Schedule for Fall 2024

9/11/2024

Speaker: Professor Andrew Gelman

Title: Modeling Weights to Generalize

Abstract: A well-known rule in practical survey research is to include weights when estimating a population average but not to use weights when fitting a regression model—as long as the regression includes as predictors all the information that went into the sampling weights. But what if you don’t know where the weights came from? We propose a quasi-Bayesian approach using a joint regression of the outcome and the sampling weight, followed by poststratifcation on the two variables, thus using design information within a model-based context to obtain inferences for small-area estimates, regressions, and other population quantities of interest. For background, see here:

 http://www.stat.columbia.edu/~gelman/research/unpublished/weight_regression.pdf

9/18/2024

Speakers: Dr. Julien Boussard and Dr. Collin Cademartori

Title: Experiences of recent graduates

Abstract: Recent graduates will share their research journey across their PhD and other valuable insights. Both speakers will start by giving a brief overview of their journey through graduate school, and their research, placing emphasis on lessons learnt through the process and what they would have done differently if they were to begin graduate school again.
Collin will elaborate on his journey from the beginnings of research to starting his current position as an assistant professor at Wake Forest. This talk will probably be most relevant to those of you who are interested in applying for academic jobs and who have at least some interest in teaching, since these are the factors that most strongly influenced his job market experience. Julien will also focus on the specifics of doing applied statistical research (as opposed to theoretical research), and try to give some advice for students interested in applied research. He will also talk briefly about the process of finding a postdoc.

9/25/2024

Speaker: Prof. Gitta Kutyniok from LMU Munich

Title: Reliable AI: Successes, Limitations, and Next Generation Computing

Abstract: The new wave of artificial intelligence is impacting industry, public life, and the sciences in an unprecedented manner. However, one current major drawback is the lack of reliability as well as the enormous energy problem of AI.
The goal of this lecture is to first provide an introduction into this new vibrant research area, and showcase some recent successes. We will then focus on limitations of reliability of AI from a mathematical perspective, in particular, with respect to computability problems caused by current (digital) hardware. This will lead us naturally to novel approaches for future AI computing such as neuromorphic computing and spiking neural networks as the associated type of neural networks.

10/2/2024

10/9/2024

10/16/2024

10/23/2024

10/30/2024

11/6/2024

11/13/2024

11/20/2024

11/27/2024

12/4/2024

12/11/2024

12/18/2024