Niloufar’s research focuses on developing high-resolution flood forecasting frameworks to enhance urban resilience in New York City. By integrating multi-sensor data and satellite observations with statistical and machine learning methods for extremes analysis, she develops predictive tools and digital twins that quantify risk in real time. Her work provides actionable insights for managing flood events within complex, high-density urban environments.
Beyond her primary research, Niloufar has contributed to regional multi-hazard risk assessment efforts along the U.S. East Coast, specifically through the development of a Coastal Risk Index to inform resilience planning. She has also refined flood inundation mapping by integrating uncertainty into stage–discharge relationships through probabilistic modeling. At The City College of New York, Niloufar serves as a Teaching Assistant for Numerical Methods and ArcGIS Pro, mentoring students in Python programming, numerical modeling, and geospatial analysis. She focuses on helping students connect computational tools with real world water resources challenges.