Research

Statistical and AI methods for biomedical research.

My research develops statistical and AI methods for biomedical data that are observational rather than experimental, often ordinal or mixed-typed, frequently distributed across institutions, and drawn from complex surveys or unstructured clinical text. The program organizes around three thrusts that share methodological foundations but address distinct questions in biostatistics, artificial intelligence, and health services research.

i

Statistical methods for complex biomedical data

Most biomedical data violate the assumptions of classical causal inference, arriving as mixed-typed variables, weighted survey samples, ordinal ratings, or records split across institutions. I work on the following projects to address these challenges.

  • Information-theoretic measures of asymmetric association that recover causal direction from observational data, with Peter X.-K. Song.
  • Minimum Bregman divergence inference for robust estimation, with Ayanendranath Basu.
  • Ordinal Bayesian network learning that handles survey weights and covariates jointly, with David Frank, Lauren Russell, and Leslie Hausmann.
  • Federated estimating equations for inference across multicenter cohorts without pooling individual-level data.
Funding
Awarded / Active

VA Office of Health Outcomes — Co-Investigator, DEVELOPING USER-CENTERED REPORTING STRATEGIES TO PROMOTE EQUITY IN THE VETERANS HEALTH ADMINISTRATION (PI: Hausmann), in progress

Proposed

National Institutes of Health (R21) — Principal Investigator, RESOLVING THE CAUSAL DIRECTION OF DEPRESSION AND INFLAMMATION: A SURVEY-WEIGHTED STRUCTURE LEARNING APPROACH, 2027–28

Completed

VA Pittsburgh CHERP, Pilot Program — Co-Investigator, CHARACTERIZING GAPS IN THE QUALITY AND EQUITY OF HOSPITAL PRESCRIBING (PIs: Anderson and Essien)
Rackham Graduate School, University of Michigan — Predoctoral Fellowship, 2023–24

Software
fastMIRFast and consistent copula-based nonparametric estimator of mutual information.
causalGEMPythonBivariate causal discovery via the generative exposure model.
causalCovGEMRCovariate-adjusted bivariate causal discovery via the generative exposure model.
swaoBNR · devSurvey-weighted, covariate-adjusted ordinal Bayesian network learning.
FedGEER · devFederated generalized estimating equations for multicenter longitudinal data.
ii

AI models for medical device safety

With colleagues at VA Pittsburgh, I co-develop AI models for adverse-event surveillance. Our current focus is insulin pumps and continuous glucose monitors — the second-largest source of medical-device-related injuries reported to the FDA. Active extensions integrate extracted risk signal into provider-facing registries and Veteran-facing safety education materials. My collaborators include:

Funding
Awarded

Breakthrough T1D — Co-Investigator, AI-ASSISTED DEVICE RISK IDENTIFICATION SYSTEM TO PERFORM RAPID AND ONGOING IDENTIFICATION OF INSULIN PUMP RISKS (PI: Codario), 2026–27

Proposed

VA Health Systems Research — Co-Investigator, SAFETY ARCHITECTURE FOR VETERAN-USE ELECTRONIC MEDICAL DEVICES (PIs: Estock and Codario), 2027–28

Selected work
in submission
Natural language processing to extract insulin pump risks from adverse event narratives
Manuscript under preparation
iii

Health services research (HSR)

I serve as the statistician on a portfolio of health services and clinical research projects, most of them at VA Pittsburgh's Center for Healthcare Evaluation, Research, and Promotion (CHERP).

Active projects span six areas:

Funding
Awarded / Active

VA Health Systems Research — Co-Investigator, CHARACTERIZING THE QUALITY AND EQUITY OF EVIDENCE-BASED PRESCRIBING FOR HOSPITALIZED VETERANS (PIs: Anderson and Essien), 2026–29
VA Health Systems Research — Co-Investigator, OPTIMIZING HPV VACCINATION STRATEGIES FOR HEAD AND NECK CANCER PREVENTION IN VETERANS (PI: Zevallos), awarded, just-in-time (JIT) review in progress, 2027–28
VA Airborne Hazards and Burn Pits Center of Excellence — Co-Investigator, THE IMPACT OF SERVICE-RELATED BURN PIT AND EMBEDDED METAL FRAGMENTS EXPOSURE ON SARCOIDOSIS INCIDENCE AND MORTALITY (PI: Seedahmed), 2026–27
VA Health Systems Research (CDA-2) — Co-Investigator, UNDERSTANDING HOW MILITARY EXPOSURES INFLUENCE SARCOIDOSIS PATTERNS, PROGNOSIS, AND TREATMENT RESPONSE TO IMPROVE OUTCOMES FOR VETERANS (PI: Seedahmed), awarded, 2027–32
Merck — Co-Investigator, HPV VACCINATION AND THE FUTURE BURDEN OF HPV+ OROPHARYNGEAL CANCER IN THE VETERANS HEALTH ADMINISTRATION (PI: Zevallos), 2025–26
VA Office of Health Outcomes — Co-Investigator, DEVELOPING USER-CENTERED REPORTING STRATEGIES TO PROMOTE EQUITY IN THE VETERANS HEALTH ADMINISTRATION (PI: Hausmann), in progress

Proposed / under review

National Institutes of Health (R21) — Co-Investigator, EVALUATING THE VA NATIONAL PHARMACOGENOMIC PROGRAM TO REDUCE ADVERSE DRUG EVENTS IN OLDER ADULTS IN THE VA NURSING HOME (PI: Kistler), JIT review in progress, 2027–28
National Institutes of Health (R01) — Co-Investigator, ADVANCING EVIDENCE-BASED PRESCRIBING FOR PATIENTS HOSPITALIZED WITH CARDIOPULMONARY CONDITIONS (PIs: Anderson and Essien), proposed, 2027–32

Completed

VA Health Systems Research — Co-Investigator, CENTER FOR HEALTHCARE EVALUATION, RESEARCH, AND PROMOTION (PIs: Suda and Groeneveld)
National Institutes of Health (Diversity Supplement) — Co-Investigator, DEVELOPMENT OF CLINICAL PREDICTION MODELS FOR PULMONARY OUTCOMES IN SARCOIDOSIS (PI: Seedahmed), 2026
VA Competitive Career Development Fund (VISN 4) — Co-Investigator, HEALTH-RELATED SOCIAL NEEDS AND SUICIDALITY AMONG SEXUAL MINORITY VETERANS (PI: Gordon), 2024–25