Information Theory in Modern Science

Information Theory is a field that brought forth the information age. It provides a rigorous mathematical framework that connects and unifies diverse areas of mathematics, including probability theory, statistics, and optimisation. Through its formalism, Claude Shannon was able to characterise and analyse the fundamental limits of compression and communication, laying the theoretical groundwork for modern digital technologies such as data compression algorithms, error-correcting codes, and contemporary communication systems ranging from 3G to 5G and beyond.

Beyond its historical role in communication, Information Theory has evolved into a versatile and powerful collection of conceptual and analytical tools. Owing to its inherently interdisciplinary nature, it now plays a central role in a wide range of modern fields, including statistical learning, inference and estimation theory, probability theory, and statistics, as well as applications in biology, neuroscience, and data science.

The purpose of this meeting is to showcase and explore the use of information-theoretic ideas and methods in contemporary research. The programme will feature a combination of lectures and presentations of recent results, illustrating how Information Theory can be leveraged to address modern scientific and technological problems, particularly in high-dimensional and data-driven settings.

Workshop website: https://www.oist.jp/conference/information-theory-modern-science

OIST is deeply committed to the advancement of women in science, in Japan and worldwide. Women are strongly encouraged to apply.