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Engineering Research at Dartmouth

Dartmouth engineering researchers work within an integrated community of experts in their fields, unencumbered by departmental divisions. Our faculty and students are versatile thinkers who can define a problem, place it within the broad social and economic contexts, and articulate a clear vision for a human-centered approach toward a solution.

Most research projects are collaborations that integrate one or more engineering disciplines with other sciences. Students working in these labs learn important lessons about the interconnectedness of the world and develop both depth and breadth that make them innovators and leaders in emerging technologies.

Research by Program Area

Biological/ Chemical

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Biomedical

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Electrical/ Computer

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Energy

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Materials Science

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Mechanical/ Operations/ Systems

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Research by Activity

Culture of Collaboration

Dartmouth Engineering is a close-knit community of scholars with a broad range of expertise. The culture of collaboration extends across the hall, across campus, and beyond. Many research projects engage colleagues from other institutions such as Dartmouth-Hitchcock, Geisel School of Medicine, Tuck School of Business, Guarini School of Graduate and Advanced Studies, and CRREL, as well as industry—and offer numerous research opportunities for undergraduates.

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Research Quick Takes

Research figure depicting transfer learning

Feb 12, 2026

Better Metamaterial Design Via Transfer Learning

PhD students Xiangbei Liu, Ya Tang, and Huan Zhao, and Professor Yan Li, are co-authors of "A transfer learning–enabled framework for rapid property prediction toward scalable and data-efficient metamaterial design" published in Results in Engineering. When faced with new requirements, conventional machine-learning approaches require substantial new datasets for retraining—basically starting from scratch. Transfer learning can significantly reduce the required amount of training data while maintaining high accuracy and stability. "This approach provides a foundation for building a scalable, data-efficient knowledge base for future applications," said Li.

Feb 12, 2026

Diagnosing Non-Melanoma Skin Cancer

Professor Arthur Pétusseau (far right) has teamed up with Research Fellow Dylan Parker, MD, and professors of dermatology Shane Chapman, MD, and Brian Simmons, MD to launch a clinical study focused on the diagnosis of non-melanoma skin cancer. The study, titled "Assessment of Skin Lesions Using a Tissue Oxygen Imager Based on Protoporphyrin IX (PpIX) Fluorescence," will enroll 120 patients to investigate whether oxygen dynamics following gentle palpation can serve as an early marker to distinguish malignant from non-malignant lesions. "Information from this study could help dermatologists better determine when surgical resection is truly necessary, potentially reducing unnecessary procedures, particularly in cosmetically-sensitive areas such as the face," said Pétusseau who developed the imager called, Pressure-Enhanced Sensing of Tissue Oxygenation (PRESTO).

Feb 05, 2026

Top Biophotonics Device

Professors Petr Brůža and Brian Pogue—along with former Thayer professor and now CTO of DoseOptics, Venkat Krishnaswamy—attended the Prism Awards Celebration at SPIE's Photonics West conference where DoseOptics' Clinical Cherenkov Imaging was listed in the top three biophotonics devices of 2025. (See Pogue's recent Commercialization Report published in Biophotonics Discovery) "What a treat to be at the world's largest biomedical optics conference with my brilliant colleagues, designers of the BeamSite Cherenkov Imaging camera. In only one year, with over 40 installations in place and planned worldwide, this technology will help keep radiation therapy advances safe for all patients," said Pogue.

Priyanshu Alluri, Zequn Chen, and Wesley Marrero

Jan 29, 2026

A Socially-Fair Framework for Measuring Student Well-Being

Priyanshu Alluri '26, PhD student Zequn Chen, and Professor Wesley Marrero presented a socially-fair framework, published in Journal of the American Medical Informatics Association - Open, that ensures homogeneous clustering performance across demographic groups while minimizing within-cluster variability. "The study integrates fairness considerations into clustering algorithms to reduce discrepancies in risk stratification and provides insights into socioeconomic drivers of student well-being," said Marrero.

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