Big Data

Tuesday, September 17, 2019 - 9:00am - 10:00am
Andrew Barron (Yale University)
For deep nets we examine contraction properties of complexity for each layer of the network. For any ReLU network there is, without loss of generality, a representation in which the sum of the absolute values of the weights into each node is exactly 1, and the input layer variables are multiplied by a value V coinciding with the total variation of the path weights. Implications are given for Gaussian complexity, Rademacher complexity, statistical risk, and metric entropy, all of which are shown to be proportional to V.
Wednesday, November 7, 2018 - 3:00pm - 3:30pm
Ray Liu (Takeda Pharmaceuticals Inc.)
Novel digital technology allows study subjects to be assessed with new metrics, monitored remotely and continuously, and deep phenotyped to reveal new patterns. Coupled with Big Data, digital technology has great potential to make the drug more efficient and fulfill the promise of personalized medicine. The presentation is introductory in nature to help researchers understand the status of digital technology implementation in clinical trials. Challenges and opportunities for analytical development will also be discussed.
Thursday, February 22, 2018 - 11:00am - 12:00pm
Sven Serneels (BASF Corporation)
This lecture will be set up as a panel discussion about the role of forecasting in a corporate big data analytics environment. As a basis for discussion, Sven Serneels will present how the Advanced Business Analytics group is set up at BASF. To create context, selected aspects from BASF's Corporate Overview will be presented, which eventually lead up to the organization and responsibilities of the Advanced Business Analytics group. Finally, successful applications in the areas of forecasting and operations research will be introduced.
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