Professor Jeffrey D Varner Cornell University
Cornell Financial Engineering Manhattan 2026 Ai & Future of Finance Conference
Jeffrey Varner is a Professor in the Smith School of Chemical and Biomolecular Engineering at Cornell University, where he has built a distinguished career at the intersection of chemical engineering, computational science, data analysis, and interdisciplinary research. He also serves as Associate Director of the Smith School, a role he has held since 2022, following his previous service as Director of Graduate Studies and Director of the Master of Engineering program.
Varner’s academic career at Cornell spans more than two decades. He joined the university as an Assistant Professor in 2005 and progressed through the ranks to become a full Professor in 2016. His leadership within the Smith School has included graduate education, professional engineering programs, and the development of interdisciplinary approaches to complex scientific and engineering problems.
In parallel with his academic work, Varner serves as Chief Quantitative Scientist at Paliquant, where he applies mathematical modeling, data analysis, and machine learning to the study of dynamic and uncertain market processes. His work reflects a broader expertise in computational methods and quantitative problem-solving, extending his research capabilities from engineering systems into financial and market applications.
Before returning to Cornell as a faculty member, Varner was a Professor and Director of the Professional Master’s of Science Program at Purdue University. Earlier in his career, he spent four years as a Research Scientist at Genencor in Palo Alto, California, bringing industrial research experience to his academic career. He also conducted postdoctoral research at ETH Zürich from 1998 to 2001.
Varner earned his Ph.D. in Chemical Engineering from Purdue University. His professional background combines deep expertise in chemical engineering and molecular biology with computational modeling, quantitative analysis, machine learning, and data-driven scientific research.
Across academia and industry, Varner has demonstrated an ability to lead interdisciplinary teams, develop novel computational approaches, and solve complex problems requiring the integration of scientific theory, quantitative methods, and advanced data analysis. His career reflects a distinctive combination of academic leadership, engineering research, computational science, and quantitative modeling.
