“Reconciling Set-Valued Policy & Dead-End Discovery in Healthcare Reinforcement Learning: An Empirical Analysis” was accepted to the Pacific Symposium on Biocomputing 2027.
Evaluating Digital Twins for Type 1 Diabetes by Decision Quality
An evaluation protocol that scores a Type 1 diabetes digital twin by whether its glucose trajectories rank candidate insulin treatments as the real patient would, not by trajectory error alone. On 30 UVA/Padova virtual patients under 21 treatments, the two criteria disagree: the worst-RMSE twin ranks second best, and a twin with half the RMSE ranks at chance.
Physiological Identifiability of Type 1 Diabetes Digital Twins Under Behavioral Heterogeneity
A study of when the physiological parameters of a Type 1 diabetes digital twin can actually be recovered from observed data, and how behavioral variation across patients limits that identifiability.
Gumbel-Based Active Sparse Mobile Crowd Sensing with Time Series Transformer
A learned Gumbel-noise sensor selection layer paired with a time series transformer, reducing reconstruction error on missing sensor data by up to 28% on Urban Air and SensorScope St-Bernard datasets.
Ensemble Learning with Early Fusion of Kernel-Transformed and Classical Electrocardiogram Features for Chagas Disease Detection
Ensemble framework over AutoGluon, ECG-FM, FFT, and wavelet features for 12-lead ECG classification. Placed 39th in the 2025 George B. Moody PhysioNet Challenge.
CCQ: A Multi-State Child Care Quality Dataset to Support AI for Children’s Health Research
A de-identified dataset of 64,479 child care providers across 12 U.S. states, curated from state QRIS portals by an LLM pipeline and released on Hugging Face, with within-state and leave-one-state-out benchmarks over tabular models and language models.
Reconciling Set-Valued Policy & Dead-End Discovery in Healthcare Reinforcement Learning: An Empirical Analysis
An empirical study of how consistently Set-Valued Policies and Dead-End Discovery agree on clinician-in-the-loop sepsis treatment, plus a partial ordering over recommended actions. Evaluated on LifeGate and MIMIC-III.
Patched Forecasting with Gumbel-Based Selector for Sparse Mobile Crowd Sensing
A follow-up extended abstract that convolutionally splits each sensing cycle into patches, each with its own selector layer, testing whether intra-cycle structure improves sensor selection.
Softening Overly Demanding Requirements in Recommendation System
Cuts item under-recommendation bias and training cost in Debiased Bayesian Personalized Ranking by replacing the adversarial debiasing network with an autoencoder plus ranking post-processing.
- Sep 2026 SVP-DeD paper accepted to PSB '27
- Apr 2026 — Type 1 diabetes digital-twin benchmark
A benchmark that scores Type 1 diabetes digital twins by decision quality — whether a twin ranks candidate insulin treatments the way the real patient would — rather than by trajectory error alone. The two criteria disagree: the worst-RMSE twin ranked treatments second best, while a twin with half the RMSE ranked at chance. We published a preprint on arXiv in Oct 2026.
- Oct 2025 — Child care quality dataset
CCQ, a de-identified dataset of 64,479 child care providers across 12 U.S. states, curated from state QRIS portals by an LLM pipeline and released on Hugging Face. Benchmarks show tree models win within-state, while zero-shot transfer to a new state lands near chance. We published a preprint on arXiv in Oct 2026.