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

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Applied Cryptography Unit banner

Applied Cryptography Unit

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.
Carlos Cid

Carlos Cid

Professor (Adjunct)

Astrophysical Big Bang Group

Astrophysical Big Bang Group

Our group focuses on unveiling lots of mysteries surrounding astrophysical explosive phenomena such as supernovae (SNe) and gamma-ray bursts (GRBs). SNe and GRBs are the most powerful explosions in the universe, yet very little is known about their explosion mechanisms. These astrophysical big bangs fascinate us with their unknown physics and puzzling astronomical phenomena such as gravitational waves, neutrinos, nucleosynthesis, non-equilibrium ionization, ultra-high-energy cosmic rays. Through our theoretical and computational approaches, we strive to reveal the complete pictures of these explosions and provide the-state-of-the-art physical interpretations for current, cutting-edge observations and useful predictions for future observations by next-generation astronomical observatories.
Shigehiro Nagataki

Shigehiro Nagataki

External Professor

Biological Nonlinear Dynamics Data Science Unit

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

Gerald Pao

Assistant Professor

cellpro

Cell Proliferation and Gene Editing Unit

Every day, millions of cells in our body divide to maintain essential tissue functions. Errors in cell division can lead to developmental disorders or cancer. The research of the unit is focused on molecular mechanisms of cell divison and quality control in normal and cancer cells to understand tumor-suppressive mechanisms and identify biomarkers that confer a cancer-specific vulnerability to chemical drugs. The unit combines high throughput imaging, gene editing and genome wide screens to open new avenues for therapeutic development.
Photo of Franz Meitinger

Franz Meitinger

Assistant Professor

Robot raising arms

Cognitive Neurorobotics Research Unit

The Cognitive Neurorobotics Research Unit is dedicated to investigating the principles of embodied cognition by conducting experimental studies in synthetic neurorobotics. The primary goals of our research are to understand:on how innate structures can be leveraged to develop cognitive constructs through iterative but limited behavioral experiences; how primary intersubjectivity in social cognition can be formed through enactive and contextual interactions with others; and how subjective experiences such as consciousness and free will can be scientifically and phenomenologically explained. In addition, our developmental neurorobotics approach is intended to uncover the underlying mechanisms of neurodevelopmental disorders, such as schizophrenia and autism. Through these researches, we can expect to deepen our ontological understanding of human beings, rather than simply creating another smart machine-learning robot.
Photo of Jun Tani

Jun Tani

Professor

Annual Reports
A yearly report from each research unit