Dr. Nurul Raihen is an accomplished Professor of Teaching in the Department of Mathematics and Statistics at the University of Toledo. With a Ph.D. in Mathematics and an M.A. in Mathematical Statistics from Wayne State University, as well as an M.S. in Applied Mathematics from the University of Toledo, Dr. Raihen brings a wealth of expertise and dedication to both teaching and research. His research spans diverse areas, including partial differential equations, mathematical modeling, machine learning, data analysis, and free boundary problems, with applications in real-world scenarios such as public health and engineering.
Dr. Raihen is the author of numerous peer-reviewed publications in high-impact journals, showcasing innovative contributions in fields like nonlinear wave equations, data mining in lung cancer research, and machine learning-based predictive modeling. He has also organized and co-organized prestigious conferences and special sessions, including events for the American Mathematical Society (AMS) and the Society for Industrial and Applied Mathematics (SIAM). In addition to his academic contributions, Dr. Raihen actively serves on editorial boards of multiple international journals and has reviewed over 120 manuscripts, reflecting his commitment to advancing scholarly excellence. His dedication to teaching is equally impressive, with over a decade of experience guiding students at various institutions. His teaching philosophy emphasizes innovation and accessibility, integrating modern tools and fostering an inclusive environment to help students succeed.
Dr. Raihen has been recognized with several awards and grants, including the Simons Foundation Research Grant, the NSF I-Corps Program Fellowship, and the Project NExT Fellowship by the Mathematical Association of America (MAA). He is also proficient in programming languages such as Python, MATLAB, and R, leveraging these skills to advance his research and teaching.
Dr. Raihen is passionate about mentoring students, fostering collaboration, and inspiring the next generation of scholars in mathematics, statistics, and data science. His professional memberships include the American Mathematical Society (AMS), the Society for Industrial and Applied Mathematics (SIAM), the Mathematical Association of America (MAA), and other professional organizations. His work continues to bridge the gap between theory and application, contributing to significant advancements in mathematics,
statistics, and data science.