
Research
Elham Azizi Awarded a Vilcek Prize for Leading Advances in Cancer Research
The Vilcek Prize honors Azizi’s innovative approach to cancer science and recognizes her role as a leader among immigrant scientists
Elham Azizi, associate professor of biomedical engineering, and the Herbert and Florence Irving Assistant Professor of Cancer Data Research at Columbia Engineering has been awarded the 2025 Vilcek Prize for Creative Promise in Biomedical Science for her contributions to cancer science. Azizi’s deep fascination with the physical properties of nature grounds her interdisciplinary work.
The Vilcek Foundation was founded to celebrate the important contributions of immigrant artists and scientists in the United States. The prizes for creative promise honor three young immigrant research scientists each year who have made significant contributions to the field. Creative Promise Prizewinners each receive a commemorative trophy and an unrestricted cash award of $50,000. Azizi is one of 14 prizewinners announced today by the Vilcek Foundation, honoring the recipients with a total of $950,000 in awards in biomedical science, in visual arts, and in curatorial work.

“This recognition inspires me to continue pushing the boundaries of what is possible in biomedical research and to help pave the way for the next generation,” says Azizi, who also is a member of the Herbert Irving Comprehensive Cancer Center.
Azizi is an Iranian-American scientist who immigrated to the United States in 2009 to pursue additional research opportunities. Because of the challenges she has faced pursuing science and engineering as a woman, Azizi is committed to supporting women or underrepresented groups in STEM.
Her research primarily focuses on composition and circuitry of cells in tumors while utilizing novel machine learning techniques and cutting-edge genomic technologies. Azizi’s approach focuses on utilizing single-cell genomic profiling and creating machine learning and statistical methods to analyze and integrate complex high-dimensional genomic data.