The opening of the Brummer & Partners MathDataLab

The opening workshop takes place at: Open Lab, Valhallavägen 79, Stockholm ( map )

Registration

Please register for the workshop here  by Nov 10. (registration is free of charge but space is limited)

Program

08:45 Breakfast

09:15 Welcome, Sigbritt Karlsson, KTH president

09:20 Presentation of Brummer & Partners MathDataLab, Henrik Hult, KTH

09:30-10:15 Randomized algorithms for large scale linear algebra and data analytics, Per-Gunnar Martinsson, Oxford University

10:15-11:00 From RNA-seq time series data to models of regulatory networks, Konstantin Mischaikow, Rutgers University

Abstract.  We will describe a novel approach to nonlinear dynamics based on topological and combinatorial ideas. An important consequence of this approach is that it is both computationally accessible and allows us to rigorously describe dynamics observable at a fixed scale over large sets of parameter values. To demonstrate the value of this approach we will consider RNA-seq time series data time series data and propose potential regulatory networks based on how robustly the network is capable of reproducing the observed dynamics.

11:00-12:30 Lunch break (lunch not included)

12:30-13:15 What is persistence? Wojciech Chacholski, KTH

Abstract.  What does it mean to understand shape? How can we measure it and make statistical conclusions about it? Do data sets have shapes and if so how to use their shape to extract information about the data? There are many possible answers to these questions. Topological data analysis (TDA) aims at providing some of them using homology. In my presentation aimed at broader audience I will describe the essence of TDA. I will illustrate how TDA can be used to give a machine intelligence to learn geometric shapes and how this ability can be used in data analysis.

13:15-14:00 Some mathematical challenges in the analysis of complex data, Henrik Hult, KTH

Abstract. In this talk I will give an overview of some recent advancement in the analysis of complex data. The talk will emphasize questions related to training and architecture of neural networks and I will try to highlight some mathematical challenges in this field.​