I have taught several topics to students with varied backgrounds:

• STAT 212: A first course in biostatistics
• STAT 434: Survival analysis
• STAT 441: The practice of applied statistics
• STAT 530: Graduate bioinformatics
• STAT 578: Topics courses for statistics PhD students

I have been selected to the University of Illinois's List of Teachers Ranked as Excellent by Their Students for multiple courses over multiple semesters.

My overall goal is to use the topic of data analysis as a vehicle to teach students of all levels how to create new ideas.

My approach is to contextualize data analysis within the larger scientific enterprise of answering substantive research questions. I illustrate how this enterprise drives the need for mathematical theories of data analysis (and how sometimes this happens the other around [1]). I then guide students to reconstruct for themselves, from first principles, existing concepts, models, and algorithms. I teach data analysis methods by using them as case studies for students to practice the general strategies and processes of methodological innovation.

I emphasize to students that it is the ability to create new ways of seeing the world that will help them stay relevant in the age of AI.

I have also been fortunate to have advised the following PhD students:

NameCurrent position
Eman AbdelatifPhD student
Alton BarbehennHealthLeap AI
William BiscarriTrading firm
Eduardo Cardenas-TorresCaterpillar
Arjama DasPhD student
Aster GuanPhD student
Young Joo LeePhD student
Yuhuan LiPhD student
Ilia LomasovPhD student
Chengyang LuPhD student
Robin TuNorthrop Grumman
Yihe WangMeta
Huiqin XinMeta
Rachel ZhouWaymo

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[1] Norman (2010). Technology first, needs last: the research-product gulf. Interactions 17, 38–42.