研究兴趣

数据安全与隐私，生成模型，生物信息学

工作经历

2024–至今 讲师，304am永利集团，永利官网

教育经历

2018，Information technology博士，Rutgers University

2016，经济学硕士，Rutgers University

2012，统计学学士，重庆大学北京大学

论文

期刊

1. Min, S., Asif, H., Wang, X., and Vaidya, J. (2025), “Cafe: Improved Federated Data Imputation by Leveraging Missing Data Heterogeneity”, IEEE Transac-tions on Knowledge and Data Engineerin, Preprints, 1-16, 2025.

2. Wang, X., Min, S., and Vaidya, J. (2025), “Descriptor: Synthetic Genomic Dataset with Diverse Ancestry (SynGen6)”, IEEE Data Descriptions, vol. 2, pp. 1-7, 2025.

3. Asif, H., Min, S., Wang, X., and Vaidya, J. (2024) ”U.S.-U.K. PETs Prize Chal-lenge: Anomaly Detection via Privacy- Enhanced Federated Learning”, IEEE Transactions on Privacy, vol. 1, pp. 3-18, 2024.

4. Wang, X., Dervishi, L., Li, W., Ayday, E., Jiang, X., and Vaidya, J.(2023), “Privacy-Preserving Federated Genome-wide Association Studies via Dynamic Sampling”, Bioinformatics.

5. Wang X, Jiang X, Vaidya J. (2021), “Efficient verification for outsourced genome-wide association studies”, Journal of biomedical informatics, 117, p.103714.

会议

6. Wang, X., Min, S., and Vaidya, J. (2024), “Exploring the use of Artificial Genomes for Genome-wide Association Studies through the lens of Utility and Privacy”, In AMIA Annual Symposium Proceedings, Vol. 2024

7. Wang, X., Asif, H., and Vaidya, J. (2023), “Preserving Missing Data Distri-bution in Synthetic Data”, In Proceedings of the ACM Web Conference 2023 (WWW ’23). pp. 2110-2121. Spotlight Paper. Acceptance rate: 19.2%(365/1900)

8. Wang, X., Dervishi, L., Li, W., Jiang, X., Ayday, E., and Vaidya, J.(2023) “Efficient Federated Kinship Relationship Identification”. AMIA Joint Summits on Translational Science proceedings, 2023, 534–543.

9. Dervishi, L.,Wang, X., Li, W., Halimi, A., Vaidya, J., Jiang, X., Ayday, E.(2022), “Facilitating Federated Genomic Data Analysis by Identifying Record Correlations while Ensuring Privacy”, In AMIA Annual Symposium Proceedings, Vol. 2022, p. 395.

Poster Abstracts

Dervishi, L.,Wang, X., Li, W., Halimi, A., Vaidya, J., Jiang, X., Ayday, E.(2023), “Facilitating Federated Genomic Data Analysis by Identifying Record Correlations while Ensuring Privacy”, The Network and Distributed System Se- curity Symposium (NDSS).

专利

1. Wang, X., et al. (2022), Generating Synthetic Data. PCT/US23/32411

2. Wang, X., et al. (2023), “Methods and System for Improved Anomaly Identifi- cation Through Privacy-Enhanced Two-Step Federated Learning”, Application at Rutgers University

3. Wang, X., et al. (2024), “Methods and Systems for Complementarity-Adjusted Federated Averaging Imputation”, Application at Rutgers University

学术奖励

2023，AMIA Informatics Summit 2023 Best Reviewer Award

2023，US-UK Privacy Enhancing Technologies Challenge

2015，Financial Crime Track, 1st place (Team Scarlet-Pets)

课程

2024秋，大数据分布式计算，永利官网

Presentations and Talks

1. 2024, “Overcoming Privacy-Utility Trade-offs: Innovative Approaches in Collaborative Genome-Wide Association Studies” , Chinese Academy of Sciences, China

2. 2024, “Beyond Privacy-Utility Trade-offs: Innovative Approaches in Collaborative GWAS and Financial Fraud Detection”, Beihang University, China

3. 2024, “Anomaly Detection via Privacy-Enhanced Federated Learning”, R X-AGI IFoDS, China

4. 2023, “Efficient Federated Kinship Relationship Identification”, AMIA 2023 Informatics Summit, USA

5. 2023, “Preserving Missing Data Distribution in Synthetic Data” , TheWebConf 2023, USA

学术兼职

SPC for AMIA Informatics Summit 2025 

PC for CODASPY 2025, ISBRA 2025