Danielle Maddix Robinson
Senior Applied Scientist, AWS AI Labs, PhD in Computational and Mathematical Engineering, Stanford University
- Bay Area, California
- Github
- Google Scholar
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Efficiently serve dozens of fine-tuned models with vLLM on Amazon SageMaker AI and Amazon Bedrock
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See our blog on AWS and on vLLM on our optimizations in vLLM for efficient multi-LoRA on MoE models, e.g., GPT-OSS and Qwen3-MoE.
Science in the Age of Foundation Models
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Published:
Featured perspective piece on how foundation models can be beneficial in scientific domains and their current limitations.
Mitra: Mixed synthetic priors for enhancing tabular foundation models
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Published:
Featured blog by Amazon Science on our tabular foundation model Mitra, which is trained purely on synthetic data and obtains state-of-the-art performance on classification and regression tasks. Mitra is available in AutoGluon 1.4, and we have also released the weights on HuggingFace. Find the Mitra classifier here and regressor here.
Physics-constrained machine learning for scientific computing
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Published:
Featured blog by Amazon Science on the research efforts that I am leading on physics-constrained machine learning for scientific computing and computational sciences.