Five Yale Engineering faculty win NSF CAREER Awards
Five Yale School of Engineering & Applied Science faculty members have received Faculty Early Career Development (CAREER) Awards from the National Science Foundation this year — one of the largest CAREER cohorts in the school's recent history. The NSF CAREER Award is a prestigious honor for young faculty members, supporting the early career activities of teachers and scholars considered most likely to become the academic leaders of the future.
This year's winners span computer science, electrical & computer engineering, and mechanical engineering, and their projects reflect just how wide-ranging that foundational work can be – from making AI more trustworthy for scientific discovery, to reading the color and twist of light on a single chip, to engineering tissue that organizes itself the way it does in the human body.
Meet this year's winners, and read each full story to learn more.
Arman Cohan: Building more reliable AI for science
Cohan, assistant professor of computer science, is strengthening the foundations for using large language models as trustworthy tools in scientific research. Current AI systems can overlook key publications or sound confident about claims the evidence doesn't support – a serious problem in high-stakes research settings. Cohan's project tackles this on three fronts: rigorously evaluating how AI performs on scientific tasks, adapting models to the structure of scientific literature, and improving the interpretability of their outputs.
"This project is about building the foundations needed to make AI systems more trustworthy, evidence-grounded, and useful for science," Cohan said.
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Mengxia Liu: Decoding light on a single chip
Liu, assistant professor of electrical & computer engineering, is developing a new class of light-sensing devices built from chiral semiconductors — materials whose atomic structure lacks mirror symmetry. Today, measuring both the color and the polarization of light typically requires a bulky tabletop setup of optics and components. Liu's approach aims to extract both signals from a single compact chip, with a neural network decoding the combined data, opening the door to smaller, faster sensors for secure communication and biological imaging.
"We're working toward a compact electronic device that will give us both answers out of a single chip," Liu said.
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Ben Fisch: Keeping private data secure and trustworthy in the cloud
Fisch, assistant professor of computer science, is developing cryptographic techniques that let sensitive data — medical records, financial histories, private research datasets — be processed securely in the cloud. His project combines Fully Homomorphic Encryption, which lets a server compute on encrypted data without ever decrypting it, with cryptographic proof systems that verify the computation was done correctly. Combining these two efficiently has proven difficult; Fisch's work aims to close that gap for real-world use.
"Cloud servers are powerful, but they are also magnets for attacks, from data breaches to application exploits," Fisch said. "The goal of this work is to make it possible to outsource computation to an untrusted server while preserving data privacy and allowing the client to verify correctness."
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Yimin Luo: Engineering self-aligning tissue
Luo, assistant professor of mechanical engineering, is developing new materials and methods to guide lab-grown cells into the organized, directional structures found in real tissue — like the aligned cells of blood vessels and heart muscle. Her approach borrows from liquid crystal physics, which describes how elongated cells can spontaneously line up in consistent patterns, and tests how patterning cell sheets in advance can shape how they fold and develop over time.
"We're trying to figure out the design rules for how sheets of cells organize," Luo said. "We want to understand what tells them to align one way instead of another, and how we can use that knowledge to build tissue scaffolds that are reliable and inexpensive to produce."
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Rex Ying: Building more powerful and efficient AI
Ying, assistant professor of computer science, is rethinking the mathematical foundations of AI by developing models that represent data in flexible, curved mathematical spaces rather than the flat geometry most systems use today. Relationships in data like scientific documents, molecules, and social networks often have a naturally curved structure — and building models that match that structure could make AI both more efficient and easier to interpret. The project includes collaborators at Yale School of Medicine, the Broad Institute, Google Research, Snap Inc., and Amazon Web Services.
"In many fields, it's not enough for the model to get the right answer,” Ying said. “A clinician or a scientist needs to understand why. The geometric properties of these spaces give us a natural way to dig under the hood."
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Published Date
Jul 23, 2026
