• 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 
• 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, 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 
• 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 

• 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 & 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 

• M. Burkhart. Fixed point conditions for non-coprime actions. Proceedings of the Royal Society of Edinburgh Section A: Mathematics (to appear) 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 
• R. Li, E. Harshfield, S. Bell, M. Burkhart, A. Tuladhar, S. Hilal, D. Tozer, F. Chappell, S. Makin, J. Lo, J. Wardlaw, F.-E. de Leeuw, 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 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 pdf 

• M. Burkhart. “A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding.” Ph.D. Dissertation, Brown University, Division of Applied Mathematics (2019) MR4158190  pdf 

Patents & Pending

• M. Burkhart & G. Ruiz. Causal inference via neuroevolutionary selection. U.S. Patent Application #17/748,891. Filed 2022. Published as US 2023/0376776 A1 pdf 
• M. Burkhart & K. Shan. User classification from data via deep segmentation for semi-supervised learning. U.S. Patent Application #16/681,239. Filed 2019. Published as US 2021/0142152 A1 pdf   Granted 2022 as US 11,455,518 B2 pdf 
• M. Burkhart & K. Modarresi. Digital experience enhancement using an ensemble deep learning model. U.S. Patent Application #16/375,627. Filed 2019. Published as US 2020/0320382 A1 pdf   Granted 2023 as US 11,816,562 B2 pdf