Papers
- 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) code 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) code pdf
- M. Burkhart, Discriminative Bayesian filtering lends momentum to the stochastic Newton method for minimizing log-convex functions, Optimization Letters 17 (2023) MR4557438 code 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 code 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
Preprints
- E. G. Woo, M. Burkhart, E. Alsentzer, & B. Beaulieu-Jones, Synthetic data distillation enables the extraction of clinical information at scale, medRxiv 2024.09.27.24314517 pdf
- M. Burkhart, Fixed point conditions for non-coprime actions, Proceedings of the Royal Society of Edinburgh Section A: Mathematics (in press) 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
Dissertation
- M. Burkhart, A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding, Ph.D. Dissertation, Division of Applied Mathematics, Brown University, Providence, 2019 MR4158190 pdf
Patents & Pending
- M. Burkhart & G. Ruiz, Causal inference via neuroevolutionary selection, U.S. Patent Application #17/748,891 filed 2022, published 2023 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