Research Units

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.

The Future-Proof Cryptography Unit studies Multi-Party Computation, Fully Homomorphic Encryption, Verifiable Computation and Cryptanalysis and how these are affected by quantum computers.

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.

Cognitive functions of the brain, such as sensory perception, learning and memory, and decision-making emerge from computations by neural networks. The advantages of biological neural comput...

The OIST Neural Computation Unit aims to develop novel algorithms and to reveal brain mechanisms for reinforcement learning and Bayesian inference by combining top-down theoretical and bottom-up experimental approaches.