Talks

ML=Machine Learning, BI=Biological Imaging

[ML] Learning Domain General Predictors
Simons Institute – Information-Theoretic Methods for Trustworthy Machine Learning, 2023. (invited).
[poster]

[ML, BI] Separating Neural Encoding and Decoding Pathways in fMRI by Disentangling Causal and Anticausal Mechanisms
University of Illinois at Urbana-Champaign Miniature Brain Machinery Retreat, 2022.

[ML, BI] PGM-augmented-ICA-AROMA: Denoising via probabilistic graphical model augmentation of ICA-AROMA
Beckman Institute Graduate Student Seminar, 2021. (invited).
University of Illinois at Urbana-Champaign Miniature Brain Machinery Retreat, 2021.
[video] [slides]

[ML] Exploiting Causal Chains for Domain Generalization
Conference on Neural Information Processing Systems (NeurIPS), Workshop on Distribution Shifts – Connecting Methods and Applications (DistShift), 2021.

[ML] Automated Incorporation of Machine Learning (AIM)
Sandia National Laboratories MARTIANS End of Summer Symposia, 2020. (invited).
[poster]

[ML] Interpretable Recurrent Convolutional Neural Networks for Cyber Alert Triaging
Sandia National Laboratories MARTIANS End of Summer Symposia, 2019. (invited).
[poster]