Linda Abriola is the director of Tufts Institute of the Environment and a university professor at Tufts University, where she holds appointments in the departments of Civil and Environmental Engineering and Chemical and Biological Engineering. She is a member of both the American Academy of Arts and Sciences and the National Academy of Engineering (NAE), and a fellow of the American Geophysical Union. From 2003 to 2015, she served as the dean of the Tufts University School of Engineering. Prior to joining Tufts, Abriola was the Horace Williams King Collegiate Professor of Environmental Engineering at the University of Michigan. An expert in the characterization, transport, fate, and recovery/destruction of contaminants in the subsurface, Abriola is the author of more than 150 refereed publications and has been the recipient of many awards and honors, including her recent appointment as a U.S. State Department Science Envoy.
Director, Tufts Institute of the Environment
University Professor, Tufts University
Professor, Department of Civil and Environmental Engineering, Tufts School of Engineering
Dean of Engineering, Tufts University School of Engineering
Director, Environmental and Water Resources Engineering program, University of Michigan at Ann Arbor
Visiting Scientist, Department of Geotechnical Engineering, Universitat Politecnica de Cataluña
Visiting Associate Professor, Department of Petroleum Engineering, University of Texas at Austin
Department of Civil and Environmental Engineering, University of Michigan at Ann Arbor
- 1996-2003: Professor
- 1990-1996: Associate Professor
- 1984-1990: Assistant Professor
Postdoctoral Researcher, Department of Civil Engineering, Princeton University
Project Engineer, Procter and Gamble Manufacturing Company
Linda Abriola's primary research area is in the mathematical modeling of the transport and fate of organic chemical contaminants in porous media. She developed one of the first mathematical models to describe the interphase mass partitioning and non-aqueous phase migration of organic liquid contaminants in the subsurface. Current and recent funding encompasses: measurement and modeling of effective mass transfer and microbial transformation rates at the field scale; use of machine learning and signal processing techniques to quantify in situ contaminant mass flux and mass distribution from sparse data; development of innovative nanoparticle tools for petroleum reservoir characterization; and exploration of the processes controlling transport of nanoparticles in subsurface environments.