Skip to main content
Cornell university
Cornell Statistics and Data Science Cornell Statistics and Data Science
  • About Us

    About Us
    Cornell's Department of Statistics and Data Science offers four programs at the undergraduate and graduate levels. Undergraduates can earn a BA in statistical science, social...

    Welcome to the Department of Statistics and Data Science
    History
    Facilities
    Statistics Graduate Society
    Recently Published Papers
  • Academics

    Academics

    Undergraduate
    PhD
    MPS
    PhD Minor in Data Science
    Courses & Course Enrollment
  • People

    People

    Faculty
    Field Faculty
    PhDs
    Emeritus Faculty
    Academic Staff
    Staff
    Research Areas of Expertise
    Statistical Consultants
  • News and Events

    News and Events

    Events
    News
  • Resources

    Resources

    Professional Societies and Meetings
    Affiliated Groups
    Career Services
    Cornell Statistical Consulting Unit
  • Alumni

    Alumni
    Cornell's Statistics and Data Science degrees prepare students for a wide variety of careers, from academia to industry.  See the After Graduation page for a general overview of...

    Alumni Profiles

Search form

You are here

  1. Home 
  2. Events 
  3. Statistics Seminars

Statistics Seminar Speaker: Wanjie Wang, 02/19/2016

Event Layout

Friday Feb 19 2016

Statistics Seminar Speaker: Wanjie Wang, 02/19/2016

4:15pm @ 226 Weill Hall
In Statistics Seminars

The Statistics Seminar Speaker for Friday, February 19, 2016, is Wanjie Wang, a Postdoctoral Associate with Department of Biostatistics and Epidemiology and Department of Statistics at the University of Pennsylvania. Wang's main research interest lies in the area of high dimensional statistical inference, including studying the separation boundary of accessibility and impossibility for some statistical problems in high dimensional setting, such as the clustering problem, signal recovery problem, and detection problem; developing new statistical methods designed for high dimensional data with rare and weak signals, such as Important Features (IF) PCA algorithm for clustering problem; Modelling and solving real world problems, such as the detection and evaluation of sparse simultaneous signals for genetic associations between two diseases based on GWAS data.

Wang is also interested in applying statistics to real world problems, such as rank-based tests for Genomics data with excessive zeros; developing Current-Threshold model for neuron coding. She received a master's degree and Ph.D. in Statistics from Carnegie Mellon University, and completed her undergraduate studies in the School of Mathematical Sciences at Peking University.

Title: Important Features PCA (IF-PCA) for Large-Scale Inference, with Applications in Gene Microarrays

Abstract: Clustering is a major problem in statistics with many applications. In the Big Data era, it faces two main challenges: (1). the number of features is much larger than the sample size; (2). the signals are sparse and weak, masked by large amount of noise. 

We propose a new tuning-free clustering procedure for large-scale data, Important Features PCA (IF-PCA). IF-PCA consists of a feature selection step, a PCA step, and a k-means step. The first two steps reduce the data dimensions recursively, while the main information is preserved. As a consequence, IF-PCA is fast and accurate, producing competitive performance in application to 10 gene microarray data sets. 

We also propose a model that can capture the rarity and weakness of signal. Under this model, the statistical limits for the clustering problem and IF-PCA has been found. 

Refreshments will be served after the seminar in 1181 Comstock Hall. 

Event Categories

  • Statistics Seminars
  • Special Events
  • Home
  • About Us
  • Contact Us
  • Careers
© Cornell University Department of Statistics and Data Science

1198 Comstock Hall, 129 Garden Ave., Ithaca, NY 14853

Social Menu

  • Facebook
  • Twitter
  • YouTube
Cornell Bowers CIS College of Computing and Information Science Cornell CALS ILR School

If you have a disability and are having trouble accessing information on this website or need materials in an alternate format, contact web-accessibility@cornell.edu for assistance.