This course will introduce modern, state of the art research on algorithms for massive data. We shall put an emphasis on works that sit on the border “between theory and practice” and on algorithmic problems with applications in machine learning, complex networks, and data science.
Modern computational systems (such as LLMs, social networks, and e-commerce systems) are required to process and analyze huge amounts of highly-structured data, and make decisions in split seconds that affect the lives of humans and the society as a whole. The amount of data, the complex structure, and the importance of fast decision making together often require the use of heuristics and algorithms that are efficient in the real world, but where the theoretical understanding is currently lacking.
We shall discuss several algorithmic problems where there is a large (and increasing) gap between theory and practice, and study and propose “optimistic” tools from the beyond worst case analysis literature to close this gap.
See syllabus tab for more details.