Research

Research Support

  • Principal Investigator. “Statistical Methods for Integrating Evidence and Data for Precision Health Management in High-Risk Alzheimer’s Disease Populations.” R01AG097722, NIA, 2026–2031.
  • Principal Investigator. “Machine Learning Methods for Optimizing Individualized Treatment Strategies for Precision Psychiatry.” R01MH123487, NIMH, 2021–2027.
  • Principal Investigator. “Statistical Methods for Integrating Mixed-type Biomarkers and Phenotypes in Neurodegenerative Disease Modeling.” R01NS073671, NINDS, 2017–2028.
  • Multiple Principal Investigator (Contact PI: Zeng). “Statistical and Machine Learning Methods to Improve Dynamic Treatment Regimens Estimation Using Real World Data.” R01GM124104, NIGMS, 2018–2028.

Research Areas

Sequential decision-making and evidence integration

My group has developed augmented outcome-weighted learning for dynamic treatment regimens (DTRs), methods that jointly select tailoring variables at multiple treatment stages, and strategies that optimize expected benefit subject to stage-specific or cumulative adverse-risk constraints. We designed an enrichment approach for sequential multiple assignment randomized trials (SMARTs) that supplements later stages with patients who have relevant prior treatment histories. Our work also includes methods for synthesizing independent stagewise trials while accounting for delayed treatment effects and for combining studies with partially overlapping treatments and populations.

EHRs, randomized trials, and real-world evidence

My group has developed matched learning methods for treatment selection from EHRs and a domain-adaptation approach that uses EHRs to learn treatment-relevant patient representations while estimating the final treatment rule from randomized trials. This allows real-world data to enrich patient characterization without relying on the no unmeasured confounding assumptions for treatment assignment. We are also adapting a pretrained EHR foundation model to heterogeneous treatment effect estimation in small clinical cohorts using dimension reduction and regularization.

Longitudinal, digital, and multimodal patient representations

We have developed mixed-response state-space models for smartphone-based digital phenotypes, subject-specific hidden Markov models for wearable sensors, temporal generative models for ecological momentary assessment, hierarchical state-space models for EEG, and latent models of neurodegenerative disease progression. Current work extends this program to neural representations of behavioral and neurophysiological data and adaptation of pretrained foundation models when clinical cohorts are small. The goal is to characterize evolving patient states in forms that are interpretable and useful for treatment decisions.

Trustworthy evaluation and AI-assisted evidence synthesis

My group is developing debiasing and sample-splitting methods for inference on learned treatment rules and their value, including settings in which tailoring variables are estimated from complex data. I am interested in extending these ideas to healthcare AI evaluation, including calibration, subgroup heterogeneity, uncertainty from learned representations, risk-sensitive criteria, and whether added model complexity produces measurable value beyond simpler alternatives. An emerging direction in my group’s research is agent-assisted evidence review.

Selected Publications

The complete list is in my CV.

* Corresponding or senior author. Underlined names identify current and former Ph.D. advisees, research assistants, and postdoctoral fellows.

Statistical Methodology

  1. Ye Z, Wang Y, Gu Y, Kang K (2026). Beyond Overall Diagnosis: A Longitudinal Multi-Domain Latent Structure Model for Domain-specific Parkinson’s Disease Progression. Journal of the American Statistical Association. In press. Code
  2. Bian Y, Zeng D, Yang H, Williams L, Wang Y* (2026). Integrative learning of individualized treatment rules from multiple studies with partially overlapping treatments. Biometrics. 82(2), ujag083. Code
  3. Fei W, Chen Y, Cai Z, Zeng D, Wang Y* (2026). Learning Optimal Early Decision Treatment Rules with Multi-domain Intermediate Outcomes. Biometrics. 82(1), ujaf167. Code
  4. Cai Z, Zeng D, Marder K, Honig L, Wang Y* (2026). Dynamic Classification of Latent Disease Progression with Auxiliary Surrogate Labels. Annals of Applied Statistics. 20(1), 641–662.
  5. Bian Y, Guo X, Wang Y* (2025). Joint modeling for learning decision-making dynamics in behavioral experiments. Annals of Applied Statistics. 19(4), 3372–3393. Code
  6. Fei W, Miao Z, Xu T, Wang Y* (2025). Joint Bayesian Hidden Markov Model with Subject-Specific Transitions for Wearable Sensor Data. Statistics in Medicine. 44(28-30), e70334. Code
  7. Guo X, Zeng D, Wang Y* (2025). HMM for discovering decision-making dynamics using reinforcement learning experiments. Biostatistics. 26(1), kxae033. Code
  8. Liu M, Wang Y, Fu H, Zeng D (2024). Controlling Cumulative Adverse Risk in Learning Optimal Dynamic Treatment Regimens. Journal of the American Statistical Association. 119(548), 2622–2633. Code
  9. Liu M, Wang Y, Fu H, Zeng D (2024). Learning Optimal Dynamic Treatment Regimens Subject to Stagewise Risk Controls. Journal of Machine Learning Research. 25(128), 1–64.
  10. Guo X, Zeng D, Wang Y* (2024). A Semiparametric Inverse Reinforcement Learning Approach to Characterize Decision Making for Mental Disorders. Journal of the American Statistical Association. 119(545), 27–38. Code
  11. Kim S, Kim Y, Wang Y* (2024). Temporal Generative Models for Learning Heterogeneous Group Dynamics of Ecological Momentary Assessment Data. Biometrics. 80(4), ujae115. Code
  12. Wang Q, Wang Y* (2024). Multi-Layer Exponential Family Factor Models for Integrative Analysis and Learning Disease Progression. Biostatistics. 25(1), 203–219. Code
  13. Guo X, Yang B, Loh J, Wang Q, Wang Y* (2024). A Hierarchical Random Effects State-space Model for Modeling Brain Activities from Electroencephalogram Data. Biometrics. 80(4), ujae130. Code
  14. Xie W, Zeng D, Wang Y (2024). Support vector machine for dynamic survival prediction with time-dependent covariates. Annals of Applied Statistics. 18(3), 2166–2186. Code
  15. Xu T, Chen Y, Zeng D, Wang Y* (2023). Mixed-Response State-Space Model for Analyzing Multi-Dimensional Digital Phenotypes. Journal of the American Statistical Association. 118(544), 2288–2300. Code
  16. Chen Y, Fei W, Wang Q, Zeng D, Wang Y* (2021). Dynamic COVID risk assessment accounting for community virus exposure from a spatial-temporal transmission model. Advances in Neural Information Processing Systems (NeurIPS), 34.
  17. Sun M, Zeng D, Wang Y* (2021). Modelling Temporal Biomarkers With Semiparametric Nonlinear Dynamical Systems. Biometrika. 108(1), 199–214.
  18. Chen Y, Zeng D, Wang Y* (2021). Learning Individualized Treatment Rules for Multiple-Domain Latent Outcomes. Journal of the American Statistical Association. 116(533), 269–282. Code
  19. Chen Y, Zeng D, Xu T, Wang Y* (2020). Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatments. Advances in Neural Information Processing Systems 33 (NeurIPS 2020). Code
  20. Wu P, Zeng D, Wang Y* (2020). Matched Learning for Optimizing Individualized Treatment Strategies Using Electronic Health Records. Journal of the American Statistical Association. 115(529), 380–392. Code
  21. Wang Q, Xie S, Wang Y*, Zeng D* (2020). Survival-Convolution Models for Predicting COVID-19 Cases and Assessing Effects of Mitigation Strategies. Frontiers in Public Health. 8, 325. Code
  22. Wang Y, Fu H, Zeng D (2018). Learning Optimal Personalized Treatment Rules in Consideration of Benefit and Risk: With an Application to Treating Type 2 Diabetes Patients With Insulin Therapies. Journal of the American Statistical Association. 113(521), 1–13.
  23. Liu Y, Wang Y, Kosorok M, Zhao Y, Zeng D (2018). Augmented Outcome-weighted Learning for Estimating Optimal Dynamic Treatment Regimens. Statistics in Medicine. 37, 3776–3788. R package DTRlearn2
  24. Wang Y*, Chen T, Zeng D (2016). Support Vector Hazards Machine: A Counting Process Framework for Learning Risk Scores for Censored Outcomes. Journal of Machine Learning Research. 17(167), 1–37. Code
  25. Jiang F, Ma Y, Wang Y (2015). Fused Kernel-Spline Smoothing for Repeatedly Measured Outcomes in a Generalized Partially Linear Model with Functional Single Index. Annals of Statistics. 43(5), 1929–1958.
  26. Wang Y*, Liang B, Tong X, Marder K, Bressman S, Orr-Urtreger A, Giladi N, Zeng D (2015). Efficient Estimation of Nonparametric Genetic Risk Function with Censored Data. Biometrika. 102(3), 515–532. R package GSSE
  27. Chen T, Wang Y*, Chen H, Marder K, Zeng D (2014). Targeted local support vector machine for age-dependent classification. Journal of the American Statistical Association. 109(507), 1174–1187.
  28. Chen H, Wang Y*, Paik CM, Choi A (2013). A marginal approach to reduced-rank penalized spline smoothing with application to multilevel functional data. Journal of the American Statistical Association. 108(504), 1216–1229.
  29. Wang Y*, Chen H, Zeng D, Mauro C, Duan N, Shear K (2013). Auxiliary marker-assisted classification in the absence of class identifiers. Journal of the American Statistical Association. 108(502), 553–565.
  30. Wang Y*, Garcia T, Ma Y (2012). Nonparametric estimation for censored mixture data with application to the Cooperative Huntington’s Observational Research Trial. Journal of the American Statistical Association. 107(500), 1324–1338. Code
  31. Wang Y*, Chen H (2012). On testing an unspecified function through a linear mixed effects model with multiple variance components. Biometrics. 68(4), 1113–1125. Code
  32. Chen H, Wang Y* (2011). A penalized spline approach to functional mixed effects model analysis. Biometrics. 67, 861–870. Code
  33. Wang Y*, Yang Q, Rabinowitz D (2011). Unbiased and locally efficient estimation of genetic effect on quantitative trait in the presence of population admixture. Biometrics. 67(2), 331–343. Code
  34. Wang Y*, Clark LN, Marder K, Rabinowitz D (2007). Nonparametric estimation of age-at-onset distributions from censored kin-cohort data. Biometrika. 94(2), 403–414.

Collaborative Biomedical Research

  1. Lavery J, Chen Y, Panageas K, Wang Y* (2025). Unveiling Non-Small Cell Lung Cancer Treatment Effect Heterogeneity: A Comparative Analysis of Statistical Methods. Journal of the National Cancer Institute. 117(10), 2062–2072.
  2. Morrow J, Gisladottir U, Xu T, Tatonetti N, Wang Y*, Walsh T* (2025). The Association between Stimulant Medication Use and Mortality. Journal of Clinical Psychopharmacology. 45(6), 554–561. Mitchell B. Balter Award, 2026.
  3. Lloyd EC, Griffen T, Wang Y, Attia E, Walsh BT (2025). Fluoxetine for anorexia nervosa after weight restoration: moderation of effect by depression. Psychological Medicine. 55, e290.
  4. Steinglass JE, Fei W, Foerde K, Touzeau C, Ruggiero J, Lloyd C, Attia E, Wang Y, Walsh BT (2024). Change in food choice during acute treatment and the effect on longer-term outcome in patients with anorexia nervosa. Psychological Medicine. 54(6), 1133–1141.
  5. Cramer EY, Ray EL, Lopez VK, …, Wang Y, …, Reich N (2022). Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States. Proceedings of the National Academy of Sciences. 119(15), e2113561119.
  6. Cook L, Schulze J, Uhlmann W, Verbrugge J, Marder K, Lee A, Wang Y, Alcalay R, Nance M, Beck J (2022). Tools for Communicating Risk for Parkinson’s Disease. npj Parkinson’s Disease. 8(1), 164.
  7. Walsh T, Xu T, Wang Y, Attia E, Kaplan A (2021). Time Course of Relapse following Acute Treatment for Anorexia Nervosa. American Journal of Psychiatry. 178, 848–853.
  8. Sieberts SK, Schaff J, Duda M, …, the Parkinson’s Disease Digital Biomarker Challenge Consortium, Wang Y, Guan Y, Brunner D, Bonato P, Mangravite LM, Omberg L (2021). Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge. npj Digital Medicine. 4, 53. Winning team of a sub-challenge of the DREAM Challenge.
  9. McDonnell EI, Wang Y, Goldman J, Marder K (2021). Age of Onset of Huntington’s Disease in Carriers of Reduced Penetrance Alleles. Movement Disorders. 36(12), 2958–2961.
  10. Goldberg T, Chen C, Wang Y, Jung E, Swanson A, Ing C, Garcia P, Whittington R, Moitra V (2020). Association of Delirium With Long-term Cognitive Decline: A Meta-Analysis. JAMA Neurology. 77(11), 1373–1381.
  11. Kimhy D, Lister A, Liu Y, Vakhrusheva J, Delespaul P, Malaspina D, Ospina LH, Mittal VA, Gross JJ, Wang Y (2020). The impact of emotion awareness and regulation on psychotic symptoms during daily functioning. npj Schizophrenia. 6(1), 1–7.
  12. Attia E, Steinglass J, Walsh T, Wang Y, Wu P, Schreyer C, Wildes J, Yilmaz Z, Guarda A, Kaplan A, Marcus M (2019). Olanzapine versus Placebo in Adult Outpatients with Anorexia Nervosa: A randomized clinical trial. American Journal of Psychiatry. 176(6), 449–456.
  13. Lee A, Wang Y, Alcalay RN, Mejia-Santana H, Saunders-Pullman R, Bressman S, Corvol JC, Brice A, Lesage S, Mangone G, Tolosa E, Pont-Sunyer C, Vilas D, Schüle B, Kausar F, Foroud T, Berg D, Brockmann K, Goldwurm S, Siri C, Asselta R, Ruiz-Martinez J, Mondragón E, Marras C, Ghate T, Giladi N, Mirelman A, Marder K, Michael J. Fox LRRK2 Cohort Consortium (2017). Penetrance Estimate of LRRK2 p.G2019S Mutation in Individuals of Non-Ashkenazi Jewish Ancestry. Movement Disorders. 32(10), 1432–1438.
  14. Mauro C, Shear MK, Reynolds CF, Simon NM, Zisook S, Skritskaya N, Wang Y, Lebowitz B, Duan N, First M, Ghesquiere A, Gribbin C, Glickman K (2017). Performance Characteristics and Clinical Utility of Diagnostic Criteria Proposals in Bereaved Treatment-seeking Patients. Psychological Medicine. 47(4), 608–615.
  15. Shear MK, Reynolds CF, Simon NM, Zisook S, Wang Y, Mauro C, Duan N, Lebowitz B, Skritskaya N (2016). Optimizing Treatment of Complicated Grief: A Randomized Clinical Trial. JAMA Psychiatry. 73(7), 685–694.
  16. Marder K, Wang Y, Alcalay RN, Mejia-Santana H, Tang M, Lee A, Ray D, Mirelman A, Saunders-Pullman R, Clark L, Ozelius L, Orr-Urtreger A, Giladi N, Bressman S, for the LRRK2 Ashkenazi Jewish Consortium (2015). Age-Specific Penetrance of LRRK2 G2019S in the Michael J. Fox Ashkenazi Jewish LRRK2 Consortium. Neurology. 85(1), 89–95.
  17. Dixon L, Goldman H, Bennett M, Wang Y, McNamara K, Mendon S, Goldstein A, Choi C, Lee R, Lieberman J, Essock S (2015). Implementing Coordinated Specialty Care for Early Psychosis: The RAISE Connection Program. Psychiatric Services. 66(7), 691–698.
  18. Shear K, Wang Y, Skritskaya N, Duan N, Mauro C, Ghesquiere A (2014). Treatment of Complicated Grief in Elderly Persons: A Randomized Clinical Trial. JAMA Psychiatry. 71(11), 1287–1295.
  19. Simpson B, Maher M, Wang Y, Bao Y, Foa E, Franklin M (2011). Patient Adherence Predicts Outcome from Cognitive-Behavioral Therapy in Obsessive-Compulsive Disorder. Journal of Consulting and Clinical Psychology. 79(2), 247–252.
  20. Poduri A, Wang Y, Gordon D, Barral-Rodriguez S, Barker-Cummings C, Ulgen A, Chitsazzadeh V, Hill R, Risch N, Hauser A, Pedley T, Walsh C, Ottman R (2009). Novel susceptibility locus at chromosome 6q16.3-22.31 in a family with GEFS+. Neurology. 73(16), 1264–1272.
  21. Wang Y, Clark LN, Louis ED, Mejia-Santana H, Harris J, Cote LJ, Waters C, Andrews D, Ford B, Frucht S, Fahn S, Ottman R, Rabinowitz D, Marder K (2008). Risk of Parkinson disease in carriers of parkin mutations: estimation using the kin-cohort method. Archives of Neurology. 65(4), 467–474. Featured in the editorial of the same issue.
  22. Jiang R, Burke G, Enright P, Newman A, Margolis H, Cushman M, Tracy R, Wang Y, Kronmal R, Barr G (2008). Inflammatory markers and longitudinal lung function decline in the elderly. American Journal of Epidemiology. 168(6), 602–610.
  23. Clark LN, Ross BM, Wang Y, Mejia-Santana H, Harris J, Louis ED, Cote LJ, Andrews H, Fahn S, Waters C, Ford B, Frucht S, Ottman R, Marder K (2007). Mutations in the Glucocerebrosidase gene are associated with Early-Onset Parkinson disease. Neurology. 69, 1270–1277.
  24. Clark LN, Wang Y, Karlins E, Saito L, Mejia-Santana H, Harris J, Louis ED, Cote LJ, Andrews H, Fahn S, Waters C, Ford B, Frucht S, Ottman R, Marder K (2006). Frequency of LRRK2 mutations in early- and late-onset Parkinson disease. Neurology. 67(10), 1786–1791.
  25. Yip N, Kawut SM, Lederer DJ, Wilt JS, Wang Y, Sternberg D, D’Ovidio F, Sonett JR, Arcasoy SM (2006). Immunoglobulin G levels before and after lung transplantation. American Journal of Respiratory and Critical Care Medicine. 173(8), 917–921.