Seed Grant Awards Description FY 2026

Arts and Humanities Seed Grant (Spring 2026)

Open/All Discipline Seed Grant (Spring 2026)

Arts and Humanities Seed Grant (Fall 2025)

Ownership and use of public lands have long been contested. However, increasing pressures from growing populations, conflicting stakeholder interests, and environmental impacts are sparking unprecedented conflicts about ownership, protections, and access. Public land issues are making headline news daily. In response, we need reminders of how magnificent our public lands are, how much we cherish them, and what is at stake if we lose them.

Built on swamplands, the urban fabric of New Orleans saw major shifts over the past three hundred years. At the beginning of the 20th century, the large-scale implementation of drainage pumps transformed massive wetlands into buildable areas. Residential neighborhoods such as Broadmoor, Gentilly, and Lakeview emerged between Lake Pontchartrain and the Mississippi River. The water that was once drained still haunts the houses built on the low land. Heavy rainstorms, hurricanes, and floods are nature’s attempts to restore the manipulated ground plane. In response, flood-prone houses have been elevated by local builders and house owners—using steel beams, hydraulic jacks, and stacked 6x6.

Hot springs, found in mountainous landscapes across the globe, are equal parts cultural and natural phenomenon. Today hot springs are often developed and designed as commercial spas and tourist resorts, although some remain as natural physical settings. Contemporary risks associated with hot springs sites span from geothermal energy plans, to declines of water within the system, to neglect and loss of historic structures and fragile natural settings.

Addressing these concerns, the grant will articulate a research agenda, and produce a detailed precis for two core Landscape Architecture classes (Fall 2026) that comprise a 9 credit immersive studio/seminar experience. Students will catalogue and document selected hot springs sites and assess their geologic and hydraulic properties; explore narratives and ideas attached to these cultural sites; and engage in a project that reimagines how people might sustainably access and utilize hot springs in Colorado. The class will connect with indigenous tribes to explore hot springs on their lands. Hot springs require thoughtful protections and sustainable designs that allow visitors access to these fragile, alluring sites.

The timeliness of this topic in light of climate change, loss of sites, and development pressures affects landscapes around the world affirms capacity for funding of future efforts. Grant products will include all work by the students and faculty; these will inform a body of additional engaged learning experiences, new research, and publications that will address the legacy and potential of Colorado’s hot springs, and direct thinking towards the future environmental and cultural value of hot springs globally.

Pleasure Principle is a dark comedy pilot about a seemingly picture-perfect 20-something teacher’s assistant who begins to unravel after her rabbi boyfriend’s surprise proposal. With pressure mounting from her overbearing psychiatrist parents, destructive habits with sex, drugs, and alcohol resurfacing, and only questionable support from her lifelong best friend, she struggles to hold her life together. The series explores mental health, Jewish identity, and female sexuality with humor and emotional depth.

Open/All Discipline Seed Grant (Fall 2025)

This project investigates the potential for scaling Community Land Trusts (CLTs) in Colorado, with a focus on how these organizations can grow while maintaining commitments to affordability, community governance, and resident accountability. CLTs provide a unique model for permanent affordability by separating land ownership from homeownership. The research will assess scaling strategies such as portfolio expansion, geographic expansion, and growth in organizational scope, and will analyze their implications for housing stability and community dynamics. A mixed-methods approach will be used, including semi-structured interviews, surveys, and document analysis. Interviews and surveys will capture the perspectives of CLT staff, residents, and partners, while document analysis will provide evidence of organizational structures, policies, practices, and financial models that shape CLT growth and operations.

The Warning Failure Database (“WFD”) project will systematically document instances in which U.S. public warning systems—principally the Emergency Alert System (EAS) and Wireless Emergency Alerts (WEA)—have failed to meet their objectives. ORS seed funding will enable compilation of the initial 50 instances of warning failure from 2015 to 2025 gleaned from media reports, government documents, and research. This proof-of-concept will, ideally, lead to funding for the scaled development of the only comprehensive, public dataset of hundreds of U.S. (and eventually international) instances of warning failure. Although relatively straightforward to assemble from public documents, previous institutional reluctance to catalogue failure has stymied the creation of a similar resource. Journalists tend to treat each new warning failure as a distinct event, overlooking systemic patterns. Therefore, each instance of warning failure added to the WFD will be coded along multiple dimensions (discussed below), and revision of cases will occur as new documentation becomes available. Database queries will permit rigorous analysis of the attributes of warning failure, which responds to an August 2025 Federal Communications Commission’s (FCC) rulemaking concerning the modernization of the nation’s alerting systems. Key FCC questions include: What objectives are not being met by current alerting systems? Which entities struggle to originate effective alerts? What kinds of information are omitted or poorly conveyed? By providing stakeholders ongoing, incident-level data via the WFD, this project will supply FCC, policymakers, practitioners, researchers, and citizens with the evidence necessary to improve public warning in the United States and internationally.

Natural gas (NG) pipeline leaks in urban areas are a significant methane source, but bottom-up inventories underestimate emissions due to limited sampling of activity factors (leaks per pipeline length) and emission factors. These parameters vary substantially across cities, making blanket assumptions unreliable. Despite mobile monitoring's effectiveness for leak detection, it hasn't been deployed in Denver, Colorado. We propose using the Denver Mobile Monitoring Laboratory (DMML) in a multi-week campaign with an Aeris MIRA Ultra gas analyzer to map methane leaks across Denver. By analyzing methane-to-ethane ratios, we'll identify which plumes originate from NG leaks versus other sources. Our study will compare Denver's leak frequency and distribution with other U.S. cities and evaluate emissions rates using two methods: a standard linear model and inverse plume modeling. We'll generate a comprehensive methane emissions estimate for Denver and compare it with existing inventories. The research will provide methodological guidance for future mobile monitoring studies quantifying methane leak emissions. Results will inform best practices for using mobile platforms to improve emission factor accuracy and support policy decisions. This pilot campaign serves as preliminary work for a planned NSF Career proposal, where the DMML would become a key tool for evidence-based air pollution policy-making. By addressing current inventory limitations and demonstrating mobile monitoring capabilities, this work will advance urban methane emission quantification and support climate mitigation efforts in Denver and beyond.

Heavy-duty trucks are the backbone of U.S. freight movement, but their operations remain inefficient, energy-intensive, and vulnerable to disruptions. While state and federal agencies are planning large-scale freight infrastructure investments, most efforts rely on aggregate assumptions rather than data-driven insights from real-world truck activity. This project leverages a large GPS dataset of short- and long-haul heavy-duty truck operations in Colorado and neighboring states, previously acquired and partially analyzed for a peer-reviewed paper at the 2025 IEEE Vehicle Power and Propulsion Conference (VPPC 2025). The next step is to transform this raw data into predictive and adaptive modeling frameworks using deep learning and reinforcement learning (RL) approaches. We propose to support one graduate student and one undergraduate student to: (i) preprocess and cluster truck trajectories into representative duty cycles, (ii) train deep learning sequence models to learn temporal patterns such as routes, dwell times, and congestion, and (iii) develop a prototype RL framework where an agent learns when and where to stop to minimize delay and cost under dynamic conditions (e.g., congestion spikes, station outages). Deliverables include a cleaned and annotated dataset, a deep learning sequence model, an RL prototype for scheduling, one peer-reviewed publication submission, and preparation of competitive external proposals. By combining big data analytics, advanced machine learning, and workforce development, this seed grant will generate preliminary results and robust machine learning (ML) that position CU Denver for competitive external funding opportunities such as the Advanced Research Projects Agency-Infrastructure (ARPA-I), U.S. Department of Transportation (DOT), American Automobile Association Traffic Safety Foundation (AAATF), Transportation Research Board’s IDEA program, and the National Science Foundation’s Energy, Power, Control, and Networks (NSF EPCN) program.

Last-mile delivery is the final step in e-commerce order fulfillment. Typically, it is modeled as a vehicle routing problem. Unfortunately, vehicle routing problems are computationally challenging and cannot capture all possible practical considerations. Still, last-mile delivery operators solve vehicle routing problems on a regular basis—daily, hourly, or even by the minute—depending on the set of orders pending fulfillment at any given moment. And every time they do so, they effectively produce a new data point which can be labeled ex post facto with its realized performance upon deployment. In this research, we will develop data-driven methods to learn patterns that are persistent in “good” delivery routes. We will store these patterns in a compact, special-purpose data structure, and we will leverage them to both speed up existing algorithms and to improve their responsiveness to drivers’ route quality feedback.

There has been increased attention on early childhood teachers’ use of exclusionary discipline that removes children from the educational setting; however, little is known about why early childhood teachers use exclusionary discipline practices such as timeout and sending a child to the office instead of proactive discipline approaches and social emotional supports. This project proposes to extend the current sparse literature base by utilizing a mixed methods approach to survey and then interview early childhood teachers regarding their use of exclusionary discipline practices. The use of both quantitative and qualitative data will provide a more complete understanding about why early childhood teachers use certain exclusionary discipline practices in their classrooms. The findings from this project will inform future research and practice-based efforts to train and support teachers in classroom management approaches. We will involve doctoral students in data collection, analysis, and writing. We plan to disseminate the findings in peer-reviewed journal articles and in conference presentations. We will use this project to obtain larger extramural funding from the U.S. Department of Education and/or the Spencer Foundation.

This project will develop a technical framework to enable secure, efficient, and cooperative Low Earth Orbit (LEO) satellite networking across independent operators. The current LEO ecosystem is fragmented, with each operator managing its own infrastructure, leading to inefficient resource use, communication bottlenecks, and limited cross-operator coordination. The research will pursue three thrusts. First, we will design decentralized, enforceable multi-party contract mechanisms that capture both competitive and cooperative interactions, using game-theoretic models and lightweight coordination protocols. Second, we will develop algorithms for bandwidth and energy allocation, distributed task scheduling, and data management to support efficient communication and computation in dynamic satellite networks. Third, we will ensure security and reliability by creating resilient routing protocols, SLA-compliant secure computation models, and secure authentication and key management protocols. The proposed solutions will be evaluated under realistic constraints, including limited onboard resources, mobility, and adversarial conditions. Together, these efforts will lay the foundation for a scalable and trustworthy collaborative satellite ecosystem.

This seed project will generate critical data to support the development of a research proposal aimed at creating a new incremental sheet forming (ISF) technology for continuous fiber-reinforced polymer (CFRP) composites with high geometric fidelity and precisely controlled fiber architecture, capabilities not attainable with existing manufacturing methods. ISF progressively deforms sheets along controlled toolpaths and uniquely enables moldless fabrication of metal and plastic shell structures with exceptional design flexibility. CFRPs are critical for a wide range of high performance engineering applications. However, extending ISF to these materials is hindered by a fundamental challenge: inextensible fibers resist the localized deformation required during forming. The proposed ISF technology addresses this barrier by using a thermally curable resin to impregnate continuous fibers when preparing ISF feedstock sheets. During forming, the uncured resin temporarily permits fiber–matrix sliding to accommodate global structural deformation; subsequent thermal treatment cures the resin and locks in robust fiber–matrix bonding. To support technological innovation, an artificial intelligence (AI)–powered modeling framework will be created to efficiently simulate the forming process, predict the mechanical properties of the resulting composites, and enable a reverse-design tool to counteract shear-induced fiber deviation, a critical challenge unique to CFRP forming.
The seed project will focus on three key activities: demonstrating the feasibility of ISF with prepreg fibers, evaluating thermoplastic–epoxy interfacial bonding strength, and validating the reverse-design approach to correct fiber deviation. The data generated will establish technical feasibility, reduce risk, and provide the foundation for a competitive full proposal to external funding agencies.

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Seed Grant Awards Description FY 2026

Arts and Humanities Seed Grant (Spring 2026)

Open/All Discipline Seed Grant (Spring 2026)

Arts and Humanities Seed Grant (Fall 2025)

Open/All Discipline Seed Grant (Fall 2025)

Research Development Office

Seed Grant Awards Description FY 2026

Arts and Humanities Seed Grant (Spring 2026)

(name) - (college) - (dept) - (title)

Open/All Discipline Seed Grant (Spring 2026)

(name) - (college) - (dept) - (title)

Arts and Humanities Seed Grant (Fall 2025)

Joann Brennan - CAM - Visual Arts - Public Lands in America: Our National Treasure

Shirley Chen - CAP - Architecture & Planning - Home Elevation

Ann Komara - CAP - Landscape Architecture - Cultural Landscapes: Hot Springs in the American West

Stephanie Kornick - CAM - Film & Television - Pleasure Principle: A Dark Comedy Television Pilot

Open/All Discipline Seed Grant (Fall 2025)

Deserai Anderson - SPA - Scaling Community Land Trusts: Balancing Affordability, Governance, and Community Dynamics

Hamilton Bean - CLAS - Communications - Database of Public Warning Failure

Priyanka deSouza - CAP - Urban & Regional Planning - Detecting Natural Gas Leaks: A Use Case of the Denver Mobile Monitoring Laboratory

Jacqueline Garrido - CEDC - Electrical - AI Approaches for Heavy-Duty Truck Operations in Colorado: Deep and Reinforcement Learning

Carlos Martinez Mori - CLAS - Mathematics - Learning Precedence Constraints for Last-Mile Delivery

Elizabeth Steed - SEHD - Early Childhood Teacher’s Use of Soft Exclusionary Discipline: A Mixed Methods Study

Xiaojian Wang - CEDC - Computer Science - Bridging Fragmentation in Space: Fostering a Collaborative, Efficient, and Secure Low Earth Orbit Satellite Ecosystem

Kai Yu - CEDC - Mechanical Engineering - Incremental Sheet Forming of Continuous Fiber Reinforced Polymer Composites

Research Development Office