Complex systems

The MB-OpenQS Unit theoretically explores hybrid many-body quantum systems composed of strongly interacting matter and light.

Geomimicry, Emergence, and Meta Soil Unit re-imagines soil as adaptive matter. We learn nature's microscopic design rules to engineer "meta soils" with tailored composition and function.

Collective Dynamics and Quantum Transport Unit explores dynamical and transport phenomena in various quantum matters and their spintronic, electronic, and quantum-information applications.

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.

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.

The Biodiversity and Biocomplexity Unit explores how ecological and evolutionary processes generate and sustain biodiversity, and how those processes are being altered by human activities.

The Integrated Open Systems Unit aims to understand the fundamental principles that govern open, complex systems and apply such knowledge for real-world applications

We seek the principles governing the behavior of whole organisms, integrating physics, biology and computational approaches to understand life's most complex and fascinating phenomena.

We investigate how mind and matter make a difference to each other — from the origins of life and agency to how individuals, technologies, and cultures shape thought and experience. Our work is anchored by irruption theory, a framework developed in the unit for understanding mental causation as structured unpredictability.

The CSE Unit analyzes the dynamics of complex adaptive systems, focusing on behavioral dynamics shaping social systems, eco-evolutionary dynamics shaping ecosystems, and their interactions.

We work in experimental nonlinear, non-equilibrium and soft matter physics. Our current research focuses on fluids, granular media, fluctuations in renewables and quantitative life sciences.

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.

The Biological Complexity Unit studies how biophysical systems, ranging from subcellular circuits to cellular populations, can function despite being subject to random fluctuations.