About The Director

Dr. Yong Chen is a Professor of Biostatistics and Founding Director of the Center for Health AI and Synthesis of Evidence (CHASE) at the University of Pennsylvania. He also directs the Penn Computing, Inference, and Learning (PennCIL) lab, focusing on evidence synthesis, machine learning/AI, and clinical evidence generation.

Dr. Chen is one of twenty Commissioners serving internationally on the Lancet Commission on Rare Diseases. He is also a Statistical Editor for the Annals of Internal Medicine, a Statistical Consultant for New England Journal of Medicine-AI, and an Associate Editor for both the JASA-ACS and The Annals of Applied Statistics. Dr. Chen has authored over 260 peer-reviewed papers in statistics and medical informatics. He is an elected Fellow of the American Statistical Association, and the American College of Medical Informatics.

Dr. Yong Chen has extensive experience leading multi-site studies across major national research networks, particularly within the PCORnet infrastructure. He maintains close collaborative relationships with several PCORnet Clinical Research Networks, including STAR Network, OneFlorida+, INSIGHT CRN, REACHnet, and PEDSnet. As contact Principal Investigator of a $8 million U01 award funded by NCATS, Dr. Chen leads a study involving over 20 health systems across OneFlorida+ and STAR, encompassing more than 35 million patients to develop AI-driven predictive models for early diagnosis of rare diseases. He also co-leads the IMPACT-MH initiative, a $13 million NIH-funded U24 project that supports a $150 million mental health research ecosystem, integrating over thirteen national consortia to build data infrastructure and deploy federated, multimodal AI analytics (project news). Additionally, Dr. Chen has recently been awarded funding for the ReCARDO initiative, which is supported by a $27.2 million U24 grant from the NIA, that aims to leverage electronic health records, claims, mobile apps, and wearable devices to accelerate discoveries in Alzheimer’s disease and related dementias. Further, Dr. Chen serves as the Biostatistics Lead for PEDSnet, where he has led and contributed to over 30 peer-reviewed studies using pediatric real-world data.

Study on Long COVID was featured by Penn Medicine News October 1, 2025.

Dr. Yong Chen was interviewed about Long COVID risk for a feature in TIME Magazine published October 1, 2025.

The Lancet Infectious Diseases published commentary on recent Long COVID study September 30, 2025.

Dr. Yong Chen was interviewed by The New York Times for a feature on Long COVID published September 30, 2025.

Dr. Yong Chen was interviewed by The Microbiologist for a feature on Long COVID published September 30, 2025.

Real-world data; clinical evidence generation; learning health system; healthcare delivery.

• Ph.D. in Biostatistics at the Bloomberg School of Public Health, the Johns Hopkins University
• Thesis Advisors: Professor Kung-Yee Liang and Professor Charles Rohde.
• M.A. in Mathematics at the Department of Mathematics, the Johns Hopkins University
• B.S. in Mathematics at the University of Science and Technology of China

• 2023. Elected Fellow, the American College of Medical Informatics (ACMI).
• 2023. Best of Annals of Applied Statistics (to be presented at JSM 2023): PALM: Patient-centered Treatment Ranking via Large-scale Multivariate Network Meta-analysis.
• 2022. Winner of the Best Paper in Biometrics by an IBS Member Award: Testing small study effects in multivariate meta-analysis. Biometrics, 76(4), 1240-1250. 2020.
• 2022. Best of Annals of Applied Statistics: Monitoring vaccine safety by studying temporal variation of adverse events using vaccine adverse event reporting system.
• 2021. The Observational Health Data Sciences and Informatics (OHDSI) Titan Award for Methodological Research to recognize extraordinary contributions by an individual, organization, or team in development or evaluation in analytical methods for clinical characterization, population-level effect estimation, or patient-level prediction
• 2021. Best paper award by the Translational Bioinformatics Year-in-Review by American Medical Informatics Association (top 25 papers among 206 papers published in Jan 2020 – March 2021)
• 2020. Elected Fellow, American Statistical Association
• 2019. Distinguished Faculty member at the Department of Biostatistics, Epidemiology and Informatics, the Perelman School of Medicine, University of Pennsylvania
• 2018. Best paper award by the International Medical Informatics Association (IMIA) Yearbook Section on Clinical Research Informatics
• 2018. Elected Member, International Statistical Institute
• 2018. Elected Member, Society for Research Synthesis Methodology
• 2015. Institute of Mathematical Statistics IMS Travel Award
• 2010. Margaret Merrell Award for excellence in research, Department of Biostatistics, The Johns Hopkins University.
• 2005 — 2010. Sommer Scholar, the Bloomberg School of Public Health, the Johns Hopkins University – Dean Alfred Sommer’s leadership training program for the next generation of public health leaders at the Bloomberg School of Public Health, the Johns Hopkins University
• The inaugural class of Hopkins Sommer Scholars in 2005

• Selected Statistical Papers:
  1. Tong, J, Hu, J, Hripcsak, G, Ning, Y, and Chen, Y (2025). DisCo–HD: Distributed causal inference with covariates shift for analyzing real-world high-dimensional data. Journal of Machine Learning Research, 26(3), 1-50.
  2. Wang, Y, Tong, J, Hu, X, Ye, Z, Tang, C. Y, and Chen, Y. (2025). Semiparametric Sieve Estimation for Survival Data with Two-layer Censoring. Biometrika, asaf006.
  3. Wang, J. Y, Ye, Z. S, Chen, Y. (2024). Likelihood-based inference under nonconvex boundary constraints. Biometrika, 111 (2), 591-607.
  4. Bai, R, Boland, M.R, Chen, Y. Scalable high-dimensional Bayesian varying coefficient models with unknown within-subject covariance. 2023. Journal of Machine Learning Research 24, 1-49
  5. Duan, R, Liang, J, Shaw, P, Tang, CY and Chen, Y. 2023 Testing the missing at random assumption in generalized linear models in the presence of instrumental variables. Scandinavian Journal of Statistics. 1–21.https://doi.org/10.1111/sjos.12685
  6. Duan, R, Ning, Y and Chen, Y. Heterogeneity-aware communication-efficient distributed statistical inference, Biometrika 109.1 (2022): 67-83.
  7. Bai, R, Moran, G, Antonelli, J, Chen, Y, and Boland, M. Spike-and-Slab Group Lassos for Grouped Regression and Sparse Generalized Additive Models. Journal of the American Statistical Association 117.537 (2022): 184-197.
  8. Huang, J, Ning, Y, Reid, N and Chen, Y, On specification tests for composite likelihood inference, Biometrika 107.4 (2020): 907-917.
  9. Huang, J, Ning, Y, Liang, K-Y, and Chen, Y, Composite likelihood inference under boundary conditions, Statistica Sinica 30.2 (2020): 1005-1025.
  10. Shen, W, Liu, S, Chen, Y, and Ning, J. Regression analysis of longitudinal data with outcome-dependent sampling and informative censoring. Scandinavian Journal of Statistics 46.3 (2019): 831-847.
  11. Chen, Y, Huang, J, Ning, Y, Liang, K-Y, and Lindsay, B. A conditional composite likelihood ratio test with boundary constraints. Biometrika 105.1 (2018): 225-232.
  12. Hong, C, Ning, Y, Wang, S, Wu, H, Carroll, RJ and Chen, Y. PLEMT: A novel pseudolikelihood-based EM test for homogeneity in generalized exponential tilt mixture models, Journal of the American Statistical Association 112.520 (2017): 1393–1404.
  13. Chen, Y, Ning, J, Ning, Y, Liang, K-Y and Bandeen-Roche, K. (2017) On pseudolikelihood inference for semiparametric models with boundary problems. Biometrika, 104 (1): 165–179.
  14. Ning, J, Chen, Y, Cai, C, Huang, X and Wang, MC. (2015) On the Dependence Structure of Bivariate Recurrent Event Processes: Inference and Estimation, Biometrika 102(2): 345-358.
  15. Ning, Y, and Chen, Y. (2015) A class of pseudolikelihood ratio tests for homogeneity in exponential tilt mixture models, Scandinavian Journal of Statistics 42 (2), 504–517.
  16. Chen, Y, and Liang, KY. (2010) On the asymptotic behavior of the pseudolikelihood ratio test statistic with boundary problems, Biometrika, 97 (3), 603–620.

• Selected Biostatistics Papers:
  1. Luo, C, Duan, R, Edmondson, M, Shi, J, Maltenfort, M, Morris, J, Forrest, C, Hubbard, R, and Chen, Y. (2023) Distributed Proportional Likelihood Ratio Model with Application to Data Integration across Clinical Sites. The Annals of Applied Statistics, 18(1), 63-79, 17.
  2. Duan, R, Tong, J, Lin, L, Levine, L, Sammel, M, Stoddard, J, Li, T, Schmid, C, Chu, H and Chen, Y. PALM: Patient-centered Treatment Ranking via Large-scale Multivariate Network Meta-analysis, The Annals of Applied Statistics (June 2022).
  3. Marks-Anglin, A, Luo, C, Piao, J, Gibbons, M, Schmid, C, Ning, J and Chen, Y. EMBRACE: an EM-based Bias Reduction Approach through Copas-Model Estimation for Quantifying the evidence of selective publishing in network meta-analysis, Biometrics 78.2 (2022): 754-765.
  4. Lian, X, Zhang, J, Hodges, J, Chen, Y, and Chu, H. Accounting for Post-randomization Variables in Meta-analysis: A Joint Meta-Regression Approach, Biometrics (Sep 2021).
  5. Huang, J, Cai, Y, Du, J., Li, R., Ellenberg, S.S., Hennessy, S., Tao, C., and Chen, Y. Monitoring vaccine safety by studying temporal variation of adverse events using vaccine adverse event reporting system. The Annals of Applied Statistics 2021, 15.1 (2021): 252-269.
  6. Hong, C, Salanti, G, Morton, S, Riley, R, Chu, H, Kimmel, S, and Chen, Y. Testing small study effects in multivariate meta-analysis, Biometrics 76.4 (2020): 1240-1250.
  7. Ning, J, Cai, C, Chen, Y, Huang, X and Wang, MC. Semiparametric Modelling and Estimation of Covariate-Adjusted Dependence between Bivariate Recurrent Events, Biometrics 76.4 (2020): 1229-1239.
  8. Duan, R, Ning, Y, Wang, S, Lindsay, B, Carroll, R, and Chen, Y. A fast score test for generalized mixture models, Biometrics 76.3 (2020): 811-820.
  9. Wang, L, Chai, X, Chen, Y, and Chen, J. Novel Two-Phase Sampling Designs for Studying Binary Outcomes, Biometrics 76.1 (2020): 210-223.
  10. Duan, R, Cao, M, Ning, Y, Zhu, M, Zhang, B, McDermott, A, Chu, H, Zhou, X, Moore, J, Ibrahim, J, Scharfstein, D and Chen, Y. Global identifiability of latent class models with applications to diagnostic test accuracy studies: a Grobner basis approach, Biometrics 76.1 (2020): 98-108.
  11. Ma, X, Lian, X, Chu, H, Ibrahim, J, and Chen, Y. A Bayesian hierarchical model for network meta-analysis of multiple diagnostic tests, Biostatistics, 19.1 (2018): 87–102.
  12. Hong, C, Ning, Y, Wei, P, Cao, Y and Chen, Y. A semiparametric model for vQTL mapping, Biometrics 73.2 (2017) : 571–581.
  13. Ning, J, Chen, Y and Piao, J. Maximum likelihood estimation and EM algorithm of Copas-like selection model for publication bias correction. Biostatistics, 18.3 (2017): 495–504.
  14. Liu, Y, Chen, Y and Chu H. A unification of models for meta-analysis of diagnostic accuracy studies without a gold standard, Biometrics, 71.2 (2017): 538–47.

• Selected Medical Informatics Papers:
  1. Tong, J, Luo, C, Islam, M, Sheils, N, Buresh, J, Edmondson, M, Merkel, P, Lautenbach, E, Duan, R and Chen, Y. An efficient distributed algorithm with application to COVID-19 data from heterogeneous clinical sites. Nature Partner Journal (NPJ) Digital Medicine, 5, 76 (2022).
  2. Liu, XK, Duan, R, Luo, C, Ogdie, A, Moore, J, Kranzler, H, Bian, J, and Chen, Y. ADAP: multisite learning with high-dimensional heterogeneous data via A Distributed Algorithm for Penalized regression. Scientific Reports, 12.1 (2022): 1-12.
  3. Luo, C., Du, J, Butler, A, Cuker, A, Lautenbach, E, Asch, D, Poland, G, Tao, C and Chen, Y. Comparability of clinical trials and spontaneous reporting data regarding COVID-19 vaccine safety, Scientific Reports, 12.1 (2022): 1-8.
  4. Luo, C., Islam, M.N., Sheils, N.E., Reps, J.M., Buresh, J., Schuemie, M.J., Doshi, J, Werner, R, Asch, D and Chen, Y. dPQL: a lossless distributed algorithm for generalized linear mixed model with application to privacy-preserving hospital profiling. Journal of the American Medical Informatics Association, 29.8 (2022): 1366-1371.
  5. Edmondson, M, Luo, C, Islam, N, Sheils, N, Buresh, J, Chen, Z, Bian, J, and Chen, Y. Distributed Quasi-Poisson Regression Algorithm for Modeling Multi-Site Count Outcomes in Distributed Data Networks. Journal of Biomedical Informatics, (2022): 104097.
  6. Luo, CL, Duan, R, Naj, A, Kranzler, H, Bian, J and Chen, Y. A One-shot Distributed Algorithm for Cox model with Heterogeneous Multicenter Data. Scientific Reports, 12.1 (2022): 1-8.
  7. Luo, C., Islam, M.N., Sheils, N.E., Buresh, J., Reps, J.M., Martijn, S, Ryan, P, Edmondson, M., Duan, R., Tong, J., Marks-Anglin, A, Bian, J, Chen, Z, Duarte- Salles, T, Fernandez-Bertolin, S, Falconer, T, Kim, C, Park, RW, Pfohl, S, Shah, Nigam, Williams, A, Xu, H, Zhou, Y, Lautenbach, E, Doshi, J, Werner, R, Asch, D and Chen, Y. (February 2022) DLMM as a lossless one-shot algorithm for collaborative multi-site distributed linear mixed models. Nature Communications, 13.1 (2022): 1-10.
  8. Liu, XK, Chubak, J, Hubbard, R and Chen, Y. SAT: a Surrogate Assisted Two- wave case boosting sampling method, with application to EHR-based association studies. Journal of the American Medical Informatics Association, Volume 29, Issue 5, May 2022, Pages 918–927.
  9. Yin, Z, Tong, J, Chen, Y, Hubbard, R, and Tang, CY. A Cost-effective Chart Review Sampling Design to Account for Phenotyping Error in EHR data. Journal of the American Medical Informatics Association, (2021) 29(1):52-61.
  10. Edmondson, M, Luo, C, Duan, R, Maltenfort, M, Chen, Z, Locke, K, Shults, J, Bian, J, Ryan, P, Forrest, C and Chen, Y. An Efficient and Accurate Distributed Learning Algorithm for Modeling Multi-Site Zero-Inflated Count Outcomes. Scientific Reports 11.1 (2021): 1-17.
  11. Du, J, Xiang, Y, Sankaranarayanapillai, M, Zhang, M, Wang, J, Si, Y, Pham, H, Xu, H, Chen, Y, Tao, C. Extracting post-marketing adverse events from safety reports in the Vaccine Adverse Event Reporting System (VAERS) using deep learning, (2021) Journal of the American Medical Informatics Association, 28.7 (2021): 1393-1400.
  12. Hubbard, R, Xu, J, Siegel, R, Chen, Y, Ihuoma Eneli. Studying pediatric health outcomes with electronic health records using Bayesian clustering and trajectory analysis, (December 2020) Journal of Biomedical Informatics, 113 (2021): 103654.
  13. Li, R, Duan, R, Zhang, X, Lumley, T, Pendergrass, S, Bauer, C, Hakonarson, H, Carrell, D, Smoller, J, Wei, W, Carroll, R, Edwards, D, Wiesner, G, Sleiman, P, Denny, J, Mosley, J, Ritchie, M, Chen, Y and Moore, J. Lossless integration of multiple electronic health records for identifying pleiotropy using summary statistics. (October 2020) Nature Communications, 12.1 (2021): 1-10.  selected as Best paper award by the Translational Bioinformatics Year-in-Review by American Medical Informatics Association (top 25 papers among 206 papers published in Jan 2020 – March 2021)
  14. Raisaro, JL, Marino, F, Troncoso-Pastoriza, J, Bernstam, E.V., Chen, Y, Got- tlieb, A, Kim, M, Klann, J, Klersy, C, Malin, B, Mean, M, Prasser, F, Scudeller, L, Wong, S, Frenkel-Morgenstern, M, Xu, H, Jiang, X, Hubaux, JP. (July 2020) SCOR: A secure international informatics infrastructure to investigate COVID-19. Journal of the American Medical Informatics Association, 27.11 (2020): 1721-1726.
  15. Duan, R, Luo, C, Schuemie, M.J., Tong, J, Liang, J., Chang, H., Boland, M.R., Bian, J., Xu, H., Holmes, J., Forrest, C., Morton, S., Berlin, J.A., Moore, J.H., Mahoney, K.B. and Chen, Y. (June 2020) Learning from local to global – an efficient distributed algorithm for modeling time to event data, Journal of the American Medical Informatics Association 27.7 (2020): 1028-1036.
  16. Duan, R, Boland, MB, Liu, ZX, Liu, Y, Chang, H, Xu, H, Chu, H, Schmid, C, For- rest, C, Holmes, J, Schuemie, M, Berlin, J.A. and Chen, Y. (Oct 2019) Learning from Electronic Health Records Across Multiple Sites: A Communication-efficient and Privacy-preserving Distributed Algorithm. Journal of the American Medical Informatics Association 27.3 (2020): 376-385.
  17. Tong, J, Huang, J, Wang, X, Moore, J, Hubbard, R and Chen, Y. (Sep. 2019) An Augmented Estimation Procedure for EHR-based Association Studies Accounting for Differential Misclassification. Journal of the American Medical Informatics Association 27.2 (2020): 244-253.
  18. Du, J, Cunningham, RM, Xiang, Y, Li, F, Jia, Y, Boom, JA, Myneni, S, Bian, J, Luo, C, Chen, Y and Tao, C. Leveraging deep learning to understand health beliefs about the Human Papillomavirus Vaccine from social media. Nature Partner Journal (NPJ) Digital Medicine, 2.1 (2019): 1-4.
  19. Li, R, Duan, R, Kember, R, Regeneron Genetic Center, Rader, D, Damrauer, S, Moore, J and Chen, Y. A regression framework to uncover pleiotropy in large-scale electronic health record data. Journal of the American Medical Informatics Association 26.10 (2019): 1083-1090.
  20. Li, R, Chen, Y, and Moore, J Integration of genetic and clinical information to improve imputation of data missing from electronic health records. Journal of the American Medical Informatics Association 26.10 (2019): 1056-1063.
  21. Huang, J, Duan, R, Hubbard, R, Wu, Y, Moore, JH, Xu, H, and Chen, Y, PIE: A prior knowledge guided integrated likelihood estimation method (PIE) for bias reduction in association studies using electronic health records data, Journal of the American Medical Informatics Association. 25.3 (2018): 345-352.

• Selected Epidemiology and Biomedical Science Papers:
  1. Zhou, T, Zhou, J, Hodges, J, Lin, L, Chen, Y, Cole, S, and Chu, H, Estimating the Complier Average Causal Effect in a Meta-analysis of Randomized Clinical Trials with Binary Outcomes Accounting for Noncompliance: A Generalized Linear Latent and Mixed Model Approach, American Journal of Epidemiology 191.1 (2022): 220-229.
  2. Xiao, M, Chen, Y, Cole, S, MacLehose RF, Richardson, DB, Chu, H. Is OR “portable” in meta-analysis? Time to consider bivariate generalized linear mixed model, Journal of Clinical Epidemiology 142 (2022): 280.
  3. Rao, S, Lee, G, Razzaghi, H, Lorman, V, Mejias, A, Pajor, N, Thacker, D, Webb, R, Dickinson, K, Bailey, C, Jhaveri, R, Christakis, D, Bennett, T, Chen, Y, and Forrest, C. Clinical Features and Burden of Postacute Sequelae of SARS-CoV-2 Infection in Children and Adolescents. JAMA Pediatrics, 176.10 (2022): 1000-1009.
  4. Lu, Y, Zandt, M, Liu, Y, Li, J, Wang, X, Chen, Y, Cho, J, Dorajoo, S, Feng, M, Hsu, M, Hsu, J, Iqbal, U, Chen, J, Jonnagaddala, J, Li, Y, Liaw, S, Lim, H, Ngiam, KY, Nguyen, P, Park, R, Pratt, N, Reich, C, Rhee, S, Sathappan, S, Shin, S, Tan, H, You, S, Zhang, X, Krumholz, H, Suchard, M, Xu, H. Analysis of dual combination therapies used in treatment of hypertension in a multinational cohort. JAMA Network Open, 5.3 (2022): e223877-e223877.
  5. Asch, D, Islam, M, Scheils, N, Chen, Y, Doshi, J, Buresh, J. and Werner, R. Patient and Hospital Factors Associated With Differences in Mortality Rates Among Black and White US Medicare Beneficiaries Hospitalized With COVID-19 Infection. JAMA Network Open, 4.6 (2021): e2112842-e2112842.
  6. Asch, D, Scheils, N, Islam, M, Chen, Y, Werner, R, Buresh, J and Doshi, J. Variation in US hospital mortality rates for patients admitted with COVID-19 during the first 6 months of the pandemic. JAMA Internal Medicine, 181.4 (2021): 471-478.
  7. Xiao, M, Chu, H, Cole, S, Chen, Y, MacLehose, R, Richardson, DB and Green- land, S. Odds ratios are far from “portable”—A call to use realistic models for effect variation in meta-analysis, Journal of Clinical Epidemiology (August 2021) (accepted).
  8. Hong, C, Duan, R, Zeng, L, Hubbard, R, Lumley, T, Riley, R, Chu, H, Kimmel, S and Chen, Y. The Galaxy plot: a new visualization tool of bivariate meta-analysis studies, American Journal of Epidemiology 189.8 (2020): 861-869.
  9. Du, J, Luo, C, Shegog, R, Bian, J, Cunningham, R, Boom, J, Poland, G, Chen, Y, Tao, C. Use of deep learning to analyze Twitter discussions about HPV vaccine beliefs and attitudes, JAMA Network Open 3.11 (2020): e2022025-e2022025.
  10. Singh, J, Kallan, M, Chen, Y, Parks, M, Ibrahim, S. Association of Race/Ethnicity with Hospital discharge disposition after Elective Total Knee Arthroplasty, JAMA Network Open, 2.10 (2019): e1914259-e1914259.
  11. Wang, L, Rouse, B, Marks-Anglin, A, Duan, R, Shi, Q, Quach, K, Chen, Y Schmid, CH, Li, T. Rapid network meta-analysis using data from Food and Drug Administration approval packages is feasible but with limitations. Journal of Clinical Epidemiology. 114:84–94.
  12. Chahoud, J, Semaan, A, Chen, Y, Cao, M, Rieber, A, Rady, P and Tyring, S. The Association between Beta-genus Human Papillomavirus and Cutaneous Squamous Cell Carcinoma in Immunocompetent Individuals: a Meta-analysis, JAMA Dermatology, 152(12):1354–1364.
  13. Gluck, C, Qiu, C, Han, S, Palmer, M, Park, J, Ko, Y, Hanson, R, Huang, J, Chen, Y, Park, A, Mantzaris, I, Verma, A, Li, H, and Susztak, K. Kidney cytosine methylation changes improve renal function decline estimation in patients with diabetic kidney disease, Nature Communications, 10.1 (2019): 1-12.
  14. Lake, E, Jordan, J, Duan, R, and Chen, Y. A Meta-Analysis of the Associations Between the Nurse Work Environment in Hospitals and 4 Sets of Outcomes, Medical Care 59(5) 353-360.12

1. Gao, Y., Zhou, J., Zhou, H., Chen, Y. and Dai, X. Learn then decide: A learning approach for designing data marketplaces. Journal of the American Statistical Association-Theory and Methods, (under revision).
2. Zhang, D., Liu, X., Ning, Y., Carroll, R., Chen, Y. Bias reduction in distributed inference for rare events. Biometrika, (under revision).
3. Duan, J., Ning, Y., Chen, X. and Chen, Y. Two-stage Hypothesis Tests for Variable Interactions with FDR Control. The Annals of Statistics, (under revision).
4. Hu, J., Tong, J., Ning, Y., Tang, C. Y., and Chen, Y. (Dec, 2024). Federated feature selection via FDR control. Journal of the Royal Statistical Society Series B, (minor revision).
5. Hu, J., Wang, Y., Wang, T., Qiu, Y., Ning, Y.and Chen, Y. Targeted Learning of Heterogeneous Sources by Informative Feature Sharing. Journal of the Royal Statistical Society Series B, (under revision).
6. Liu, X., Hu, J., Jing, N., Ning, Y., Tang, CY., Li, R and Chen, Y. Targeted learning via probabilistic subpopulation matching. Journal of the American Statistical Association-Theory and Methods, (under revision).
7. Wang, Y., Liu, X., Ning, Y., Carroll, R. J., and Chen, Y. (2025+) Integration of Heterogeneous Data via a One-shot Distributed EM Algorithm, Journal of the American Statistical Association-Theory and Methods, (under revision).
8. Chen, Y., Wang, H., Lumley, T., Dai, X., and Chen, Y. (2025+) Surrogate-Powered Inference: Regularization and Adaptivity, Journal of the American Statistical Association-Theory and Methods, (under revision).
9. Wang, Y, Zhu, Z, Chen, Y. (2025+) Distributed inference for heterogeneous distributions of response. Biometrika, (under revision).
10. Liu, X, Yang, Y, Sun, Y, Bian, J, Ma, Y, Carroll, R, Chen Y. (2025+) Distributed inference for heterogeneous mixture models using multi-site data, Journal of Machine Learning Research, (under revision).
11. Duan, R, Ning, Y, Shi, J, Carroll, R, Cai, T and Chen, Y. Global identifiability of logistic regression models with misclassified outcomes. Bernoulli (invited revision).

• Senior Fellow at the Institute of Biomedical Informatics, University of Pennsylvania
• Senior Scholar at the Center for Evidence-based Practice at Penn School of Medicine, University of Pennsylvania
• Faculty member at the Applied Mathematics & Computational Science Program, Penn Arts & Sciences, University of Pennsylvania