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Pushing the limits of scientific discovery with machine learning
Computer code.

Image: iStock/iambuff

Pushing the limits of scientific discovery with machine learning

Penn Engineering’s Nat Trask is combining applied mathematics and traditional physics modeling with the powers of machine learning to design some of his first machine-learning-powered, self-driving labs at Penn.

Melissa Pappas

Embracing the power of deep learning in safety-critical systems
From left: Pengyuan Eric Lu, Insup Lee, and Oleg Sokolsky.

From left: Pengyuan Eric Lu and his mentors, Insup Lee, Cecilia Fitler Moore Professor in Computer and Information Science; and Oleg Sokolsky, research professor in CIS.

(Image: Courtesy of Penn Engineering)

Embracing the power of deep learning in safety-critical systems

Pengyuan Eric Lu, a Ph.D. candidate at the Penn Research in Embedded Computing and Integrated Systems Engineering Center, focuses his research on enhancing the reliability and safety of cyber-physical systems, in which “smart” technology interacts with the physical world.

From Penn Engineering

Racing to the future
A small racecar in a makeshift track in Penn Engineering with student spectators.

“Understanding the human factors and ethical implications of autonomous systems is just as crucial as the technical components,” says Mangharam. “This holistic approach aims to produce well-rounded engineers capable of addressing the multifaceted challenges of autonomous vehicle technology. Our goal is to equip them with the tools and mindset to tackle the challenges and opportunities of tomorrow.”

nocred

Racing to the future

Rahul Mangharam’s scaled-down, self-driving race cars are revamping engineering education at Penn.
Building solutions for brain disorders
Flavia Vitale holding a vial with a gloved hand.

Flavia Vitale is an associate professor in bioengineering in Penn Engineering and in neurology in Penn Medicine.

(Image: Melissa Pappas)

Building solutions for brain disorders

Penn Engineering’s Flavia Vitale’s work developing devices that help people living with brain disorders has earned her a CAREER award, which will support her lab’s research in materials and devices that interface with different chemical and electrical signals inside the brain.

Melissa Pappas

Shedding light on cellular metabolism to fight disease
Yihui Shen.

Yihui Shen is the J. Peter and Geri Skirkanich Assistant Professor of Innovation in Bioengineering in the School of Engineering and Applied Science.

(Image: Courtesy of Penn Engineering Today)

Shedding light on cellular metabolism to fight disease

In Yihui Shen’s lab, the assistant professor of innovation in bioengineering, aims to advance the understanding of metabolism and open doors to new cancer treatments and therapies.

From Penn Engineering Today

Showing up for Penn in London
Penn president J. Larry Jameson speaking at a microphone in London.

Interim Penn President J. Larry Jameson addresses the audience at Penn’s academic symposium in London on Friday, June 21, 2024.

(Image: Courtesy of Penn Giving)

Showing up for Penn in London

A capacity audience attended an academic symposium in London titled “Frontiers of Knowledge and Discovery: Leading in a Changing World.”
Penn pioneers a ‘one-pot platform’ to promptly produce mRNA delivery particles
3D illustration showing cross-section of the lipid nanoparticle carrying mRNA of the virus entering a human cell.

Lipid nanoparticles present one of the most advanced drug delivery platforms to shuttle promising therapeutics such as mRNA but are limited by the time it takes to synthesize cationic lipids, a key component. Now, Michael Mitchell and his team at the School of Engineering and Applied Science have developed a faster way to make cationic lipids that are also more versatile, able to carry different kinds of treatments to target specific organs.

(Image: iStock / Dr_Microbe)

Penn pioneers a ‘one-pot platform’ to promptly produce mRNA delivery particles

New lipid platform enables rapid synthesis of molecules that can shuttle therapeutics for a range of diseases with a high degree of organ specificity.
A first, physical system to learn nonlinear tasks without a traditional computer processor
Contrastive local learning network.

University of Pennsylvania physics and engineering researchers have created a contrastive local learning network, an analog system that is fast, low-power, scalable, and able to learn nonlinear tasks.

(Image: Erica Moser)

A first, physical system to learn nonlinear tasks without a traditional computer processor

Physics and engineering researchers created a contrastive local learning network that is fast, low-power, and scalable.