Data analysis and stochastic control: where do statistics and applied probability come together?

Data analysis and stochastic control: where do statistics and applied probability come together?

Date: Wednesday 09 December 2020, 1.30PM
Location: Online
Section Group Meeting


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Evolving challenges in data analysis are driving new perspectives on traditional topics in stochastic processes and their applications. In this workshop, we will examine some of these new perspectives. We will discuss recent advances in robust filtering and nonlinear expectation by Samuel Cohen, sparking interest in stochastic control from a statistical perspective. We then turn to data generation and data simulation environments for control problems in three short talks of Beatrice Acciaio, Blanka Horvath and John Moriarty. In a panel discussion we will dive deeper into the evolution of the field of data-driven stochastic control in theory and practice with a particular focus on new and emerging intersections between probability and statistics.
 

The meeting will include four talks and a panel discussion. Our speakers and panellists are:

Beatrice Acciaio (ETH Zurich)

Sam Cohen (University of Oxford)

Blanka Horvath (King’s College, London)

John Moriarty (Queen Mary University, London)

who will present their recent work in the field of data-driven stochastic control and will share their views on what applied probability can bring to the analysis of data. We encourage the participation of a heterogeneous audience of mathematicians and statisticians to contribute to a lively interdisciplinary discussion.

Outline of the schedule:

1.30pm, first session: talks by Sam Cohen and Beatrice Acciaio

2.45pm, second session: talks by Blanka Horvath and John Moriarty

3.30pm: Panel discussion starts

 

Beatrice Acciaio (ETH Zurich)

Sam Cohen (University of Oxford)

Blanka Horvath (King’s College, London)

John Moriarty (Queen Mary University, London)

 

Alexander Cox (University of Bath)

Tiziano De Angelis (University of Turin)

Kathrin Glau (Queen Mary University, London)

Nic Freeman (University of Sheffield)

for the RSS Applied Probability Section
 
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