Technical Reports

  • High-dimensional spectral sampling [report]
  • Uncertainty quantification in scientific machine learning [report]


  • 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


  • 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]


  • 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]