Anandamayee Majumdar

Assistant Professor in the Department of Mathematics, College of Science & Engineering, San Francisco State University

Bio

Anandamayee Majumdar works at the Department of Mathematics, College of Science & Engineering, San Francisco State University, CA, US. Anandamayee finished her Ph.D. and M.S. in Statistics at the University of Connecticut and Michigan State University, respectively. Moreover, she received a master's degree in mathematical statistics and probability and a bachelor's in statistics from the Indian Statistical Institute (I.S.I.).

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Research Interests:

Prospective Students: 

I am currently looking for enthusiastic students (both undergrad and grad) to work with me in various research projects. If you are interested in any of my research topics, please feel free to send me an email with your resume and transcripts.

Research Topics:

For the last decade and longer I have been conducting research on diverse topics of statistics while collaborating with Statisticians, Econometricians, and other scientists.


My research interests include spatial processes and space-time processes. Some of this data is difficult to explain and predict due to missing observations, or inherent variability that can not be explained by spatial covariates alone. Sometimes there are more than one variables that are being studied together that are associated. Data can be both numerical or categorical. Then there are challenges when different sources of data have to be combined to get better predictions. Sometimes there are changepoints in time. Statistical modeling is useful in such cases because it utilizes the available data to build efficient and possibly robust probability models that can make sense and be used for future projections.  Also sometimes there are null responses in datasets, and understanding these responses are also important in mixed modeling scenarios since they can not be understood using regular models. In the past, I had been working with studying such data with various challenges. I have also used robust modeling that can predict extreme values in multivariate processes. I have also used non-parametric and semi-parametric modeling (quantile and expectile regression) for time series and space-time data. 


I am currently focused on building robust models for better prediction when adjacency (of space-time) information alone can not explain the data. Incorporating expert opinion directly into the model is then important; I am exploring the field of space-time models where this can be done. I am also interested in public health data that is new and emerging, where there are many open questions about the public health preferences of individuals in the population.

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Contact

San Francisco State University, Department of Mathematics, 1600 Holloway Avenue, San Francisco, CA 94132

Phone: TBD

Email: amajumdar (at) sfsu.edu