2017 Apr 18

# STAT 300: Luis Campos and Xinran Li

12:00pm to 1:00pm

## Location:

Science Center Rm. 705
"Disambiguating Sources II: Extending Through Time" by Luis TBD by Xinran
2017 May 19

4:00pm to 5:20pm

SC 705
2016 Sep 08

# Study Card Day

(All day)

Also known as Course Registration Day.
2016 Oct 24

# Colloq: Tracy Ke

4:15pm to 5:15pm

## Location:

Science Center Rm. 300H

We have collected a data set for the social networks of statisticians. The data set consists of the meta information (e.g., authors, abstracts, citation counts) of about 70,000 papers in 36 representative journals in statistics and related fields, from 1984-2015. Our data collection project (which we may call it the Phase II) is a continuation of the recent data collection project by Ji and Jin (which we may call the Phase I)....

2016 Nov 22

# STAT 310: Kaisey Mandel

1:00pm to 2:30pm

## Location:

Science Center Rm. 706

Type Ia supernovae (SN Ia) are faraway exploding stars used as standardizable candles'' to determine cosmological distances, measure the accelerating expansion of the Universe, and constrain the properties of dark energy. Inferring peak luminosities of SN Ia from distance-independent observables, such as the shapes and colors of their light curves (time series), underpins the evidence for cosmic...

2017 Jan 24

# STAT 300: Iavor Bojinov and Reagan Rose

12:00pm to 1:00pm

## Location:

Science Center Rm. 705
2017 Feb 28

# STAT 310: Hyungsuk Tak and Xufei Wang

1:00pm to 2:30pm

## Location:

Science Center Rm. 706

"A Mixture of Gaussian and Student's t Errors for a Robust and Accurate Inference," by Hyunsuk Tak

"Spacings estimates and good regions," by Xufei Wang

2017 Apr 24

# Colloq: Dempster Prize Winners Espen Bernton and Xinran Li

4:15pm to 5:15pm

## Location:

Science Center Rm. 705
Reception to follow on the 7th floor of the Science Center.
2016 Sep 12

# Colloq: Xiaole Shirley Liu

4:15pm to 5:15pm

## Location:

Science Center Rm. 300H

We developed a computational approach to study tumor-infiltrating immune cells and their interactions with cancer cells. Analysis of over ten thousand RNA-seq samples from the Cancer Genome Atlas (TCGA) identified strong association between immune infiltrates and patient clinical features, viral infection status, and cancer genetic alterations. We found that melanomas with high...

2016 Oct 04

# STAT 310: Vinay Kashyap, Aneta Siemiginowska, and Andreas Zezas

1:00pm to 2:30pm

## Location:

Science Center Rm. 706
2016 Nov 14

# Colloq: Sarah Filippi

4:15pm to 5:15pm

## Location:

Science Center Rm. 300H

Bayesian nonparametric approaches to quantifying dependence between random variables

Nonparametric and nonlinear measures of statistical dependence between pairs of random variables have proved themselves important tools in modern data analysis, where the emergence of large data sets can support the relaxation of linearity assumptions implicit in traditional association scores such as correlation. In this talk, I will present two Bayesian nonparametric...

2016 Jul 12

# AstroStatTalk: Saku Vrtilek

1:00pm to 2:30pm

## Location:

Science Center Rm. 706
2016 Sep 19

# Colloq: Jingchen Liu

4:15pm to 5:15pm

## Location:

Science Center Rm. 300H
One of the main tasks of statistical models is to characterize the dependence structures of multi-dimensional distributions. Latent variable model takes advantage of the fact that the dependence of a high dimensional random vector is often induced by just a few latent (unobserved) factors. In this talk, we present several problems regarding latent variable models. When the dimension grows higher and the dependence structure becomes more complicated, it is hardly possible to find a low dimensional parametric latent variable model that fits well. We enrich the model by including a graphical... Read more about Colloq: Jingchen Liu
2017 Feb 06

# Colloq: Rob Tibshirani

4:15pm to 5:15pm

## Location:

Science Center Hall D

Recent Advances in Post-Selection Statistical Inference

We describe the problem of “post-selection inference.” This addresses the following challenge: Having mined a set of data to find potential associations, how do we properly assess the strength of these associations? The fact that we have “cherry-picked”—searched for the strongest associations—means that we must set a higher bar for declaring significant the associations that we see. This challenge becomes more important in the era of big data and complex statistical modeling. The cherry tree (dataset) can be very large...