Technical Reports
- High-dimensional spectral sampling [report]
- Uncertainty quantification in scientific machine learning [report]
Software
- Spectral Sampling Library: Designing uniform samples in high-dimensions for surrogate modeling and machine learning (coming soon)
- DDxNet: A Multi-Specialty Diagnostic Model for Clinical Time-Series
- GAN priors for unsupervised source separation
- MACC: Manifold and cyclical surrogates for scientific applications
- Function Preserving Projections: A scalable linear projection technique for HD analysis
- GrAMME: Semi-Supervised Learning using Multi-layered Graph Attention Models
- Deep metric learning pipelines for speaker diarization
Presentations
- Designing calibrated uncertainty estimators for deep models, DLCT (ML Collective) [Slides]
- Improving reliability and interpretability of AI models in clinical diagnosis, LLNL DSI AI in Healthcare Workshop [Slides]
- Building reliable and generalizable deep models using prediction calibration [Slides]
- Thoughts on building machine learning models with scientific data [Slides]
- Explaining real-word shifts and explaining under shifts [Slides]
- Understanding behavior of clinical models under domain shifts [Slides]
- Tutorial on unsupervised learning [Part 1] [Part 2] [Part 3]
- Teaching machines to be intelligent: Pitfalls, Challenges and Opportunities [Slides]
Posters
- Research overview of the CASC Machine Intelligence group [Poster]
- A spectral approach for the design of experiments in high dimensions [Poster]
- Designing Inverses for History Matching in Scientific Problems [Poster]
- Building Calibrated Deep Models Via Uncertainty Matching [Poster]
- Modeling Human Brain Connectomes using Structured Neural Networks [Poster]
- Understanding Behavior of Clinical Models under Domain Shifts [Poster]
- Improved Deep Embeddings for Inferencing with Multi-Layered Graphs [Poster]
- Unsupervised Feature Selection using a Blue Noise Graph Spectrum [Poster]
