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Goda Lab
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The lab studies Monte Carlo methods for estimating uncertainty numerically, and their applications.
AboutAbout the lab
Methods for evaluating uncertainty numerically.
The Goda Lab studies methods for evaluating uncertainty numerically, with a focus on Monte Carlo methods.
Monte Carlo methods are computational methods that estimate quantities such as averages by repeating random trials.
The lab mainly studies these methods themselves, working on theory such as quasi-Monte Carlo methods, which approach the true value faster.
Another research theme is how to use the evaluated uncertainty in decision making and experimental design.
Key terms
- Monte Carlo method
- A computational method that repeats trials using random numbers many times and estimates values such as averages from the results
- Quasi-Monte Carlo method
- A method that estimates values using points chosen to be spread evenly, instead of random numbers. It greatly improves the speed at which estimates approach the true value
- Sensitivity analysis
- A method for numerically examining which inputs affect the variability of results, and by how much
NewsLab news
Paper on quasi-Monte Carlo methods accepted by a mathematics journal
Paper on function approximation accepted by Constructive Approximation
Paper from joint industry-academia research with Kioxia Corporation accepted by an IEEE journal
Associate Professor Goda awarded the Joseph F. Traub Prize
Associate Professor Goda and co-authors publish a book on Monte Carlo and quasi-Monte Carlo methods (Japanese title: 重点解説 モンテカルロ法と
準モンテカルロ法)
ResearchResearch themes
The lab aims to evaluate the uncertainty of stochastic events numerically and use it for rational decision making and system optimization. It studies the theory of Monte Carlo methods, which underpins this goal, and their applications.
*Illustrative imageUsing uncertainty in decision making
This research considers how to make use of evaluated uncertainty. The lab works on sensitivity analysis, which identifies the inputs that affect the variability of results, on evaluating the value of data yet to be collected, and on optimizing experimental design.
*Illustrative imageTheory of Monte Carlo methods
The lab studies ways to make computations that repeat random trials approach the answer faster. It covers quasi-Monte Carlo methods and multilevel Monte Carlo methods, among others.
*Illustrative imageApplications to other fields
The lab extends the findings from its Monte Carlo research to other fields. In one example, a proposed estimation method was applied to computations related to variational autoencoders.
MembersFaculty
The members of the Goda Lab consist of faculty and students in the doctoral, master's, and undergraduate programs.As of 2026
- Associate ProfessorTakashi GodaPublications ↗
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Dice ( )” Daniel Dionne / CC BY-SA 2.0 (cropped)
- Using uncertainty in decision making: “Rig wind river” The Pinedale Field office of the BLM Wyoming State Office / Public domain (cropped)
- Theory of Monte Carlo methods: “Pi 30K” nicoguaro / CC BY 3.0 (cropped)
- Applications to other fields: “T-SNE Embedding of MNIST” Kyle McDonald / CC BY 2.0 (cropped)
- Related lab (Sakata-Mori-Asatani-Nishimoto Lab): “Social Network Visualization” Martin Grandjean / CC BY-SA 4.0 (cropped)
- Related lab (Shimada Lab): “A starling murmuration - geograph.org.uk - 6389346” Walter Baxter / CC BY-SA 2.0 (cropped)
- Related lab (Toriumi Lab): “Social Network Visualization” Martin Grandjean / CC BY-SA 4.0 (cropped)
- Related lab (Ohsawa Lab): “Wikidata in the Linked Open Data cloud” Thomas Shafee / CC BY 4.0 (cropped)
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