Research Units

The mission of the Analysis and PDE unit is to reveal and analyze the mathematical principles reflecting natural phenomena expressed by partial differential equations and advance the boundar...

Analysis on Metric Spaces Unit explores analytic and geometric problems arising in diverse spaces, especially those with no priori smooth structures. Our research focuses on partial diff...

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. Particular areas of interest include the design and analysis of quantum / post-quantum cryptography schemes, the algebraic cryptanalysis of symmetric and asymmetric key algorithms, as well as the design and analysis of primitives for privacy-preserving cryptographic mechanisms.

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

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.

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.

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.

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

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 are developing theoretical and experimental projects in cognitive science, guided by the hypothesis that agent-environment interaction is an essential part of mental activity.

Analysis of partial differential equations is a very rich mathematics subject, which is broadly applied in a large variety of fields of science. It is particularly important to study nonlinear PDEs that arise in geometry and many related areas. The Geometric Partial Differential Equations Unit aims to develop new analytic methods to understand behavior of solutions to various geometric evolutions and explore solvability of nonlinear equations in general geometric settings such as sub-Riemannian manifolds and metric spaces. Our research is motivated by numerous applications in material sciences, crystal growth, image processing and is also closely connected with topics in optimal control, game theory and machine learning, etc.

The Gravity, Quantum Geometry and Field Theory Unit’s research interest lies in revealing the quantum nature of spacetime. Geometrical aspects of gravity, manifested in General Relativity, h...

The Information Theory, Probability and Statistics Unit performs theoretical research at the intersection of the fields described in the name with applications to various areas that include Estimation Theory, Computational Biology, Hypothesis Testing, etc.

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

The Marine Physics and Engineering Unit advances the forecast of ocean dynamics and the development of hydrodynamic disaster mitigation alternatives, paving the way for novel ocean technologies.

The Model-Based Evolutionary Genomics Unit works at the crossroads of computational and evolutionary biology. Our long-term goal is to achieve an integrative understanding of the evolution of Life on Earth and the origins and emergence of complexity across different biological scales, from individual proteins to ecosystems. To move towards this goal, we develop and apply model-driven evolutionary genomics methods to reconstruct the Tree of Life and the major evolutionary transitions that have occurred along its branches.

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.