Arthur Pentland Dempster, a distinguished American mathematician and statistician whose groundbreaking work profoundly influenced the fields of statistics and artificial intelligence, passed away on January 30, 2026, at the age of 96. Born on October 8, 1929, in Toronto, Ontario, Canada, Dempster became a revered professor emeritus in the Department of Statistics at Harvard University, where he was instrumental in its early development.

Dempster's academic journey began at the University of Toronto, where he earned his B.A. in mathematics and physics in 1952, followed by an M.A. in mathematics in 1953. He then pursued his doctoral studies at Princeton University, receiving his Ph.D. in mathematical statistics in 1956. His foundational research laid the groundwork for significant advancements in statistical methodology.

In 1957, Dempster joined Harvard University as one of the four founding faculty members of its Department of Statistics, a testament to his burgeoning influence in the discipline. He served multiple terms as Chairman of the department, guiding its growth and academic direction. His most notable contributions include the co-development of the Dempster-Shafer theory, also known as Evidence Theory, a framework for reasoning with uncertainty, and his pivotal role in the creation of the Expectation-Maximization (EM) algorithm, a powerful tool for finding maximum likelihood estimates of parameters in statistical models.

Throughout his illustrious career, Professor Dempster was recognized with numerous accolades for his intellectual prowess and contributions to statistics. He was named a Putnam Fellow in 1951 and was elected a Fellow of the Institute of Mathematical Statistics in 1963, and the American Statistical Association in 1964. His dedication to advancing scientific understanding was further acknowledged with a Guggenheim Fellowship in 1967-1968 and his election as a Fellow of the American Academy of Arts and Sciences in 1997.

Arthur P. Dempster's legacy extends beyond his published works and theories; it lives on through the generations of students he mentored and the continued application of his statistical innovations across various scientific disciplines. His passing marks the end of an era for the statistical community, but his profound intellectual contributions will continue to shape research and understanding for years to come.