Maren Mahsereci

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I am a research scientist, currently leading the Prediction Modelling area at Yahoo DSP, driving real-time bidding models in Ads-Tech from research through to production at scale.

Previously, I have been a postdoctoral researcher at the University of Tübingen, and a Machine Learning Scientist at Amazon Web Services (AWS) in Berlin and Amazon Retail (AMZN) in Cambridge. I graduated from the Max-Planck Institute for Intelligent Systems (MPI) Tübingen.

My research interests include real-time bidding and Ads-Tech, stochastic optimization methods for deep learning, probabilistic numerics, and statistical emulation.

I am also an active contributor and maintainer of open source software projects on GitHub:

  • EmuKit: A Python toolkit for decision-making under uncertainty, providing methods for Bayesian optimization, Bayesian quadrature, and experimental design.
  • Kernel Embedding Dictionary: A reference collection of kernel mean embeddings and their closed-form expressions for common kernel and distribution pairs.
  • ProbNum (inactive): A Python library of probabilistic numerical solvers that quantify the uncertainty arising from finite computation.