Teaching Experience

Instructor

Graduate-level course

• STT 997: Advanced Topics in Statistics (Spring 2025) at Michigan State University [Course Material]
  • Developed a new course for recent topics in deep generative models and their applications
• STT 890: Statistical Problems (Summer 2025) at Michigan State University
  • Individualized study on selected problems

Undergraduate-level course

• STT 441: Probability and Statistics I: Probability (Fall 2025) at Michigan State University
  • Topics include probability models and basic statistics at an intermediate mathematical level, such as normal approximation, sampling distributions, parameter estimation, and elementary tests of hypotheses.
• STT 351: Probability and Statistics for Engineering (Summer 2025) at Michigan State University
  • Topics include probability models and random variables, estimation, confidence intervals, tests of hypotheses, simple linear regression, and applications to engineering.

Guest Lecturer

• Statistical Practice and Research for Interdisciplinary Sciences (Spring 2025) at Columbia University
  • Graduate-level course on interdisciplinary research topics in Biostatistics.
  • Gave the lecture “Variational Autoencoders and Their Applications to Multi-modal Data Analysis”.
• STT 990: Statistics & Probability (Fall 2024) at Michigan State University
  • Graduate-level seminar course.
  • Gave the lecture “Deep Generative Model: A Statistical Perspective”. [Presentation Slide]
• Statistical Practice and Research for Interdisciplinary Sciences (Spring 2024) at Columbia University
  • Graduate-level course on interdisciplinary research topics in Biostatistics.
  • Gave the lecture “Recent Topics on Conditional Generative Models”.
• Deep Learning: A Statistical Perspective (Fall 2021) at Seoul National University
  • Graduate-level course on deep learning.
  • Gave the lecture “Conditional Image Synthesis and Its Applications” in English.

Mentorship Experience

Co-mentoring Graduate Students

• Bin Yang, Ph.D. Candidate, Department of Biostatistics, Columbia University (April 2024 - Present)
  • Conducted regular weekly meetings with the student and Dr. Yuanjia Wang.
  • Provided mentorship on the following work: Yang B., Kim, Y.-G., and Wang Y. Deep representation learning for optimizing individualized treatment decisions. Work in progress; This work was selected as the Runner-up in the student paper competition for the Statistics in Imaging Section of the ASA.
• Soohyun Kim, Ph.D., Department of Biostatistics, Columbia University (Mar. 2022 -Sep. 2024)
  • Conducted regular weekly meetings with the student and Dr. Yuanjia Wang.
  • Provided mentorship on the doctoral dissertation and the following paper: Kim, S., Kim, Y.-G., and Wang, Y. (2024). Temporal generative models for learning heterogeneous group dynamics of ecological momentary data. Biometrics. [Paper]
• Zekai Jin, Master Student, Department of Biostatistics, Columbia University (Dec. 2022 - Oct. 2023)
  • Conducted regular bi-weekly meetings with the student and Dr. Seonjoo Lee.
  • Provided mentorship on deep learning-based EEG denoising methods.