I am an idealistic, risk-taking, introverted, nerdy foodie, studying computational approaches for agriculture. In particular, my research focuses on applications of novel methods to agriculture, including machine learning, Bayesian optimization and complex systems, in order to answer the key questions better.

I grew up digital in Japan. While turning to physics and mathematics in high school and (first) college, computers had never ceased to fascinate me. Circa 2000, when starting tech businesses was ever so cool, as a natural course of action, I dropped out, became a software engineer, and eventually started my own business. While developing resource management software for small-scale farmers and interacting with them, I found growing plants fascinating and much room for improving food systems. The idea of local food system began to dawn on me. To test my hunch, I developed an online service that delivered fresh produce to local people and facilitated interactions among them. I grew most vegetable by myself and directly communicate with a number of consumers—a firsthand experience of local food.

In 2010, being convinced of its potential and seeking systematic knowledge, I went to New Zealand to study agribusiness at Massey University. However, as my interests drifted from entrepreneurship to research, I decided to pursue an academic career that, I believed, could make me more useful for the society. In 2015, the year after completing an honours degree in economics at the Australian National University, I came to Madison, WI for PhD.

I enjoy cooking, playing pool, and a simple life in general. Kanan, a beloved nematologist, keeps my life not-so-simple yet full of bliss.


Because of the professional background, I am competent in software design and coding. In 2017, to broaden the research toolkit, I attended Complex Systems Summer School at Santa Fe Institute. Finally, at UW-Madison, I took classes that closely reflect my interest. I am basically trained in economics and machine learning at a graduate level and mathematics at an undergraduate level.

  • ECON 711: Economic Theory - Microeconomics Sequence
  • ECON 713: Economic Theory - Microeconomics Sequence
  • ECON 712: Economic Theory - Macroeconomics Sequence
  • ECON 709: Economic Statistics and Econometrics I
  • ECON 710: Economic Statistics and Econometrics II
  • AAE 641: Foundations of Agricultural Economics
  • AAE 706: Applied Risk Analysis
  • AAE 731: Frontiers in Development Economics 2
  • AAE 520: Community Economic Analysis
  • REAL EST 720: Urban Economics
  • MATH 521: Analysis I
  • MATH 531: Probability Theory
  • MATH 541: Modern Algebra
  • MATH 629: Introduction to Measure and Integration
  • MATH 632: Introduction to Stochastic Processes
Machine learning
  • CS 540: Introduction to Artificial Intelligence
  • CS 532: Theory and Applications of Pattern Recognition
  • CS 761: Mathematical Foundations of Machine Learning
  • CS 861: Theoretical Foundations of Machine Learning
  • CS 524: Introduction to Optimization
  • CS 723: Dynamic Programming and Associated Topics
  • SOC 901: Social Network Analysis