I am a Research Statistician Developer at SAS where I am a part of a small team in Advanced Analytics R&D that designs and develops graphical user interfaces (“tasks”) in SAS® Studio to help make the analytical methods in SAS easier to learn and use.
SAS Studio is a more modern interface for SAS that you can use in place of the SAS Windowing Environment or Enterprise Guide®. SAS Studio is not a stand-alone product but is available with Base SAS® or SAS® Viya®. SAS Studio is also the primary interface that comes with SAS® University Edition, which is free to students, educators, and learners for non-commercial use, and can be installed on all three major operating systems. Yes, it actually works easily on Mac and Linux! If you use statistical software, I encourage you to check it out!
Before joining SAS, I finished my PhD in statistics at North Carolina State University under the direction of Hua Zhou and Eric Chi. My research interests include machine learning, big and high-dimensional data, computational statistics, and statistics education. While in grad school, I served as an instructor for ST311 (introductory statistics) for several semesters. I also gained real-world experience as a Data Scientist Intern at MaxPoint Interactive and as a statistical consultant for the NC State University College of Veterinary Medicine. Please take a look around my website and feel free to contact me if you have any questions or comments.
- I am excited that I will be attending the United States Conference on Teaching Statistics (USCOTS) 2019 on behalf of SAS. In addition to staffing an exhibitor table, I will also giving a presentation, Training Modern Statisticians with SAS® Viya® for Learners. It will be on Friday, May 17th at 4:25pm in room 109 – I hope to see you there!
SAS Global Forum 2019
- I had a great time at SAS Global Forum again this year. I loved talking with users to hear how they use SAS, answer their questions, and see their excitement for my work that makes analytics more accessible for everyone.
- I presented a paper and two software demos:
|Paper||Learning Data Science with SAS® University Edition and JupyterLab|
|Super Demo||Build Fully Connected Neural Networks with SAS® Studio Tasks and SAS® Viya®|
|Super Demo||Easily Perform Competing-Risks Survival Analysis with SAS® Studio Tasks|
- You can explore and reproduce the examples in my paper by first installing SAS® University Edition (which is free for academic, non-commercial use) and then downloading the Jupyter notebooks and data from the SAS Global Form 2019 repository on GitHub. Your comments and questions are encouraged!
- I plan to eventually create videos of my super demos, and I will post them once they are available.