Eugene Katsevich

Eugene Katsevich
  • Assistant Professor of Statistics and Data Science

Contact Information

  • office Address:

    311 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 )

  • STAT961 - Statistical Methodology

    This is a course that prepares 1st year PhD students in statistics for a research career. This is not an applied statistics course. Topics covered include: linear models and their high-dimensional geometry, statistical inference illustrated with linear models, diagnostics for linear models, bootstrap and permutation inference, principal component analysis, smoothing and cross-validation.

    STAT961001 ( 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.

  • STAT961 - STATISTICAL METHODOLOGY

    This is a course that prepares 1st year PhD students in statistics for a research career. This is not an applied statistics course. Topics covered include: linear models and their high-dimensional geometry, statistical inference illustrated with linear models, diagnostics for linear models, bootstrap and permutation inference, principal component analysis, smoothing and cross-validation.

  • STAT991 - SEM IN ADV APPL OF STAT

    This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics vary from year to year and are chosen from advance probability, statistical inference, robust methods, and decision theory with principal emphasis on applications.

  • STAT999 - INDEPENDENT STUDY

    Written permission of instructor and the department course coordinator required to enroll.

Activity

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