Online seminar in May 2020

We have the following seminar via google meet.

1.
Title: Geometric formulation of the Wasserstein distance in the optimal transport problem
Time: May 21st (Thursday) 14:00 – 14:40
Speaker: Dr. Gihyun Lee (SNU)
Abstract:
In this survey talk, we introduce the relation between the spectral distance of noncommutative geometry and the notion of Wasserstein distance considered in the optimal transport problem. This relation was first observed by Rieffel (1999), and its proof was given by D’Andrea-Martinetti (2010).

2.
Title: Investigation of ‘Flash Crash” via Topological Data Analysis (TDA)
Time: May 21st (Thursday) 14:45 – 15:25
Speaker: Dr. Wonse Kim (SNU)
Abstract:
There is by now a quite extensive literature on applications of TDA. But there are only a few literatures on applications of TDA to financial data, including the recent result of Gidea-Katz(2018). Interestingly, Gidea-Katz (2018) showed that a stock market crash can be foreseen via TDA by utilizing daily US stock market indices data (e.g., S&P 500, DJIA, NASDAQ, and Russell 2000). However, the Flash Crash on 6 May 2010 showed that the market can be substantially destabilized in as little as about 30 min. Since the Flash Crash, analyses of market crash of the intraday-horizon has also become important parts of the study of market crash. In this talk, I will demonstrate that the TDA methodology based on Gidea-Katz (2018) can be used in forecasting short term market crash such as Flash Crash.

3.
Title: Model Predictive Control with Random Batch Methods for a guiding problem
Time: May 21st (Thursday) 15:30 – 16:10
Speaker: Dr. Dongnam Ko (University of Deusto)
Abstract:
We model, simulate and control the guiding problem for a herd of sheep under the action of shepherd dogs. The problem is formulated in the optimal control framework, which is an open-loop control strategy commonly used for the heat or wave equations. However, simulating a herd of sheep quickly becomes unfeasible from the large number of interactions. To overcome this, we use the Random Batch Method (RBM) for a computationally cheap approximation. Moreover, we follow Model Predictive Control (MPC) to ensure the convergence of the algorithm in a more concrete way, compared to the arguments of Stochastic Gradient Descent (SGD).



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