Thank you to everyone who attended the 2023 Department of Bioengineering Juneteenth Address. For those who were unable to attend or who may wish to share the opportunity to view the lecture, a recording of Dr. Kevin Johnson’s talk, “A White Neighbor, a Black Surgeon, and a Mormon Computer Scientist Walk into a Bar…” is available below.
Speaker: Kevin B. Johnson, MD, MS, FAAP, FAMIA, FACMI
David L. Cohen University Professor
Computer and Information Science
Biostatistics, Epidemiology and Informatics
Bioengineering
Annenberg School for Communication
Pediatrics
VP for Applied Informatics (UPHS), University of Pennsylvania
Abstract:
As we recognize Juneteenth, a holiday that brings awareness to what journalist Corey Mitchell calls “…a complex understanding of the nation’s past,” we also need to understand how many of our neighbors, staff, and faculty — even those born in the last 100 years — continue to navigate through the environment that made Juneteenth remarkable. In this talk, Dr. Johnson shares a bit of his personal story and how this story informs his national service and passion for teaching.
Nader Engheta was puzzled when he got a call from the psychology department about a fish. In the early 1990s, Engheta, a newly minted associate professor of electrical engineering in Penn’s School of Engineering and Applied Science, was a respected expert in radio wave technologies. But in recent years, his work had been expanding into subjects at once more eccentric and fundamental.
Nader Engheta was puzzled when he got a call from the psychology department about a fish.
In the early 1990s, Engheta, a newly minted associate professor of electrical engineering in Penn’s School of Engineering and Applied Science, was a respected expert in radio wave technologies. But in recent years, his work had been expanding into subjects at once more eccentric and fundamental.
Engheta’s interest in electromagnetic waves was not limited to radio frequencies, as a spate of fresh publications could attest. Some studies investigated a range of wave interactions with a class of matter known as a “chiral media,” materials with molecular configurations that exhibit qualities of left or right “handedness.” Others established practical electromagnetic applications for a bewildering branch of mathematics called “fractional calculus,” an area with the same Newtonian roots as calculus proper but a premise as eyebrow-raising as the suggestion a family might literally include two-and-a-half children.
Electromagnetic waves are organized on a spectrum of wavelengths. On the shorter end of the spectrum are high-energy waves, such as X-rays. In the middle, there is the limited range we see as visible light. And on the longer end are the lower-energy regimes of radio and heat.
Researchers tend to focus on one kind of wave or one section of the spectrum, exploring quirks and functions unique to each. But all waves, electromagnetic or not, share the same characteristics: They consist of a repeating pattern with a certain height (amplitude), rate of vibration (frequency), and distance between peaks (wavelength). These qualities can define a laser beam, a broadcasting voice, a wind-swept lake, or a violin string.
Engheta has never been the kind of scholar to limit the scope of his curiosity to a single field of research. He is interested in waves, and his fascination lies equally in the physics that determine wave behavior and the experimental technologies that push the boundaries of those laws.
So, when Edward Pugh, a mathematical psychologist studying the physiology of visual perception, explained that green sunfish might possess an evolutionary advantage for seeing underwater, Engheta listened.
Soon, the two Penn professors were pouring over microscope images of green sunfish retinas.
Read Devorah Fischler’s full story about Nader Engheta and watch an accompanying video at Penn Today.
Nader Engheta is H. Nedwill Ramsey Professor of Electrical and Systems Engineering at Penn Engineering, with secondary appointments in the departments of Bioengineering, Materials Science and Engineering, and Physics and Astronomy in the School of Arts & Sciences.
We hope you will join us for the 2023 Department of Bioengineering Juneteenth Address by Dr. Kevin B. Johnson.
Date: Wednesday, June 14, 2023
Start Time: 11:00 AM ET
Location: Berger Auditorium (Skirkanich Hall basement room 013)
Zoom link
Meeting ID: 925 0325 6013
Passcode: 801060
Following the event, a limited number of box lunches will be available for in-person attendees. If you would like a box lunch, please RSVP here by Monday, June 12 so we can get an accurate headcount.
Speaker: Kevin B. Johnson, MD, MS, FAAP, FAMIA, FACMI
David L. Cohen University Professor
Annenberg School for Communication, Bioengineering, Biostatistics, Epidemiology and Informatics, Computer and Information Science, Pediatrics
VP for Applied Informatics (UPHS), University of Pennsylvania
Title: “A White Neighbor, a Black Surgeon, and a Mormon Computer Scientist Walk into a Bar…”
Abstract: As we recognize Juneteenth, a holiday that brings awareness to what journalist Corey Mitchell calls “…a complex understanding of the nation’s past”, we also need to understand how many of our neighbors, staff, and faculty—even those born in the last 100 years—continue to navigate through the environment that made Juneteenth remarkable. Dr. Johnson will share a bit of his personal story and how this story informs his national service and passion for teaching.
Bio: Dr. Johnson is a leader of medical information technologies to improve patient care and safety. He is well regarded and widely known for pioneering discoveries in clinical informatics, leading to advances in data acquisition, medication management, and information aggregation in medical settings.
He is a board-certified pediatrician who has aligned the powers of medicine, engineering and technology to improve the health of individuals and communities. In work that bridges biomedical informatics, bioengineering and computer science, he has championed the development and implementation of clinical information systems and artificial intelligence to drive medical research. He has encouraged the effective use of technology at the bedside, and he has empowered patients to use new tools that help them to understand how medications and supplements may affect their health. He is interested in using advanced technologies such as smart devices and in developing computer-based documentation systems for the point of care. He also is an emerging champion of the use of digital media to enhance science communication, with a successful feature-length documentary describing health information exchange, a podcast (Informatics in the Round) and most recently, a children’s book series aimed at STEM education featuring scientists underrepresented in healthcare.
Dr. Johnson holds joint appointments in the Department of Computer and Information Science of the School of Engineering and Applied Science, and secondary appointments in Bioengineering and the Annenberg School for Communication. He serves as Vice President for Applied Informatics in the University of Pennsylvania Health System and as a Professor of Pediatrics at the Children’s Hospital of Philadelphia.
Before arriving at Penn, he served as the Cornelius Vanderbilt Professor and Chair of the Department of Biomedical Informatics at the Vanderbilt University School of Medicine, where he had taught since 2002. As Senior Vice President for Health Information Technology at the Vanderbilt University Medical Center, he led the development of clinical systems that enabled doctors to make better treatment and care decisions for individual patients, and introduced new systems to integrate artificial intelligence into patient care workflows.
The author of more than 150 publications, Dr. Johnson has held numerous leadership positions in the American Medical Informatics Association and the American Academy of Pediatrics. He leads the American Board of Pediatrics Informatics Advisory Committee, directs the Board of Scientific Counselors of the National Library of Medicine, and is a member of the NIH Council of Councils. He is an elected member of the National Academy of Medicine, American College of Medical Informatics and Academic Pediatric Society. He has received awards from the Robert Wood Johnson Foundation and American Academy of Pediatrics, among many others.
Folding@home is led by Gregory Bowman, a Penn Integrates Knowledge Professor who has appointments in the Departments of Biochemistry and Biophysics in the Perelman School of Medicine and the Department of Bioengineering in the School of Engineering and Applied Science. (Image: Courtesy of Penn Medicine News)
Two heads are better than one. The ethos behind the scientific research project Folding@home is that same idea, multiplied: 50,000 computers are better than one.
Folding@home is a distributed computing project which is used to simulate protein folding, or how protein molecules assemble themselves into 3-D shapes. Research into protein folding allows scientists to better understand how these molecules function or malfunction inside the human body. Often, mutations in proteins influence the progression of many diseases like Alzheimer’s disease, cancer, and even COVID-19.
Penn is home to both the computer brains and human minds behind the Folding@home project which, with its network, forms the largest supercomputer in the world. All of that computing power continually works together to answer scientific questions such as what areas of specific protein implicated in Parkinson’s disease may be susceptible to medication or other treatment.
Using the network hub at Penn, Bowman and his team assign experiments to each individual computer which communicates with other computers and feeds info back to Philly. To date, the network is comprised of more than 50,000 computers spread across the world.
“What we do is like drawing a map,” said Bowman, explaining how the networked computers work together in a type of system that experts call Markov state models. “Each computer is like a driver visiting different places and reporting back info on those locations so we can get a sense of the landscape.”
Individuals can participate by signing up and then installing software to their standard personal desktop or laptop. Participants can direct the software to run in the background and limit it to a certain percentage of processing power or have the software run only when the computer is idle.
When the software is at work, it’s conducting unique experiments designed and assigned by Bowman and his team back at Penn. Users can play scientist and watch the results of simulations and monitor the data in real time, or they can simply let their computer do the work while they go about their lives.
Machine learning (ML) programs computers to learn the way we do – through the continual assessment of data and identification of patterns based on past outcomes. ML can quickly pick out trends in big datasets, operate with little to no human interaction and improve its predictions over time. Due to these abilities, it is rapidly finding its way into medical research.
People with breast cancer may soon be diagnosed through ML faster than through a biopsy. Those suffering from depression might be able to predict mood changes through smart phone recordings of daily activities such as the time they wake up and amount of time they spend exercising. ML may also help paralyzed people regain autonomy using prosthetics controlled by patterns identified in brain scan data. ML research promises these and many other possibilities to help people lead healthier lives.
But while the number of ML studies grow, the actual use of it in doctors’ offices has not expanded much past simple functions such as converting voice to text for notetaking.
The limitations lie in medical research’s small sample sizes and unique datasets. This small data makes it hard for machines to identify meaningful patterns. The more data, the more accuracy in ML diagnoses and predictions. For many diagnostic uses, massive numbers of subjects in the thousands would be needed, but most studies use smaller numbers in the dozens of subjects.
But there are ways to find significant results from small datasets if you know how to manipulate the numbers. Running statistical tests over and over again with different subsets of your data can indicate significance in a dataset that in reality may be just random outliers.
This tactic, known as P-hacking or feature hacking in ML, leads to the creation of predictive models that are too limited to be useful in the real world. What looks good on paper doesn’t translate to a doctor’s ability to diagnose or treat us.
These statistical mistakes, oftentimes done unknowingly, can lead to dangerous conclusions.
To help scientists avoid these mistakes and push ML applications forward, Konrad Kording, Nathan Francis Mossell University Professor with appointments in the Departments of Bioengineering and Computer and Information Science in Penn Engineering and the Department of Neuroscience at Penn’s Perelman School of Medicine, is leading an aspect of a large, NIH-funded program known as CENTER – Creating an Educational Nexus for Training in Experimental Rigor. Kording will lead Penn’s cohort by creating the Community for Rigor which will provide open-access resources on conducting sound science. Members of this inclusive scientific community will be able to engage with ML simulations and discussion-based courses.
“The reason for the lack of ML in real-world scenarios is due to statistical misuse rather than the limitations of the tool itself,” says Kording. “If a study publishes a claim that seems too good to be true, it usually is, and many times we can track that back to their use of statistics.”
Such studies that make their way into peer-reviewed journals contribute to misinformation and mistrust in science and are more common than one might expect.
A tailored silicon nanopattern coupled with a semi-transparent gold mirror can solve a complex mathematical equation using light. (Image credit: Ella Maru studio)
Researchers at the University of Pennsylvania, AMOLF, and the City University of New York (CUNY) have created a surface with a nanostructure capable of solving mathematical equations.
Powered by light and free of electronics, this discovery introduces exciting new prospects for the future of computing.
Engheta is the founder of the influential field of “optical metatronics.” He creates materials that interact with photons to manipulate data at the speed of light. Engheta’s contribution to this study marks an important advance in his quest to use light-matter interactions to surpass the speed and energy limitations of digital electronics, bringing analog computing out of the past and into the future.
“I began the work on optical metatronics in 2005,” says Engheta, “wondering if it were possible to recreate the elements of a standard electronic circuit at nanoscale. At this tiny size, it would be possible to manipulate the circuit with light, rather than electricity. After achieving this, we became more ambitious, envisioning collections of these nanocircuits as processors. In 2014, we were designing materials that used these optical nanostructures to perform mathematical operations, and in 2019, we anted up to entire mathematical equations using microwaves. Now, my collaborators and I have created a surface that can solve equations using light waves, a significant step closer to our larger goals for computing materials.”
The study, recently published in Nature Nanotechnology, demonstrates the possibility of solving complex mathematical problems and a generic matrix inversion at speeds far beyond those of typical digital computing methods.
The solution converges in about 349 femtoseconds (less than one trillionth of a second), orders of magnitude faster than the clock speed of a conventional processor.
Nader Engheta is the H. Nedwill Ramsey Professor in the Departments of Electrical and Systems Engineering and in Bioengineering in the School of Engineering and Applied Science and Professor in Physics and Astronomy in the School of Arts & Sciences at the University of Pennsylvania.
Gregory Bowman, the Louis Heyman University Professor, has joint appointments in the Department of Biochemistry and Biophysics in the Perelman School of Medicine and the Department of Bioengineering in the School of Engineering and Applied Science. (Image: Courtesy of School of Engineering and Applied Sciences)
His research aims to combat global health threats such as COVID-19 and Alzheimer’s disease by better understanding how proteins function and malfunction, especially through new computational and experimental methods that map protein structures. This understanding of protein dynamics can lead to effective new treatments for even the most seemingly resistant diseases.
“Delivering the right treatment to the right person at the right time is vital to sustaining—and saving—lives,” Magill said. “Greg Bowman’s novel work holds enormous promise and potential to advance new forms of personalized medicine, an area of considerable strength for Penn. A gifted researcher and consummate collaborator, we are delighted to count him among our distinguished PIK University Professors.”
Bowman came to Penn from the Washington University School of Medicine’s Department of Biochemistry and Molecular Biophysics, where he served on the faculty since 2014. He previously completed a three-year postdoctoral fellowship at the University of California, Berkeley.
Bowman’s research utilizes high-performance supercomputers for simulations that can better explain how mutations and disease change a protein’s functions. These simulations are enabled in part through the innovative Folding@home project, which Bowman directs. Folding@home empowers anyone with a computer to run simulations alongside a consortium of universities, with more than 200,000 participants worldwide.
His research has been supported by the National Science Foundation, National Institutes of Health, National Institute on Aging, and Packard Foundation, among others, and he has received a CAREER Award from the NSF, Career Award at the Scientific Interface from the Burroughs Wellcome Fund, and Thomas Kuhn Paradigm Shift Award from the American Chemical Society. He received a Ph.D. in biophysics from Stanford University and a B.S. (summa cum laude) in computer science, with a minor in biomedical engineering, from Cornell University.
“Greg Bowman’s highly innovative work,” Winkelstein said, “exemplifies the power of our interdisciplinary mission at Penn. He brings together supercomputers, biophysics, and biochemistry to make a vital impact on public health. This brilliant fusion of methods—in the service of improving people’s lives around the world—will be a tremendous model for the research of our faculty, students, and postdocs in the years ahead.”
The Penn Integrates Knowledge program is a University-wide initiative to recruit exceptional faculty members whose research and teaching exemplify the integration of knowledge across disciplines and who are appointed in at least two schools at Penn.
The Louis Heyman University Professorship is a gift of Stephen J. Heyman, a 1959 graduate of the Wharton School, and his wife, Barbara Heyman, in honor of Stephen Heyman’s uncle. Stephen Heyman is a University Emeritus Trustee and member of the School of Nursing Board of Advisors. He is Managing Partner at Nadel and Gussman LLC in Tulsa, Oklahoma.
nucleus and membrane of pathogen micro organisms in blue background
Up to 50 percent of cancer-signaling proteins once believed to be immune to drug treatments due to a lack of targetable protein regions may actually be treatable, according to a new study from the Perelman School of Medicine at the University of Pennsylvania. The findings, published this month in Nature Communications, suggest there may be new opportunities to treat cancer with new or existing drugs.
Researchers, clinicians, and pharmacologists looking to identify new ways to treat medical conditions—from cancer to autoimmune diseases—often focus on protein pockets, areas within protein structures to which certain proteins or molecules can bind. While some pockets are easily identifiable within a protein structure, others are not. Those hidden pockets, referred to as cryptic pockets, can provide new opportunities for drugs to bind to. The more pockets scientists and clinicians have to target with drugs, the more opportunities they have to control disease.
The research team identified new pockets using a Penn-designed neural network, called PocketMiner, which is artificial intelligence that predicts where cryptic pockets are likely to form from a single protein structure and learns from itself. Using PocketMiner—which was trained on simulations run on the world’s largest super computer—researchers simulated single protein structures and successfully predicted the locations of cryptic pockets in 35 cancer-related protein structures in thousands of areas of the body. These once-hidden targets, now identified, open up new approaches for potentially treating existing cancer.
What’s more, while successfully predicting the cryptic pockets, the method scientists used in this study was much faster than previous simulation or machine-learning methods. The network allows researchers to nearly instantaneously decide if a protein is likely to have cryptic pockets before investing in more expensive simulations or experiments to pursue a predicted pocket further.
“More than half of human proteins are considered undruggable due to an apparent lack of binding proteins in the snapshots we have,” said Gregory R. Bowman, PhD, a professor of Biochemistry and Biophysics and Bioengineering at Penn and the lead author of the study. “This PocketMiner research and other research like it not only predict druggable pockets in critical protein structures related to cancer but suggest most human proteins likely have druggable pockets, too. It’s a finding that offers hope to those with currently untreatable diseases.”
University of Pennsylvania scientist Nader Engheta has been selected as a 2023 recipient of the Benjamin Franklin Medal, one of the world’s oldest science and technology awards. The laureates will be honored on April 27 at a ceremony at the Franklin Institute in Philadelphia.
Engheta, H. Nedwill Ramsey Professor in Electrical and Systems Engineering, is among nine outstanding individuals recognized with Benjamin Franklin Medals this year for their achievements in extraordinary scientific, engineering and business leadership.
“As a scientist and a Philadelphian, I am deeply honored and humbled to receive the Franklin Medal. It is the highest compliment to receive an award whose past recipients include some of my scientific heroes such as Albert Einstein, Nikola Tesla, Alexander Graham Bell, and Max Planck. I am very thankful to the Franklin Institute for bestowing this honor upon me.”
Larry Dubinski, President and CEO of The Franklin Institute, says, “We are proud to continue The Franklin Institute’s longtime legacy of recognizing individuals for their contributions to humanity. These extraordinary advancements in areas of such importance as social equity, sustainability, and safety are significantly moving the needle in the direction of positive change and therefore laying the groundwork for a remarkable future.”
The 2023 Benjamin Franklin Medal in Electrical Engineering goes to Engheta for his transformative innovations in engineering novel materials that interact with electromagnetic waves in unprecedented ways, with broad applications in ultrafast computing and communication technologies.
“Professor Engheta’s pioneering work in metamaterials and nano-optics points the way to new and truly revolutionary computing capabilities in the future,” says University of Pennsylvania President Liz Magill. “Penn inaugurated the age of computers by creating the world’s first programmable digital computer in 1945. Professor Engheta’s work continues this tradition of groundbreaking research and discovery that will transform tomorrow. We are thrilled to see him receive the recognition of the Benjamin Franklin Medal.”
Engheta founded the field of optical nanocircuits (“optical metatronics”), which merges nanoelectronics and nanophotonics. He is also known for establishing and& developing the field of near-zero-index optics and epsilon-near-zero (ENZ) materials with near-zero electric permittivity. Through his work he has opened many new frontiers, including optical computation at the nanoscale and scattering control for cloaking and transparency. His work has far-reaching implications in various branches of electrical engineering, materials science, optics, microwaves, and quantum electrodynamics.
“This award recognizes Dr. Engheta’s trailblazing advances in engineering and physics,” says Vijay Kumar, Nemirovsky Family Dean of Penn Engineering.“ The swift and sustainable technologies his research in metamaterials and metatronics offers the world are the result of a lifelong commitment to scientific curiosity. For over 35 years, Nader Engheta has personified Penn Engineering’s mission of inventing the future.”
Nader Engheta is the H. Nedwill Ramsey Professor in the Departments of Electrical and Systems Engineering and Bioengineering in the School of Engineering and Applied Science and professor of physics and astronomy in the School of Arts & Sciences at the University of Pennsylvania.
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.
Machine learning (ML) programs computers to learn the way we do – through the continual assessment of data and identification of patterns based on past outcomes. ML can quickly pick out trends in big datasets, operate with little to no human interaction and improve its predictions over time. Due to these abilities, it is rapidly finding its way into medical research.
