Description of the summer 2020 REU project on Machine learning approaches to oscillator and clock synchronization: 

PI: Hanbaek Lyu

If a group of people is given local clocks with arbitrarily set times, and there is no global reference (for example GPS), is it possible for the group to synchronize all clocks by only communicating with nearby members? In order for a distributed system to be able to perform high-level tasks that may go beyond the capability of an individual agent, the system must first solve a “clock synchronization” problem to establish a shared notion of time. The study of clock synchronization (or coupled oscillators) has been an important subject of research in mathematics and various areas of science for decades, with fruitful applications in many areas including wildfire monitoring, electric power networks, robotic vehicle networks, large-scale information fusion, and wireless sensor networks. However, there has been a gap between our theoretical understanding of systems of coupled oscillators and practical requirements for clock synchronization algorithms in modern application contexts. This project will develop systematic approaches for bridging this gap based on combinatorial and probabilistic methods. The use of discrete oscillators will be a key thread in developing more robust and efficient clock synchronization algorithms, extending the current proof techniques for convergence guarantee, and providing a foundation for a data-driven approach to the clock synchronization problems.

During the 8-weeks long summer REU 2020 project, the team will take an interdisciplinary approach to the problem of oscillator and clock synchronization using some of the modern machine learning techniques and a family of discrete oscillators due to the PI (called the Firefly Cellular Automata).

Minimum experience in python programming and dynamical systems is required.

Probability, combinatorics, and complex systems

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