Yale Engineering

06/25/2026

Thermal sensing and imaging are widely used in robots, drones, self-driving vehicles, night-vision systems, remote thermometers, and thermal cameras. They are also important for emerging thermal neuristors - neuromorphic devices that exploit thermal dynamics to emulate brain-inspired computation.

A team led by Prof. Fengnian Xia has turned a long-known semiconductor carrier-feedback effect into a functional platform for thermal sensing that’s both high quality and practical, revealing a powerful capability that had not previously been recognized. Read the full paper in Nature Sensors: https://loom.ly/brr1w2g

06/18/2026

AI tools are already reshaping how scientists work, helping researchers sift through vast bodies of literature, summarize findings, and generate hypotheses. But in high-stakes scientific settings, the limitations are real. These systems can overlook key publications, cite weak evidence, or express confidence in claims that aren't supported by current science.

Yale Engineering's Arman Cohan has received an NSF CAREER Award to address that directly. His work focuses on building more trustworthy, evidence-grounded AI for scientific research – developing rigorous evaluation frameworks, better methods for adapting large language models to scientific domains, and tools that make AI outputs more transparent and interpretable.

The goal, according to Cohan, is understanding when these systems can be trusted, where they fail, and how to make them more reliable across scientific disciplines.

Read more: https://loom.ly/spFu5Z4

06/17/2026

There’s approximately 10,000 different types of proteins in our bodies, and how they join together is critical to fighting diseases. It’s also an incredibly mysterious process, given the millions of potential protein combinations that exist (we know some of them, but not nearly enough). A team of researchers says the current methods for predicting how proteins bind are inadequate. They offer a simpler and more accurate way: https://loom.ly/5h2JH2M

06/15/2026

AI models make mistakes. But there's an important distinction between a model that is misinformed, meaning it lacks the right knowledge, and one that is misaligned, subtly steering you toward an outcome that doesn't reflect your intentions. Understanding the difference matters enormously.

Yale Engineering professors Yang Cai and Nicole Immorlica, along with colleagues from economics and statistics, are developing frameworks to tell the two apart. Yale News sat down with the researchers to find out how.

Two multidisciplinary research teams are seeking to understand why AI systems become misinformed or misaligned with users’ intentions and to develop ways to make them safer, more reliable, and more accountable.

06/12/2026

Most large power transformers in the U.S. are over 25 years old with a typical life expectancy of just 30 years. As electricity demand surges from data centers, electric vehicles, and renewable energy, the grid's aging infrastructure is under dramatic strain. The weakest link is the insulation breakdown. It's the most common cause of transformer failure, costing consumers tens of billions of dollars annually.

A Yale-led interdisciplinary team has developed a potential solution from an unlikely source: wood. By treating natural wood veneer and soaking it in insulating oil, the team created a material called oil-impregnated densified wood (ODW) that outperforms the century-old insulation technology currently used in most transformers. It's stronger, conducts heat better, and is far more resistant to electrical breakdown. The work also points to broader applications beyond transformers, including motors and printed circuit boards.

Read more: https://loom.ly/cfHYA1o

06/08/2026

Congratulations to Prof. Leandros Tassiulas, for receiving the ACM e-Energy Test of Time Award – given to research that has opened new areas of inquiry or solved problems of lasting importance, at least a decade after publication.

His 2011 paper tackled a central challenge in smart grid design: how to schedule consumer power demands in real time to minimize grid operational costs. The mathematical framework he developed has since become a foundational reference in smart grid research. The award will be presented at ACM e-Energy 2026 later this month.

Read more: https://loom.ly/9E-xDZ4

06/05/2026

Fluid mechanics is everywhere. It's in the way cream swirls into coffee, the way raindrops splay out on your car’s windshield, the way pollutants move through soil. At Yale Engineering, Profs. Amir Pahlavan and Bauyrzhan Primkulov are uncovering the hidden physics behind it all, with implications for everything from environmental remediation to quantum mechanics.

An added benefit: The research can also be stunning to look at.

Read more: https://loom.ly/Ugl1Si0

📸 Tanner Pendleton

06/04/2026

Yesterday, students, faculty, staff, and Yale community members gathered for the "Women in STEM: Breaking Barriers. Building Futures" panel discussion hosted by the Working Women's Network. Distinguished Biomedical Engineering faculty — Anjelica Gonzalez, Kathryn Miller-Jensen, Cristina Rodriguez, and Shreya Saxena — shared their career journeys, research insights, and perspectives on advocacy in STEM.

Thank you to the panelists for such an inspiring conversation!

06/04/2026

Redox flow batteries are among the most promising candidates for grid-scale renewable energy storage. But finding the right molecular compounds to make them work efficiently is a slow, complex process. Yale Engineering's David Kwabi and a team from Microsoft are using an AI system that reasons like a scientist to speed that up, combining human experimentation with AI's ability to map vast chemical design spaces. Their first test run produced a winning molecule.

Read more: https://loom.ly/EGKw25c

06/01/2026

Transistors have been shrinking for decades, and the next frontier requires semiconductors just one atom thick. The challenge has been that the methods capable of producing these materials at industrial scale tend to compromise quality.

Yale Engineering's Cong Su and collaborators found a remarkably simple fix. By pre-treating chemical ingredients with acid, the team anchored them to the surface during growth – eliminating the defective, patchy layers that have long plagued scalable production. The result is record-quality atomically thin materials with direct implications for faster, more efficient electronic and quantum devices.

Read more: https://loom.ly/BAfPs9M