Optimization and Game Theory

    Research in this area focuses on developing efficient and scalable algorithms for solving large scale optimization problems in engineering, data science and machine learning. Our work also studies optimal decision making in networked settings, including communication networks, energy systems and social networks. The multi-agent nature of many of these systems also has led to several research activities that rely on game-theoretic approaches.

    Latest news in optimization and game theory

    The National Science Foundation (NSF) announced today an investment of $220 million to establish 11 artificial intelligence (AI) institutes, each receiving $20 million over five years. One of these,

    A tunable coupler can switch the qubit-qubit interaction on and off. Unwanted, residual (ZZ) interaction between the two qubits is eliminated by harnessing higher energy levels of the

    Realtime Robotics has developed a combination of proprietary software and hardware that reduces system deployment time by 70 percent or more, reduces deployment costs by 30 percent or

    A new simulation environment, PlasticineLab, is designed to make robot learning more intuitive. Tasks like the ones pictured are designed to train agents to manipulate soft and deformable

    Lars Erik Matsson Fagernæs, Bernhard Paus Græsdal, and Herman Øie Kolden (l-r) met in Cambridge, Massachusetts, during orientation for an MIT Professional Education program in 2019. In 2020,

    Upcoming events