Stephen T. Wong

Stephen T. Wong, PhD

John S. Dunn Presidential Distinguished Chair in Biomedical Engineering, Full Member, Research Institute
Professor of Computer Science and Bioengineering in Oncology, Academic Institute
Associate Director, Cores, Biostatistics and Bioinformatics
Houston Methodist
Weill Cornell Medical College


stwong@houstonmethodist.org
Biography

Dr. Wong holds the John S. Dunn, Sr. Distinguished Endowed Chair in Biomedical Engineering; he is also a Professor of Radiology, Pathology, Laboratory Medicine, Neurology, and Neurosciences, the Associate Director of Translational Research at Methodist Cancer Center, and Chief of Medical Physics and Chief Research Information Officer at Houston Methodist Hospital. In addition, he serves as the Founding Director of the Ting Tsung and Wei Fong Chao Center for BRAIN (Bioinformatics Research and Imaging in the Neurosciences) and Founding Director of the Center for Modeling Cancer Development at Houston Methodist Research Institute. He also holds a dozen of other academic posts across institutions in Texas Medical Center as well as overseas universities and medical schools.

An internationally acclaimed bioengineer and imaging scientist, Dr. Stephen Wong has led teams that developed production automation for first very large scale integration (VLSI) 1MB computer memory chip and the largest online brokerage trading system, and contributed to the first hospital-wide digital radiology image management system in US academic medical centers.

Dr. Wong has more than twenty years of research and management experience in industry and academia, including Hewlett-Packard, AT&T Bell Laboratories, the Japanese Fifth Generation Computer Systems Project, Philips Medical Systems and Royal Philips Electronics, Charles Schwab, University of California - San Francisco/Berkeley, Harvard University and Houston Methodist Hospital. He received his senior executive education from the MIT Sloan School of Management, Stanford University Graduate School of Business and Columbia University Graduate School of Business. He holds many patents and has published over 300 peer-reviewed papers and four books. He also serves on and chairs NIH study panels, conference program committees, and the editorial boards of twelve scientific journals. As an international authority, he is sought as a speaker on medical imaging, systems biology, healthcare IT, drug development, biophotonics, clinical neuroscience and other related topics.

Description of Research

Dr. Wong's research focuses on understanding health and disease from a systems perspective in order to generate cost-effective strategies and solutions for disease management. His research approaches combine both experimental and high throughput biology with rigorous computational, bioinformatics, biophysics, and imaging methods to achieve a deep understanding of the functions of each component in biological systems and integration of them with multilevel systems analysis. For example, the consequences of genetic variation, cell-cell interactions and environmental factors are included to reflect the circumstances and microenvironments of the disease condition. Current research projects include pathway modeling and high throughput phenotyping approaches for target discovery and drug repurposing in combating neurological disorders and cancer, and for understanding the mechanisms of these diseases. Additional projects include reformulation of small molecule drugs using chemical biology approaches, the development of point-of-care molecular diagnosis and monitoring devices using biophotonics and nanotechnology techniques, and the development of label-free molecular image-guided cancer therapy platforms.

Areas Of Expertise

Systems biology Bioinformatics Biological and medical imaging Drug repositioning Drug combinations Image-guided therapy Translational biophotonics Molecular imaging Diagnosis Mechanisms of cancer Mechanisms of neurological disorders
Education & Training

Postdoctoral Fellowship, Japan Science and Technology Agency
MS, Lehigh University
PhD, Lehigh University
Postdoctoral Fellowship, National Science Foundation, ICOT, The Fifth Generation Computer Systems Project, MITI, Tokyo
Publications

Image-to-image translation of label-free molecular vibrational images for a histopathological review using the UNet+/seg-cGAN model
He, Y, Li, J, Shen, S, Liu, K, WONG, KELVINK, He, T & WONG, STEPHENTC 2022, , Biomedical Optics Express, vol. 13, no. 4, pp. 1924-1938. https://doi.org/10.1364/BOE.445319

hDirect-MAP: projection-free single-cell modeling of response to checkpoint immunotherapy
Lu, Y, Xue, G, Zheng, N, Han, K, Yang, W, Wang, R-S, Wu, L, Miller, LD, Pardee, T, Triozzi, PL, Lo, H-W, Watabe, K, Wong, STC, Pasche, BC, Zhang, W & Jin, G 2022, , Briefings in bioinformatics, vol. 23, no. 2. https://doi.org/10.1093/bib/bbab575

HDirect-MAP: Projection-free single-cell modeling of response to checkpoint immunotherapy
Lu, Y, Xue, G, Zheng, N, Han, K, Yang, W, Wang, RS, Wu, L, Miller, LD, Pardee, T, Triozzi, PL, Lo, HW, Watabe, K, Wong, STC, Pasche, BC, Zhang, W & Jin, G 2022, , Briefings in bioinformatics, vol. 23, no. 2, bbab575. https://doi.org/10.1093/bib/bbab575

Author Correction: Radiation-activated secretory proteins of Scgb1a1 + club cells increase the efficacy of immune checkpoint blockade in lung cancer (Nature Cancer, (2021), 2, 9, (919-931), 10.1038/s43018-021-00245-1)
Ban, Y, Markowitz, GJ, Zou, Y, Ramchandani, D, Kraynak, J, Sheng, J, Lee, SB, Wong, STC, Altorki, NK, Gao, D & Mittal, V 2022, , Nature Cancer, vol. 3, no. 2, pp. 262. https://doi.org/10.1038/s43018-022-00330-z

Edge-enhanced dual discriminator generative adversarial network for fast MRI with parallel imaging using multi-view information
Huang, J, Ding, W, Lv, J, Yang, J, Dong, H, Del Ser, J, Xia, J, Ren, T, Wong, ST & Yang, G 2022, , Applied Intelligence. https://doi.org/10.1007/s10489-021-03092-w

Affective Computing for Late-Life Mood and Cognitive Disorders
Smith, E, Storch, EA, Vahia, I, Wong, STC, Lavretsky, H, Cummings, JL & Eyre, HA 2021, , Frontiers in Psychiatry, vol. 12, 782183. https://doi.org/10.3389/fpsyt.2021.782183

Artificial Intelligence Unifies Knowledge and Actions in Drug Repositioning
Yin, Z & Wong, ST 2021, , Emerging topics in life sciences. https://doi.org/10.1042/ETLS20210223

Retrospective study of deep learning to reduce noise in non-contrast head CT images
Wong, KK, Cummock, JS, He, Y, Ghosh, R, Volpi, JJ & Wong, STC 2021, , Computerized Medical Imaging and Graphics, vol. 94, 101996. https://doi.org/10.1016/j.compmedimag.2021.101996

Artificial intelligence unifies knowledge and actions in drug repositioning
Yin, Z & Wong, STC 2021, , Emerging Topics in Life Sciences, vol. 5, no. 6, pp. 803-813. https://doi.org/10.1042/ETLS20210223

An Intelligent Augmented Lifelike Avatar App for Virtual Physical Examination of Suspected Strokes
Yao, K, Wong, KK, Yu, X, Volpi, J & Wong, STC 2021, , Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, vol. 2021, pp. 1727-1730. https://doi.org/10.1109/EMBC46164.2021.9629720

Converging Multi-Modality Datasets to Build Efficient Drug Repositioning Pipelines against Alzheimer's Disease and Related Dementias
Yin, Z & Wong, ST 2021, , Medical Review.

Artificial intelligence-augmented, label-free molecular imaging method for tissue identification, cancer diagnosis, and cancer margin detection
Li, J, Liu, J, Wang, Y, He, Y, Liu, K, Raghunathan, R, Shen, SS, He, T, Yu, X, Danforth, R, Zheng, F, Zhao, H & Wong, STC 2021, , Biomedical Optics Express, vol. 12, no. 9, pp. 5559-5582. https://doi.org/10.1364/BOE.428738

Radiation-activated secretory proteins of Scgb1a1 + club cells increase the efficacy of immune checkpoint blockade in lung cancer
Ban, Y, Markowitz, GJ, Zou, Y, Ramchandani, D, Kraynak, J, Sheng, J, Lee, SB, Wong, STC, Altorki, NK, Gao, D & Mittal, V 2021, , Nature Cancer, vol. 2, no. 9, pp. 919-931. https://doi.org/10.1038/s43018-021-00245-1

Retrospective study of deep learning denoising to improve contrast-to-noise ratio in missed hyperacute ischemic stroke lesions
Ghosh, R, Cummock, JS, Wong, K, Volpi, JJ & Wong, ST 2021, , European Stroke Organization Conference 2021, 9/1/21 - 9/3/21 pp. 1717. https://doi.org/10.1177%2F23969873211034932

The Progress of Label-Free Optical Imaging in Alzheimer’s Disease Screening and Diagnosis
Liu, K, Li, J, Raghunathan, R, Zhao, H, Li, X & Wong, STC 2021, , Frontiers in Aging Neuroscience, vol. 13, 699024. https://doi.org/10.3389/fnagi.2021.699024

Novel STAT3 small-molecule inhibitors identified by structure-based virtual ligand screening incorporating SH2 domain flexibility
Kong, R, Bharadwaj, U, Eckols, TK, Kolosov, M, Wu, H, Cruz-Pavlovich, FJS, Shaw, A, Ifelayo, OI, Zhao, H, Kasembeli, MM, Wong, STC & Tweardy, DJ 2021, , Pharmacological Research, vol. 169, 105637. https://doi.org/10.1016/j.phrs.2021.105637

Risk factors for and frequency of ct scans, steroid use, and repeat visits in inflammatory bowel disease patients seen at a single-center emergency department: A retrospective cohort study
Euers, L, Abughazaleh, S, Glassner, K, Gajula, P, Jones-Pauley, M, Ezeana, C, Puppala, M, Wang, L, Wong, S, Oglat, A, Nickerson, S & Abraham, BP 2021, , Journal of Clinical Medicine, vol. 10, no. 12, 2679. https://doi.org/10.3390/jcm10122679

The bone microenvironment invigorates metastatic seeds for further dissemination
Zhang, W, Bado, IL, Hu, J, Wan, YW, Wu, L, Wang, H, Gao, Y, Jeong, HH, Xu, Z, Hao, X, Lege, BM, Al-Ouran, R, Li, L, Li, J, Yu, L, Singh, S, Lo, HC, Niu, M, Liu, J, Jiang, W, Li, Y, Wong, STC, Cheng, C, Liu, Z & Zhang, XHF 2021, , Cell, vol. 184, no. 9, pp. 2471-2486.e20. https://doi.org/10.1016/j.cell.2021.03.011

Sio: A spatioimageomics pipeline to identify prognostic biomarkers associated with the ovarian tumor microenvironment
Zhu, Y, Ferri-Borgogno, S, Sheng, J, Yeung, TL, Burks, JK, Cappello, P, Jazaeri, AA, Kim, JH, Han, GH, Birrer, MJ, Mok, SC & Wong, STC 2021, , Cancers, vol. 13, no. 8, 1777. https://doi.org/10.3390/cancers13081777

An imageomics and multi-network based deep learning model for risk assessment of liver transplantation for hepatocellular cancer
He, T, Fong, JN, Moore, LW, Ezeana, CF, Victor, D, Divatia, M, Vasquez, M, Ghobrial, RM & Wong, STC 2021, , Computerized Medical Imaging and Graphics, vol. 89, 101894. https://doi.org/10.1016/j.compmedimag.2021.101894