Machine Learning

Related Faculty

Dale R. Durran
Adjunct Professor of Applied Mathematics
Professor of Atmospheric Sciences
Megan Ebers
Postdoctoral Scholar
Bamdad Hosseini
Assistant Professor
J. Nathan Kutz
Robert Bolles and Yasuko Endo Professor
Tim Leung
Boeing Endowed Professor
Director of Computational Finance and Risk Management Program
Krithika Manohar
Adjunct Assistant Professor of Mechanical Engineering
Armita Nourmohammad
Adjunct Associate Professor in Applied Mathematics
Associate Professor in Physics

Related Research

Le, V., Nagpal, C., & Dubrawski, A. (2023). Identification of patients with stable coronary artery disease who benefit from ACE inhibitors using Cox mixture model for heterogeneous treatment effects. Journal of Critical Care, 74, 154208.
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.
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.
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.
Liu, Y.H., Smith, S.J., Mihalas S., Shea-Brown E., Sumbul U., “Cell-type–specific neuromodulation guides synaptic credit assignment in a spiking neural network”, Proceedings of the National Academy of Sciences, 2021.
Liu, Y.H., Smith, S.J., Mihalas S., Shea-Brown E., Sumbul U., “A solution to temporal credit assignment using cell-type-specific modulatory signals”, bioRxiv, 2020.
Ahlstrom, Austin, "Computational Regiospecific Analysis of Brain Lipidomic Profiles" (2019). Undergraduate Honors Theses. 70. https://scholarsarchive.byu.edu/studentpub_uht/70
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).