Grants

Sponsor: NSF
PI: Kanwal Kaur, NSME
Total Award: $144,994.00
Start Date: 10/1/2022
End Date: 9/30/2025
On a No Cost Extension till: 9/30/2026

This research will design a 5G-enabled smart Artificial Intelligence (AI) based microinverter for a pole-mounted solar power system that is equipped with an energy storage system which will allow it to connect to low-voltage distribution networks in order to operate as a Virtual Power Plant. To ensure uninterrupted power, the 5G communication between the photovoltaic (PV) and the battery system, the smart microinverter, and the Supervisory Control and Data Acquisition (SCADA) should be secure.

Sponsor: California Learning Lab
PI: Alberto Cruz, NSME
Total Award: $149,840.90
Start Date: 12/1/2025
End Date: 6/30/2026

Academia must embrace AI tools in the classroom to maintain the relevance of California’s public education system. This change is outpacing curricular updates from bodies such as ACM and IEEE, and it falls on educators to drive curricular changes. Students AI use is already widespread. Educators must guide students in this process, and any proposed solutions must be swiftly implemented. Reform requires university support to address socioeconomic and pedagogical factors, which cannot be resolved with students self-regulating their learning with ChatGPT. Methods are required to constructively integrate AI into the advancement cycle. We propose a program to prototype personalized AI tutors for students at the California State University, Bakersfield (CSUB). AI can enhance equitable participation and advancement of minorities who faced inadequate access in K-12 education.

Sponsor: The Regents of UC, UC Davis
PI: Emerson Case, A & H
Total Award: $50,074.00
Start Date: 1/1/2025
End Date: 12/31/2025: possible year 2 coming

Guide writing instructors to integrate GenAI feedback with peer review, critical reflection on both types of feedback and its relevance for their writing goals, and discussions of short readings on AI.

Sponsor: NSF
PI: Jane Dong, NSME
Total Award: $399,226.00 
Start Date: 10/1/2025
End Date: 9/30/2028

The ASPIRE-AI initiative unites four institutions—California State University, Bakersfield (CSUB), New York City College of Technology (City Tech), University of Bridgeport (UB), and University of San Francisco (USF)—to foster sustainable external partnerships in artificial intelligence (AI) and related fields and applying AI to drive innovation, research, and entrepreneurship to benefit regional economic growth and workforce development. The project aims to establish institutional structure for partnership development, enhance faculty and student development, and advance workforce preparedness. Each institution brings unique strengths to this collaboration, leveraging their regional and disciplinary expertise to address systemic barriers such as limited infrastructure and fragmented institutional culture.

Sponsor: NSF - University of California, Riverside
PI: Isabel Sumaya
Total Award (Sub award): $800,000.00
Start Date: 10/1/2026

The University of California, Riverside Inland BRIDGE, serving as a regional technology transfer (TT) hub, provides an optimal solution to California State University, Bakersfield’s  (CSUB) "innovation gap" where TT remains nascent and underdeveloped. With fewer research expenditures as a regional, non-R1 campus, CSUB lacks the patent infrastructure, and commercialization depth that characterize the University of California (UC) system. The UC possesses mature technology transfer offices, experienced licensing professionals, and innovation best practices built on decades of high research intensity and doctoral education. In contrast, many CSU research programs, including at CSUB, are applied research–driven with regional relevance, but they do not generate invention disclosures at volumes sufficient to justify investing in standalone commercialization operations. At CSUB, institutional priorities tend to focus on teaching activities and student success, leaving support for technology transfer activities difficult to justify.

As a sub awardee, CSUB will greatly benefit from a partnership with UCR Inland BRIDGE to infuse needed support that will spur innovation among faculty and students in building first time TT capabilities, training opportunities, and utilizing AI-enabled validation tools. Our focus is to develop innovation spaces where faculty and students collaborate on regionally relevant solutions and are supported in translating those solutions into market-ready outcomes—effectively closing the gap between research, innovation, and commercialization at CSUB.

Sponsor: Jose State University Research Foundation
PI: Ehsan Reihani
Total Award: $74,998.00
Start Date: 7/1/2026

The goal of this project is to quantify how specific design elements at multi lane roundabouts influence safety outcomes using AI based video analytics and surrogate safety measures, and to translate those findings into practical guidance that reduces property damage only crashes and improves public confidence in roundabout operations across California.

Sponsor: Andrew W Mellon Foundation (Mellon)
PI: Joseph Florez
Total Award: $500,000
Start Date: 8/1/2026

Artificial intelligence systems increasingly rely on universalized models of knowledge that obscure the cultural, historical, and material conditions through which understanding is generated and curated. This proposal aims to foreground forms of intelligence that run counter to standardization and quantification through the design and implementation of an interdisciplinary AI and Ethics curriculum at California State University, Bakersfield (CSUB) that brings philosophy, computer science, and place-based humanities pedagogies into conversation with local epistemologies in California’s Central Valley.

Sponsor: National Institute of Food and Agriculture of the USDA (NIFA)
PI: Ehsan Reihani
Total Award: $489,834.00
Start Date: 7/1/2026

Small and medium-sized farms face intensifying pressure to reduce water and energy consumption while maintaining profitability. In California’s San Joaquin Valley, the Sustainable Groundwater Management Act (SGMA) mandates pumping reductions of 20–50%, yet most small farms lack access to the data-driven decision tools available to large commercial operations. This project addresses that gap by developing an AI-enabled edge-cloud decision support platform that optimizes irrigation scheduling for water and energy efficiency on resource-constrained farms. The proposed Ag-Edge-ML framework integrates multi-disciplinary data streams—soil moisture sensors, weather forecasts, pump energy signatures, satellite imagery, and utility pricing—through novel temporal fusion algorithms designed for the asynchronous, noisy, and sparse data environments typical of small-farm deployments. Machine learning models predict crop water demand and system performance, while a multi-objective optimization engine generates irrigation schedules that balance water conservation, energy costs, and yield protection. Critically, the system incorporates human-in-the-loop constraints reflecting producer labor availability, ensuring recommendations are practically actionable. Lightweight edge computing enables operation under limited connectivity.

Field trials in Kern County, California will validate system performance across diverse crops and irrigation systems, targeting 15–25% water reduction, 10–20% energy savings, and positive return on investment within four irrigation seasons. The platform will be released as open-source software, with training materials developed in partnership with Cooperative Extension to support broad adoption.