Research Scientist Wencheng Wu’s fields of expertise include video processing, computer vision, machine learning, image processing (specialized in defect detection), simulation/modeling, and psychophysical assessment. His current interest lies in applying and advancing computer vision and machine learning for various applications such as Transportation and HealthCare. At University of Rochester he has responsibility for projects in the Rochester Data Science Consortium, which is focused on advancing regional economic development and supports a range of partnerships with industry in areas of data science research, training, technology development and access to research computing expertise and resources.
Prior to join University of Rochester, Wencheng worked at PARC, where he was a key technical contributor on two computer vision research projects: rail road image defect detection and printer defect diagnostics. Prior to that he spent 17 years at Xerox/PARC/Conduent where he worked on image quality metric developments, printer and sensor characterizations, image simulation and color modeling, color consistency measurement, image processing algorithms for defect detection, video processing and analytics for transportation and retail domains.
Dr. Wu received his Ph.D. in Electrical Engineering from Purdue University, Indiana. He holds 117 patents, has 32 patents pending, and has published a book chapter and 35 conference / journal papers. He is a senior member of IEEE.
- Lin, B. Xu, W. Wu, T. Richardson, E. A. Bernal, B. Martens, C. Thornton, C. Heatwole, “Deep Metric Learning with Triplet Networks: Application to Hand-grip Myotonia,” IEEE EMB Special Topic Conference on Healthcare Innovations and Point-of-Care Technologies, 2019.
- Wu, B. Xu, E. A. Bernal, R. L. Hill, E. B. Brown, and D. Desa “Breast Cancer Tissue Sub-Type Classification from Second Harmonic Generation Images via Machine Learning,” IEEE EMB Special Topic Conference on Healthcare Innovations and Point-of-Care Technologies, 2019.
- Cheng, B. Xu, W. Wu, L. Lin, T. Richardson, and E. A. Bernal, “An Unsupervised Machine Learning Framework for Parkinson’s Disease Progression Analysis and Subtyping,” IEEE EMB Special Topic Conference on Healthcare Innovations and Point-of-Care Technologies, 2019.
- L. Lin, B. Xu, W. Wu, T. Richardson, E. Bernal. Interpretable Diagnosis of Myotonic Dystrophy from Handgrip Time Series Data with Attention-based Temporal Convolutional Network, 2019 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, June 2019.
- Shreve, R. Bala, W. Wu, B. Xu, P. Matts, A, Purwar, “Deep CNNs for Facial Skin Age Modeling,” accepted to International Conference on Machine Vision Applications, May 27–31 2019, Tokyo, Japan.
- Gavai, W. Wu, B. Xu, et. al., “Hybrid Image-based Defect Detection for Railroad Maintenance,” 2019 IS&T International Symposium on Electronic Imaging, Jan. 13-17, 2019, Burlingame, CA.
- Wencheng Wu, “On-street parked vehicle detection via view-normalized classifier,” IS&T International Symposium on Electronic Imaging 2019: Image Processing: Algorithms and Systems XVII proceedings, 2019.
- Arko Barman, Wencheng Wu, Robert Loce, Aaron Burry, “Person Re-Identification Using Overhead View Fisheye Lens Cameras,” 2018 IEEE International Symposium on Technologies for Homeland Security, 2018
Conference Proceedings and Presentations
- L. Lin, B. Xu, W. Wu, T. Richardson, E. Bernal. Deep Metric Learning with Triplet Networks: Application to Myotonic Dystrophy Diagnosis. CEIS, April 2019.
- W. Wu, R. Hill, E. Brown, B. Xu, E. Bernal, E. Patak. Image-Based Biomarkers for Cancer Recurrence Prediction using SHG imaging, CEIS April 2019. (Best Poster Award)
- B.Xu, W. Wen, S. Wshah, R. Elmoudi, L. Lin. Advanced Modeling of Power System Dynamics Using Machine Learning. NYISO, Albany, August 2019.
- W. Wu. Applying Region-based CNN for Micro-Feature Image Analysis, Artificial Intelligence Seminar series, Rochester Institute of Technology, Rochester, NY, October 2018.
- B.Xu, W. Wen, S. Wshah, R. Elmoudi. Advanced Modeling of Power System Dynamics Using Machine Learning. NYSERDA, Albany, Nov 2018.
- Wu. Person Re-Identification Using Overhead View Fisheye Lens Cameras, Monthly IS&T Imaging Seminar series, IS&T local chapter, Rochester, NY, October 2018.