- M. Burkhart, B. Ramadan, L. Solo, W. Parker, & B. Beaulieu-Jones, Quantifying surprise in clinical care: detecting highly informative events in electronic health records with foundation models, Pacific Symposium on Biocomputing 31 (2026) pdf
- S. Sethi, D. Chen, M. Burkhart, N. Bhandari, B. Ramadan, & B. Beaulieu-Jones, Prototype learning to create refined interpretable digital phenotypes from ECGs, Pacific Symposium on Biocomputing 31 (2026) pdf
- B. Ramadan, M.-C. Liu, M. Burkhart, W. Parker, & B. Beaulieu-Jones, Diagnostic codes in AI prediction models and label leakage of same-admission clinical outcomes, JAMA Network Open 8 (2025) pdf
- S. Sethi, D. Chen, T. Statchen, M. Burkhart, N. Bhandari, B. Ramadan, & B. Beaulieu-Jones, ProtoECGNet: case-based interpretable deep learning for multi-label ECG classification with contrastive learning, Proceedings of the 10th Machine Learning for Healthcare Conference, PMLR 298 (2025) pdf
- M. Burkhart, Actions of nilpotent groups on nilpotent groups, Glasgow Mathematical Journal 67 (2025) MR4883973 pdf
- M. Burkhart, Fixed point conditions for non-coprime actions, Proceedings of the Royal Society of Edinburgh Section A: Mathematics 155 (2025) MR4899574 pdf
- E. G. Woo, M. Burkhart, E. Alsentzer, & B. Beaulieu-Jones, Synthetic data distillation enables the extraction of clinical information at scale, npj Digital Medicine 8 (2025) pdf
- M. Burkhart, L. Lee, D. Vaghari, A. Toh, E. Chong, C. Chen, P. Tiňo, & Z. Kourtzi, Unsupervised multimodal modeling of cognitive and brain health trajectories for early dementia prediction, Scientific Reports 14 (2024) pdf
- L. Lee, D. Vaghari, M. Burkhart, P. Tiňo, M. Montagnese, Z. Li, K. Zühlsdorff, J. Giorgio, G. Williams, E. Chong, C. Chen, B. Underwood, T. Rittman, & Z. Kourtzi, Robust and interpretable AI-guided marker for early dementia prediction in real-world clinical settings, eClinicalMedicine 74 (2024) pdf
- M. Burkhart & G. Ruiz, Neuroevolutionary representations for learning heterogeneous treatment effects, Journal of Computational Science 71 (2023) pdf
- M. Burkhart, Discriminative Bayesian filtering lends momentum to the stochastic Newton method for minimizing log-convex functions, Optimization Letters 17 (2023) MR4557438 pdf
- R. Li, E. Harshfield, S. Bell, M. Burkhart, A. Tuladhar, …, C. Chen, Z. Kourtzi, & H. Markus, Predicting incident dementia in cerebral small vessel disease: comparison of machine learning and traditional statistical models, Cerebral Circulation – Cognition and Behavior 5 (2023) pdf
- R. Borchert, T. Azevedo, A. Badhwar, J. Bernal, M. Betts, R. Bruffaerts, M. Burkhart, I. Dewachter, …, D. Llewellyn, M. Veldsman, & T. Rittman, Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: a systematic review, Alzheimer's & Dementia 19 (2023) pdf
- M. Burkhart, Conjugacy conditions for supersoluble complements of an abelian base and a fixed point result for non-coprime actions, Proceedings of the Edinburgh Mathematical Society 65 (2022) MR4542651 pdf
- M. Burkhart & G. Ruiz, Neuroevolutionary feature representations for causal inference, Computational Science – ICCS 2022 pdf
- M. Burkhart, Discriminative Bayesian filtering for the semi-supervised augmentation of sequential observation data, Computational Science – ICCS 2021 MR4371656 pdf
- M. Burkhart & K. Shan, Deep low-density separation for semi-supervised classification, Computational Science – ICCS 2020 MR4152505 pdf
- M. Burkhart, D. Brandman, B. Franco, L. Hochberg, & M. Harrison, The discriminative Kalman filter for Bayesian filtering with nonlinear and nongaussian observation models, Neural Computation 32 (2020) MR4101168 pdf
- M. Burkhart & K. Modarresi, Determining adaptive loss functions and algorithms for predictive models, Computational Science – ICCS 2019 pdf
- M. Burkhart & K. Modarresi, Adaptive objective functions and distance metrics for recommendation systems, Computational Science – ICCS 2019 MR3975427 pdf
- D. Brandman, M. Burkhart, J. Kelemen, B. Franco, M. Harrison, & L. Hochberg, Robust closed-loop control of a cursor in a person with tetraplegia using Gaussian process regression, Neural Computation 30 (2018) MR3873814 pdf
- D. Brandman, T. Hosman, J. Saab, M. Burkhart, B. Shanahan, J. Ciancibello, …, M. Harrison, J. Simeral, & L. Hochberg, Rapid calibration of an intracortical brain computer interface for people with tetraplegia, Journal of Neural Engineering 15 (2018) pdf
- M. Burkhart, Y. Heo, & V. Zavala, Measurement and verification of building systems under uncertain data: a Gaussian process modeling approach, Energy and Buildings 75 (2014) pdf
- I. Lee, L. Solo, M. Burkhart, B. Ramadan, W. Parker, & B. Beaulieu-Jones, Representation before training: a fixed-budget benchmark for generative medical event models, arXiv:2604.16775 pdf
- L. Solo, M. McDermott, W. Parker, B. Ramadan, M. Burkhart, & B. Beaulieu-Jones, Efficient generative prediction for EHR foundation models: the SCOPE and REACH estimators, arXiv:2602.03730 pdf
- N. Hahn, E. Stein, BrainGate Consortium (128 named members including M. Burkhart, D. Brandman, E. Brown, & M. Harrison), J. Donoghue, J. Simeral, L. Hochberg, & F. Willett, Long-term performance of intracortical microelectrode arrays in 14 BrainGate clinical trial participants, medRxiv:2025.07.02.25330310 pdf
- M. Burkhart, B. Ramadan, Z. Liao, K. Chhikara, J. Rojas, W. Parker, & B. Beaulieu-Jones, Foundation models for electronic health records: representation dynamics and transferability, arXiv:2504.10422 pdf
- M. Abroshan, M. Burkhart, O. Giles, S. Greenbury, Z. Kourtzi, J. Roberts, M. van der Schaar, J. Steyn, A. Wilson, & M. Yong, Safe AI for health and beyond – monitoring to transform a health service, arXiv:2303.01513 pdf
- M. Burkhart, Linear transformations & the multivariate generating function, arXiv:1209.3357 pdf
- M. Burkhart & G. Ruiz, Causal inference via neuroevolutionary selection, U.S. Patent 12,602,589 filed 2022, granted 2026 pdf
- M. Burkhart & K. Shan, User classification from data via deep segmentation for semi-supervised learning, U.S. Patent 11,455,518 filed 2019, granted 2022 pdf
- M. Burkhart & K. Modarresi, Digital experience enhancement using an ensemble deep learning model, U.S. Patent 11,816,562 filed 2019, granted 2023 pdf