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I am working at the University of Freiburg, before that I worked 7 years at Amazon as a Senior Applied Scientist. 
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My research focuses on machine-learning, in particular in AutoML, LLMs, and probabilistic time-series forecasting. In the past, I also worked on Computational Topology and Geometry Processing.
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I deeply enjoy taking inspiration of real-problems to do academic research (and the other way around too!). Some of this research went into AWS services such as the forecasting service <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html">DeepAR</a>, 
<a href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning.html">AWS hyperparameter tuning service </a>
or more recently <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/inference-recommender.html">AWS SageMaker inference recommender</a>.
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I am also very keen on working on open-source, I am a core developer of <a href="https://github.com/syne-tune/syne-tune">Syne Tune</a> (Hyperparameter Optimization), I was also a core-developer of <a href="https://github.com/awslabs/gluon-ts">Gluon-ts</a> (time series forecasting), <a href="https://github.com/awslabs/datawig">Datawig</a> (data imputation) and <a href="https://gudhi.inria.fr">Gudhi</a> (computational topology).
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You can find my CV <a href="/downloads/davidsalinas_cv_11_2023.pdf?attredirects=0"> here</a>.
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My list of publications can be found on <a href="http://scholar.google.fr/citations?user=D0WjJlsAAAAJ&amp;hl=en">Google scholar.</a><br />
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