| Bradley C. Naylor, Christian N. K. Anderson, Marcus Hadfield, David H. Parkinson, Austin Ahlstrom, Austin Hannemann, Chad R. Quilling, Kyle J. Cutler, Russell L. Denton, Robert Adamson, Thomas E. Angel, Rebecca S. Burlett, Paul S. Hafen, John. C. Dallon, Mark K. Transtrum, Robert D. Hyldahl, and John C. Price.
"Utilizing Nonequilibrium Isotope Enrichments to Dramatically Increase Turnover Measurement Ranges in Single Biopsy Samples from Humans" (Sep 2022).
Journal of Proteome Research 2022 21 (11), 2703-2714.
DOI: 10.1021/acs.jproteome.2c00380.
Link: https://pubs.acs.org/doi/10.1021/acs.jproteome.2c00380 |
Publications, Articles |
Applied Mathematics, Biochemistry |
| Liu, Y.H., Smith, S., Mihalas S., Shea-Brown E., Sumbul U., “Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators”, Advances in Neural Information Processing Systems, 2022. |
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Applied Mathematics, Computer Science, Machine Learning, Mathematical Neuroscience |
| Angelini E, Wang Y, Zhou JX, Qian H, Huang S (2022) A model for the intrinsic limit of cancer therapy: Duality of treatment-induced cell death and treatment-induced stemness. PLoS Comput Biol 18(7): e1010319. https://doi.org/10.1371/journal.pcbi.1010319 |
Publications |
Applied Mathematics, Cancer Modeling, Dynamical Systems, Mathematical Biology |
| Liu, Y.H., Ghosh A., Richards B. A., Shea-Brown E., Lajoie G., “Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules”, Advances in Neural Information Processing Systems, 2022. |
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Applied Linear Algebra, Applied Mathematics, Computer Science, Dynamical Systems, Machine Learning, Mathematical Neuroscience |
| Maass, K., Aravkin, A., & Kim, M. (2021). A feasibility study of a hyperparameter tuning approach to automated inverse planning in radiotherapy. arXiv preprint arXiv: arXiv:2105.07024. |
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Applied Mathematics, Cancer Modeling, Machine Learning, Optimization and Variational Analysis |
| Maass, K., Kim, M., & Aravkin, A. (2020). A nonconvex optimization approach to IMRT planning with dose-volume constraints. arXiv preprint arXiv:1907.10712. |
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Applied Mathematics, Cancer Modeling, Inverse Problems, Optimization and Variational Analysis |
| Ahlstrom, Austin, "Computational Regiospecific Analysis of Brain Lipidomic Profiles" (2019). Undergraduate Honors Theses. 70. https://scholarsarchive.byu.edu/studentpub_uht/70 |
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Applied Mathematics, Biochemistry, Machine Learning, Mathematical Biology |
| Maass, K., & Kim, M. (2019). A Markov decision process approach to optimizing cancer therapy using multiple modalities. Mathematical Medicine and Biology: A Journal of the IMA. doi:10.1093/imammb/dqz004. |
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Applied Mathematics, Cancer Modeling, Optimization and Variational Analysis |
| D. Rim, S. Moe, and R. J. LeVeque, Transport Reversal for Model Reduction of Hyperbolic Partial Differential Equations, SIAM/ASA Journal on Uncertainty Quantification 6(2018), pp. 118-150. doi:10.1137/17M1113679 |
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Applied Mathematics, Computational Methods, Numerical Analysis, Reduced-Order Models, Scientific Computing |
| Mark Kot. (2014) A First Course in the Calculus of Variations. American Mathematical Society. |
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Applied Mathematics |
| Ka-Kit Tung. (2007) Topics in Mathematical Modeling. Princeton University Press |
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Applied Mathematics |
| Randall LeVeque. (2007) Finite Difference Methods for Ordinary and Partial Differential Equations. SIAM. |
Publications, Books |
Applied Mathematics, Mathematical Methods, Partial Differential Equations |
| Randall LeVeque. (2002) Finite Volume Methods for Hyperbolic Problems. Cambridge University Press. |
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Applied Mathematics, Mathematical Methods |
| Mark Kot. (2001) Elements of Mathematical Ecology. Cambridge University Press. |
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Applied Mathematics, Mathematical Ecology |
| Randall LeVeque, D. Mihalas, E. Dorfi, E. Mueller. (1998) Computational Methods for Astrophysical Fluid Flow. Springer-Verlag. |
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Applied Mathematics, Computational Methods |
| Randall LeVeque. (1990) Numerical Methods for Conservation Laws. Birkhauser-Verlag Basel. |
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Applied Mathematics |
| Anne Greenbaum. (1987) Iterative Methods for Solving Linear Systems. SIAM. |
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Applied Mathematics |
| Liu, Y.H., Baratin A., Cornford J., Mihalas S., Shea-Brown E., Lajoie G., “How connectivity structure shapes rich and lazy learning in neural circuits”, International Conference on Learning Representations (ICLR), 2024 (accepted). |
Publications, Articles |
Applied Mathematics, Computer Science, Machine Learning, Mathematical Neuroscience |
