## Upcoming Talks

### January 18, 2018

#### Winter Term Kick-off Problem Session

Welcome back to the Winter Term, and the Winter 2019 Series of the Left to the Reader Seminar! To start things off this term, we will have an open problem session again, where audience members will share the problems they are working on, or are interested in working on. Each person will take five minutes to share their current interests to a general mathematical audience, and explain some of the tools most commonly used in their field. Afterward, we will fill up the time slots of the coming term. Hope to see you all there!

## Past Talks

### November 26, 2018

#### On Efimov's Problem

Every topological space is infinite and Hausdorff (just while you read this, of course)

Question 1: Does Every compact space contain an infinite convergent sequence?

To see why the answer to Question 1 is surprisingly negative, we will talk about compactifications and just a little bit about ultrafilters. In fact $\beta\mathbb{N}$ the Stone-Çech compactifcations of the integers (or the set of ultrafilters over $\mathbb{N}$) is a counterexample.

Efimov's Problem asks whether $\beta\mathbb{N}$ is the counterexample to Question 1; that is:

Question 2 (a.k.a. Efimov's Problem): Does every compact space contain either:

• An infinite convergent sequence.

### October 5, 2017

#### Title: Artificial Intelligence and Analysis of Non-stationary Spatial-temporal Data

In this talk, I will first give a brief introduction to the area of Artificial Intelligence (AI) in which the main differences between Strong AI and Weak AI, and some of the mathematical as well as philosophical theories formulated for describing each of these subareas will be discussed. I will further review the advances in Machine Learning research and current state-of-the-art in Deep Learning, Statistical Learning and Tensor Decomposition-based Learning methods. The rest of the talk will focus on the main challenges and difficulties confronted when analyzing the non-stationary spatial-temporal data using the machine learning approach. As a real-world example of such a complex data, electroencephalogram recordings of patients having major depressive disorder will be demonstrated on which I will share some of my recent research achievements. Our proposed framework utilized for analyzing the mentioned data involves the use of various techniques developed in statistics, operations research, machine learning and big data

### September 28, 2017

#### Change-point detection for noisy non-stationary biological signals

Experimentally and clinically collected time series data are often contaminated with significant confounding noise, creating short, non-stationary time series. This noise, due to natural variability and measurement error, poses a challenge to conventional change point detection methods. We proposed a novel, real-time change point detection method for effectively extracting important time points in non-stationary, noisy time series. We validated our approach with three simulated time series, as well as with a physiological data set of simulated labour experiments in fetal sheep. Our methodology allows for the first time the detection of fetal acidemia from changes in the fetus' heart rate variability, rather than traditional invasive methods. We believe that our method demonstrates a first step towards the development of effective, non-invasive real time monitoring during labour from signals which may be easily collected.

### September 21, 2017

#### Kick-off Problem Session

This week we are having audience members introduce a problem they are interested in studying. Each person will take only 5 minutes to give the problem statement and some of the tools used. This session will act as a preview for what may appear in the upcoming weeks. Afterward we will try to fill up the time slots with volunteer speakers.