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
Find a Faculty Member or Research Unit
Algorithms for Ecological and Evolutionary Genomics
The Algorithms for Ecological and Evolutionary Genomics Unit develops computer algorithms for core problems in genomics to study the genomes of every extant species on our planet.
Gene Myers
Professor (Adjunct)
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
Professor (Adjunct)
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
Complex Fluids and Flows Unit
We study multiscale and multiphysics problems related to fluid dynamics by numerical simulations. Our research is focused on turbulence, non-Newtonian fluids and multiphase flows.
Marco Edoardo Rosti
Assistant Professor
Computational Neuroscience Unit
We study how neurons and microcircuits in the brain operate and explore the influences of neuronal morphology and excitability on common neural functions such as synaptic plasticity and learning, and determine how molecular mechanisms enable these functions.
Erik De Schutter
Professor
Embodied Cognitive Science Unit
We are developing theoretical and experimental projects in cognitive science, guided by the hypothesis that agent-environment interaction is an essential part of mental activity.
Tom Froese
Assistant Professor
Machine Learning and Data Science Unit
In the machine learning and data science (MLDS) 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 automatically find a new scientific discoveries from data.
Makoto Yamada
Associate Professor
Networked Quantum Devices Unit
The ambition of NetQ, the Networked Quantum Devices unit, is to develop the necessary theoretical tools such as novel error correction mechanisms, cryptographic protocols, and simulation alg...
David Elkouss
Associate Professor
Quantum Information Security Unit
The research unit will conduct theoretical research into all aspects of quantum information processing with focus on the nature of randomness and its applications in secure communication.
Artur Ekert
Professor (Adjunct)