Srisha Nippani

Mentored by IDIES member Professor Mauro Maggioni

Systems of interacting agents arise in various academic disciplines, where agents may represent particles, cells, animals, people, rational agents, etc… Recent work by Professor Mauro Maggioni has explored the following questions: Given observations of trajectories from an interacting system of agents, how can one estimate the underlying interaction laws from the data? Which learning algorithms are suitable for this task, and what theoretical guarantees can be provided to ensure the reliability of these estimators? Our current work extends this research by relaxing the assumption of pairwise interactions, allowing for higher-order interactions—starting with ternary interactions. In addition, we aim to improve the computational aspects of the algorithms used to construct the estimators, enabling them to scale to larger data sets.

Sri is a sophomore at Johns Hopkins University majoring in Mathematics with minors in Economics and Computer Science. He aims to pursue graduate studies in Economics and is enthusiastic about contributing to interdisciplinary research that leverages quantitative and computational methods.