César de la Fuente, Presidential Assistant Professor in Psychiatry, Bioengineering, Microbiology, and in Chemical and Biomolecular Engineering has been honored with a 2022 Young Investigator Award by the Royal Spanish Society of Chemistry (RSEQ) for his pioneering research efforts to combine the power of machines and biology to help prevent, detect, and treat infectious diseases.
Five University of Pennsylvania undergraduates have received 2022 Goldwater Scholarships, including Laila Barakat Norford, a third year Bioengineering major from Wayne, Pennsylvania. Goldwater Scholarships are awarded to sophomores or juniors planning research careers in mathematics, the natural sciences, or engineering.
Penn has produced 23 Goldwater Scholars in the past seven years and a total of 55 since Congress established the scholarship in 1986.
Laila Barakat Norford is majoring in bioengineering with minors in computer science and bioethics in Penn Engineering. As a Rachleff Scholar, Norford has been engaged in systems biology research since her first year. Her current research uses machine learning to predict cell types in intestinal organoids from live-cell images, enabling the mechanisms of development and disease to be characterized in detail. At Penn, she is an Orientation Peer Advisor, a volunteer with Advancing Women in Engineering and the Penn Society of Women Engineers, and a teaching assistant for introductory computer science. She is secretary of the Penn Band, plays the clarinet, and is a member of the Band’s Fanfare Honor Society for service and leadership. Norford registers voters with Penn Leads the Vote and canvasses for state government candidates. She is also involved in Penn’s LGBTQ+ community as a member of PennAces. Norford plans to pursue a Ph.D. in computational biology, aspiring to build computational tools to address understudied diseases and health disparities.
Konrad Kording, Nathan Francis Mossell University Professor in Bioengineering, Neuroscience, and Computer and Information Sciences, was appointed the Co-Director of the CIFAR Program in Learning in Machines & Brains. The appointment will start April 1, 2022.
CIFAR is a global research organization that convenes extraordinary minds to address the most important questions facing science and humanity. CIFAR was founded in 1982 and now includes over 400 interdisciplinary fellows and scholars, representing over 130 institutions and 22 countries. CIFAR supports research at all levels of development in areas ranging from Artificial Intelligence and child and brain development, to astrophysics and quantum computing. The program in Learning in Machines & Brains brings together international scientists to examine “how artificial neural networks could be inspired by the human brain, and developing the powerful technique of deep learning.” Scientists, industry experts, and policymakers in the program are working to understand the computational and mathematical principles behind learning, whether in brains or in machines, in order to understand human intelligence and improve the engineering of machine learning. As Co-Director, Kording will oversee the collective intellectual development of the LMB program which includes over 30 Fellows, Advisors, and Global Scholars. The program is also co-directed by Yoshua Benigo, the Canada CIFAR AI Chair and Professor in Computer Science and Operations Research at Université de Montréal.
Kording, a Penn Integrates Knowledge (PIK) Professor, was previously named an associate fellow of CIFAR in 2017. Kording’s groundbreaking interdisciplinary research uses data science to advance a broad range of topics that include understanding brain function, improving personalized medicine, collaborating with clinicians to diagnose diseases based on mobile phone data and even understanding the careers of professors. Across many areas of biomedical research, his group analyzes large datasets to test new models and thus get closer to an understanding of complex problems in bioengineering, neuroscience and beyond.
Visit Kording’s lab website and CIFAR profile page to learn more about his work in neuroscience, data science, and deep learning.
The Very Large Scale Microfluidic Integration (VLSMI) platform, a technology developed by the Penn researchers, contains hundreds of mixing channels for mass-producing mRNA-carrying lipid nanoparticles.
Penn Engineering secured a multi-million-dollar contract with Wellcome Leap under the organization’s $60 million RNA Readiness + Response (R3) program, which is jointly funded with the Coalition for Epidemic Preparedness Innovations (CEPI). Penn Engineers aim to create “on-demand” manufacturing technology that can produce a range of RNA-based vaccines.
The Penn Engineering team features Daeyeon Lee, Evan C Thompson Term Chair for Excellence in Teaching and Professor in Chemical and Biomolecular Engineering, Michael Mitchell, Skirkanich Assistant Professor of Innovation in Bioengineering, David Issadore, Associate Professor in Bioengineering and Electrical and Systems Engineering, and Sagar Yadavali, a former postdoctoral researcher in the Issadore and Lee labs and now the CEO of InfiniFluidics, a spinoff company based on their research. Drew Weissman of the Perelman School of Medicine, whose foundational research directly continued to the development of mRNA-based COVID-19 vaccines, is also a part of this interdisciplinary team.
The success of these COVID-19 vaccines has inspired a fresh perspective and wave of research funding for RNA therapeutics across a wide range of difficult diseases and health issues. These therapeutics now need to be equitably and efficiently distributed, something currently limited by the inefficient mRNA vaccine manufacturing processes which would rapidly translate technologies from the lab to the clinic.
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.
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.”
“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.
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’sComplex 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.
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.
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.
The impending danger of bacterial resistance to antibiotics is well-documented within the scientific community. Bacteria are the most efficient evolvers, and their ability to develop tolerance to drugs, in addition to antibiotic overuse and misuse, means that researchers have had to get particularly resourceful to ensure the future of modern medicine.
WIRED’s Max G. Levy recently spoke with de la Fuente and postdoctoral researcher and study collaborator Marcelo Torres about the urgency of the team’s work, and why developing these solutions is critical to the survival of civilization as we know it. The team’s algorithm, based on pattern recognition software used to analyze images, makes an otherwise insurmountable feat tangible.
De la Fuente’s lab specializes in using AI to discover and design new drugs. Rather than making some all-new peptide molecules that fit the bill, they hypothesized that an algorithm could use machine learning to winnow down the huge repository of natural peptide sequences in the human proteome into a select few candidates.
“We know those patterns—the multiple patterns—that we’re looking for,” says de la Fuente. “So that allows us to use the algorithm as a search function.”
Each year, Penn Engineering’s seniors present their Senior Design projects, a year-long effort that challenges them to test and develop solutions to real-world problems, to their individual departments. The top three projects from each department go on to compete in the annual Senior Design Competition, sponsored by the Engineering Alumni Society, which involves pitching projects to a panel of judges who evaluate their potential in the market. While the pandemic made this year’s competition logistically challenging, students and organizers were able to come together virtually to continue the tradition.
This year’s virtual format provided an opportunity for judges from around the country to participate in evaluating projects. Brad Richards, Director of Alumni Relations at Penn Engineering who helped plan the competition, was able to help recruit more than 60 volunteers to serve on the panel.
“The broad number of judges from varying industries made this competition incredibly meaningful, we will absolutely be integrating a virtual component to allow for more judges in the future.”
Eighteen teams total, three from each department, virtually presented to the panel of judges, who awarded $2,000 prizes in four categories.
Technology & Innovation Prize
This award recognized the team whose project represents the highest and best use of technology and innovation to leverage engineering principles.
Winner: Team OtoAI Department: Bioengineering Team Members: Krishna Suresh, Nikhil Maheshwari, Yash Lahoti, Jonathan Mairena, Uday Tripathi Advisor: Steven Eliades, Assistant Professor of Otorhinolaryngology in Penn’s Perelman School of Medicine Abstract: OtoAI is a novel digital otoscope that enables primary care physicians to take images of the inner ear and leverages machine learning to diagnose abnormal ear pathologies.
Rising Bioengineering Sophomore Catherine Michelluti (BSE 2023) has been featured on Penn’s SNF Paideia Program Instagram which discusses her diverse interests in machine learning in medicine, computer science, playing the violin and more. Catherine is a pre-med student who is pursuing an uncoordinated dual degree between the School of Engineering and Applied Science and the Wharton School of Business (BS in Economics 2023). She is also an incoming fellow in the SNF Paideia Program, which is supported by the Stavros Niarchos Foundation, is an interdisciplinary program which “encourage[s] the free exchange of ideas, civil and robust discussion of divergent views, and the integration of individual and community wellness, service, and citizenship through SNF Paideia designated courses, a fellows program, and campus events” (SNF Paideia website).
Once hailed as medical miracles, antibiotics are losing their effectiveness due to the rapid increase of bacterial immunity.
Researchers are scrambling to keep up with evolution, and they are currently exploring how machine learning can be applied to microbiology to develop more effective treatments.
In the past, researchers have studied bacteria behavior and used their findings to work against the natural patterns of bacterial life. In the 1980s, computer-assisted screening methods helped researchers in their efforts but few developments surfaced from their work. It seemed that there were no new antibiotics to be found using traditional methods, and pharmaceutical companies stepped away from funding antibiotic development in favor of more profitable drugs used to treat chronic conditions. But a new field of research shows a way forward, thanks to the massive advances in computing that have occurred over the intervening decades.
Among the pioneering researchers in this field is César de La Fuente, Presidential Assistant Professor in Psychiatry, Microbiology and Bioengineering. De La Fuente is accelerating the discovery of new antibiotics with his Drug Repurposing Hub, a library of more than 6,000 compounds that is using machine learning algorithms to seek out possible solutions for human disease. With his compound library, de La Fuente is able to examine drugs already approved by the FDA and hunt for new, more effective applications.
In addition to this work, de La Fuente and his colleagues are interested in using machine learning to innovate drug design itself. His lab uses a machine learning platform to generate new molecules in silico and perform experiments on them. Once the results of the experiments come in, they are fed back into the computer so the machine learning platform can continuously learn and improve its findings from the data.
In a recent interview with Katherine Harmon Courage in Quanta Magazine, de La Fuente said:
“The hypothesis is that nature has run out of inspiration in terms of providing us with new antibiotics. That’s why we think that machines … could diversify natural molecules to convert them to synthetic versions that would be much more effective.”
Originally posted on the Penn Engineering blog. Read more about de La Fuente’s work and other researchers exploring the computational design of new antibiotics in Quanta Magazine or The Atlantic.
