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Data Science Lab
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The lab studies new methods and theory in medical statistics and machine learning as part of data science.
AboutAbout the lab
Drawing reliable insights from data.
The Data Science Research Group develops statistical methods and machine learning methods that derive new knowledge from multivariate data, and studies their mathematical statistics and learning theory.
Built on a foundation of mathematical statistics, the lab works on causal inference, machine learning, analysis of medical and health data, survival analysis, meta-analysis, and more.
It considers what can be concluded, and how far, when data are incomplete, as with missing data and censoring.
On the lab's website, it is introduced under the name "Sugimoto Lab".
Key terms
- survival analysis
- Data analysis that targets the time until an event occurs. One of the research themes in medical statistics
- causal inference
- Statistical methods for examining cause-and-effect relationships from data
- reinforcement learning
- A machine learning approach that learns better ways of making choices by accumulating choices and their outcomes
ResearchResearch themes
Using statistical theory, applied mathematical methods, and computers, the lab develops new statistical methods and reinforcement learning methods and studies their theory. It aims to connect the statistical learning methods it develops to applications in the empirical sciences.
Theory of stratified analysis
For stratified analysis that adjusts for confounders, the lab derives explicit formulas for the exact distribution of the representative random-effects model. It is also interested in applications to clusters built by unsupervised learning and in extending the theory.
Correlation analysis of multiple event times
When handling several types of event times, the correlation between counting processes can be expressed by formulas that take the characteristics of the events into account. Combined with multivariate modeling using copulas, this makes it possible to develop new statistical methods.
*Illustrative imageBandit problems and reinforcement learning
The lab works on reinforcement learning methods such as bandit problems, and on their information theory. Using statistical theory, it examines the trade-off between exploring for better choices and exploiting existing knowledge when data arrive sequentially.
*Illustrative imageTheory guaranteeing the behavior of machine learning
The lab develops methods that guarantee, through theory and numerical experiments, how machine learning behaves on test data. Focusing on which algorithm was used for training, it studies statistical learning theory using PAC-Bayes and information theory.
MembersFaculty
The members of the Data Science Research Group are faculty, project professors, visiting faculty and visiting researchers, doctoral, master's, and undergraduate students, and administrative and secretarial staff.
- ProfessorTomoyuki Sugimoto
- Associate ProfessorFutoshi Futami
- Assistant ProfessorYozo Tonaki
VoicesStudent voices
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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: “Antique one-armed bandit, Ventnor, Isle of Wight, UK” Ronald Saunders from Warrington, UK / CC BY-SA 4.0 (cropped)
- Bandit problems and reinforcement learning: “Slot machine” Jeff Kubina from the milky way galaxy / CC BY-SA 2.0 (cropped)
- Theory guaranteeing the behavior of machine learning: “Bayes' Theorem MMB 01” mattbuck (category) / CC BY-SA 3.0 (cropped)
- Related lab (Senoo Lab): “Fluidigm C1 Single-Cell Auto Prep system” Konrad Förstner / CC BY 2.0 (cropped)
- Related lab (Kawahara Lab): “Artificial Neural Network with Chip” mikemacmarketing / original posted on flickr Liam Huang / clipped and posted on flickr / CC BY 2.0 (cropped)
- Related lab (Nakashima Lab): “Sample view of data collection process using artificial intelligence application” Captain Andrew M. Freeman, Air Force Institute of Technology / Air Force Research Laboratories (AFRL), Sensors, ATR, Target Recognition Branch. / Public domain (cropped)
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