More Updates…

2020

  • Presenting our work on connecting sample design and generalization in ML in Neurips 2020. [paper]
  • Work on using prediction calibration as a training objective for building regression models in scientific problems published in Nature Communications. [paper]
  • Need to build chest X-ray based diagnostic models with limited data? Check out our recently accepted SPIE paper!! [preprint]
  • DDxNet, a general deep architecture for time-varying clinical data (ECG/EEG/EHR), published in Nature Scientific Reports. [paper] [code]
  • Received the LLNL Director’s 2020 Early Career Recognition award.
  • Our paper on “A Statistical Mechanics Framework for Task-Agnostic Sample Design” accepted at Neurips 2020.
  • Invited Talk at the NNSA Next-Gen AI for Proliferation Detection Meeting on AI explainability [slides]
  • DOE Proposal on the integration of knowledge graphs into predictive modeling awarded.
  • Our paper on using prediction calibration to obtain reliable models in healthcare AI accepted for presentation at the UNSURE workshop, MICCAI 2020.
  • Paper on Function-preserving linear projections (FPP) for high dimensional scientific data accepted for publication in the journal Machine Learning: Science and Technology. [code]
  • Our paper on unsupervised audio source separation using GAN priors accepted for presentation at Interspeech 2020. [code]
  • Presented our paper on Task-agnostic sample design in the Workshop on Real World Experiment Design and Active Learning at ICML 2020.
  • News article about our recent PNAS paper on using deep learning for surrogate modeling in scientific applications.
  • Feature article on AI-based analysis of clinical diagnosis models and COVID-19 infections.
  • New podcast altert: DataSkeptic podcast where I talked about some recent work on interpretability in healthcare AI [Apple] [Spotify].
  • A preprint of our work at LLNL on designing accurate emulators for scientific processes that is currently under consideration at Nature Communications.
  • A new approach to build reliable and interpretable deep models for Healthcare AI. In collaboration with IBM Research and Arizona State University.
  • Paper on building accurate neural network surrogates for inertial confinement fusion published in Proceedings of the National Academy of Sciences. [code]
  • Coverage-based designs for hyper-parameter optimization in neural nets accepted for publication at IEEE Transactions on Neural Networks and Learning Systems.
  • Our paper on MimicGAN, an easy and effective way to robustly project onto the image manifold, is accepted to IJCV Special Issue on GANs.
  • Listen to our presentation on Learn-by-Calibrating at IEEE ICASSP 2020.
  • New paper alert: How does prediction calibration affect the performance of lottery tickets? Our new paper answers this question!
  • A regularized GAT model for robust semi-supervised learning under node injection attacks accepted as an oral talk at ICASSP 2020.
  • Our paper Learn-by-calibrating that explores the use of a prior-free calibration objective for training regression models accepted at ICASSP 2020.
  • At AAAI presenting our work on building calibrated deep models for regression, time-series forecasting and object localization [poster].

Prior to 2020