Daniel Egert
Postdoctoral Scientist at University of California, San Francisco
San Francisco, California, United States
Summary
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RockstarDaniel Egert is a postdoctoral scientist based in San Francisco with nine years of experience applying machine learning to research problems. Describing himself as a "machine learning worker bee," he blends hands-on model development with rigorous scientific inquiry at the University of California, San Francisco. His background suggests a strong focus on translating experimental insights into reproducible ML workflows and publications. Comfortable operating at the intersection of academia and engineering, he likely contributes to both codebases and collaborative research projects. Colleagues can expect a pragmatic, detail-oriented researcher who prioritizes reproducibility and incremental impact.
9 years of coding experience
Github Skills (102)
python10
text-to-speech10
automatic-speech-recognition10
deep-learning10
speech10
gpu10
gpu-acceleration10
machine-translation10
multimodal10
numpy10
audio10
generative-ai10
ai10
neural-network10
tensor10
Programming languages (8)
Github contributions (5)
trias702/NeMo
Apr 2022 - Sep 2023
NeMo: a toolkit for conversational AI
Contributions:144 pushes, 26 branches in 1 year 5 months
nlpconversationalconversational-aichatbotnemo
trias702/apex
Apr 2021 - Sep 2023
A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch
Contributions:10 pushes, 1 branch in 2 years 5 months
pytorchmixed-precisiondeep-learningdistributed-trainingtraining
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