Speaker: Franziska Bell

Senior Data Science Manager @Uber

Franziska Bell is lead data scientist of the Intelligent Decision Systems team at Uber, which focuses on developing new models for real-time outage and outlier detection. Since joining Uber in late 2014, these models have broken new ground in detection accuracy and speed whilst being sufficiently computationally tractable to be applied to 100,000s of time series in real-time. Before Uber, Franziska was a Postdoc at Caltech where she developed a novel, highly accurate approximate quantum molecular dynamics theory to calculate chemical reactions for large, complex systems, such as enzymes. Franziska earned her Ph.D. in theoretical chemistry from UC Berkeley focusing on developing highly accurate, yet computationally efficient approaches which helped unravel the mechanism of non-silicon-based solar cells and properties of organic conductors.

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2019 Tracks

  • Groking Timeseries & Sequential Data

    Techniques, practices, and approaches around time series and sequential data. Expect topics including image recognition, NLP/NLU, preprocess, & crunching of related algorithms.

  • Deep Learning in Practice

    Deep learning use cases around edge computing, deep learning for search, explainability, fairness, and perception.