Faculty and Research Units 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. Faculty and Research Units Discover Research Specialties Browse research disciplines and specialities. Discover more Find a Faculty Member or Research Unit Research Unit | Faculty Member (-) Biology Chemistry Computer Science Ecology and Evolution Engineering and Applied Sciences Marine Sciences Mathematics Neuroscience Physics Facet Research Discipline Biochemistry Bioinformatics Biology Biophysics Biotechnology Botany Cell biology Complex systems Developmental biology Evolutionary biology Genetics Genomics Health sciences Immunology Medicine Molecular biology Nanoscience Physiology Structural biology Synthetic biology Theoretical sciences (-) Virology Facet Specialty Clear filters Biological Nonlinear Dynamics Data Science Unit 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 Assistant Professor Molecular Cryo-Electron Microscopy Unit The Molecular Cryo-Electron Microscopy Unit investigates the structure of macromolecular complexes with an emphasis on viruses, ion channels, and membrane proteins. The unit seeks better und... Matthias Wolf Professor Optical Neuroimaging Unit The Optical Neuroimaging Unit uses home-built two-photon microscopes and special fluorescent dyes to image neuronal and astrocytic activity on a cellular level in behaving mice. Bernd Kuhn Professor Annual Reports A yearly report from each research unit Visit the page
Biological Nonlinear Dynamics Data Science Unit 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 Assistant Professor
Molecular Cryo-Electron Microscopy Unit The Molecular Cryo-Electron Microscopy Unit investigates the structure of macromolecular complexes with an emphasis on viruses, ion channels, and membrane proteins. The unit seeks better und... Matthias Wolf Professor
Optical Neuroimaging Unit The Optical Neuroimaging Unit uses home-built two-photon microscopes and special fluorescent dyes to image neuronal and astrocytic activity on a cellular level in behaving mice. Bernd Kuhn Professor