Her research focuses on nonparametric regression, measurement error models, robust estimation techniques, and empirical finance. Through her work, she explores methodological challenges in statistical modelling and develops approaches that improve the reliability and accuracy of quantitative analysis. Her research contributes to the growing body of knowledge in advanced statistical methods while also addressing applications relevant to finance and business research.
Dr. Srivastava has published her research in respected statistical journals, including Communications in Statistics – Theory and Methods, and has additional accepted work in areas related to measurement error models and regression estimation. She has actively engaged with the academic community through participation in international conferences and workshops, including the Bernoulli-IMS World Congress in Probability and Statistics and advanced programmes in statistical learning and Bayesian analysis.
Prior to pursuing her doctoral studies, Dr. Srivastava worked as a Statistical Programmer with Novartis Healthcare, gaining valuable industry exposure to data-driven analysis and applied quantitative methods. Her teaching interests include statistics, probability, statistical inference, regression analysis, time series, and research methodology. At MICA, she brings a strong foundation in quantitative research and analytics, helping students develop the analytical capabilities required for evidence-based decision-making and business problem-solving.