研究ユニット

カイラル表現論ユニットは、量子場の理論に現れる対称性を研究します。より具体的には、無限次元リー代数、例えば Kac–Moody 代数や、より一般的には頂点代数の表現論に焦点を当てています。

ネットワーク量子デバイスユニット「NetQ」の目指すところは、量子ネットワークとその応用の原理実証を可能にするために必要な理論的ツールを開発することです。具体的には、新しい誤り訂正メカニズム、暗号化プロトコル、シミュレーションアルゴリズムなどです。

Utilizing cutting-edge time-resolved techniques, including ultrafast PEEM and ARPES, the Femtosecond Spectroscopy Unit explores extreme light-matter interaction on the nanometer and femtosecond scale.

The Model-Based Evolutionary Genomics Unit works at the crossroads of computational and evolutionary biology. Our long-term goal is to achieve an integrative understanding of the evolution of Life on Earth and the origins and emergence of complexity across different biological scales, from individual proteins to ecosystems. To move towards this goal, we develop and apply model-driven evolutionary genomics methods to reconstruct the Tree of Life and the major evolutionary transitions that have occurred along its branches.

The Algebraic Combinatorics and Fundamental Physics Unit investigates new algebro-combinatorial and geometric structures underlying quantum field theory, focusing on scattering amplitudes, total positivity, amplituhedra, and cluster algebras.

脳細胞の活動と動物の行動を同時に詳細に記録することで、脳内での情報処理機構について調べています。

我々の研究室は、超新星・ガンマ線バーストに関する様々な謎の解明に向け、理論的研究を行います。超新星・ガンマ線バーストは宇宙最大規模の爆発現象であり、その爆発メカニズムは良く分かっていません。また超新星・ガンマ線バーストは物理と謎の宝庫であり（重力波、ニュートリノ、元素合成、非平衡電離、最高エネルギー宇宙線等）、極限宇宙物理学の最高峰とも言うべき現象です。我々はこれら様々な謎の解明に向けて最先端の理論的・数値的研究を行い、この宇宙最大爆発現象の全貌を明らかにします。我々の理論研究は、超新星・ガンマ線バーストに関する最先端の観測に物理的解釈を与え、次世代観測に対する予言・提言を発信します。

The Solar-Terrestrial Environment and Climate Unit aims to deepen our understanding of the Sun, its surrounding environment, and their impacts on Earth’s weather and climate.

偏微分方程式の解析は豊富な内容を持つ数学の分野で、科学の様々な学問領域において幅広く応用されています。特に幾何学及び関連分野に現れる非線形偏微分方程式を考察することが重要です。幾何学的偏微分方程式ユニットでは、新たな解析手法を考案することによって、幾何学的発展方程式の解の振る舞いについて理解し、そしてサブリーマン多様体や距離空間などの一般的な幾何学的設定における非線形方程式の可解性問題を探究します。研究の動機として、材料科学、結晶成長，画像処理への応用が多く知られていて、最適制御やゲーム理論、機械学習などのテーマにも密接に関係しています。

幾何学的群論ユニットでは、無限群の大域的幾何構造を研究し、双曲空間や非正曲率空間、群の増大度や射影複体に焦点を当てています。

The Applied Cryptography Unit investigates the design and analysis of modern cryptographic primitives and schemes used to protect the confidentiality and integrity of data – at rest, being communicated or computed upon – both in the classical and the quantum settings. Areas of interest include the algebraic cryptanalysis of symmetric and asymmetric key algorithms; design and analysis of primitives for privacy-preserving cryptographic mechanisms; and the design and analysis of quantum-safe cryptographic constructions.

The Information Theory, Probability and Statistics Unit performs theoretical research at the intersection of the fields described in the name with applications to various areas that include Estimation Theory, Computational Biology, Hypothesis Testing, etc.

In machine learning and data science unit, we focus on developing fundamental machine learning algorithms and solving important scientific problems using machine learning. We are currently interested in statistical modeling for high-dimensional data including kernel and deep learning models and geometric machine learning algorithms, including graph neural networks (GNN) and optimal transport problems. In addition to developing ML models, we focus on developing new machine learning methods to find a new scientific discoveries from data automatically.

The Marine Physics and Engineering Unit advances the forecast of ocean dynamics and the development of hydrodynamic disaster mitigation alternatives, paving the way for novel ocean technologies.

液滴・ソフトマター ユニットは、ソフトマター、液滴物理学、界面現象が交わる豊かな研究領域を探究しています。

物理学者たちは長い間、物質およびエネルギーの本質を説明できる普遍的法則を探し求めてきましたが、最近まで複雑な生物系の研究は困難でした。理論生物物理学ユニットでは...

The biological nonlinear dynamics data science unit investigates complex systems explicitly taking into account the role of time. We do this by instead of averaging occurrences using their statistics, we treat observations as frames of a movie and if patterns reoccur then we can use their behaviors in the past to predict their future. In most cases the systems that we study are part of complex networks of interactions and cover multiple scales. These include but are not limited to systems neuroscience, gene expression, posttranscriptional regulatory processes, to ecology, but also include societal and economic systems that have complex interdependencies. The processes that we are most interested in are those where the data has a particular geometry known as low dimensional manifolds. These are geometrical objects generated from embeddings of data that allows us to predict their future behaviors, investigate causal relationships, find if a system is becoming unstable, find early warning signs of critical transitions or catastrophes and more. Our computational approaches are based on tools that have their origin in the generalized Takens theorem, and are collectively known as empirical dynamic modeling (EDM). As a lab we are both a wet and dry lab where we design wet lab experiments that maximize the capabilities of our mathematical methods. The results from this data driven science approach then allows us to generate mechanistic hypotheses that can be again tested experimentally for empirical confirmation. This approach merges traditional hypothesis driven science and the more modern Data driven science approaches into a single virtuous cycle of discovery.

生物複雑性ユニットでは、生体分子回路から細胞集団までの様々な生物物理学システムにおいてどのように確率的変動下でも正常に機能しているのかを対象に研究をしています。