Tag: Dani Bassett

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

by Devorah Fischler

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.

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Dani Smith Bassett Receives 2022-23 Heilmeier Award

by Olivia J. McMahon

Dani Bassett, Ph.D.

Dani Smith Bassett, J. Peter Skirkanich Professor in Bioengineering and in Electrical and Systems Engineering in Penn Engineering, has been named the recipient of the 2022-23 George H. Heilmeier Faculty Award for Excellence in Research for “groundbreaking contributions to modeling and control of brain networks in the contexts of learning, disease and aging.”

The Heilmeier Award honors a Penn Engineering faculty member whose work is scientifically meritorious and has high technological impact and visibility. It is named for the late George H. Heilmeier, a Penn Engineering alumnus and member of the School’s Board of Advisors, whose technological contributions include the development of liquid crystal displays and whose honors include the National Medal of Science and Kyoto Prize.

Bassett, who also holds appointments in Physics & Astronomy in Penn Arts & Sciences and in Neurology and Psychiatry in the Perelman School of Medicine, is a pioneer in the field of network neuroscience, an emerging subfield which incorporates elements of mathematics, physics, biology and systems engineering to better understand how the overall shape of connections between individual neurons influences cognitive traits. They lead the Complex Systems lab, which tackles problems at the intersection of science, engineering and medicine using systems-level approaches, exploring fields such as curiosity, dynamic networks in neuroscience, and psychiatric disease.

Bassett will deliver the 2022-23 Heilmeier Award Lecture in Spring 2023.

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Grace Hopper Distinguished Lecture: “How Memory Guides Value-Based Decisions” (Daphna Shohamy, Columbia University)

We hope you will join us for the 2022 Grace Hopper Distinguished Lecture by Dr. Jennifer Lewis, presented by the Department of Bioengineering and hosted by Dani S. Bassett, J. Peter Skirkanich Professor in Bioengineering, Electrical and Systems Engineering, Physics & Astronomy, Neurology and Psychiatry.

Date: Thursday, December 8, 2022
Start Time: 3:30 PM EST
Location: Glandt Forum, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA 19104

Join us after the live lecture for a light reception!

Daphna Shohamy, Ph.D.

 

Speaker: Daphna Shohamy, Ph.D.
Kavli Professor of Brain Science, Co-Director of the Kavli Institute for Brain Science, Professor in the Department of Psychology & Zuckerman Mind Brain Behavior Institute
Columbia University

Title: “How Memory Guides Value-Based Decisions”

Zoom link
Passcode: 704696

 

Lecture Abstract:

From robots to humans, the ability to learn from experience turns a rigid response system into a flexible, adaptive one. In the past several decades, major advances have been made in understanding how humans and other animals learn from experience to make decisions. However, most of this progress has focused on rather simple forms of stimulus-response learning, such as automatic responses or habits. In this talk, I will turn to consider how past experience guides more complex decisions, such as those requiring flexible reasoning, inference, and deliberation. Across a range of behavioral contexts, I will demonstrate a critical role for memory in such decisions and will discuss how multiple brain regions interact to support learning, what this means for how memories are used, and the consequences for how decisions are made. Uncovering the pervasive role of memory in decision-making challenges the way we think about what memory is for, suggesting that memory’s primary purpose may be to guide future behavior and that storing a record of the past is just one way to do so.

Dr. Shohamy Bio:

Daphna Shohamy, PhD is a professor at Columbia University where she co-directs the Kavli Center for Neural Sciences and is Associate Director of the Zuckerman Mind, Brain Behavior Institute. Dr. Shohamy’s work focuses on the link between memory, and decision-making. Combining brain imaging in healthy humans with studies of patients with neurological and psychiatric disorders, Dr. Shohamy seeks to understand how the brain transforms experiences into memories; how memories shape decisions and actions; and how motivation and exploration affect human behavior.

Information on the Grace Hopper Lecture:
In support of its educational mission of promoting the role of all engineers in society, the School of Engineering and Applied Science presents the Grace Hopper Lecture Series. This series is intended to serve the dual purpose of recognizing successful women in engineering and of inspiring students to achieve at the highest level.

Rear Admiral Grace Hopper was a mathematician, computer scientist, systems designer and the inventor of the compiler. Her outstanding contributions to computer science benefited academia, industry and the military. In 1928 she graduated from Vassar College with a B.A. in mathematics and physics and joined the Vassar faculty. While an instructor, she continued her studies in mathematics at Yale University where she earned an M.A. in 1930 and a Ph.D. in 1934. Grace Hopper is known worldwide for her work with the first large-scale digital computer, the Navy’s Mark I. In 1949 she joined Philadelphia’s Eckert-Mauchly, founded by the builders of ENIAC, which was building UNIVAC I. Her work on compilers and on making machines understand ordinary language instructions lead ultimately to the development of the business language, COBOL. Grace Hopper served on the faculty of the Moore School for 15 years, and in 1974 received an honorary degree from the University. In support of the accomplishments of women in engineering, each department within the School invites a prominent speaker for a one or two-day visit that incorporates a public lecture, various mini-talks and opportunities to interact with undergraduate and graduate students and faculty.

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Listen: ‘Curious Minds’ on NPR’s ‘Detroit Today’

by Ebonee Johnson

Twin siblings and scholars Dani S. Bassett of Penn and Perry Zurn of American University collaborated over half a dozen years to write “Curious Minds: The Power of Connection.” (Image: Tony and Tracy Wood Photography)

Twin academics Dani S. Basset, J. Peter Skirkanich Professor and director of the Complex Systems Lab, and Perry Zurn, a professor of philosophy at American University, were recently featured as guests on NPR radio show “Detroit Today” to discuss their new book, “Curious Mind: The Power of Connection.”

In their book, Basset and Zurn draw on their previous research, as well as an expansive network of ideas from philosophy, history, education and art to explore how and why people experience curiosity, as well as the different types it can take.

Basset, who holds appointments in the Departments of Bioengineering and Electrical and Systems Engineering, as well as the Department of Physics and Astronomy in Penn Arts & Science, and the Departments of Neuroscience and Psychiatry in Penn Perelman’s School of Medicine, and Zurn spoke with “Detroit Today” producer Sam Corey about what types of things make people curious, and how to stimulate more curiosity in our everyday lives.

According to the twin experts, curiosity is not a standalone facet of one’s personality. Basset and Zurn’s work has shown that a person’s capacity for inquiry is very much tied to the overall state of their health.

“There’s a lot of scientific research focusing on intellectual humility and also openness to ideas,” says Bassett. “And there are really interesting relationships between someone’s openness to ideas, someone’s intellectual humility and their curiosity and also their wellbeing or flourishing,”

Listen to “What makes people curious and how to encourage the act” at “Detroit Today.”

Register for a book signing event for “Curious Minds: The Power of Connection,” on Friday, December 9th at the Penn Bookstore.

This story originally appeared in Penn Engineering Today.

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‘Curious Minds: The Power of Connection’

Twin siblings and scholars Dani S. Bassett of Penn and Perry Zurn of American University collaborated over half a dozen years to write “Curious Minds: The Power of Connection.” (Image: Tony and Tracy Wood Photography)

With appointments in the Departments of Bioengineering and Electrical and Systems Engineering, as well as the Department of Physics and Astronomy in Penn Arts & Science, and the Departments of Neuroscience and Psychiatry in Penn Perelman’s School of Medicine, Dani S. Bassett is no stranger to following the thread of an idea, no matter where it might lead.

Curious Minds book cover

Those wide-ranging fields and disciplines orbit around an appropriate central question: how does the tangle of neurons in our brains wire itself up to learn new things? Bassett, J. Peter Skirkanich Professor and director of the Complex Systems Lab, studies the relationship between the shape of those networks of neurons and the brain’s abilities, especially the way the shape of the network grows and changes with the addition of new knowledge.

 

To get at the fundamentals of the question of curiosity, Bassett needed to draw on even more disciplines. Fortunately, they didn’t have to look far; Bassett’s identical twin is Perry Zurn, a professor of philosophy at American University, and the two have investigated the many different ways a person can exhibit curiosity.

Bassett and Zurn have now published a new book on the subject. In Curious Minds: The Power of Connection, the twins draw on their previous research, as well as an expansive network of ideas from philosophy, history, education and art.

In an interview with The Guardian, Bassett explains how these threads wove together:

“It wasn’t clear at the beginning of our careers that we would even ever have a chance to write a book together because our areas were so wildly different,” Bassett says – but then, as postgraduates, Zurn was studying the philosophy of curiosity while Bassett was working on the neuroscience of learning. “And so that’s when we started talking. That talking led to seven years of doing research together,” Bassett says. “This book is a culmination of that.”

How exactly do philosophy and neuroscience complement each other? It all starts with the book’s first, and most deceptively simple question: what is curiosity? “Several investigators in science have underscored that perhaps the field isn’t even ready to define curiosity and how it’s different from other cognitive processes,” says Bassett. The ambiguity in the neuroscience literature motivated Bassett to turn to philosophy, “where there are really rich historical definitions and styles and subtypes that we can then put back into neuroscience and ask: ‘Can we see these in the brain?’”

Curious Minds: The Power of Connection is available now. Read Amelia Tait’s review “Are you a busybody, a hunter or a dancer? A new book about curiosity reveals all,” in The Guardian. 

This story originally appeared in Penn Engineering Today.

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Moving Away From ‘Average,’ Toward the Individual

by Michele W. Berger

In a course from Annenberg’s David Lydon-Staley, seven graduate students conducted single-participant experiments. This approach, what’s known as an “n of 1,” may better capture the nuances of a diverse population than randomized control trials can.

David Lydon-Staley is an assistant professor of communication and principal investigator of the Addiction, Health, & Adolescence Lab in the Annenberg School for Communication.

To prep for an upcoming course he was teaching, Penn researcher David Lydon-Staley decided to conduct an experiment: Might melatonin gummies—supplements touted to improve sleep—help him, as an individual, fall asleep faster?

For two weeks, he took two gummies on intervention nights and none on control nights. The point, however, wasn’t really to find out whether the gummies worked for him (which they didn’t), but rather to see how an experiment with a single participant played out, what’s known as an “n of 1.”

Randomized control experiments typically include hundreds or thousands of participants. Their aim is to show, on average, how the intervention being studied affects people in the treatment group. But often “there’s a failure to include women and members of minoritized racial and ethnic groups in those clinical trials,” says Lydon-Staley, an assistant professor in the Annenberg School for Communication. “The single-case approach says, instead of randomizing a lot of people, we’re going to take one person at a time and measure them intensively.”

In Lydon-Staley’s spring semester class, Diversity and the End of Average, seven graduate students conducted their own n-of-1 experiments—on themselves—testing whether dynamic stretching might improve basketball performance or whether yoga might decrease stress. One wanted to understand the effect of journaling on emotional clarity. They also learned about representation in science, plus which analytical approaches might best capture the nuance of a diverse population and individuals with many intersecting identities.

“It’s not just an ‘n of 1’ trying to do what the big studies are doing. It’s a different perspective,” says Lydon-Staley. “Though it’s just one person, you’re getting a much more thorough characterization of how they’re changing from moment to moment.”

Read the full story in Penn Today.

David Lydon-Staley is an Assistant Professor of communication and principal investigator of the Addiction, Health, & Adolescence Lab in the Annenberg School for Communication at the University of Pennsylvania. Lydon-Staley is a former postdoctoral research in the Complex Systems Lab of Dani S. Bassett, J. Peter Skirkanich Professor in Bioengineering and in Electrical and Systems Engineering.

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The Changing Face of Portraiture at Penn

by Katherine Unger Baillie

One of the new portraits in Leidy depicts Jane Hinton, one of the first two Black women to earn a doctorate in veterinary medicine from Penn. Captions on the photos chronicle the achievements of those displayed, but also, in some cases, the challenges they faced due to their race or gender.

A grand split staircase inside the entrance to Leidy Labs invites visitors into the home of the School of Arts & Sciences’ Biology Department. As students ascend or descend on their way to lab meetings and classes, a set of faces looks down on them—not the old, gilt-framed portraits that long hung in the stairwell, but 14 new photos in chestnut-colored wooden frames, depicting scientists who have close connections to Penn and the department. The gallery now highlights a more diverse suite of individuals, such as Emily Gregory, the first female teaching fellow at Penn, and Roger Arliner Young, the first African American woman to earn a doctorate in zoology.

The new art is part of a collective effort by the department, working with guidance from the University Curator’s office, to rethink how portraiture and representation operate in the halls of their buildings. Many other University departments, schools, and leaders are in the process of undertaking similar initiatives, driven in part by the question: How can the walls of campus buildings better reflect the communities they serve?

“We have about 1,500 to 1,600 portraits in our collection,” says University curator Lynn Marsden-Atlass. “Most of them are paintings by white men of white men. Since I have been the University curator, my goal has really been to bring in more visible diversity to our art collection. And now we’ve been getting increasing numbers of requests, like from the Biology Department, to take on some of this themselves.”

The changes are meant to enhance a sense of inclusion for all at Penn, notably students, says history of art professor Gwendolyn DuBois Shaw. “There are certain contexts that students, in particular, want to assert that they belong,” she says, “that they are not just at Penn, but they’re of Penn.”

Pushing against homogeny

At Penn and many institutions like it, portraits find their way onto walls through a variety of means. Portraits honor department chairs, deans, or others who have ascended to the top ranks of the academy. Sometimes they depict thought leaders in a field, who may or may not have a direct connection to the University. And occasionally donors write into their gift agreement that a portrait will be hung in recognition of their philanthropy.

The result, however, can mean building walls that function like memorials or museums, highlighting the past but not the current community, or a hoped-for future one.

Located at one of the unofficial “entrances” to Penn’s campus at 34th and Walnut streets, the 16-foot-tall bronze form of Brick House, by artist Simone Leigh, makes a statement. Installed in November 2020, it is the first campus sculpture of and by a Black woman.

“I’ve had such an interesting set of conversations about what the walls of Penn are for,” says Dani Bassett, a professor in the School of Engineering and Applied Science. “We as an institution have used the walls to display our history. But there’s a sense in which the students who walk the halls feel that, especially when those faces are not diverse, this kind of art can be really oppressive, saying that, ‘This space is not for me, it’s only for white men.’ So, the question is, how do we venerate our history without hurting our students? Are our walls the place for history or the place for the future?”

In June 2020, amid widespread Black Lives Matter protests, Bassett, together with Junhyong Kim, chair of the Biology Department, as well as other faculty and staff, addressed an open letter requesting institutional and financial support for diversifying portraiture at Penn.

“Many spaces at Penn reflect its history but do not reflect our core values of diversity and inclusion, nor do they accurately reflect the student, staff, and faculty bodies that comprise the Penn of today, or those we envision to comprise the Penn of tomorrow,” they wrote. More than 430 members of the Penn community signed the letter.

Bassett has felt the need to act—and felt it most viscerally—when they interact with students, who have identified the issue of portraiture as an area that makes them feel uncomfortable, even unwelcome. For example, Bassett notes, one room in which students present their thesis proposals (and later defend their Ph.D. theses) is lined with portraits of white men. “The students walk into this room and think, ‘Here is this space where I will be evaluated and I will be evaluated, most likely, by people who are not like me,’” Bassett says. “It was those conversations with students that made me realize this is so important to address.”

Read the full story in Penn Today.

Dani Bassett is the J. Peter Skirkanich Professor, with appointments in the Departments of Bioengineering and Electrical & Systems Engineering in the School of Engineering and Applied Science, the Department of Physics & Astronomy in the School of Arts & Sciences, and the Departments of Neurology and Psychiatry in the Perelman School of Medicine.

 

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Refining Data into Knowledge, Turning Knowledge into Action

by Janelle Weaver

Heatmaps are used by researchers in the lab of Jennifer Phillips-Cremins to visualize which physically distant genes are brought into contact when the genome is in its folded state.

More data is being produced across diverse fields within science, engineering, and medicine than ever before, and our ability to collect, store, and manipulate it grows by the day. With scientists of all stripes reaping the raw materials of the digital age, there is an increasing focus on developing better strategies and techniques for refining this data into knowledge, and that knowledge into action.

Enter data science, where researchers try to sift through and combine this information to understand relevant phenomena, build or augment models, and make predictions.

One powerful technique in data science’s armamentarium is machine learning, a type of artificial intelligence that enables computers to automatically generate insights from data without being explicitly programmed as to which correlations they should attempt to draw.

Advances in computational power, storage, and sharing have enabled machine learning to be more easily and widely applied, but new tools for collecting reams of data from massive, messy, and complex systems—from electron microscopes to smart watches—are what have allowed it to turn entire fields on their heads.

“This is where data science comes in,” says Susan Davidson, Weiss Professor in Computer and Information Science (CIS) at Penn’s School of Engineering and Applied Science. “In contrast to fields where we have well-defined models, like in physics, where we have Newton’s laws and the theory of relativity, the goal of data science is to make predictions where we don’t have good models: a data-first approach using machine learning rather than using simulation.”

Penn Engineering’s formal data science efforts include the establishment of the Warren Center for Network & Data Sciences, which brings together researchers from across Penn with the goal of fostering research and innovation in interconnected social, economic and technological systems. Other research communities, including Penn Research in Machine Learning and the student-run Penn Data Science Group, bridge the gap between schools, as well as between industry and academia. Programmatic opportunities for Penn students include a Data Science minor for undergraduates, and a Master of Science in Engineering in Data Science, which is directed by Davidson and jointly administered by CIS and Electrical and Systems Engineering.

Penn academic programs and researchers on the leading edge of the data science field will soon have a new place to call home: Amy Gutmann Hall. The 116,000-square-foot, six-floor building, located on the northeast corner of 34th and Chestnut Streets near Lauder College House, will centralize resources for researchers and scholars across Penn’s 12 schools and numerous academic centers while making the tools of data analysis more accessible to the entire Penn community.

Faculty from all six departments in Penn Engineering are at the forefront of developing innovative data science solutions, primarily relying on machine learning, to tackle a wide range of challenges. Researchers show how they use data science in their work to answer fundamental questions in topics as diverse as genetics, “information pollution,” medical imaging, nanoscale microscopy, materials design, and the spread of infectious diseases.

Bioengineering: Unraveling the 3D genomic code

Scattered throughout the genomes of healthy people are tens of thousands of repetitive DNA sequences called short tandem repeats (STRs). But the unstable expansion of these repetitions is at the root of dozens of inherited disorders, including Fragile X syndrome, Huntington’s disease, and ALS. Why these STRs are susceptible to this disease-causing expansion, whereas most remain relatively stable, remains a major conundrum.

Complicating this effort is the fact that disease-associated STR tracts exhibit tremendous diversity in sequence, length, and localization in the genome. Moreover, that localization has a three-dimensional element because of how the genome is folded within the nucleus. Mammalian genomes are organized into a hierarchy of structures called topologically associated domains (TADs). Each one spans millions of nucleotides and contains smaller subTADs, which are separated by linker regions called boundaries.

Associate professor and Dean’s Faculty Fellow Jennifer E. Phillips-Cremins.

“The genetic code is made up of three billion base pairs. Stretched out end to end, it is 6 feet 5 inches long, and must be subsequently folded into a nucleus that is roughly the size of a head of a pin,” says Jennifer Phillips-Cremins, associate professor and dean’s faculty fellow in Bioengineering. “Genome folding is an exciting problem for engineers to study because it is a problem of big data. We not only need to look for patterns along the axis of three billion base pairs of letters, but also along the axis of how the letters are folded into higher-order structures.”

To address this challenge, Phillips-Cremins and her team recently developed a new mathematical approach called 3DNetMod to accurately detect these chromatin domains in 3D maps of the genome in collaboration with the lab of Dani Bassett, J. Peter Skirkanich Professor in Bioengineering.

“In our group, we use an integrated, interdisciplinary approach relying on cutting-edge computational and molecular technologies to uncover biologically meaningful patterns in large data sets,” Phillips-Cremins says. “Our approach has enabled us to find patterns in data that classic biology training might overlook.”

In a recent study, Phillips-Cremins and her team used 3DNetMod to identify tens of thousands of subTADs in human brain tissue. They found that nearly all disease-associated STRs are located at boundaries demarcating 3D chromatin domains. Additional analyses of cells and brain tissue from patients with Fragile X syndrome revealed severe boundary disruption at a specific disease-associated STR.

“To our knowledge, these findings represent the first report of a possible link between STR instability and the mammalian genome’s 3D folding patterns,” Phillips-Cremins says. “The knowledge gained may shed new light into how genome structure governs function across development and during the onset and progression of disease. Ultimately, this information could be used to create molecular tools to engineer the 3D genome to control repeat instability.”

Read the full story in Penn Today.

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Investing in Penn’s Data Science Ecosystem

by Erica K. Brockmeier

As part of a major University-wide investment in science, engineering, and medicine, the Innovation in Data Engineering and Science Initiative aims to help Penn become a leader in developing data-driven approaches that can transform scientific discovery, engineering research, and technological innovation.

From smartphones and fitness trackers to social media posts and COVID-19 cases, the past few years have seen an explosion in the amount and types of data that are generated daily. To help make sense of these large, complex datasets, the field of data science has grown, providing methodologies, tools, and perspectives across a wide range of academic disciplines.

But the challenges that lie ahead for data scientists and engineers, from developing algorithms that don’t exacerbate biases to ensuring privacy protections, are equally complex and, in some instances, require entirely new ways of thinking.

As part of its $750 million investment in science, engineering, and medicine, the University has committed to supporting the future needs of this field. To this end, the Innovation in Data Engineering and Science (IDEAS) initiative will help Penn become a leader in developing data-driven approaches that can transform scientific discovery, engineering research, and technological innovation.

“The IDEAS initiative is game-changing for our University,” says President Amy Gutmann. “This new investment allows us to boost our interdisciplinary efforts across campus, recruit phenomenal additional team members, and generate an even more sound foundation for discovery, experimentation, and design. This initiative is a clear statement that Penn is committed to taking data science head-on.”

Building on a foundation of existing expertise

Led by the School of Engineering and Applied Science, the IDEAS initiative builds upon the steadily gathering momentum of its data-centric research. The Warren Center for Network and Data Sciences has been a major catalyst for this type of work, generating foundational research on ethical algorithms and data privacy, as well as collaborations that have drawn in faculty from the Wharton School, Law School, Perelman School of Medicine, and beyond. In addition, Wharton’s Department of Statistics and Data Science is an active partner in research and teaching initiatives that apply statistical modeling across a wide variety of fields.

“One of the unique things about data science and data engineering is that it’s a very horizontal technology, one that is going to be impacting every department on campus,” says George Pappas, Electrical and Systems Engineering Department chair. “When you have a horizontal technology in a competitive area, we have to figure out specific areas where Penn can become a worldwide leader.”

To do this, IDEAS aims to recruit new faculty across three research areas: artificial intelligence (AI) to transform scientific discovery, trustworthy AI for autonomous systems, and understanding connections between the human brain and AI.

Penn already has a strong foundation in using AI for scientific discovery thanks in part to investments in basic research facilities such as the Singh Center for Nanotechnology and the Laboratory for Research on the Structure of Matter. Additionally, there are centers focused on connecting researchers from different fields to address complex scientific questions, including the Center for Soft and Living Matter, Center for Engineering Mechanobiology, and Penn Institute for Computational Science.

Developing “trustworthy” algorithms, ones that work reliably outside of situations in which they are trained, is another key component of the IDEAS initiative. Ongoing research at the Penn Research in Embedded Computing and Integrated Systems Engineering (PRECISE) Center, the General Robotics, Automation, Sensing & Perception (GRASP) Lab, and DARPA-funded projects on the safety of AI-based aircraft control provide a starting point for furthering Penn’s research portfolio on safe, explainable, and trustworthy autonomous systems.

In the area of neuroscience and how the human brain is similar to AI and machine learning approaches, research from PIK Professor Konrad Kording and Dani Bassett’s Complex Systems lab exemplifies the types of cross-disciplinary efforts that are essential for addressing complex questions. By recruiting additional faculty in this area, IDEAS will help Penn make strides in bio-inspired computing and in future life-changing discoveries that could address cognitive disorders and nervous system diseases.

Read the full story in Penn Today.

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Penn Bioengineering Celebrates Five Researchers on Highly Cited Researchers 2021 List

The Department of Bioengineering is proud to announce that five of our faculty have been named on the annual Highly Cited Researchers™ 2021 list from Clarivate:

Dani Bassett, Ph.D.

Dani S. Bassett, J. Peter Skirkanich Professor in Bioengineering and in Electrical and Systems Engineering
Bassett runs the Complex Systems lab which tackles problems at the intersection of science, engineering, and medicine using systems-level approaches, exploring fields such as curiosity, dynamic networks in neuroscience, and psychiatric disease. They are a pioneer in the emerging field of network science which combines mathematics, physics, biology and systems engineering to better understand how the overall shape of connections between individual neurons influences cognitive traits.

Robert D. Bent Chair
Jason Burdick, Ph.D.

Jason A. Burdick, Robert D. Bent Professor in Bioengineering
Burdick runs the Polymeric Biomaterials Laboratory which develops polymer networks for fundamental and applied studies with biomedical applications with a specific emphasis on tissue regeneration and drug delivery. The specific targets of his research include: scaffolding for cartilage regeneration, controlling stem cell differentiation through material signals, electrospinning and 3D printing for scaffold fabrication, and injectable hydrogels for therapies after a heart attack.

César de la Fuente, Ph.D.

César de la Fuente, Presidential Assistant Professor in Bioengineering and Chemical & Biomedical Engineering in Penn Engineering and in Microbiology and Psychiatry in the Perelman School of Medicine
De la Fuente runs the Machine Biology Group which combines the power of machines and biology to prevent, detect, and treat infectious diseases. He pioneered the development of the first antibiotic designed by a computer with efficacy in animals, designed algorithms for antibiotic discovery, and invented rapid low-cost diagnostics for COVID-19 and other infections.

Carl June, M.D.

Carl H. June, Richard W. Vague Professor in Immunotherapy in the Perelman School of Medicine and member of the Bioengineering Graduate Group
June is the Director for the Center for Cellular Immunotherapies and the Parker Institute for Cancer Therapy and runs the June Lab which develops new forms of T cell based therapies. June’s pioneering research in gene therapy led to the FDA approval for CAR T therapy for treating acute lymphoblastic leukemia (ALL), one of the most common childhood cancers.

Vivek Shenoy, Ph.D.

Vivek Shenoy, Eduardo D. Glandt President’s Distinguished Professor in Bioengineering, Mechanical Engineering and Applied Mechanics (MEAM), and in Materials Science and Engineering (MSE)
Shenoy runs the Theoretical Mechanobiology and Materials Lab which develops theoretical concepts and numerical principles for understanding engineering and biological systems. His analytical methods and multiscale modeling techniques gain insight into a myriad of problems in materials science and biomechanics.

The highly anticipated annual list identifies researchers who demonstrated significant influence in their chosen field or fields through the publication of multiple highly cited papers during the last decade. Their names are drawn from the publications that rank in the top 1% by citations for field and publication year in the Web of Science™ citation index.

Bassett and Burdick were both on the Highly Cited Researchers list in 2019 and 2020.

The methodology that determines the “who’s who” of influential researchers draws on the data and analysis performed by bibliometric experts and data scientists at the Institute for Scientific Information™ at Clarivate. It also uses the tallies to identify the countries and research institutions where these scientific elite are based.

David Pendlebury, Senior Citation Analyst at the Institute for Scientific Information at Clarivate, said: “In the race for knowledge, it is human capital that is fundamental and this list identifies and celebrates exceptional individual researchers who are having a great impact on the research community as measured by the rate at which their work is being cited by others.”

The full 2021 Highly Cited Researchers list and executive summary can be found online here.