Research

My research develops statistical methods and open-source software for valid inference when AI/ML predictions are used alongside or in place of observed data, and applies deep learning tools to clinical problems. I also have a body of applied work in public health across maternal health, child welfare, intimate partner violence, and infectious disease.

Inference on predicted data

As AI/ML models increasingly replace traditional data collection, researchers face a new challenge: how do you draw valid statistical conclusions when your outcome variable was predicted by an algorithm rather than directly observed? Naive use of predicted data produces biased estimates and invalid confidence intervals.

I work on methods that correct for this, and co-develop the ipd R package, which implements multiple approaches (PostPI, PPI, PPI++, PSPA) in a unified framework. Current extensions target survival/censored outcomes (the VIPS project), longitudinal data analyzed with generalized estimating equations, and finite-sample variance estimation for penalized GEE.

Key papers:

Industry research

During my June–August 2026 internship as a Senior Data Scientist Intern at Analysis Group in Boston, I optimized digital-twin machine-learning models to improve predictive performance and developed a new statistical estimator to support valid effect estimation and inference from digital-twin-generated outcomes.

I also applied graph neural networks to insurance claims data to analyze and interpret healthcare provider networks across the United States.

Deep learning for clinical screening

I am developing a multimodal deep learning model that combines chest X-ray embeddings and 12-lead electrocardiogram waveforms to screen for pulmonary embolism using the MIMIC-IV critical care database. The project focuses on whether routinely collected data can support objective clinical screening. A conference abstract from this work was published in the THSNA 2026 Proceedings in American Journal of Hematology, 101(S2).

This project is built using scorcher, an R package I co-develop that makes deep learning accessible to clinical researchers without specialized programming backgrounds. Scorcher is supported by the Stuart and Molly Sloan Precision Oncology Institute Technology Dissemination Award.

Collaborators: Dr. Jeffrey T. Leek (Fred Hutch), Dr. Stephen Salerno (Fred Hutch), Dr. Barbara Lam (Fred Hutch/UW), Dr. Leo Anthony Celi (MIT/Harvard)

Biomedical software evaluation

As first author, I led an assessment of impact metrics for NCI-funded cancer research software through the ITCR program, finding that traditional citation counts inadequately capture software usage and that infrastructure like documentation quality and developer accessibility are associated with higher adoption.

Key paper: Best practices to evaluate the impact of biomedical research software – Bioinformatics, 2024

Applied public health

I have published on a range of public health topics using nationally representative survey data from Bangladesh, including maternal health (antenatal care, cesarean sections, menstrual hygiene), child welfare (stunting, child abuse), intimate partner violence, and COVID-19 (vaccine prioritization, pandemic preparedness, underreporting). Recent work includes a study of gaps in hypertension and diabetes awareness in Bangladesh, published in BMC Public Health in July 2026. See my full publication list for details.