Sima is a biostatistics Ph.D. student dedicated to building reliable, data-driven tools for infectious-disease modeling and public-health decision-making. Her current work focuses on machine-learning–based calibration for agent-based epidemic models, including a three-layer bidirectional LSTM calibrator that improves accuracy and runtime over ABC and is being productized as epiworldRcalibrate, and on the comparative evaluation of effective reproduction number (Rt) estimators using agent-based network models. Sima collaborates closely with Dr. Bernardo Modenesi, Dr. Yue Zhang, and Dr. George Vega Yon, and she contributes to open-source tools such as epiworldR.
Forecasting and Surveillance of Infectious Threats and Epidemics (ForeSITE)
An Insight Net Center for Integration