A hyperlink network from English Wikipedia, with only 0.1% of articles (nodes) and their connections (edges) visualized. Seven different reader journeys through this network are highlighted in various colors. The network is organized by topic and displayed using a layout that groups related articles together. (Image: Dale Zhou)
At one point or another, you may have gone online looking for a specific bit of information and found yourself “going down the Wiki rabbit hole” as you discover wholly new, ever-more fascinating related topics — some trivial, some relevant — and you may have gone so far down the hole it’s difficult to piece together what brought you there to begin with.
According to the University of Pennsylvania’s Dani Bassett, who recently worked with a collaborative team of researcher to examine the browsing habits of 482,760 Wikipedia readers from 50 different countries, this style of information acquisition is called the “busybody.” This is someone who goes from one idea or piece of information to another, and the two pieces may not relate to each other much.
“The busybody loves any and all kinds of newness, they’re happy to jump from here to there, with seemingly no rhyme or reason, and this is contrasted by the ‘hunter,’ which is a more goal-oriented, focused person who seeks to solve a problem, find a missing factor, or fill out a model of the world,” says Bassett.
In the research, published in the journal Science Advances, Bassett and colleagues discovered stark differences in browsing habits between countries with more education and gender equality versus less equality, raising key questions about the impact of culture on curiosity and learning.
Dani S. Bassett is the J. Peter Skirkanich Professor at the University of Pennsylvania with a primary appointment in the School of Engineering and Applied Science’s Department of Bioengineering and secondary appointments in the School of Arts & Sciences’ Department of Physics & Astronomy, Penn Engineering’s Department of Electrical and Systems Engineering, and the Perelman School of Medicine’s Departments of Neurology and Psychiatry.
Representing Bach’s pieces as networks reveals hidden structures in his music. (Credit: Suman Kulkarni)
Even today, centuries after he lived, Johann Sebastian Bach remains one of the world’s most popular composers. On Spotify, close to seven million people stream his music per month, and his listener count is higher than that of Mozart and even Beethoven. The Prélude to his Cello Suite No. 1 in G Major has been listened to hundreds of millions of times.
What makes Bach’s music so enduring? Music critics might point to his innovative harmonies, complex use of counterpoint and symmetrical compositions. Represent Bach’s music as a network, however, where each node stands for one musical note, and each edge the transition from one note to another, and a wholly different picture emerges.
In a recent paper in Physical Review Research, Dani S. Bassett, J. Peter Skirkanich Professor in Bioengineering and in Electrical and Systems Engineering within the School of Engineering and Applied Science, in Physics & Astronomy within the School of Arts & Sciences, and in Neurology and Psychiatry within the Perelman School of Medicine, and Suman Kulkarni, a doctoral student in Physics & Astronomy, applied network theory to Bach’s entire oeuvre.
The paper sheds new light on the unique qualities of Bach’s music and demonstrates the potential for analyzing music through the lens of networks. Such analysis could yield benefits for music therapists, musicians, composers and music producers, by giving them unprecedented quantitative insight into the structure of different musical compositions.
“This paper provides a starting point for how one can boil down these complexities in music and start with a simple representation to dig into how these pieces are structured,” says Kulkarni, the paper’s lead author. “We applied this framework to a dozen types of Bach’s compositions and were able to observe quantitative differences in how they were structured.”
How do we measure chaos and why would we want to? Together, Penn engineers Dani S. Bassett, J. Peter Skirkanich Professor in Bioengineering and in Electrical and Systems Engineering, and postdoctoral researcher Kieran Murphy leverage the power of machine learning to better understand chaotic systems, opening doors for new information analyses in both theoretical modeling and real-world scenarios.
Humans have been trying to understand and predict chaotic systems such as weather patterns, the movement of planets and population ecology for thousands of years. While our models have continued to improve over time, there will always remain a barrier to perfect prediction. That’s because these systems are inherently chaotic. Not in the sense that blue skies and sunshine can turn into thunderstorms and torrential downpours in a second, although that does happen, but in the sense that mathematically, weather patterns and other chaotic systems are governed by physics with nonlinear characteristics.
“This nonlinearity is fundamental to chaotic systems,” says Murphy. “Unlike linear systems, where the information you start with to predict what will happen at timepoints in the future stays consistent over time, information in nonlinear systems can be both lost and generated through time.”
Like a game of telephone where information from the original source gets lost as it travels from person to person while new words and phrases are added to fill in the blanks, outcomes in chaotic systems become harder to predict as time passes. This information decay thwarts our best efforts to accurately forecast the weather more than a few days out.
“You could put millions of probes in the atmosphere to measure wind speed, temperature and precipitation, but you cannot measure every single atom in the system,” says Murphy. “You must have some amount of uncertainty, which will then grow, and grow quickly. So while a prediction for the weather in a few hours might be fairly accurate, that growth in uncertainty over time makes it impossible to predict the weather a month from now.”
In their recent paper published in Physical Review Letters, Murphy and Bassett applied machine learning to classic models of chaos, physicists’ reproductions of chaotic systems that do not contain any external noise or modeling imperfections, to design a near-perfect measurement of chaotic systems to one day improve our understanding of systems including weather patterns.
“These controlled systems are testbeds for our experiments,” says Murphy. “They allow us to compare with theoretical predictions and carefully evaluate our method before moving to real-world systems where things are messy and much less is known. Eventually, our goal is to make ‘information maps’ of real-world systems, indicating where information is created and identifying what pieces of information in a sea of seemingly random data are important.”
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.
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.
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
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, PhDis 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 GraceHopper 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 GraceHopper 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 GraceHopper 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. GraceHopper 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. GraceHopper 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.
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,”
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.
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.
“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?’”
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.”
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.”
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.
