Hofer_EPFL

Maximilian Hofer

Through Sigma Squared, Maximilian applies the latest machine learning and advanced analytics methods to make a difference for businesses. His industry experience ranges from Amazon Munich and M&C Saatchi London to Quid in San Francisco.

A University College London (UCL) and University of Oxford graduate, Maximilian is passionate about data-driven problem-solving in today's complex business environment.

More on LinkedIn.

Resources on medical text analysis

Maximilian Hofer, Andrey Kormilitzin, Paul Goldberg, and Alejo Nevado-Holgado. Few-Shot Learning for Named Entity Recognition in Medical Text. ArXiv:1811.05468 [Cs, Stat], November 13, 2018. http://arxiv.org/abs/1811.05468. [pdf]

Named Entity Recognition from Medical Text with Bi-Directional LSTMs presentation, explaining how to model text data with recurrent neural networks (RNNs) and how to apply RNNs to medical text [pdf]

Academic experience

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BSc Management Science
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MSc Computer Science
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PhD Management of Technology

Industry experience

Product manager at Quid Inc.
Software engineer at M&C Saatchi
Supply chain at Amazon Inc.