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
A new system, known as Murakkab, optimizes the design and deployment of multistep workflows that power AI applications.
MIT researchers provide a major upgrade to the nearly century-old idea of random utility models.
MIT researchers developed a way to identify the smallest dataset that guarantees optimal solutions to complex problems.
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Neural network controllers provide complex robots with stability guarantees, paving the way for the safer deployment of autonomous vehicles and industrial machines.