My research interests focus on understanding complex systems and emergent phenomena through a blend of theoretical, computational, and machine learning approaches. I explore Self-Organized Criticality (SOC), which describes how systems naturally evolve to a critical state where even small perturbations can lead to large-scale changes. SOC is a powerful framework for understanding diverse systems, from avalanches and earthquakes to neural networks and ecosystems. Investigating how these critical states arise and their implications for system behavior is central to my work.
I also delve into Nonlinear Dynamics, where I study the intricate and often chaotic behavior of systems governed by nonlinear interactions. These systems defy simple predictions, yet they are ubiquitous in nature, from weather systems to fluid dynamics. Using tools from Statistical and Computational Physics, I aim to uncover the underlying principles governing these behaviors, often relying on numerical simulations and statistical methods to analyze data and validate models. Another key area of my research is the study of Extreme Events, which occur in the tails of probability distributions but have outsized impacts. Understanding how and when these events emerge, whether in financial markets, climate systems, or power grids, is critical to both prediction and mitigation.Â
I am also interested in Reservoir Computing and Machine Learning extends these insights by applying data-driven techniques to model complex dynamical systems, leveraging machine learning to make predictions, discover patterns, and enhance computational efficiency in solving these problems.