Selected Talks and Presentations

ML=Machine Learning, BI=Biological Imaging

[ML] Clued-in to Clueless: Navigating Distribution Shifts with Varying Levels of Target Distribution Information
Computer Science Seminar, Texas State University, 2024. (invited)
Institute for Foundations of Machine Learning (IFML), UT Austin, 2024. (invited).

[ML] Towards Externally Valid Evaluation of AI Systems
MobiliT.AI forum, 2024. (invited).
Machine Learning Seminar, University of Illinois at Urbana-Champaign, 2024. (invited).
Shah Lab, Stanford University, 2024.(invited).

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

[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.
[slides]

[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]