Hello and welcome to my webzone!
I am currently a Teaching Assistant Professor for the Department of Statistics at the University of Illinois at Urbana-Champaign.
After a BS in Mathematics at Illinois, I completed a PhD in Statistics at the Illinois with Ping Ma. I then spent four years as a Visiting Assistant Professor, then Lecturer and MS Academic Advisor for Statistics @ Illinois.
I recently spent a year as an Associated Assistant Professor and Data Analytics Program Specialist for the Department of Statistics at The Ohio State University. Go Bucks? While away from Illinois, I was an Adjunct Teaching Assistant Professor for Statistics @ Illinois where my course Statistical Modeling in R is part of the online Master of Computer Science in Data Science.
But now I have returned to spend even more time at Illinois. Yes, I have spent a lot of time at UIUC. I’m still mad they don’t use UIUC anymore. Oh well. Go Illini!
The best way to reach me is by email, which I check far too frequently. I have a phone, but honestly, I don’t know the number and I respond faster to email anyway. As I type this, I am currently without an office.
You can also find me on GitHub and Reddit.
For the Fall 2019 semester I will (tentatively) be teaching:
My Fall 2019 office hours are to be determined.
Currently, all office hours are “open” office hours, meaning you can stop by for class questions, or really anything at all, but questions about class will be given priority.
If you need to schedule a meeting outside of these times, please send an email suggesting two possible times, on two different days. (A total of four suggested times.) I have a preference for time-slots directly adjacent to current office hours or directly after a class. Please also indicate a brief agenda for the meeting. If possible, I will also try to resolve the issue immediately via email.
R [Summer 2017]
R for use with the course. Soon to partially be released as public MOOCs on the Coursera platform.R. I currently maintain an open-source supplemental textbook, R for Statistical Learning which focuses on using R to perform statistical learning techniques.R
R and regression modeling for students with undergraduate experience in probability and statistics. Currently in use for STAT 420 both in-person and online at UIUC for the Departments of Statistics and Computer Science.R for Statistical Learning
R code and rmarkdown examples, the text is starting to become a complete reference text for STAT 432 - Basics of Statistical Learning at UIUC for the Departments of Statistics. Target audience is advanced undergraduate students in statistics with previous experience with R and regression.