“This is What a Data Scientist Looks Like”

Speakers at the second annual Women in Data Science @ Penn Conference.

Last month, the second annual Women in Data Science (WiDS) @ Penn Conference virtually gathered nearly 500 registrants to participate in a week’s worth of academic and industry talks, live speaker Q&A sessions, and networking opportunities.

Hosted by Penn Engineering, Analytics at WhartonWharton Customer Analytics and Wharton’s Statistics Department, the conference’s theme — “This is What a Data Scientist Looks Like” – emphasized the depth, breadth, and diversity of data science, both in terms of the subjects the field covers and the people who enter it.

Following welcoming remarks from Erika James, Dean of the Wharton School, and Vijay Kumar, Nemirovsky Family Dean of Penn Engineering, the conference began with a keynote address from President of Microsoft US and Wharton alumna Kate Johnson.

Conference sessions continued throughout the week, featuring panels of academic data scientists from around Penn and beyond, industry leaders from IKEA Digital, Facebook and Poshmark, and lightning talks from students speakers who presented their data science research.

All of the conference’s sessions are now available on YouTube and the 2021 WiDS Conference Recap, including a talk titled “How Humans Build Models for the World” by Danielle Bassett, J. Peter Skirkanich Professor in Bioengineering and Electrical and Systems Engineering.

Read more about the conference at Wharton Stories: “How Women in Data Science Rise to the Top.

Originally posted in Penn Engineering Today.

Studying ‘Hunters and Busybodies,’ Penn and American University Researchers Measure Different Types of Curiosity

by Melissa Pappas

Knowledge networks were created as participants browsed Wikipedia, where pages became nodes and relatedness between pages became edges. Two diverging styles emerged — “the busybody” and “the hunter.” (Illustrations by Melissa Pappas)

Curiosity has been found to play a role in our learning and emotional well-being, but due to the open-ended nature of how curiosity is actually practiced, measuring it is challenging. Psychological studies have attempted to gauge participants’ curiosity through their engagement in specific activities, such as asking questions, playing trivia games, and gossiping. However, such methods focus on quantifying a person’s curiosity rather than understanding the different ways it can be expressed.

Efforts to better understand what curiosity actually looks like for different people have underappreciated roots in the field of philosophy. Varying styles have been described with loose archetypes, like “hunter” and “busybody” — evocative, but hard to objectively measure when it comes to studying how people collect new information.

A new study led by researchers at the University of Pennsylvania’s School of Engineering and Applied Science, the Annenberg School for Communication, and the Department of Philosophy and Religion at American University, uses Wikipedia browsing as a method for describing curiosity styles. Using a branch of mathematics known as graph theory, their analysis of curiosity opens doors for using it as a tool to improve learning and life satisfaction.

The interdisciplinary study, published in Nature Human Behavior, was undertaken by Danielle Bassett, J. Peter Skirkanich Professor in Penn Engineering’s Departments of Bioengineering and Electrical and Systems Engineering, David Lydon-Staley, then a post-doctoral fellow in her lab, now an assistant professor in the Annenberg School of Communication, two members of Bassett’s Complex Systems Lab, graduate student Dale Zhou and postdoctoral fellow Ann Sizemore Blevins, and Perry Zurn, assistant professor from American University’s Department of Philosophy.

“The reason this paper exists is because of the participation of many people from different fields,” says Lydon-Staley. “Perry has been researching curiosity in novel ways that show the spectrum of curious practice and Dani has been using networks to describe form and function in many different systems. My background in human behavior allowed me to design and conduct a study linking the styles of curiosity to a measurable activity: Wikipedia searches.”

Zurn’s research on how different people express curiosity provided a framework for the study.

Read the full story in Penn Engineering Today.

Danielle Bassett and Jason Burdick are Among World’s Most Highly Cited Researchers

Danielle Bassett and Jason Burdick
Danielle Bassett and Jason Burdick

The nature of scientific progress is often summarized by the Isaac Newton quotation, “If I have seen further it is by standing on the shoulders of giants.” Each new study draws on dozens of earlier ones, forming a chain of knowledge stretching back to Newton and the scientific giants his work referenced.

Scientific publishing and referencing has become more formal since Newton’s time, with databases of citations allowing for sophisticated quantitative analyses of that flow of information between researchers.

The Institute for Scientific Information and the Web of Science Group provide a yearly snapshot of this flow, publishing a list of the researchers who are in the top 1 percent of their respective fields when it comes to the number of times their work has been cited.

Danielle Bassett, J. Peter Skirkanich Professor in the departments of Bioengineering and Electrical and Systems Engineering, and Jason Burdick, Robert D. Bent Professor in the department of Bioengineering, are among the 6,389 researchers named to the 2020 list.

Bassett is a pioneer in the field of network neuroscience, 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. Burdick is an expert in tissue engineering and the design of biomaterials for regenerative medicine; by precisely tailoring the microenvironment within these materials, they can influence stem cell differentiation or trigger the release of therapeutics.

Bassett and Burdick were named to the Web of Science’s 2019 Highly Cited Researchers list as well.

Originally posted in Penn Engineering Today.

Danielle Bassett on ‘A Radical New Model of the Brain’

In a ‘Wired’ feature, Bassett helps explain the growing field of network neuroscience and how the form and function of the brain are connected.

Danielle Bassett, Ph.D.

Early attempts to understand how the brain works included the pseudoscience of phrenology, which theorized that various mental functions could be determined through the shape of the skull. While those theories have long been debunked, modern neuroscience has shown a kernel of truth to them: those functions are highly localized to different regions of the brain.

Now, Danielle Bassett, Professor of J. Peter Skirkanich Professor of Bioengineering and Electrical and Systems Engineering, is pioneering a new subfield that goes even deeper into the connection between the brain’s form and function: network neuroscience.

In a recent feature article in Wired, Bassett explains the concepts behind this new subfield. While prior understanding has long relied on the idea that certain areas of the brain control certain functions, Bassett and other network neuroscientists are using advances in imaging and machine learning to reveal the role the connections between those areas play.

For Bassett, one of the first indicators that these connections mattered more than previously realized was the shape of the neurons themselves.

Speaking with Wired’s Grace Huckins, Bassett says:

“Neurons are not spherical — neurons have a cell body, and then they have this long tail that allows them to connect to many other cells. You can even look at the morphology of the neuron and say, ‘Oh, well, connectivity has to matter. Otherwise, it wouldn’t look like this.’”

Read more about Bassett and the field of network neuroscience in Wired.

Originally posted on the Penn Engineering blog.

What do ‘Bohemian Rhapsody,’ ‘Macbeth,’ and a list of Facebook Friends All Have in Common?

New research finds that works of literature, musical pieces, and social networks have a similar underlying structure that allows them to share large amounts of information efficiently.

Examples of statistical network analysis of characters in two of Shakespeare’s tragedies. Two characters are connected by a line, or edge, if they appear in the same scene. The size of the circles that represent these characters, called nodes, indicate how many other characters one is connected to. The network’s density relates to how complete the graph is, with 100% density meaning that it has all of the characters are connected. (Image: Martin Grandjean)

 

By Erica K. Brockmeier

To an English scholar or avid reader, the Shakespeare Canon represents some of the greatest literary works of the English language. To a network scientist, Shakespeare’s 37 plays and the 884,421 words they contain also represent a massively complex communication network. Network scientists, who employ math, physics, and computer science to study vast and interconnected systems, are tasked with using statistically rigorous approaches to understand how complex networks, like all of Shakespeare, convey information to the human brain.

New research published in Nature Physics uses tools from network science to explain how complex communication networks can efficiently convey large amounts of information to the human brain. Conducted by postdoc Christopher Lynn, graduate students Ari Kahn and Lia Papadopoulos, and professor Danielle S. Bassett, the study found that different types of networks, including those found in works of literature, musical pieces, and social connections, have a similar underlying structure that allows them to share information rapidly and efficiently.

Technically speaking, a network is simply a statistical and graphical representation of connections, known as edges, between different endpoints, called nodes. In pieces of literature, for example, a node can be a word, and an edge can connect words when they appear next to each other (“my” — “kingdom” — “for” — “a” — “horse”) or when they convey similar ideas or concepts (“yellow” — “orange” — “red”).

The advantage of using network science to study things like languages, says Lynn, is that once relationships are defined on a small scale, researchers can use those connections to make inferences about a network’s structure on a much larger scale. “Once you define the nodes and edges, you can zoom out and start to ask about what the structure of this whole object looks like and why it has that specific structure,” says Lynn.

Building on the group’s recent study that models how the brain processes complex information, the researchers developed a new analytical framework for determining how much information a network conveys and how efficient it is in conveying that information. “In order to calculate the efficiency of the communication, you need a model of how humans receive the information,” he says.

Continue reading at Penn Today.

To Err is Human, to Learn, Divine

Researchers develop a new model for how the brain processes complex information: by striking a balance between accuracy and simplicity while making mistakes along the way.

By Erica K. Brockmeier

New research finds that the human brain detects patterns in complex networks by striking a balance between simplicity and complexity, much like how a pointillist painting can be viewed up close to see the finer details or from a distance to see its overall structure.

The human brain is a highly advanced information processor composed of more than 86 billion neurons. Humans are adept at recognizing patterns from complex networks, such as languages, without any formal instruction. Previously, cognitive scientists tried to explain this ability by depicting the brain as a highly optimized computer, but there is now discussion among neuroscientists that this model might not accurately reflect how the brain works.

Now, Penn researchers have developed a different model for how the brain interprets patterns from complex networks. Published in Nature Communications, this new model shows that the ability to detect patterns stems in part from the brain’s goal to represent things in the simplest way possible. Their model depicts the brain as constantly balancing accuracy with simplicity when making decisions. The work was conducted by physics Ph.D. student Christopher Lynn, neuroscience Ph.D. student Ari Kahn, and Danielle Bassett, J. Peter Skirkanich Professor in the departments of Bioengineering and Electrical and Systems Engineering.

This new model is built upon the idea that people make mistakes while trying to make sense of patterns, and these errors are essential to get a glimpse of the bigger picture. “If you look at a pointillist painting up close, you can correctly identify every dot. If you step back 20 feet, the details get fuzzy, but you’ll gain a better sense of the overall structure,” says Lynn.

To test their hypothesis, the researchers ran a set of experiments similar to a previous study by Kahn. That study found that when participants were shown repeating elements in a sequence, such as A-B-C-B, etc., they were automatically sensitive to certain patterns without being explicitly aware that the patterns existed. “If you experience a sequence of information, such as listening to speech, you can pick up on certain statistics between elements without being aware of what those statistics are,” says Kahn.

To understand how the brain automatically understands such complex associations within sequences, 360 study participants were shown a computer screen with five gray squares corresponding to five keys on a keyboard. As two of the five squares changed from gray to red, the participants had to strike the computer keys that corresponded to the changing squares. For the participants, the pattern of color-changing squares was random, but the sequences were actually generated using two kinds of networks.

The researchers found that the structure of the network impacted how quickly the participants could respond to the stimuli, an indication of their expectations of the underlying patterns. Responses were quicker when participants were shown sequences that were generated using a modular network compared to sequences coming from a lattice network.

Continue reading on Penn Today.

This paper was also profiled on the website Big Think.

Danielle Bassett Named AIMBE Fellow

Danielle Bassett, Ph.D.

Danielle Bassett, J. Peter Skirkanich Professor of Bioengineering, has been named an American Institute for Medical and Biological Engineering (AIMBE) Fellow.

Election to the AIMBE College of Fellows is among the highest professional distinctions accorded to a medical and biological engineer. College membership honors those who have made outstanding contributions to “engineering and medicine research, practice, or education” and to “the pioneering of new and developing fields of technology, making major advancements in traditional fields of medical and biological engineering, or developing/implementing innovative approaches to bioengineering education.”

Bassett was nominated, reviewed, and elected by peers and members of the College of Fellows for “significant contributions to the application of neural network theory for understanding both physio and patho-physiological brain function.”

As a result of health concerns, AIMBE’s annual meeting and induction ceremony scheduled for March 29–30, 2020, was cancelled. Under special procedures, Bassett was remotely inducted along with 156 colleagues who make up the AIMBE College of Fellows Class of 2020.

Originally posted on the Penn Engineering blog.

Listen: Danielle Bassett Uses Network Science to Find Links in Human Curiosity

Danielle Bassett, Ph.D.

Danielle Bassett, J. Peter Skirkanich Professor of Bioengineering and Electrical and Systems Engineering, is a curious scientist.

Featured on a recent episode of “Choosing to be Curious” on WERA 96.7 Radio Arlington, Bassett discussed her work in studying curiosity and the potential neural mechanisms behind it. In her work, Bassett strives to re-conceptualize curiosity itself, defining it as not just seeking new bits information, but striving to understand the path through which those bits are connected.

Bassett is a pioneering researcher in the field of network science and how its tools can be applied to understand the brain. Now, Bassett and her research team are using the tools of network science and complex systems theory to uncover what common styles of curiosity people share and how individual styles differ. In addition, the team is exploring if there are canonical types of curiosity among humans or if each person’s curiosity architecture is unique.

This isn’t the first time Bassett has combined the tools of disparate fields to pursue her research. For as long as she can remember, Bassett has been insatiably curious and, while she was homeschooled as a child, she often wandered from one subject to the next and let her own interest guide her path. For Bassett, studying curiosity with the tools of physical, biology, and engineering is a natural step in her research journey.

In her interview with host Lynn Borton, Bassett says:

“What took me to curiosity is the observation that there’s a problem in defining the ways in which we search for knowledge. And that perhaps the understanding of curiosity could be benefitted by a scientific and mathematical approach. And that maybe the tools and conceptions that we have in mathematics and physics and other areas of science are useful for understanding curiosity. Which most people would consider to be more in the world of the humanities than the sciences….“Part of what I’m hoping to do is to illustrate that there are connections between disciplines that seem completely separate. Sometimes some of the best ideas in science are inspired not by a scientific result but by something else.”

To hear more about Bassett’s research on curiosity, listen to the full episode of Choosing to Be Curious.

Originally posted on the Penn Engineering blog.

Dr. Danielle Bassett and Dr. Jason Burdick Named to Highly Cited Researchers List

by Sophie Burkholder

One way to measure the success or influence of a researcher is to consider how many times they’re cited by other researchers. Every published paper requires a reference section listing relevant earlier papers, and the Web of Science Group keeps track of how many times different authors are cited over the course of a year.

Danielle Bassett, Ph.D.

In 2019, two members of the Penn Bioengineering department, Jason Burdick, Ph.D., and Danielle Bassett, Ph.D., were named Highly Cited Researchers, indicating that each of them placed within the top 1% of citations in their field based on the Web of Science’s index. For the past year, only 6,300 researchers were recognized with this honor, a number that makes up a mere 0.1% of researchers worldwide. Bassett’s lab looks at the use of knowledge, brain, and dynamic networks to understand bioengineering problems at a systems-level analysis, while Burdick’s lab focuses on advancements in tissue engineering through polymer design and development.

Robert D. Bent Chair
Jason Burdick, PhD

Burdick’s and Bassett’s naming to the list of Highly Cited Researchers demonstrates that their research had an outsized influence over current work in the field of bioengineering in the last year, and that new innovations continue to be developed from foundations these two Penn researchers created. To be included among such a small percentage of researchers worldwide indicates that Bassett and Burdick are sources of great impact and influence in bioengineering advancements today.

Danielle Bassett Receives New Scholarly Chair

Danielle Bassett, Ph.D.

Danielle Bassett has been named the J. Peter Skirkanich Professor of Bioengineering.

Dr. Bassett is a Professor in the department of Bioengineering at the School of Engineering and Applied Science. She holds a Ph.D. in Physics from the University of Cambridge and completed her postdoctoral training at the University of California, Santa Barbara, before joining Penn in 2013.

Dr. Bassett has received numerous awards for her research, including an Alfred P Sloan Research Fellowship, a MacArthur Fellowship, an Office of Naval Research Young Investigator Award, a National Science Foundation CAREER Award and, most recently, an Erdos-Renyi Prize in Network Science to name but a few. She has authored over 190 peer-reviewed publications as well as numerous book chapters and teaching materials. She is the founding director of the Penn Network Visualization Program, a combined undergraduate art internship and K-12 outreach program bridging network science and the visual arts.

Continue reading at the Penn Engineering blog.