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The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...
Modern integrated circuits are tiny yet incredibly complex technological artifacts, composed of millions and billions of individual structures working in unison.Managing their complexity and facilitating their design drove part of the co-evolution of moder ...
Large omics datasets are nowadays routinely generated to provide insights into cellular processes. Nevertheless, making sense of omics data and determining intracellular metabolic states remains challenging. Kinetic models of metabolism are crucial for int ...
Deep neural networks have completely revolutionized the field of machinelearning by achieving state-of-the-art results on various tasks ranging fromcomputer vision to protein folding. However, their application is hindered bytheir large computational and m ...
This project focuses on the design and production of an electronic circuit, whose goal is to control the opening and closing of magneto-rheological (MR) valves embedded in a shoe. ...
Neuromorphic computing is a wide research field aimed to the realization of brain-inspired
hardware, apt to tackle computation of unstructured data more efficiently than currently done
with standard computational units. Oscillatory neural networks are know ...
Quantum many-body control is a central milestone en route to harnessing quantum technologies. However, the exponential growth of the Hilbert space dimension with the number of qubits makes it challenging to classically simulate quantum many-body systems an ...
Maxwell's equations govern light propagation and its interaction with matter. Therefore, the solution of Maxwell's equations using computational electromagnetic simulations plays a critical role in understanding light-matter interaction and designing optic ...
We address the problem of designing stabilizing control policies for nonlinear systems in discrete-time, while minimizing an arbitrary cost function. When the system is linear and the cost is convex, the System Level Synthesis (SLS) approach offers an effe ...
Large-scale cyber-physical systems require that control policies are distributed, that is, that they only rely on local real-time measurements and communication with neighboring agents. Optimal Distributed Control (ODC) problems are, however, highly intrac ...