Urban communities are agglomerations of dynamic cultures, social norms, and traditions. They function as complex systems, where evolving human behaviors and social interactions are constrained within static frameworks of spatial design, planning, and policy. Understanding these complexities requires a critical examination of the spatio-temporal variances that emerge from contextual differences across communities, particularly to support informed policy and planning decisions. In my research, I leverage recent advances in network analysis and econometrics to quantify and model the intricate and evolving patterns of urban life. This allows me to analyze interdependencies and construct counterfactual scenarios that can guide more responsive and data-informed urban interventions. 

My intellectual path has been deeply interdisciplinary rooted in architecture, urban design, and computational urban science which positions me uniquely to examine the relationship between people and the spaces they inhabit. This perspective allows me to approach quantitative analysis with a qualitative sensitivity, emphasizing the value of lived experiences in shaping meaningful, equitable, and context-aware urban policies. This foundation informs the complex systems approach that underpins my work. My current work as a Phd Candidate at Resilient Urban Networks Lab, New York University at the department of Technology Management and Innovation and Centre for Urban Science and progress focuses on investigating post disaster decisions, resilience and indicators of tipping points in disaster affected communities by leveraging complex systems and network science concepts and methods. Response and recovery of communities affected by disaster varies with respect to their socio-demographic and social connectedness context and the type of disaster that affects them. These factors also depend on frequency and recurrence of disasters. Therefore, to efficiently analyze these dynamic factors, a dynamic-complex systems approach is needed. It is also necessary to ensure that this approach is guided by context specific strategies.