研究ユニット

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.

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.

（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...

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」...

量子情報科学・技術とは、量子力学と情報理論の融合によって生まれ、量子コンピュータや量子通信、量子計測などを含みます。

量子マシンユニットでは、量子サブシステムが個々の能力を凌ぐ機能を集合的に発揮できる量子デバイスの実用化を目指して、理論と実験の両面から研究しています。

量子理論ユニット（TQM）では物性物理学や統計物理学の幅広い問題を研究しています。