Seidenberg School of CSIS
Computer Science NY
- @New York City
163 William Street 233
New York City
Dr. Shan joined Pace University in August 2013. She is currently an Associate Professor in the Department of Computer Science. Prior to joining Pace, she was an Assistant Professor in the Department of Mathematics and Computer Science at Benedictine College from 2011 to 2013. Her primary research interest is the application of machine learning to medical image analysis and computer-aided diagnosis.
PhD, Utah State University, Logan, UT, 2011
BS, Harbin Institute of Technology, Harbin, China, 2004
Awards and Honors
Pace University, June 2016 - Kenan Award
Pace University, April 2014 - Research Day 2014 Awardee
Intermountain Graduate Research Symposium, April 2011 - First Place Research Paper
Automatic Hand Segmentation from Hand X-rays Using Minimized Training Samples and Machine Learning Models
Yang, Z., Guida, C., Shan, J., Blackadar, J., Cheung, T., Driban, J., McAlindon, T. & Zhang, M. (2012, August).
Bone Marrow Lesion Segmentation Using Synthetic Data and Deep Learning Models
Michaely, B., Zhang, M. & Shan, J. (2021, August).
Knee Osteoarthritis Classification Using 3D CNN and MRI
Guida, C., Zhang, M. & Shan, J. (2021, August). Applied Sciences. Vol 11 (Issue 11) http://www.mdpi.com/2076-3417/11/11/5196
Automated cell division classification in early mouse and human embryos using convolutional neural networks
Malmsten, J., Zaninovic, N., Zhan, Q., Rosenwaks, Z. & Shan, J. (2021, August). Neural Computing and Applications.
Knee Bone Segmentation on Three-Dimensional MRI
Almajalid, R., Shan, J., Zhang, M., Stonis, G. & Zhang, M. (2020, June).
Identification of Knee Cartilage Changing Pattern
Almajalid, R., Shan, J. & Du, Y. (2019, December 16). Applied Sciences. Vol 9 (Issue 17) , pages 3469. http://www.mdpi.com/2076-3417/9/17/3469
Automated cell stage predictions in early mouse and human embryos using convolutional neural networks
Malmsten, J. & Shan, J. (2019, August 22).
Bone Segmentation in 3D Knee MRI Images Using U-Net
Alon, T., Shan, J., Zhang, M., Delvecchio, J. & Zhang, M. (2019, May 19).
Convolutional Neural Networks for Breast Ultrasound Image Segmentation
Liang, Y., Shan, J., Benjamin, D. & Almajalid, R. (2019, May 19).
Development of a Deep-Learning-Based Method for Breast Ultrasound Image Segmentation
Almajalid, R., Shan, J., Du, Y. & Zhang, M. (2019, May 19).
Using Whole Knee Cartilage Damage Index to Predict Knee Osteoarthritis: A Two-year Longitudinal Study
Du, Y., Shan, J., Almajalid, R. & Zhang, M. (2018, December 17).
Whole Knee Cartilage Quantification Based on Informative Locations
Zhang, M., Shan, J., Du, Y. & Almajalid, R. (2018, December 3).
Knee Osteoarthritis Severity Level Classification Using Whole Knee Cartilage Damage Index and ANN
Du, Y., Shan, J., Almajalid, R. & Zhang, M. (2018, December 3).
A Novel Method to Predict Knee Osteoarthritis Progression on MRI Using Machine Learning Methods
Du, Y., Almajalid, R., SHAN, J. & Zhang, M. (2018, September 26). IEEE Trans. on NanoBioscience. Vol 17 (Issue 3) , pages 228-236. http://ieeexplore.ieee.org/document/8364638/
Microaneurysm Detection Using Principal Component Analysis and Machine Learning Methods
Cao, W., Zarnek, N., SHAN, J. & Li, L. (2018, May). IEEE Trans. on NanoBioscience. Vol 17 (Issue 3) , pages 191-198. http://ieeexplore.ieee.org/document/8364584/
Innovations of Phishing Defense: The Mechanism, Measurement and Defense Strategies
Thakur, K., SHAN, J. & Pathan, A. (2018, May). International Journal of Communication Networks and Information Security.
Automated Microaneurysm Detection in Fundus Images through Region Growing
Li, L. & Shan, J. (2018, March).
Knee Osteoarthritis Prediction on MR Images Using Cartilage Damage Index and Machine Learning Methods
Du, Y. & Shan, J. (2017, December).
Microaneurysm Detection in Fundus Images by Small Image Patches and Machine Learning Methods
Cao, W., Czarnek, N. & SHAN, J. (2017, November).
Automated Microaneurysm Detection in Fundus Images by Region Growing
Li, L. & Shan, J. (2017, November).
A Novel and Robust Automatic Seed Point Selection Method for Breast Ultrasound Images
Mukaddim, R. A., Shan, J., Kabir, I., Ashik, A., Abid, R., Yan, Z., Metaxas, D., Garra, B., Islam, K. & Alam, S. (2017, February).
A New Scheme to Evaluate the Accuracy of Knowledge Representation in Automated Breast Cancer Diagnosis
Shan, J. (2016, December).
SHAN, J. (2018, September 27). The Third IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies. Knee Osteoarthritis Severity Level Classification Using Whole Knee Cartilage Damage Index and ANN. Washington, D.C..
SHAN, J. (2018, September 25). National Science Foundation PI Annual Meeting. 3D Image Predictive Model for Osteoarthritis Disease. NSF, Washington, D.C..
Dr. Shan's research interests include machine learning, medical image processing, and computer-aided diagnosis (CAD) systems. Her primary focus is developing robust and efficient CAD algorithms to help doctors analyzing medical images, discovering distinguishing features, and classifying data utilizing machine learning methods. Her on-going research projects include CAD systems for breast cancer, diabetic retinopathy, and knee osteoarthritis.
Grants, Sponsored Research and Contracts
SCH: EAGER: RUI: Collaborative Research: A Novel 3D Image Predictive Model for Knee Osteoarthritis Disease
Shan, J. September 2017 - August 2021.National Science Foundation,Federal,$208,107.00.Funded.
DAISEC: Data Analytics in Cybersecurity
Chen, L., Genc, Y. & Shan, J. September 2017 - August 2019.Department of Defense,Federal,$188,564.98.Funded.This project in particular aims to incorporate hands-on teaching of data analytics in cybersecurity.
Innovative Teaching Grant
Shan, J. January 2017 - June 2017.Pace University,Pace University,$5,000.00.Funded.
Scholarly Research Committee Grant
Shan, J. November 2016 - May 2017.Pace University,Pace University,$2,900.00.Funded.
CRA-W Distinguished Lecture Series at Pace University -- Workshop on Deep Learning
Shan, J. April 2017 - April 2017.Computing Research Association -Women (CRA-W),Other,$750.00.Funded.
IEEE Women in Engineering
- Editorial Board Member of Seidenberg School Technical Report Series[Committee Member]
- Faculty Satisfaction Survey Committee[Committee Member]
Desc: Generate faculty satisfaction survey for year 2018, collect survey data and generate survey report.