Herman P. Schwan Distinguished Lecture: “Seeing the Unseen: How AI Redefines Bioengineering” (Dorin Comaniciu, Siemens Healthineers)

Dorin Comaniciu, Ph.D.

We hope you will join us for the 2023 Herman P. Schwan Distinguished Lecture by Dr. Dorin Comaniciu, hosted by the Department of Bioengineering.

Wednesday, December 13, 2023
1:00 PM ET
Location: Wu & Chen Auditorium (Levine 101)
The lecture and Q&A will be followed by a light reception in Levine Lobby.

Speaker: Dorin Comaniciu, Ph.D.
Senior Vice President
Artificial Intelligence and Digital Innovations
Siemens Healthineers

About Dorin Comaniciu:

Dr. Comaniciu serves as Senior Vice President for Artificial Intelligence and Digital Innovation at Siemens Healthineers. His scientific contributions to machine intelligence and computational imaging have translated to multiple clinical products focused on improving the quality of care, specifically in the fields of diagnostic imaging, image-guided therapy, and precision medicine.

Comaniciu is a member of the National Academy of Medicine, the Romanian Academy, and a Top Innovator of Siemens. He is a Fellow of the IEEE, ACM, MICCAI Society, and AIMBE, and a recipient of the IEEE Longuet-Higgins Prize for fundamental contributions to computer vision. Recent recognition of his work includes an honorary doctorate from Friedrich-Alexander University of Erlangen-Nuremberg.

He has co-authored 550 granted patents and 350 peer-reviewed publications that have received 61,000 citations, with an h-index of 102, in the areas of machine intelligence, medical imaging, and precision medicine.

A graduate of University of Pennsylvania’s Wharton School, Comaniciu received a doctorate in electrical and computer engineering from Rutgers University and a doctorate in electronics and telecommunications from Polytechnic University of Bucharest.

He is an advocate for technological innovations that save and enhance lives, addressing critical issues in global health.

About the Schwan Lecture:

The Herman P. Schwan Distinguished Lecture is in honor of one of the founding members of the Department of Bioengineering, who emigrated from Germany after World War II and helped create the field of bioengineering in the US. It recognizes people with a similar transformative impact on the field of bioengineering.

Harnessing Artificial Intelligence for Real Biological Advances—Meet César de la Fuente

by Eric Horvath

In an era peppered by breathless discussions about artificial intelligence—pro and con—it makes sense to feel uncertain, or at least want to slow down and get a better grasp of where this is all headed. Trusting machines to do things typically reserved for humans is a little fantastical, historically reserved for science fiction rather than science. 

Not so much for César de la Fuente, PhD, the Presidential Assistant Professor in Psychiatry, Microbiology, Chemical and Biomolecular Engineering, and Bioengineering in Penn’s Perelman School of Medicine and School of Engineering and Applied Science. Driven by his transdisciplinary background, de la Fuente leads the Machine Biology Group at Penn: aimed at harnessing machines to drive biological and medical advances. 

A newly minted National Academy of Medicine Emerging Leaders in Health and Medicine (ELHM) Scholar, among earning a host of other awards and honors (over 60), de la Fuente can sound almost diplomatic when describing the intersection of humanity, machines and medicine where he has made his way—ensuring multiple functions work together in harmony. 

“Biology is complexity, right? You need chemistry, you need mathematics, physics and computer science, and principles and concepts from all these different areas, to try to begin to understand the complexity of biology,” he said. “That’s how I became a scientist.”

Read the full story in Penn Medicine News.

Cesar de la Fuente On the “Next Frontier” of Antibiotics

César de la Fuente
César de la Fuente

In a recent CNN feature, César de la Fuente, Presidential Assistant Professor in Bioengineering, Psychiatry, Microbiology, and in Chemical and Biomolecular Engineering commented on a study about a new type of antibiotic that was discovered with artificial intelligence:

“I think AI, as we’ve seen, can be applied successfully in many domains, and I think drug discovery is sort of the next frontier.”

The de la Fuente lab uses machine learning and biology to help prevent, detect, and treat infectious diseases, and is pioneering the research and discovery of new antibiotics.

Read “A new antibiotic, discovered with artificial intelligence, may defeat a dangerous superbug” in CNN Health.

Why is Machine Learning Trending in Medical Research but not in Our Doctor’s Offices?

by Melissa Pappas

Illustration of a robot in a white room with medical equipment.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.

Read the full story in Penn Engineering Today.

Penn Bioengineering Senior Discusses Remote Research Experience

Yi-An Hsieh (BE 2023)

Yi-An Hsieh, a fourth year Bioengineering student from Anaheim, California, worked remotely this summer on a team that spanned three labs, including the Kamoun Lab at the Hospital of the University of Pennsylvania. Hsieh credits her research on kidney graft failure with enriching her scientific skill set, exposing her to machine learning and real-time interaction with genetic datasets. In a guest post for the Career Services Blog, Hseih writes about her remote summer internship experience. “It showed me that this type of research energy that could not be dampened despite the distance,” she writes.

Read “Exploring How Amino Acid Polymorphisms Affect Graft Survival” in the Career Services Blog.

Training the Next Generation of Scientists on Soft Materials, Machine Learning and Science Policy

by Melissa Pappas

Developing new soft materials requires new data-driven research techniques, such as autonomous experimentation. Data regarding nanometer-scale material structure, taken by X-ray measurements at a synchrotron, can be fed into an algorithm that identifies the most relevant features, represented here as red dots. The algorithm then determines the optimum conditions for the next set of measurements and directs their execution without human intervention. Brookhaven National Laboratory’s Kevin Yager, who helped develop this technique, will co-teach a course on it as part of a new Penn project on Data Driven Soft Materials Research.

The National Science Foundation’s Research Traineeship Program aims to support graduate students, educate the STEM leaders of tomorrow and strengthen the national research infrastructure. The program’s latest series of grants are going toward university programs focused on artificial intelligence and quantum information science and engineering – two areas of high priority in academia, industry and government.

Chinedum Osuji, Eduardo D. Glandt Presidential Professor and Chair of the Department of Chemical and Biomolecular Engineering (CBE), has received one of these grants to apply data science and machine learning to the field of soft materials. The grant will provide five years of support and a total of $3 million for a new Penn project on Data Driven Soft Materials Research.

Osuji will work with co-PIs Russell Composto, Professor and Howell Family Faculty Fellow in Materials Science and Engineering, Bioengineering, and in CBE, Zahra Fakhraai, Associate Professor of Chemistry in Penn’s School of Arts & Sciences (SAS) with a secondary appointment in CBE, Paris Perdikaris, Assistant Professor in Mechanical Engineering and Applied Mechanics, and Andrea Liu, Hepburn Professor of Physics and Astronomy in SAS, all of whom will help run the program and provide the connections between the multiple fields of study where its students will train.

These and other affiliated faculty members will work closely with co-PI Kristin Field, who will serve as Program Coordinator and Director of Education.

Read the full story in Penn Engineering Today.

César de la Fuente Receives 2022 RSEQ Young Investigator Award

César de la Fuente, PhD

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.

Read the RSEQ’s announcement here.

This story originally appeared in Penn Medicine News’s Awards & Accolades post for April 2022.

 

Penn Bioengineering Student Laila Barakat Norford Named Goldwater Scholar

Laila Barakat Norford (Class of 2023)

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.

She is among the 417 students named 2022 Goldwater Scholars from the 1,242 students nominated by 433 academic institutions in the United States, according to the Barry Goldwater Scholarship & Excellence in Education Foundation. Each scholarship provides as much as $7,500 each year for as many as two years of undergraduate study.

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.

The students applied for the Goldwater Scholarship with assistance from Penn’s Center for Undergraduate Research and Fellowships.

Read about all five 2022 Penn Goldwater Scholars in Penn Today.

Konrad Kording Appointed Co-Director the CIFAR Learning in Machines & Brains Program

Konrad Kording, PhD (Photo by Eric Sucar)

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.

Penn Engineers Secure Wellcome Leap Contract for Lipid Nanoparticle Research Essential in Delivery of RNA Therapies

by Melissa Pappas

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.

Read more in Penn Engineering Today.