ASSET Center Inaugural Seed Grants Will Fund Trustworthy AI Research in Healthcare


Illustration credit: Melissa Pappas

Penn Engineering’s newly established ASSET Center aims to make AI-enabled systems more “safe, explainable and trustworthy” by studying the fundamentals of the artificial neural networks that organize and interpret data to solve problems.

ASSET’s first funding collaboration is with Penn’s Perelman School of Medicine (PSOM) and the Penn Institute for Biomedical Informatics (IBI). Together, they have launched a series of seed grants that will fund research at the intersection of AI and healthcare.

Teams featuring faculty members from Penn Engineering, Penn Medicine and the Wharton School applied for these grants, to be funded annually at $100,000. A committee consisting of faculty from both Penn Engineering and PSOM evaluated 18 applications and  judged the proposals based on clinical relevance, AI foundations and potential for impact.

Artificial intelligence and machine learning promise to revolutionize nearly every field, sifting through massive amounts of data to find insights that humans would miss, making faster and more accurate decisions and predictions as a result.

Applying those insights to healthcare could yield life-saving benefits. For example, AI-enabled systems could analyze medical imaging for hard-to-spot tumors, collate multiple streams of disparate patient information for faster diagnoses or more accurately predict the course of disease.

Given the stakes, however, understanding exactly how these technologies arrive at their conclusions is critical. Doctors, nurses and other healthcare providers won’t use such technologies if they don’t trust that their internal logic is sound.

“We are developing techniques that will allow AI-based decision systems to provide both quantifiable guarantees and explanations of their predictions,” says Rajeev Alur, Zisman Family Professor in Computer and Information Science and Director of the ASSET Center. “Transparency and accuracy are key.”

“Development of explainable and trustworthy AI is critical for adoption in the practice of medicine,” adds Marylyn Ritchie, Professor of Genetics and Director of the Penn Institute for Biomedical Informatics. “We are thrilled about this partnership between ASSET and IBI to fund these innovative and exciting projects.”

 Seven projects were selected in the inaugural class, including projects from Dani S. Bassett, J. Peter Skirkanich Professor in the Departments of Bioengineering, Electrical and Systems Engineering, Physics & Astronomy, Neurology, and Psychiatry, and several members of the Penn Bioengineering Graduate Group: Despina Kontos, Matthew J. Wilson Professor of Research Radiology II, Department of Radiology, Penn Medicine and Lyle Ungar, Professor, Department of Computer and Information Science, Penn Engineering; Spyridon Bakas, Assistant Professor, Departments of Pathology and Laboratory Medicine and Radiology, Penn Medicine; and Walter R. Witschey, Associate Professor, Department of Radiology, Penn Medicine.

Optimizing clinical monitoring for delivery room resuscitation using novel interpretable AI

Elizabeth Foglia, Associate Professor, Department of Pediatrics, Penn Medicine and the Children’s Hospital of Philadelphia

Dani S. Bassett, J. Peter Skirkanich Professor, Departments of Bioengineering and Electrical and Systems Engineering, Penn Engineering

 This project will apply a novel interpretable machine learning approach, known as the Distributed Information Bottleneck, to solve pressing problems in identifying and displaying critical information during time-sensitive clinical encounters. This project will develop a framework for the optimal integration of information from multiple physiologic measures that are continuously monitored during delivery room resuscitation. The team’s immediate goal is to detect and display key target respiratory parameters during delivery room resuscitation to prevent acute and chronic lung injury for preterm infants. Because this approach is generalizable to any setting in which complex relations between information-rich variables are predictive of health outcomes, the project will lay the groundwork for future applications to other clinical scenarios.

Read the full list of projects and abstracts in Penn Engineering Today.

Erin Berlew and Rhea Chitalia Receive Solomon R. Pollack Awards for Excellence in Graduate Bioengineering Research

The Solomon R. Pollack Award for Excellence in Graduate Bioengineering Research is given annually to the most deserving Bioengineering graduate students who have successfully completed research that is original and recognized as being at the forefront of their field. This year Penn Bioengineering recognizes the outstanding work of two graduate students in Bioengineering: Erin Berlew and Rhea Chitalia.

Erin Berlew, Ph.D. candidate in Bioengineering

Erin Berlew is a Ph.D. candidate in the lab of Brian Chow, Associate Professor in Bioengineering. She successfully defended her thesis, titled “Single-component optogenetic tools for cytoskeletal rearrangements,” in December 2021. In her research, she used the BcLOV4 optogenetic platform discovered/developed in the Chow lab to control RhoGTPase signaling. Erin earned a B.S. in Chemistry from Haverford College in 2015 and was an Americorps member with City Year Philadelphia from 2015-2016. “Erin is a world-class bioengineering with an uncommon record of productivity gained through her complementary expertise in molecular, cellular, and computational biology,” says Chow. “She embodies everything wonderful, both academically and culturally, about our graduate program and its distinguished history.” Erin’s hobbies outside the lab include spending time with family, reading mystery novels, enjoying Philadelphia, and crossword puzzles. In the future, she hopes to continue to teach for the BE department (she has already taught ENGR 105 and served as a TA for undergraduate and graduate courses) and to conduct further research at Penn.

Rhea Chitalia, Ph.D. candidate in Bioengineering

Rhea Chitalia is a Ph.D. candidate in Bioengineering and a member of the Computational Biomarker Imaging Group (CBIG), advised by Despina Kontos, Matthew J. Wilson Associate Professor of Research Radiology II in the Perelman School of Medicine. Rhea completed her B.S.E. in Biomedical Engineering at Duke University in 2015. Her doctoral research concerns leveraging machine learning, bioinformatics, and computer vision to develop computational imaging biomarkers for improved precision cancer care. In December 2021 she successfully defended her thesis titled “Computational imaging biomarkers for precision medicine: characterizing intratumor heterogeneity in breast cancer.” “It has been such a privilege to mentor Rhea on her dissertation research,” says Kontos. “Rhea has been a star graduate student. Her work has made fundamental contributions in developing computational methods that will allow us to gain important insight into tumor heterogeneity by utilizing a multi-modality imaging approach.” David Mankoff, Matthew J. Wilson Professor of Research Radiology in the Perelman School of Medicine, served as Rhea’s second thesis advisor. “It was a true pleasure for me to work with Rhea and to Chair her BE Thesis Committee,” Mankoff adds. “Rhea’s Ph.D. thesis and thesis presentation was one of the best I have had the chance to be involved with in my graduate mentoring career.” After graduation, Rhea hopes to further precision medicine initiatives through the use of real world, multi-omic data in translational industry settings. She will be joining Invicro as an Imaging Scientist. In her spare time, Rhea enjoys trying new restaurants, reading, and spending time with friends and family.