Machine Learning Research Staff
Current- Researching out-of-distribution and adversarial robustness, dynamic neural network adaptation for robustness (to out-of-distribution data) and compression, and catastrophic forgetting; Published at NeurIPS 2022
- Designed neural network pruning and quantization experiments yielding 60× memory and 4× latency reduction on edge device (NVIDIA Jetson) while maintaining 95% of test performance compared to baseline
- Proposed LDRD exploratory research project on gradient-free deep learning and secured funding for team of 4 researchers for 3 years (as Principal Investigator)
- Managing project with team of 4 LLNL research staff, 2 university subcontracts, and 2 summer interns
- Awarded LLNL Deputy Director Science and Technology (DDS&T) Publication Excellence Award