About Me

Welcome to my personal page!

My name is Yunfei Wang. I am currently a senior machine learning engineer at Instacart, San Francisco, California. Previously, I worked as a senior statistician at GM Cruise.

I obtained my Ph.D. degree in Statistics from University of Texas at Dallas (UTD) in 2016. My advisor was Professor Robert Serfling and I have learned from him much more than just Statistics. My research interests include nonparametric and robust multivariate statistical methods, high-dimensional data analysis and statistical computing, and my disseration is about Foundations for Multivariate Rank Functions and Sign and Signed-Rank Statistics.



Publications

Talks and Presentations

  • Contributed talk. On Liu's Simplicial Depth and Randles' Interdirections. Co-author: Robert Serfling. Joint Statistical Meetings (JSM) 2016, Chicago, IL, USA. 08/2016.
  • Contributed talk. On Fast Affine Equivariant Robust Scatter Estimation. Co-author: Robert Serfling. Joint Statistical Meetings (JSM) 2014, Boston, MA, USA. 08/2014.
  • Poster presentation. On Fast Affine Equivariant Robust Scatter Estimation. Co-author: Robert Serfling. Conference of Texas Statisticians (COTS) 2014, Richardson, TX, USA. 03/2014.
  • Poster presentation. On Fast Affine Equivariant Robust Scatter Estimation. Co-author: Robert Serfling. Ordered Data Analysis, Models and Health Research Methods Conference, Richardson, TX, USA. 03/2014.


Good Tools

Nice and Helpful People

WORST things in the World

  • American Home Shield (the WORST home warranty I have used - they does NOT keep any promise with me)


Email

meyunfei.wang



Stat 3360.001, Probability and Statistics for Management and Economics, Spring 2016

  • Lecture Notes

    • Part 1: Graphical and Numerical Descriptions of Data
      • About the Notes
      • Sections 2.1: Basics
      • Sections 2.1 - 2.3: Graphical Descriptive Techniques for Categorical Data
      • Sections 3.1 - 3.2: Graphical Descriptive Techniques for Interval Data
      • Sections 4.1 - 4.3: Numerical Descriptive Techniques for Interval Data
    • Part 2: Relationship between Interval Variables
      • Section 3.3: Scatter Plot
      • Section 4.4: Measure of Linear Relationship
      • Section 4.4: Simple Linear Regression
    • Part 3: Events
      • Section 6.1 - 6.3: Experiment, Simple Event, Event
      • Section 6.1 - 6.3: Intersection of Events, Union of Events, Complement of Event
      • Section 6.1 - 6.3: Rephrasing Complicated Events
    • Part 4: Probabilities of Events
      • Section 6.1 - 6.3: Probability of Event
      • Section 6.1 - 6.3: Joint Probability, Marginal Probability, Independence of Events
      • Section 6.1 - 6.3: Complement Rule, Addition Rule
      • Section 6.1 - 6.3: Conditional Probability, Multiplication Rule, Probability Tree
      • Section 6.1 - 6.3: Guidelines for Solving Word Problems
    • Part 5: Random Variable and Probability Distribution
      • Section 7.1: Random Variable, Discrete Random Variable, Continuous Random Variable
      • Section 7.1: Probability Distribution
    • Part 6: Discrete Probability Distribution
      • Section 7.1: Probability Mass Function
      • Section 7.1: Population Mean, Variance and Standard Deviation
      • Section 7.2: Joint and Marginal Probability Mass Function
      • Section 7.2: Population Covariance, Independence of Random Variables
      • Section 7.3: Portfolio Investment
    • Part 7: Special Discrete Distributions
      • Section 7.4: Binomial Distribution
      • Section 7.5: Poisson Distribution
      • Section 7.4 - 7.5: Cumulative Probability
      • Section 7.4 - 7.5: Cumulative Distribution Tables
    • Part 8: Normal Distribution
      • Section 8.1: Continuous Probability Distribution, Probability Density Function
      • Section 8.2: Normal Distribution
      • Section 9.1: Sampling Distribution of Sample Mean
    • Part 9: Estimation
      • Section 10.1: Concepts of Estimation, Point Estimate, Confidence Interval
      • Section 10.2: Estimating Population Mean with Known Population Standard Deviation
      • Section 10.3: Selecting the Sample Size
    • Part 11: One-Population Inference
      • Section 10.2: Confidence Interval of Population Mean with Known Population Standard Deviation
      • Section 11.2: Testing Population Mean with Known Population Standard Deviation
      • Section 12.1: Confidence Interval of Population Mean with Unknown Population Standard Deviation
      • Section 12.1: Testing Population Mean with Unknown Population Standard Deviation
      • Section 12.2: Confidence Interval and Testing of Population Variance
      • Section 12.3: Confidence Interval and Testing of Population Proportion
    • Part 12: Two-Population Inference
      • Section 13.3: Inference on Difference between Two Population Means: Matched Pairs
      • Section 13.1: Inference on Difference between Two Population Means: Independent Populations
      • Section 13.5: Inference on Difference between Two Population Proportions
    • Part 13: Summary of One and Two-Population Inference
      • An Overview of Part 11 and Part 12
    • Part 14: Chi-Squared Tests
      • Section 15.1: Goodness-of-Fit Test for One Random Variable
      • Section 15.2: Independent (Homogeneity) Test for Two Random Variables