Y. Hsi, F. Niu, X. Huang, Z. Karpyn, and P. Shokouhi, (2021). Comparison of Supervised Machine Learning Image Segmentation Algorithms for Petrophysical Characterization of a Saturated Porous Medium, AGU Fall Meeting 2021.
Xue, Y., J. Ye, Q. Zhou, L. R. Long, S. Antani, Z. Xue, C. Cornwell, R. Zaino, K. C. Cheng, X. Huang, (2021). Selective synthetic augmentation with HistoGAN for improved histopathology image classification, Medical Image Analysis, v. 67, [101816],
Ju, R. P. Zhou, S. Wen, W. Wie, Y. Xue, X. Huang, and X. Yang, (2020). 3D-CNN-SPP: A Patient Risk Prediction System From Electronic Health Records via 3D CNN and Spatial Pyramid Pooling, IEEE Transactions on Emerging Topics in Computational Intelligence.
Liang, D. Y. C. Guo, S. K. Zhang, T. J. Mu, and X. Huang, (2020). Lane Detection: A Survey with New Results, Journal of Computer Science and Technology, v. 35(3), pp. 493-505,
Ni, H., Y. Xue, Q. Zhang, and X. Huang, (2020). SiamParseNet: Joint Body Parsing and Label Propagation in Infant Movement Videos, Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings, v. 12264, pp. 396-405,
Ou, Y., Y. Xue, Y. Yuan, T. Xu, V. Pisztora, J. Li, and X. Huang, (2020). Semi-Supervised Cervical Dysplasia Classification with Learnable Graph Convolutional Network, Proceedings 2020 IEEE International Symposium on Biomedical Imaging, pp. 1720-1724,
Purswani, P., Z. T. Karpyn, K. Enab, Y. Xue, and X. Huang, 2020). “Evaluation of Image Segmentation Techniques for Image-Based Rock Property Estimation,” Journal of Petroleum Science and Engineering, v. 195, pp. 166-172.