Eugene Katsevich

Eugene Katsevich
  • Assistant Professor of Statistics

Contact Information

  • office Address:

    311 Wharton Academic Research Building
    265 South 37th Street
    Philadelphia, PA 19104

Research Interests: multiple testing, high-dimensional inference, selective inference, and applications to genomics

Links: Personal Website

Overview

Education

Ph.D. in Statistics, Stanford University, 2019
A.B. in Mathematics, Princeton University, 2014

Academic Positions Held

Postdoctoral Researcher, Department of Statistics and Data Science, Carnegie Mellon University, 2019-2020

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Research

Teaching

Current Courses

  • STAT471 - Modern Data Mining

    Modern Data Mining: Statistics or Data Science has been evolving rapidly to keep up with the modern world. While classical multiple regression and logistic regression technique continue to be the major tools we go beyond to include methods built on top of linear models such as LASSO and Ridge regression. Contemporary methods such as KNN (K nearest neighbor), Random Forest, Support Vector Machines, Principal Component Analyses (PCA), the bootstrap and others are also covered. Text mining especially through PCA is another topic of the course. While learning all the techniques, we keep in mind that our goal is to tackle real problems. Not only do we go through a large collection of interesting, challenging real-life data sets but we also learn how to use the free, powerful software "R" in connection with each of the methods exposed in the class. This course may be taken concurrently with the prerequisite with instructor permission.

    STAT471001 ( Syllabus )

    STAT471002 ( Syllabus )

Past Courses

  • STAT471 - MODERN DATA MINING

    Modern Data Mining: Statistics or Data Science has been evolving rapidly to keep up with the modern world. While classical multiple regression and logistic regression technique continue to be the major tools we go beyond to include methods built on top of linear models such as LASSO and Ridge regression. Contemporary methods such as KNN (K nearest neighbor), Random Forest, Support Vector Machines, Principal Component Analyses (PCA), the bootstrap and others are also covered. Text mining especially through PCA is another topic of the course. While learning all the techniques, we keep in mind that our goal is to tackle real problems. Not only do we go through a large collection of interesting, challenging real-life data sets but we also learn how to use the free, powerful software "R" in connection with each of the methods exposed in the class. This course may be taken concurrently with the prerequisite with instructor permission.

Activity

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