| Rapid calibration of an intracortical brain–computer interface for people with tetraplegia DM Brandman, T Hosman, J Saab, MC Burkhart, BE Shanahan, ... Journal of neural engineering 15 (2), 026007, 2018 | 126 | 2018 |
| Measurement and verification of building systems under uncertain data: A Gaussian process modeling approach MC Burkhart, Y Heo, VM Zavala Energy and Buildings 75, 189-198, 2014 | 74 | 2014 |
| Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review R Borchert, T Azevedo, A Badhwar, J Bernal, M Betts, R Bruffaerts, ... Alzheimer's & Dementia 19 (12), 5885-5904, 2023 | 31 | 2023 |
| The discriminative Kalman filter for Bayesian filtering with nonlinear and nongaussian observation models MC Burkhart, DM Brandman, B Franco, LR Hochberg, MT Harrison Neural computation 32 (5), 969-1017, 2020 | 27* | 2020 |
| Robust closed-loop control of a cursor in a person with tetraplegia using Gaussian process regression DM Brandman, MC Burkhart, J Kelemen, B Franco, MT Harrison, ... Neural computation 30 (11), 2986-3008, 2018 | 24 | 2018 |
| Deep Low-Density Separation for Semi-Supervised Classification MC Burkhart, K Shan International Conference on Computational Science, 297-311, 2020 | 9 | 2020 |
| A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding MC Burkhart Brown University, 2019 | 7 | 2019 |
| Digital Experience Enhancement Using An Ensemble Deep Learning Model MC Burkhart, K Modarresi US Patent 11,816,562, 2023 | 4 | 2023 |
| Neuroevolutionary representations for learning heterogeneous treatment effects MC Burkhart, G Ruiz Journal of Computational Science 71, 102054, 2023 | 2 | 2023 |
| Predicting incident dementia in cerebral small vessel disease: comparison of machine learning and traditional statistical models R Li, EL Harshfield, S Bell, M Burkhart, AM Tuladhar, S Hilal, DJ Tozer, ... Cerebral Circulation-Cognition and Behavior 5, 100179, 2023 | 2 | 2023 |
| Neuroevolutionary Feature Representations for Causal Inference MC Burkhart, G Ruiz International Conference on Computational Science, 3-10, 2022 | 2 | 2022 |
| Linear Transformations & the Multivariate Generating Function MC Burkhart arXiv preprint arXiv:1209.3357, 2012 | 2 | 2012 |
| Mercurial signatures implementation. Github (2020) M Burkhart | 2 | |
| Causal inference via neuroevolutionary selection MC Burkhart, G Ruiz US Patent App. 17/748,891, 2023 | 1 | 2023 |
| Conjugacy conditions for supersoluble complements of an abelian base and a fixed point result for non-coprime actions MC Burkhart Proceedings of the Edinburgh Mathematical Society 65 (4), 1075-1079, 2022 | 1 | 2022 |
| Determining Adaptive Loss Functions and Algorithms for Predictive Models MC Burkhart, K Modarresi International Conference on Computational Science, 595-607, 2019 | 1 | 2019 |
| Conditional Probabilities of Multivariate Poisson Distributions MC Burkhart arXiv preprint arXiv:1209.4957, 2012 | 1 | 2012 |
| Unsupervised multimodal modeling of cognitive and brain health trajectories for early dementia prediction MC Burkhart, LY Lee, D Vaghari, AQ Toh, E Chong, C Chen, P Tiňo, ... Scientific Reports 14 (1), 10755, 2024 | | 2024 |
| AI‐guided patient stratification for neurodegenerative disorders using unsupervised trajectory modelling MC Burkhart, LY Lee, D Vaghari, J Venton, S Thomas, N Smith, ... Alzheimer's & Dementia 19, e074696, 2023 | | 2023 |
| Validating the clinical utility of AI‐guided tools for early dementia prediction D Vaghari, LY Lee, MC Burkhart, M Montagnese, K Zuhlsdorff, J Giorgio, ... Alzheimer's & Dementia 19, e074628, 2023 | | 2023 |
