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

Support points – a new way to reduce big and high-dimensional data
Simon Mak, Georgia Institute of Technology
Jan 28, 2019 4:00pm
1011 Evans Hall
Abstract:
This talk presents a new method for reducing big and high-dimensional data into a smaller dataset, called support points (SPs). In an era where data is plentiful but downstream analysis is oftentimes expensive, SPs can be used to tackle many big data challenges in statistics, engineering and machine learning. SPs have two key advantages over existing methods. First, SPs provide optimal and...
The Stratified Micro-randomized Trial Design: Sample Size Considerations for Testing Nested Causal Effects of Time-varying Treatment
Walter Dempsey, Harvard University
Jan 30, 2019 4:00pm
1011 Evans Hall
Abstract:
Technological advancements in the field of mobile devices and wearable sensors have helped overcome obstacles in the delivery of care, making it possible to deliver behavioral treatments anytime and anywhere. Delivery of these treatments is increasingly triggered by detections/predictions of vulnerability and receptivity, which may have been impacted by prior treatments. Furthermore the...
Statistical inference for infectious disease modeling
Po-ling Loh, University of Wisconsin-Madison
Feb 4, 2019 4:00pm
1011 Evans Hall
Abstract:
We discuss two recent results concerning disease modeling on networks. The infection is assumed to spread via contagion (e.g., transmission over the edges of an underlying network). In the first scenario, we observe the infection status of individuals at a particular time instance and the goal is to identify a confidence set of nodes that contain the source of the infection with high probability....
Statistics on Shape Data: Correcting an Asymptotic Bias in Template Shape Estimation
Nina Miolane, Stanford University
Feb 20, 2019 4:00pm
1011 Evans Hall
Abstract:
Computational Anatomy aims to model and analyze healthy and pathological distributions of organ shapes. We are interested in the computational representation of the brain anatomy using brain MRIs (Magnetic Resonance Imaging). How can we define the notion of brain shapes and how can we learn their distribution in the population? Landmarks’ shapes, curve shapes or surface shapes can be seen as the...
Mean Estimation with Sub-Gaussian Rates in Polynomial Time
Sam Hopkins, UC Berkeley
Feb 27, 2019 4:00pm
1011 Evans Hall
Abstract:
We study polynomial time algorithms for estimating the mean of a heavy-tailed multivariate random vector. We assume only that the random vector X has finite mean and covariance. In this setting, the radius of confidence intervals achieved by the empirical mean are large compared to the case that X is Gaussian or sub-Gaussian. We offer the first polynomial time algorithm to estimate the mean with...
Towards honest inference from real-world healthcare data
David Madigan, Columbia University
Mar 6, 2019 4:00pm
1011 Evans Hall
Abstract:
In practice, our learning healthcare system relies primarily on observational studies generating one effect estimate at a time using customized study designs with unknown operating characteristics and publishing – or not – one estimate at a time. When we investigate the distribution of estimates that this process has produced, we see clear evidence of its shortcomings, including an apparent...
More Data, More Problems: What Society Needs from the Statistical and Data Sciences
Brent Durbin, Smith College
Mar 13, 2019 4:00pm
1011 Evans Hall
Abstract:
Despite more than 20 years of increasing reliance on data-intensive digital tools for commerce, governance, and social interaction, society has been slow to respond to both the promise and the perils of the phenomenon we now call Big Data. This lack of adaptation to new ways of collecting, storing, and analyzing data has been especially apparent within universities and the public sector, both of...
Facebook Disaster Maps: Aggregate Insights for Crisis Response and Recovery
Shankar Iyer, Facebook
Mar 20, 2019 4:00pm
1011 Evans Hall
Abstract:
After a natural disaster or other crisis, humanitarian organizations need to know where affected people are located and what resources they need. While this information is difficult to capture quickly through conventional methods, aggregate usage patterns of social media apps like Facebook can help fill these information gaps. In this talk, I'll describe the data and methodology that power...
Tajima coalescent
Julia Palacios, Stanford University
Apr 2, 2019 4:00pm
141 McCone Hall
Abstract:
In this talk I will present the Tajima coalescent, a model on the ancestral relationships of molecular samples. This model is then used as a prior model on unlabeled genealogies to infer evolutionary parameters with a Bayesian nonparametric method. I will then show that conditionally on observed data and a particular mutation model, the cardinality of the hidden state space of Tajima’s...
Renewable Estimation and Incremental Inference in Generalized Linear Models with Streaming Data
Peter Song, University of Michigan
Apr 10, 2019 4:00pm
1011 Evans Hall
Abstract:
I will present a new statistical paradigm for the analysis of streaming data based on renewable estimation and incremental inference in the context of generalized linear models. Our proposed renewable estimation enables us to sequentially update the maximum likelihood estimation and inference with current data and summary statistics of historic data, but with no use of any historic raw data...
From correlation to causation — measuring ad effectiveness at scale
Tamara Greasby, The Trade Desk
Apr 17, 2019 4:00pm
1011 Evans Hall
Abstract:
Everyone has had that one ad for that one pair of shoes seem to follow them everywhere they go on the internet. Why does that happen? Especially if you already bought the shoes? To make sense of this, it's worth understanding how marketers have historically measured ad effectiveness -- and why the problem is harder than it seems. Beyond improvements in measuring ad effectiveness, this talk with...
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