Pitt | Swanson Engineering
Two Swanson School Projects Win University of Pittsburgh Scaling Grants
Credit: University of Pittsburgh

PITTSBURGH (April 6, 2020) — Two projects from the Swanson School of Engineering have received University of Pittsburgh Scaling Grants.The first, tackling the global problem of plastic waste, is headed by Eric Beckman, PhD, Bevier Professor of Chemical and Petroleum Engineering and co-director of the Mascaro Center for Sustainable Innovation. The second project, which will support the push for artificial intelligence innovation in medical imaging, was also awarded a Scaling Grant and is led by Shandong Wu, PhD, associate professor in the Department of Radiology.

The Scaling Grants provide $400,000 over two years to support detailed project planning, gathering proof-of-concept results, and reduction of technical risk for teams pursuing an identified large extramural funding opportunity. The Scaling Grants are part of the University’s Pitt Momentum Funds, which offer funding across multiple stages of large, ambitious projects.

Addressing the Global Waste Challenge

The problem of plastic waste is growing on a global scale, with an annual global production rate of more than 500 million tons per year and predicted to triple by 2036. The project, “Attacking the Global Plastics Waste Problem,” seeks to create a convergent academic center welcoming expertise from across the University that will focus on the circular economy as a solution.

“For most new technologies, one group creates the technology in the lab as a pilot, then at full scale. The group launches it, and only later decides if there are environmental and/or policy and/or legal issues,” says Beckman. “We're proposing to do these analyses in parallel, so that each section of the work informs the others. Further, the technology we are proposing to develop is a mixture of chemical engineering, chemistry, and materials science.”

The interdisciplinary team will take advantage of its deep expertise in both the science of plast ics recycling and the legal and governance frameworks that will help governments implement a circular economy for plastics. In addition to Beckman, the team consists of Melissa Bilec, PhD, Roberta A. Luxbacher Faculty Fellow, associate professor in civil and environmental engineering (CEE), and deputy director of MCSI; Vikas Khanna, PhD, Wellington C. Carl Faculty Fellow and associate professor in CEE and Chemical and Petroleum Engineering; Gotz Veser, PhD, professor in chemical and petroleum engineering; Peng Liu, PhD, associate professor in the Department of Chemistry; Amy Wildermuth, professor and dean of the University of Pittsburgh School of Law; and Joshua Galperin, visiting associate professor in the School of Law.

“Recycling can only do so much. A circular economy framework is a promising solution to the complex, urgent problem that plastic pollution presents,” says Bilec, who is part of a five-university team that received a two-year National Science Foundation grant for $1.3 million to pursue convergence research on the circular economy as a plastic waste solution. “Our proposed center will integrate the science and engineering of plastics recycling, using a novel approach on both the recycling and manufacturing sides, into frameworks tracking its environmental and economic impact.” 

Applying Artificial Intelligence to Medical Research

The second project to receive a Scaling Grant is the “Pittsburgh Center for Artificial Intelligence Innovation in Medical Imaging,” a collaboration between the Departments of Radiology, Bioengineering, Biomedical Informatics, and Computer Science. This work, led by Wu, aims to use artificial intelligence (AI) to reshape medical imaging in radiology and pathology.

Through the Pittsburgh Health and Data Alliance, the region is already at work using machine learning to translate “big data” generated in health care to treatments and services that could benefit human health.

"The advancement in AI, especially in deep learning, provides a powerful approach for machine learning on big healthcare data,” said Wu. “Deep learning enables large-scale data mining with substantially increased accuracy and efficiency in data analysis."

The multidisciplinary research team will work to develop AI imaging methodology and translational applications with the ultimate goal of creating tools that are clinically useful, accurate, explainable and safe.

“AI can substantially improve quantitative analysis to medical imaging data and computational modeling of clinical tasks using medical images for disease diagnosis and outcome prediction," explained Wu.

David A. Vorp, associate dean for research and John. A. Swanson Professor of Bioengineering, will help facilitate this collaboration in engineering.

“Artificial intelligence nicely complements bioengineering and medical research,” said Vorp. “My lab uses AI with CT scans to help predict the prognosis and improve treatment of aortic aneurysm, and that is just one example of how this cutting-edge technology can be applied to medical images. Rather than relying on the naked eye, we can use AI to analyze these images and have a more sensitive detector to identify disease, improve health and save lives.”

The group’s long-term vision is to combine the computational expertise and clinical resources across Pitt, UPMC and Carnegie Mellon University to build a center for innovative AI in clinical translational medical imaging. 

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Maggie Pavlick and Leah Russell, 4/6/2020

Contact: Maggie Pavlick