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
Math 1326.009, Applied Calculus II, Fall 2015
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