talk-data.com

We talked about:

Polina's background How common it is for PhD students to build ML pipelines end-to-end Simultaneous PhD and industry experience Support from both the academic and industry sides How common the industrial PhD setup is and how to get into one Organizational trust theory How price relates to trust How trust relates to explainability The importance of actionability Explainability vs interpretability vs actionability Complex glass box models Does the explainability of a model follow explainability? What explainable AI bring to customers and end users Can all trust be turned into KPI?

Links:

LinkedIn: https://www.linkedin.com/in/polina-mosolova/ Neural Additive Models paper: https://proceedings.neurips.cc/paper/2021/file/251bd0442dfcc53b5a761e050f8022b8-Paper.pdf Neural Basis Model paper: https://arxiv.org/pdf/2205.14120.pdf Interpretable Feature Spaces paper: https://kdd.org/exploration_files/vol24issue1_1._Interpretable_Feature_Spaces_revised.pdf

Getting a PhD in Industry - Polina Mosolova

About the event

Outline:

• Doing a PhD in Industry
• Academia vs Industry
• Interpretable AI and ML

About the speaker:

I am a data scientist at SAP, passionate about bringing the full potential of current machine learning research to business applications. I am interested in creative combinations of statistical and machine learning methods for use cases addressing real-world problems. In my PhD dissertation, I created an applied machine learning framework for churn prediction, enhanced by organisational trust theory and explainable machine learning methods.

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