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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

Jiahui "Gary" Luo standing with his research poster

Mar 05, 2026

Better Liver Transplant Decisions

PhD student Jiahui "Gary" Luo and Professor Wesley Marrero, with researchers at U Michigan, developed a new simulation framework to analyze liver transplant decisions, which was published in IEEE's 2025 Winter Simulation Conference. The team created a continuous-time simulation that models patient health and organ arrivals while mimicking real-world, varied organ acceptance practices. "The study concludes that high selectivity is a major obstacle to saving lives. Because small quality differences have modest survival effects, accepting a broader range of medically suitable organs can significantly reduce waiting times and maximize the lifesaving potential of the donor pool," said Marrero.

Figure depicting reversible all-liquid conversion path

Feb 26, 2026

Next-Gen Batteries for Grid Storage

Research Associate Peiyu Wang Th'25, PhD students Huilin Qing, Baiheng Li, and Ruiwen Zhang, and Professor Weiyang (Fiona) Li co-authored "Semi-liquid lithium−sulfur batteries for large-scale energy storage" published in Nature Reviews Clean Technology. This review examines catholyte chemistry and design, static and redox flow configurations, and strategies to improve performance and scalability for large-scale energy storage. "Lithium–sulfur batteries offer high energy density and cost-effectiveness but are limited by the precipitation of solid sulfur species, which has driven interest in semi-liquid systems," said Li.

Feb 19, 2026

Machine-Learning-Enabled Phototransistors

PhD student Simon Agnew '22, Research Associate Xavier Cadet, and professors Peter Chin and Will Scheideler co-authored "Decoding disorder: Machine learning unlocks multi-wavelength and intensity sensing in a single indium oxysulfide phototransistor" published in Device. The paper presents machine-learning-enabled phototransistors that decode both light wavelength and intensity from a single printed device—no filters or sensor arrays required. This work points toward simpler, lower-cost, and more scalable multi-parameter sensing for flexible optoelectronics. "By combining scalable liquid-metal printing of ultrathin indium oxysulfide with data-driven analysis, we show how disorder—often viewed as a limitation in printed semiconductors—can be turned into a powerful sensing feature," said Scheideler.

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.

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