Razvan Teodorescu

Many complex systems from fields as diverse as social networks, mathematical biology, or smart electrical power grids can be modeled as a graph of statistical correlations. The task of making a decision based on such a model (e.g., identifying an imminent hazard, or predicting an oncoming surge of activity on social networks) may appear as being hopelessly complicated and out of reach. This presentation describes a few advanced algorithms that greatly reduce the complexity of decision-making, by following the evolution of "beliefs" (or Bayesian data) on the graph.

There is a popular fascination with various ideas of "time travel" and "multiverse", fueled in part by movies and shows which incorporate actual science to various degrees. Aimed at a general audience, this talk clarifies concepts and ideas in an accessible way, with ample references to movies and shows dominating this generation.

With the advent of widely-available AI tools, the role of traditional teaching/learning in STEM fields comes under increased scrutiny. Is STEM teaching an "endangered profession"? What is the new role of the classroom teacher, when every student can claim to have an expert tutor readily available on their device? This presentation takes the audience through the finer points of scientific learning and points to the new meanings of STEM education in the current paradigm.

Learning mathematics (at any level) is not all that different from other learning experiences, and yet misconceptions and misleading approaches about this field abound. This talk gives a learner's perspective (and experience), along with specific demonstrations for how diverse this process can be.