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Data Science


Track 3

11.00 to 11.40

Solving Natural Language problems with scarce data

Álvaro Barbero Jiménez - IIC

11.45 to 12.25

The case for a common Metadata Layer for Machine Learning

Jörg Schad - ArangoDB

Track 4

12.35 to 13.15

Time-Efficient Aircraft Fault Isolation Procedures with NLP techniques

Miguel Martín Acosta - Airbus Defense and Space

Rocío Martín Martín - Airbus Defense and Space

16.20 to 17.00

Fairing: Bringing Kubernetes for Data Scientists

Karthik Ramasamy - Google

Vaibhav Singh - Google

17.05 to 17.45

Operationalizing Data Science using the Azure stack

María Medina - Microsoft

17.50 to 18.30

Generative models (Gaussian Mixture Models) for images and time series

Alok N Singh - IBM

Track 5

11.45 to 12.25

Fashion Recommendations at Scale, lessons learned

Humberto Corona - Zalando


Track 2

13.10 to 13.50

Disentangling risks, activity and performance through US corporate reports

Tomasa Rodrigo - BBVA Research

Track 3

13.10 to 13.50

AI and Medicine: From medical text, medical imaging… to genomics

Aurelia Bustos Moreno - Medbravo

15.25 to 16.05

Machine Learning for federated privacy-preserving scenarios

Roberto Díaz Morales - Tree Technology

Track 4

13.55 to 14.35

Distributed Deep Learning with Keras/TensorFlow on Spark: yes you can!

Guglielmo Iozzia - MSD