I am a Machine Learning/AI researcher in the Machine Intelligence group at Lawrence Livermore National Laboratories. My research interest broadly spans machine learning, statistics and signal processing for applications in computer vision, healthcare, scientific machine learning and network analysis. I collaborate with several researchers and practitioners to enable the use of machine learning and AI technologies to solve challenging real-world problems.
And yes… you can call me Jay.
A quick peek into my research in machine learning
- Three papers accepted to AAAI 2021 — robust explanations via loss estimation [preprint], uncertainty matching Graph Neural Networks [preprint] and attribute-guided adversarial augmentation [preprint].
- 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]
- New paper alert: Need to build chest X-ray based diagnostic models with limited data? Check out our recently accepted SPIE paper!! [preprint]
- New paper alert: 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.
- New paper alert: Our paper on “A Statistical Mechanics Framework for Task-Agnostic Sample Design” accepted at Neurips 2020.
- Invited Talk at the 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]