"To consult the statistician after an experiment is finished is often merely to ask him to conduct a post mortem examination. He can perhaps say what the experiment died of."
"To consult the statistician after an experiment is finished is often merely to ask him to conduct a post mortem examination. He can perhaps say what the experiment died of."
Md Sazib Hasan, Ph.D.
Associate Professor
225 South University Avenue
Saint George, Utah, 87770
Phone: 435 652 7765
email: mdsazib.hasan@utahtech.edu
Md Sazib Hasan joined Utah Tech University in 2019 after completing his Ph.D. at the University of Louisiana at Lafayette. His primary areas of interest in the study include statistical modeling. Dr Hasan's area of research focuses on Fiducial Inference, small sample problems, Interval estimations, prediction intervals and statistical learning. He is highly enthusiastic about teaching and thinks that deep interactions foster student achievement.
By kindling their inner self, he hopes to provide students the chance to think critically via applied and authentic learning. His goal is to provide an inclusive educational environment in which all students are treated equally and have equal access to opportunities. As a Project NExT fellow, he discovered that engaging students in project-based learning are one of the most effective ways to prepare them for future jobs. He has established several student contests, including DataFest, to provide students with real-world experience. He is involved in mentoring undergraduate students who are tackling community issues. He encourages students to submit their research findings to various conferences and research symposiums.
As a statistician, he is passionate about working on projects that are related to serving the community. He is particularly interested in involving my students in the learning process, as I believe it provides valuable practical experience. Some of the notable projects he has undertaken or is currently working on with my students include predicting lung cancer mortality in Utah using various statistical and machine learning techniques, identifying critical water management facilities in Washington County for efficient budget distribution, predicting water quality measurements in stream water in Utah, utilizing statistical and machine learning techniques for breast cancer prediction, and understanding community sentiment through text analysis. His goal is to continue working with my students to help them apply statistical and machine learning techniques to solve community projects.
Dr Hasan is serving as a Vice Chair of MAA Intermountain Section.