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Published in Engineering in Medicine and Biology Conference, 2020
This paper outlines the pipeline for semi-automatically localizing and labeling SEEG depth electrodes in 3D images of brain volumes. Fully automating this task is left to future work.
Recommended citation: Huynh C, Li, A Gonzalez-Martinez J, Sarma SV. Towards Automatic Localization and Anatomical Labeling of Intracranial Depth Electrodes in Brain Images. Conf Proc IEEE Eng Med Biol Soc. 2020. https://sarmalab.icm.jhu.edu/publications/
Published in Nature Neuroscience, 2021
This paper proposes a computational statistic – neural fragility – for determining seizure onset zones in epilepsy patients.
Recommended citation: Li, A., Huynh, C., Fitzgerald, Z. et al. Neural fragility as an EEG marker of the seizure onset zone. Nat Neurosci 24, 1465–1474 (2021). https://doi.org/10.1038/s41593-021-00901-w
Published in arXiv Machine Learning, 2021
This paper proposes a manifold-aware variation of the random forest algorithm for structured data, which has empirically demonstrated high statistical efficiency relative to deep neural networks.
Recommended citation: Perry, R., Li, A., Huynh, C. et al. Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks. arXiv Machine Learning (2019). https://doi.org/10.48550/arXiv.1909.11799
Published in arXiv Machine Learning, 2022
This paper studies the analytical properties of the rank-one perturbation matrices in linear dynamical systems. This provides additional analysis on neural fragility, which is a recently proposed model by Li et al. (Nature Neuroscience, 2021) predicting surgical outcome for epilepsy patients.
Recommended citation: Li, A., Huynh, C.. Analysis of Neural Fragility: Bounding the Norm of a Rank-One Perturbation Matrix (2022). https://doi.org/10.48550/arXiv.2202.07026
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Tutoring, Johns Hopkins University, Office of Academic Support, 2018
PILOT Leader for 030.205 Organic Chemistry I (Fall 2018) and 553.171 Discrete Math (Spring 2019).
Undergraduate course, Johns Hopkins University, Applied Math & Statistics Department, 2019
Teaching assistant for 553.420/620 Introduction to Probability taught by Prof. John Wierman in Fall 2019.
Undergraduate course, Johns Hopkins University, Computer Science Department, 2020
Course assistant for 601.464 Artificial Intelligence taught by Prof. Adam Poliak in Spring 2020.
Undergraduate course, Johns Hopkins University, Applied Math & Statistics Department, 2020
Teaching assistant for 553.430/630 Introduction to Statistics taught by Prof. Avanti Athreya in Fall 2020.
Undergraduate course, Johns Hopkins University, Computer Science Department, 2021
Course assistant for 601.464 Artificial Intelligence taught by Prof. Musad Haque in Spring 2021.