Neyman Seminar

The Neyman seminar is the statistics seminar in the Department. Historically, it has been focused on applications of Statistics to other fields. Nowadays, it has a very broad scope, with topics ranging from applications of statistics to theory.

The seminar is held on Wednesdays from 4 to 5 in the Jerzy Neyman room, 1011 Evans.

Details of individual seminar events are published in the campus' event system.

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Recent & Upcoming Neyman Seminars

Giles Hooker, Cornell University
Jan 22, 2020 4:00pm
1011 Evans Hall
Abstract:
This talk examines the design of stochastic experimental systems so as to best able to estimate parameters of the underlying dynamics. In systems ranging from ecology, neurobiology and economics, models of system dynamics can be paired with laboratory experiments to estimate parameters and gain insight into their underlying dynamics. When this is done, several experimental parameters can be...
Neyman Seminar
Tim Sullivan, Freie Universität Berlin and Zuse Institute Berlin
Jan 29, 2020 4:00pm
1011 Evans Hall
Abstract:
Numerical computation --- such as numerical solution of a PDE, or quadrature --- can modelled as a statistical inverse problem in its own right. In particular, we can apply the Bayesian approach to inversion, so that a posterior distribution is induced over the object of interest (e.g. the PDE's solution) by conditioning a prior distribution on the same finite information that would be used in a...
Neyman Seminar
Claire Donnat, Stanford University
Feb 3, 2020 4:00pm
1011 Evans Hall
Abstract:
From social networks to neurosciences, graphs have rapidly become ubiquitous by offering a versatile modeling framework in which data points are represented as nodes, and various aspects of the underlying organization of the data are captured through edges. Brain Connectomics---a developing field in cognitive neuroscience---is a case in point, as it strives to understand cognitive processes and...
Neyman Seminar
Song Mei, Stanford University
Feb 12, 2020 4:00pm
1011 Evans Hall
Abstract:
Deep learning methods operate in regimes that defy the traditional statistical mindset. Despite the non-convexity of empirical risks and the huge complexity of neural network architectures, stochastic gradient algorithms can often find the global minimizer of the training loss and achieve small generalization error on test data. As one possible explanation to the training efficiency of neural...
Neyman Seminar
Carsen Stringer, HHMI Janelia Research
Feb 19, 2020 4:00pm
1011 Evans Hall
Abstract:
Interpreting high-dimensional datasets requires new computational and analytical methods. I have developed such methods to extract and analyze neural activity from 20,000 neurons recorded simultaneously in awake, behaving mice. The neural activity was not low-dimensional as commonly thought, but instead was high-dimensional and obeyed a power-law scaling across its eigenvalues. We developed a...
Neyman Seminar
Ryan Tibshirani, Carnegie Mellon University
Feb 24, 2020 4:00pm
1011 Evans Hall
Abstract:
This talk is centered around trend filtering, a relatively recent method for nonparametric regresson based on penalizing the L1 norm of discrete derivatives. We will discuss some of the unique features of this method that "make it work", and briefly cover extensions to additive models and graphs. We will finish by discussing connections to what are an old topic in numerical analysis---discrete...
Neyman Seminar
John Williams
Mar 11, 2020 4:00pm
1011 Evans Hall
Abstract:
Applied statistics typically involves working with other people’s data and often with other people’s models, which can lead to trouble if not done carefully. This talk examines an example: a study of the effects of flow in the Mekong River on an important fishery in Cambodia, prominently published in Science. Problems with the data, the model, and the analysis demonstrate the importance of...
Neyman Seminar
Stuart Russell, UC Berkeley
Apr 1, 2020 4:00pm
Zoom meeting 2224 Piedmont (Center for Digital Archaeology )
Abstract:
It is reasonable to expect that AI capabilities will eventually exceed those of humans across a range of real-world-decision making scenarios. Should this be a cause for concern, as Alan Turing and and others have suggested? While some in the mainstream AI community dismiss the issue, I will argue instead that a fundamental reorientation of the field is required. Instead of building...
Neyman Seminar