Catalyzing Research Teaming
Background
One of the key Research & Innovation actions within the 2024 College Strategic Plan is to:
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Over the past several years the College has invested internal funding resources in support of research initiatives (e.g., Interdisciplinary Research Themes – IRT; Sandia National Labs Collaborative Fund) to incentivize faculty in the College to develop teams to advance collaborative, interdisciplinary research. These efforts have enhanced faculty collaborations across units and resulted in significant external research funding awards from federal agencies, including multi-million-dollar awards for Institutes, Centers and Industry-University collaborations.Ìý
To further the Strategic Plan’s goals, the college has committed to create a Catalyzing Research Teaming opportunity for collaborative, interdisciplinary research teams amongst our faculty. The overall intent is to provide seed-grant funding to research teams of interdisciplinary faculty to ideate new engineering and scientific advances and to plan for submission to specific, large-scale external funding opportunities forthcoming in 2025-26. Submitting teams will be required to identify downstream funding opportunities at the Center/Institute levels of funding that span multiple years (>$1.5M/yr).
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CRT Winning Teams
The Team: Cresten Mansfeldt (PI), CEAE, EVEN; Shideh Dashti – CEAE; Stephen Kissler – CS; Julie Korak – CEAE, EVEN; Abbie Liel – CEAE ; Sheldon Masters, CEAE, EVEN; Nicole Xu – ME, ROBO, BME, BioFrontiers Institute
The Impact:Ìý
The Watering Whole at É«ÊÓƵÏÂÔØ is transforming how we manage water systems by shifting from a unit-by-unit approach to a flow-history perspective. This new approach emphasizes the interconnectedness of human, material, and environmental interactions within the One Water cycle. The initiative focuses on three key research areas:
Their work focuses on:
- Human-infrastructure interaction, helping people identify and respond to water system failures.
- Policy and materials management, guiding decisions on safer, more sustainable infrastructure.
- Bio-inspired sensor technology, driving the next generation of smart water monitoring tools.
This wide range of applications reflects both the flexibility of the research, and the deep, cross-disciplinary expertise involved.
The Team: Mija Hubler – CEAE, MSE; Anthony Straub – CEAE, EVEN, MSE; Maryam Shakiba – ASEN; Wil Srubar – CEAE, MSE; Sherri Cook – CEAE, EVENÌý
The Impact:Ìý
The BIO-CEM Filters team at É«ÊÓƵÏÂÔØ is developing sustainable water treatment membranes by combining renewable biopolymers with low-cost, 3D-printed ceramic materials. This innovative approach addresses key challenges in water filtration—improving membrane strength, reducing costs, and enhancing contaminant removal.
Their work focuses on:
- Optimizing biopolymer performance for filtration and sensing.
- Designing durable ceramic supports using accessible materials like sand and clay.
- Using modeling and machine learning to streamline membrane development.
- Assessing real-world impact through life-cycle analysis and targeted applications.
Together, these advances promise cleaner, more affordable water treatment technologies for a sustainable future.
The Team: Todd Murray - ME, BME; Longji Cui - ME, MSE; Nick Bottenus - ME ,BME; Rafael Piestun - ECEE; Nicole Bienert - ECEE
The Impact:Ìý
This project is revolutionizing how we see the world at microscopic scales by pushing beyond the traditional limits of imaging. At the heart of this innovation is Hermite-Gaussian (HG) mode sorting, a novel method that reconstructs images using spatial wave modes instead of pixel-by-pixel intensity—enabling super-resolution imaging far beyond the diffraction limit.
Unlike existing technologies that require complex hardware or active sample manipulation, this passive and computational approach works across a wide range of wave types—from optical and acoustic to infrared and microwave—making it a versatile tool for both engineering and biomedical applications.
Their work focuses on:
- Building a unified computational and experimental framework for HG-mode imaging across wavelengths.
- Achieving super-resolution in acoustic/photoacoustic systems for material and medical imaging.
- Expanding the technique into infrared and radio/microwave sensing.
- Advancing virtual mode sorting with machine learning for practical, real-world deployment.
Together, this work paves the way for next-generation imaging systems capable of detecting ultra-fine features previously thought impossible to resolve.