18 April 2024 Thu 4 pm

Opinions shape our societies, driving decision-making processes and influencing collective behaviors. Understanding the mechanisms behind opinion formation and evolution is crucial for predicting societal trends. Network topologies play a crucial role in shaping opinion dynamics since the structure of the underlying network greatly influences how opinions spread, converge, or diverge within a population. Leveraging the Ising model—a powerful tool adapted from statistical physics— we explore opinion dynamics across diverse network topologies: Watts-Strogatz (W-S), Barabasi-Albert (B-A), and Erdos-Renyi (E-R). Through comprehensive simulations, we explore how network topology impacts key aspects of opinion dynamics. In this presentation, I will discuss how combining network science with statistical physics can help us better understand opinion dynamics, a key challenge in complex systems.

Additionally, I will briefly discuss my research involving the application of machine learning in advanced spectroscopies, including techniques such as Muon-Spin Relaxation and Laser-Induced Breakdown spectroscopies. I will also cover the machine-learning application in Muon tomography and battery research.

< Previous

Next >