Toward Detection and Monitoring of Gait Pathology using Inertial Sensors under Rotation, Scale, and Offset Invariant Dynamic Time Warping. - Matthew Engelhard; Sriram Raju Dandu; John C. Lach; Myla D. Goldman; Stephen D. Patek (BODYNETS, 2015)

Correlations between Inertial Body Sensor Measures and Clinical Measures in Multiple Sclerosis. - Jiaqi Gong; Matthew Engelhard; Myla D. Goldman; John C. Lach (BODYNETS, 2015)

Determining physiological significance of inertial gait features in multiple sclerosis. - Sriram Raju Dandu; Matthew Engelhard; Myla D. Goldman; John C. Lach (BSN, 2016)

Adaptive symptom reporting for mobile patient-reported disability assessment. - Matthew M. Engelhard; John C. Lach; Karen M. Schmidt; Myla D. Goldman; Stephen D. Patek (Wireless Health, 2016)

Relationship between kernel density function estimates of gait time series and clinical data. - Asma Qureshi; Maïté Brandt-Pearce; Matthew M. Engelhard; Myla D. Goldman (BHI, 2017)

Demonstrating the real-world significance of the mid-swing to heel strike part of the gait cycle using spectral features. - Asma Qureshi; Matthew M. Engelhard; Maïté Brandt-Pearce; Myla D. Goldman (BSN, 2017)

Remotely engaged: Lessons from remote monitoring in multiple sclerosis. - Matthew M. Engelhard; Stephen D. Patek; Kristina Sheridan; John C. Lach; Myla D. Goldman (Int. J. Medical Informatics, 2017)

Predicting Smoking Events with a Time-Varying Semi-Parametric Hawkes Process Model. - Matthew Engelhard; Hongteng Xu; Lawrence Carin; Jason A. Oliver; Matthew Hallyburton; F. Joseph McClernon (MLHC, 2018)

Understanding the Physiological Significance of Four Inertial Gait Features in Multiple Sclerosis. - Sriram Raju Dandu; Matthew M. Engelhard; Asma Qureshi; Jiaqi Gong; John C. Lach; Maïté Brandt-Pearce; Myla D. Goldman (IEEE J. Biomed. Health Informatics, 2018)

A Generic Algorithm for Sleep-Wake Cycle Detection using Unlabeled Actigraphy Data. - Shanshan Chen; Robert Perera; Matthew M. Engelhard; Jessica R. Lunsford-Avery; Scott H. Kollins; Bernard F. Fuemmeler (BHI, 2019)

Neural Conditional Event Time Models. - Matthew Engelhard; Samuel Berchuck; Joshua D'Arcy; Ricardo Henao (MLHC, 2020)

Digital envirotyping: quantifying environmental determinants of health and behavior. - Matthew M. Engelhard; Jason A. Oliver; F. Joseph McClernon (npj Digit. Medicine, 2020)

SpanPredict: Extraction of Predictive Document Spans with Neural Attention. - Vivek Subramanian; Matthew Engelhard; Samuel Berchuck; Liqun Chen 001; Ricardo Henao; Lawrence Carin (NAACL-HLT, 2021)

Disentangling Whether from When in a Neural Mixture Cure Model for Failure Time Data. - Matthew Engelhard; Ricardo Henao (AISTATS, 2022)

On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models. - Boyao Li; Alexandar J. Thomson; Matthew M. Engelhard; David Page (CoRR, 2023)

Improving Event Time Prediction by Learning to Partition the Event Time Space. - Jimmy Hickey; Ricardo Henao; Daniel Wojdyla; Michael J. Pencina; Matthew M. Engelhard (CoRR, 2023)

Enhancing early autism prediction based on electronic records using clinical narratives. - Junya Chen; Matthew Engelhard; Ricardo Henao; Samuel Berchuck; Brian Eichner; Eliana M. Perrin; Guillermo Sapiro; Geraldine Dawson (J. Biomed. Informatics, 2023)

Adaptive Discretization for Event PredicTion (ADEPT). - Jimmy Hickey; Ricardo Henao; Daniel Wojdyla; Michael J. Pencina; Matthew Engelhard (AISTATS, 2024)

MALADE: Orchestration of LLM-powered Agents with Retrieval Augmented Generation for Pharmacovigilance. - Jihye Choi; Nils Palumbo; Prasad Chalasani; Matthew M. Engelhard; Somesh Jha; Anivarya Kumar; David Page (MLHC, 2024)

On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models. - Boyao Li; Alexander Thomson; Houssam Nassif; Matthew Engelhard; David Page (NeurIPS, 2024)

Common Event Tethering to Improve Prediction of Rare Clinical Events. - Quinn Lanners; Qin Weng; Marie-Louise Meng; Matthew M. Engelhard (UAI, 2024)

A conditional multi-label model to improve prediction of a rare outcome: An illustration predicting autism diagnosis. - Wei A. Huang; Matthew Engelhard; Marika Coffman; Elliot D. Hill; Qin Weng; Abby Scheer; Gary Maslow; Ricardo Henao; Geraldine Dawson; Benjamin Alan Goldstein (J. Biomed. Informatics, 2024)

Incorporating informatively collected laboratory data from EHR in clinical prediction models. - Minghui Sun; Matthew M. Engelhard; Armando Bedoya; Benjamin Goldstein 001 (BMC Medical Informatics Decis. Mak., 2024)

IRIS: Interpretable Retrieval-Augmented Classification for Long Interspersed Document Sequences. - Fengnan Li; Elliot D. Hill; Jiang Shu; Jiaxin Gao; Matthew M. Engelhard (ACL (1), 2025)

Predicting Partially Observed Long-Term Outcomes with Adversarial Positive-Unlabeled Domain Adaptation. - Mengying Yan; Meng Xia; Wei Angel Huang; Chuan Hong; Benjamin Goldstein 001; Matthew M. Engelhard (CHIL, 2025)

Exploring trade-offs in equitable stroke risk prediction with parity-constrained and race-free models. - Matthew Engelhard; Daniel Wojdyla; Haoyuan Wang; Michael J. Pencina; Ricardo Henao (Artif. Intell. Medicine, 2025)

Balancing Interpretability and Flexibility in Modeling Diagnostic Trajectories with an Embedded Neural Hawkes Process Model. - Yuankang Zhao; Matthew Engelhard (CoRR, 2025)

Infinite hierarchical contrastive clustering for personal digital envirotyping. - Ya-Yun Huang; F. Joseph McClernon; Jason A. Oliver; Matthew M. Engelhard (CoRR, 2025)

CLEAR: Unlearning Spurious Style-Content Associations with Contrastive LEarning with Anti-contrastive Regularization. - Minghui Sun; Benjamin Alan Goldstein; Matthew M. Engelhard (CoRR, 2025)