2016 - 2017

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

Social Networks for Statisticians

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...

Read more about Colloq: Tracy Ke
2016 Nov 22

STAT 310: Kaisey Mandel

1:00pm to 2:30pm

Location: 

Science Center Rm. 706

The Type Ia Supernova Color­-Magnitude Relation and Host Galaxy Dust: A Simple Hierarchical Bayesian Model

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...

Read more about STAT 310: Kaisey Mandel
2016 Sep 12

Colloq: Xiaole Shirley Liu

4:15pm to 5:15pm

Location: 

Science Center Rm. 300H

Inference of Tumor Immunity and T-cell Receptor Repertoire from TCGA RNA-seq Data

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...

Read more about Colloq: Xiaole Shirley Liu
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...

Read more about Colloq: Sarah Filippi
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...

Read more about Colloq: Rob Tibshirani

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