- Graduate mathematical statistics II. (S2)
- Introductory Bayesian statistics. (S1@ISM?)

At U Tokyo

- "Mathematical Statistics I & II" S12 2020, 2022

Elective for juniors and seniors of economics, this course offers the methodological basics in probability, distributional theory, asymptotic theory, point estimation and hypothesis testing, to take those who has completed "Statistics I & II" to the higher level of statistical science.

The contents covered in this lecture were selected from the textbook by Tatsuya Kubokawa (in Japanese), which required the same level of mathematical rigor as Casella and Berger (2006). I decided I wanted to teach by the classical chalk-and-talk, but also complemented it by the demonstration of statistical computing by R and the brief introduction to the recent research topics by slides. Problem sets were provided weekly and the final exam was based on those problems. The personal support from the lecturer and TA was available via emails and online meeting. In the 2020 academic year, 200+ students were registered for MS I, and 140+ for MS II.

"Good practice of online teaching at UTokyo," article in Japanese

- Undergraduate independent study "Proactive learning seminar" A12 2018

Reading group class for undergraduate students of economics. Textbook: Williams "Probability with Martingales", Chapter 9-12.

- "Mathematics I" S12 2018, 2020

Introductory linear algebra, open for both undergraduate and graduate students of economics. The conceptual aspects of linear algebra were emphasized, covering the general definition and properties of linear spaces and linear maps, and the examples included the functional space, differential equations, and others. Students were evaluated based on (almost) weekly homeworks, midterm and final. 100+ students registered for this class.

- "Statistics I & II", A12 2017, 2019, 2021

Mandatory for sophomore students of economics major and elective for those of law science, this lecture covers the basics of statistics at a higher level to have students prepared for the advanced courses such as econometrics. The topics include EDA, introductory probability theory, point estimation and hypothesis testing. Computation with R is the integral part of the lecture. 400+ students registered for this lecture.

- "Topics in Bayesian Statistics" (grad), 2016, 2018, 2019, 2021, 2022

[2021] Reading group: Multivariate dynamic modeling (DDNM, SGDLM), Bayesian predictive synthesis, Polya-gamma mixture, among others.

[2020] Reading group: Prado, Ferreira and West (2021), Chapters 4, 10, 11 and 6.

[2018-2019] Discussion and lecture: non-informative priors for linear models, Bayesian Lasso and horseshoe, DLMs, FFBS, stochastic volatility models, mixture sampler, in addition to the contents covered in 2016.

[2016] Lecture: multivariate distributions, the basics of state space models, or DLMs, filtering and smoothing, the concept of discount factors, multivariate and matrix-variate extension, graphical models and modeling by hyper-(inverse) Wishart distribution.

Student presentation: graphical models, objective Bayes and g-prior, stochastic shotgun search, and parallel computation by GPUs.

At Duke

- Discrete Data (grad), Prof. Li Ma, Fall 2015
- Categorical Data Analysis (grad), Prof. Li Ma, Fall 2014

At U Tokyo

- Econometrics II (grad), Prof. Hidehiko Ichimura, Summer 2012
- Mathematical Statistics (grad), Prof. Hidehiko Ichimura, Summer 2011
- Math I (grad, in Japanese), Prof. Yoshihiro Yajima, Summer 2011
- Mathematics for Economists (grad), Prof. Akihiko Matsui, Summer 2010