教員・研究ユニット

OIST research units take a cross-disciplinary approach to research, and the PhD program encourages students to explore the intersections of disparate fields of science and technology. Find the research unit of your interest below.

教員・研究ユニット

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Blue strings spreding like a tree

モデルベース進化ゲノミクスユニット

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.
Gergely János Szöllősi profile photo

ゲルゲイ・ヤーノシュ

准教授

Biological Nonlinear Dynamics Data Science Unit

生物の非線形力学データサイエンス研究ユニット

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.
Gerald Pao

ジェラルド・パオ

准教授

Robot raising arms

認知脳ロボティクス研究ユニット

認知脳ロボティクス研究ユニットの研究目標はニューロロボティクスの実験研究を通じて身体性認知の原理を理解することにあります。主要な研究課題は、先天的な脳構造を活用して反復的で限られた行動体験を通じて認知機能がどのように発達するか、社会的認知における間主観性が他者との身体的かつ文脈的な相互作用を通じてどのように形成されるか、そして、意識や自由意志などの主観的体験が科学的および現象学的にどのように説明できるかについてです。さらに、私たちの発達ニューロロボティクス手法を用いた、統合失調症、自閉症などの神経発達障害の原因メカニズムの解明も目指しています。
谷 淳の写真

谷 淳

教授

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