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。

生物が正常に機能するためには、適切なタイミングおよび場所で遺伝子のオン・オフを確実に切り替えなくてはなりません。遺伝子発現の制御は複雑なプロセスであり、多くの調...

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。

ヒト進化ゲノミクスユニットでは、現生人類に最も近い進化の近縁である、ネアンデルタール人とデニソワ人のゲノムを使用し、現代の人間に固有するゲノム変異を特定します。

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 Future-Proof Cryptography Unit studies Multi-Party Computation, Fully Homomorphic Encryption, Verifiable Computation and Cryptanalysis and how these are affected by quantum computers.

当ユニットでは、我々が現在直面する様々な環境変動が、植物の共生菌や病原菌および炭素や窒素などの物質循環を駆動する土壌微生物に与える影響を、分子生物学および化学的手法を組み合わせて研究しています。

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.

（2025年6月より閉店）物理生物学ユニットでは、主に生物系の研究を対象とした物理科学ベースのツールを開発しています。ゲノム進化および集団ゲノム科学などを主な対象とし、遺伝的変異が自然選択と進化とを結合する仕組みに関する新たな知見を得ようとしています。

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...

生態・進化ゲノミクスアルゴリズムユニットでは、地球上の現存する種のゲノムの組立てや比較などを研究し、ゲノミクスのコア問題の為のアルゴリズムを開発します。

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.

マウス脳において行動コンテキストが、どのように臭覚情報処理を調整するかを研究しています。手法は、神経生理学、イメージジング、回路解析、行動科学などを用います。

感覚知覚、学習と記憶、および意思決定などの脳の認知機能は、神経回路による計算から出現します。神経情報・脳計算ユニットでは、機械計算と比較して生物学的神経計算が有...

神経計算ユニットでは、脳のように柔軟かつ確実な学習を実現するアルゴリズムの開発と、それによる脳の学習の仕組みの解明に取り組んでいます。最大のテーマは「強化学習」...