Teaching at SMU

Decision Analysis (Undergraduate Core)

• Term 1, AY2026–2027 (Fall 2026), 2 sections
• Term 1, AY2025–2026 (Fall 2025), 2 sections

Teaching at Purdue

• Data Science in Public Health, Fall 2023
  • Enrollment: 48 Engineering Undergraduate Students
  • Deliver three lectures and two recitation sessions on data prepossessing
  • Design course syllabus, and prepare course materials and homework
  • Student testimonials on clear lectures
    • “The lecture was easy to follow and was informative. Xiaoquan is very good at lecturing. She is clear and confident.”
    • “She is passionate about what she’s talking about.”
    • “I like that Xiaoquan makes examples on google colab for us to apply what we learn on the slides. She also speaks clearly and explains things well.”
    • “I think she is very organized and cares about our learning.”
    • “I think that Xiaoquan is a really great instructor. She outlines clear examples and how things work.”
  • Student testimonials on engaging the class
    • “I love how interactive Xiaoquan is with the class! She asks questions and really tries to get people engaged.”
    • “She does a good job engaging the class and facilitating class discussion.”
    • “I think explaining what we need to do before the coding and asking us to make us think about it really helps.”
    • “I appreciate that she is very connected with the class and always asks if we understand and goes at a good pace.”
    • “I think the instructor provides good examples for each of the concepts reviewed in class.”

Student Mentoring

• Mentored a Purdue Aeronautics and Astronautics Engineering master student: developed a user interface to visualize occupancy trends and crime rate forecasting and worked on a paper together
• Mentored a Purdue Electrical and Computer Engineering undergraduate student: built interpretable machine learning models for hospital readmission prediction and submitted a paper together