Shuang Wang is an assistant professor at Department of Biomedical Informatics, University of California San Diego. His research interests include healthcare and genomic data privacy and security, federated data analysis, secure cloud computing and GPU computing. As the PI, he is awarded a NIH K99/R00HG008175 career grant for genome data privacy research and a NIH R13HG009072 grant for organizing genome privacy protection competition workshops, which have been reported by Nature News and GenomeWeb. He is the PI of two NIH cloud computing credit grants (CCREQ-2017-03-00036 and CCREQ-2017-03-00037). He has published more than 80 journal/conference papers, one book and three book chapters. He received outstanding achievement award due to the research work on secure genomic data analysis using Intel Software Guard Extensions (SGX) from Intel Corporation, as well as the best paper award in the American Medical Informatics Association (AMIA) 2016 Joint Summits on Translational Science. He is a senior member of IEEE and a member of AMIA.
Research Interests:
- Medical and genomic data privacy and security
- Federated biomedical data analysis
- Secure cloud computing
- GPU based high performance computing
- Source and channel coding
- Image and signal processing
Research Support:
CURRENT
Develop privacy-preserving genomic data protection techniques based on homomorphic encryption, secure multiparty computation and secure hardware.
We host an international genome privacy competition to evaluate the state-of-the-art solutions of protecting genome privacy in data sharing and analysis.
Develop secure cloud computation algorithms and software for supporting privacy-preserving biomedical data outsourcing.
This project will allow distributed queries across VINCI (the VA national enterprise data warehouse), the five University of California medical center clinical data warehouses, and three federally qualified health systems in the LA area.
We propose to develop encryption methods for biomedical data mining, and to implement these methods in open-source software that can be used by biomedical researchers in a plug-and-play manner for the statistical analysis of encrypted biomedical data. Following our approach, biomedical data will be protected by encryption once they are generated, and the subsequent analysis and sharing will always be performed on the encrypted form, which thus can achieve a high security standard for privacy protection in biomedical data science.
A big challenge in biomedical information sharing is to maintain privacy, as inappropriate data handling can put patient's and their family members' sensitive personal information at risk. We will develop a privacy-preserving decentralized framework for dynamic data dissemination and analysis to support cross-institutional collaboration.
B. PAST
The major goal is to create a National Center for Biomedical Computing that will provide high performance computing infrastructure, develop new data anonymization algorithms to enable privacy-protecting sharing and data analyses of heterogeneous data types, and train the new generation of biomedical informaticians.
The goal is to study how to share and analysis of human genomic data in a privacy-preserving manner.
Build mathematic models for AC spectrum and provide theoretical guides for the applications and decoder designs of extended AC
The objective of this research is to advance the state-of-the-art of distributed source coding (DSC) for next-generation sensor networks by remodeling DSC as graphical inference problems. The resulting technologies are expected to lead to significant reduction of power consumption for communications and thus prolonged life span of such networks.
Develop GPGPU-based data privacy protection algorithms to support privacy-preserving data dissemination.
The goal is to develop new strategies and tools to allow secure and privacy-protecting electronic health information exchange for research.
- Protecting the pRivacy Of Genomes in Research StudieS (PROGRESS) PI: Wang
Develop privacy-preserving genomic data protection techniques based on homomorphic encryption, secure multiparty computation and secure hardware.
- SERGENT: SEcuRe GEnome Analysis Competition PIs: Ohno-Machado, Jiang, Wang
We host an international genome privacy competition to evaluate the state-of-the-art solutions of protecting genome privacy in data sharing and analysis.
- Two NIH Cloud computing credit grants PI: Wang
Develop secure cloud computation algorithms and software for supporting privacy-preserving biomedical data outsourcing.
- Patient-oriented SCAlable National Network for Effectiveness Research (pSCANNER) PI: Ohno-Machado
This project will allow distributed queries across VINCI (the VA national enterprise data warehouse), the five University of California medical center clinical data warehouses, and three federally qualified health systems in the LA area.
- Encryption methods and software for privacy-preserving analysis of biomedical data PI: Tang
We propose to develop encryption methods for biomedical data mining, and to implement these methods in open-source software that can be used by biomedical researchers in a plug-and-play manner for the statistical analysis of encrypted biomedical data. Following our approach, biomedical data will be protected by encryption once they are generated, and the subsequent analysis and sharing will always be performed on the encrypted form, which thus can achieve a high security standard for privacy protection in biomedical data science.
- Decentralized differentially-private methods for dynamic data release and analysis PI: Jiang, Ohno-Machado
A big challenge in biomedical information sharing is to maintain privacy, as inappropriate data handling can put patient's and their family members' sensitive personal information at risk. We will develop a privacy-preserving decentralized framework for dynamic data dissemination and analysis to support cross-institutional collaboration.
B. PAST
- iDASH: Integrating Data for Analysis, Anonymization and Sharing PI: Lucila Ohno-Machado
The major goal is to create a National Center for Biomedical Computing that will provide high performance computing infrastructure, develop new data anonymization algorithms to enable privacy-protecting sharing and data analyses of heterogeneous data types, and train the new generation of biomedical informaticians.
- Privacy-preserved cloud computing for mapping sensitive human genomic sequences PI: Tang
The goal is to study how to share and analysis of human genomic data in a privacy-preserving manner.
- Research on Arithmetic Coding Spectrum and its Applications PI: Yong Fang
Build mathematic models for AC spectrum and provide theoretical guides for the applications and decoder designs of extended AC
- Reformulating Distributed Source Coding using Graphical Inference for Sensor Networks PI: Samuel Cheng
The objective of this research is to advance the state-of-the-art of distributed source coding (DSC) for next-generation sensor networks by remodeling DSC as graphical inference problems. The resulting technologies are expected to lead to significant reduction of power consumption for communications and thus prolonged life span of such networks.
- Biomedical Data Processing with GPU Acceleration PI: Shuang Wang
Develop GPGPU-based data privacy protection algorithms to support privacy-preserving data dissemination.
- SCANNER: Scalable National Network for Effectiveness Research PI: Ohno-Machado
The goal is to develop new strategies and tools to allow secure and privacy-protecting electronic health information exchange for research.