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Interdisciplinary Fusion 1 (Data-Driven Science Hypothesis Generation Unit) Lab
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A research unit building a new kind of science that generates hypotheses from data.
AboutAbout the lab
Generating hypotheses from data.
The Data-Driven Science Hypothesis Generation Unit is a research unit of the Science Collaboration and Exploration Center (SACRA), affiliated with the Graduate School of Science. Researchers in mathematics, physics, geology, and other fields come together to explore new scientific principles for the age of data science and extend them to various fields.
Using data analysis and machine learning, the unit pursues new research that builds governing equations and performs causal inference from large amounts of data, aiming to systematize the process of generating hypotheses from data.
At the graduate level, it carries out research and education as the Interdisciplinary Fusion Course of the Graduate School of Science.
Key terms
- Topology
- Mathematics that studies the properties of the "shape" of figures and spaces as features that remain unchanged under continuous deformation
- Machine learning
- A method in which computers learn patterns from data
- Causal inference
- Inferring cause-and-effect relationships from data
ResearchResearch themes
The lab systematizes the process of generating hypotheses from data and explores new principles for data-driven science.
*Illustrative imageData-Driven Science Hypothesis Generation Hub
The lab advances methods that construct governing equations and causal inference from large amounts of data using data analysis and machine learning, with a view to building "meta-science," which describes the discovery of natural laws itself as an emergent phenomenon.
*Illustrative imageIntroducing Mathematical Structure into Data
The lab introduces mathematical structures into collections of data that are difficult to handle, establishing methods to describe phenomena mathematically or apply machine learning to them, and proposes a new form of science that generates hypotheses directly from data.
*Illustrative imageData Analysis Using Topology
To complement the overview of the overall structure of data, which deep learning struggles with, the lab applies mathematical concepts centered on topology and geometry, and studies new analysis methods that give concrete form to objects that were previously difficult to quantify.
MembersFaculty
- ProfessorShizuo Kaji
- Program-Specific Assistant ProfessorGRAIFF ZURITA Sebastian Elias
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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.
- Top image: “Academic works by discipline over time (visualization of studies data in OpenAlex)” Shubhanshu Mishra made the first version which I then modified and the latest version is made anew / CC BY 4.0 (cropped)
- Data-Driven Science Hypothesis Generation Hub: “Whole-game-of-data-science” Hadley Wickham, Mine Çetinkaya-Rundel, Garrett Grolemund / CC BY 3.0 (cropped)
- Introducing Mathematical Structure into Data: “Change of number of scientific works by field (visualization of OpenAlex data)” Shubhanshu Mishra and own work / CC BY 4.0 (cropped)
- Data Analysis Using Topology: “An example of the 0-dimensional persistence barcode of a filtered complex” Sérgio Branco, João G. Carvalho, Marco S. Reis, Nuno V. Lopes, Jorge Cabral / CC BY 4.0 (cropped)
- Related lab (Interdisciplinary Fusion 2 (Unexplored Quantum Measurement Unit) Lab): “Squeezer device (optical parameter oscillator), used in 2011 in initial experiments with squeezed light at the Hanford LIGO observatory - MIT Museum - Cambridge, MA - DSC09057” Daderot / CC0 (cropped)
- Related lab (Interdisciplinary Fusion 3 (Earth-Life Co-evolution Research Unit) Lab): “Geological time spiral” United States Geological Survey / Public domain (cropped)
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