CAHSI-Google Institutional Research Program

CAHSI-Google Institutional Research Program is sponsored by Google (in partnership with CAHSI). Google has partnered with CAHSI (Computing Alliance of Hispanic-Serving Institutions) to enhance research collaborations and build research capacity among faculty and students at CAHSI institutions, addressing disparities in computer science.

CAHSI Institutional Research Program 2025 – 2026 Call for Abstracts The Computing Alliance of Hispanic-Serving Institutions (CAHSI) is partnering with Google on the CAHSI-Google Institutional Research Program (IRP) to fund computing research projects in the area of Artificial Intelligence for Social Impact.

Involving people with different perspectives, experiences, and disciplinary knowledge in solving problems is a key factor in our nation’s ability to innovate and compete in a global economy. The goal of the CAHSI IRP program is to build competitive research capacity of faculty and domestic students at CAHSI institutions, aligned with Google’s research interests.

In addition to potential collaboration with Google researchers and building on Google research interests, the purpose of this funding program is to initiate collaborative research partnerships across CAHSI. The CAHSI IRP awards up to 10 computing research projects across the CAHSI Alliance with deserving projects funded at up to $80,000 with the additional opportunity for Google Cloud Platform credits.

Each year of the program, faculty members submit research abstracts, and these abstracts are used to identify promising projects of interest to Google and amongst CAHSI researchers. An ideation process is used to provide feedback from experts in their field and Google researchers and an opportunity to meet potential collaborators.

Promising projects are invited to submit full proposals and undergo a National Science Foundation-style review process. Awardees are notified in the summer with funded projects beginning in the fall. Each project partners a CAHSI doctoral-granting computing program and a CAHSI non-doctoral granting computing program, each involving one faculty member and at least one domestic student.

The aim is that CAHSI-Google IRP projects will build the research capacity of faculty and students at both institutions with the hope of bridging students into CAHSI computing doctoral programs. Research projects are intended to support cutting edge fundamental and applied research in the research area of Artificial Intelligence (AI) for social impact .

Research abstracts should propose AI-related research projects to include topics such as: * AI Privacy and Security (e.g., protection of training data, resilience to malicious attacks) * AI Transparency (e.g., explainable AI systems, instructable AI) * AI Grounding (e.g., robust AI, models transferability) * AI Alignment (e.g., ethical AI systems, human-AI interaction) Research focused on AI for Social Impact should give priority to use-inspired research that addresses challenges faced by underserved and underprivileged communities.

Particular emphasis is requested on framing the research proposal around specific real-world challenges faced by such communities. Examples: Smart and Sustainable Agriculture , Smart Energy Grid Resiliency and Reliability , Water Sustainability , Smart and Connected Health, AI for civic and public good.

Each awarded project will be provided a budget of up to $80,000 in direct costs with an opportunity for supplemental Google Cloud Platform (GCP) credits. There will be a separate call issued in mid-spring for currently funded investigators to apply for renewal funding. The project length is 12 months and projects should be scoped accordingly.

Funded projects are expected to begin in Fall 2025. Project Requirements: Funded projects ultimately must involve one researcher from a doctoral-granting CAHSI department and one researcher from a non-doctoral granting CAHSI department, and they must be conducting technical computing research in the research area of “AI for social impact.

” Only one faculty member from each institution will be funded per project; multiple faculty members from the same institution will not be funded within one project. In addition, the grant must fund at least one domestic student (undergraduate or graduate) from each institution. At the time of the abstract submission, it is not necessary to have identified a collaborator nor a student.

Who May Submit Abstracts: Abstracts must be submitted by a faculty member from either a two- or four-year CAHSI institution. By September 2025 all involved researchers must be an appointed faculty member in an active CAHSI computing department as verified by CAHSI regional leadership. Adjunct faculty are ineligible.

Investigators must be conducting technical computing research as part of their proposed research projects and show a record of scholarship and research mentorship. If the PI is from a doctoral-granting CAHSI department, then the Co-PI must be from a non-doctoral-granting CAHSI department. If the PI is from a non-doctoral granting CAHSI department, then the Co-PI must be from a doctoral-granting CAHSI department.

At the time of abstract submission it is not necessary to have identified a collaborator. There will be sufficient time and many opportunities to identify a potential collaborator. Investigators previously or currently funded through the CAHSI-Google IRP are ineligible to serve as a collaborator on a 2025-2026 abstract.

Limit on Number of Abstracts per Investigator: An individual researcher may be included as a PI or Co-PI in only one abstract for the January 16th, 2025 deadline. Later in the ideation and research refinement process investigators will have the option to withdraw an abstract in order to participate in another project.

Abstract Deadline: January 16, 2025 Expertise Connector Profile : Applicants must have a complete profile on the CAHSI Expertise Connector system or have submitted a request for one by the abstract deadline of January 16, 2025. Confidentiality Disclosure Agreement : As part of the ideation and research refinement process, all submitted project abstracts will be viewable by other participants.

Because this is your and your peers’ research and we want to protect it. Therefore, all participants and reviewers will be required to sign a Confidentiality Disclosure Agreement. All applicants (to include collaborators) must have submitted their agreement by the abstract deadline of January 16, 2025 in order to proceed in the process.

Research Abstract : There are 3 narrative components required for the abstract submission: Research Problem : What is the problem or need? (650 characters with spaces maximum). Justification : Why should people care?

(500 characters with spaces maximum). Goals/Approach : (2500 characters with spaces maximum). These narrative components will be submitted through a Google form .

Formatting (bold, underlined, and italics) will not carry into the text boxes. Images are not allowed. It is expected that faculty will actively participate in the ideation and research refinement sessions, as CAHSI is working toward an engaged and collaborative Faculty Researcher Network.

Faculty ultimately funded through the CAHSI-Google IRP will be expected to serve as a research mentor to at least one student from their institution and to collaborate with a peer researcher at another CAHSI institution. Additionally, faculty who have not received an ARG certificate must complete Affinity Research Group (ARG) training by time of award in mid-June 2025.

Furthermore, it is very important that the faculty member’s department be considered engaged in regional activities and working to advance the mission of CAHSI. During the spring, regional leadership will be reaching out to departmental POCs in instances where departments are lacking in engagement. Are indirect costs/overhead/F&A allowed?

No. Indirect costs/overhead/F&A are not allowed. Are computer science education or social science projects allowed? No. While education and social science research is very much needed and appreciated, this call is intended to fund technical computing research.

At the crux of every project, there should be a technical computing research question(s) and/or measurable objectives that when answered or achieved will help advance the science of computing. Can a collaborator from outside a computing program be funded on this project?

No. Though CAHSI very much values interdisciplinary research, for the purposes of this funding mechanism, faculty mentors funded through the project must be conducting technical computing research and housed in a program providing computing education. I am a junior investigator. Should I bring in a more experienced or senior faculty member to my project so that I have a better chance of being funded?

This funding opportunity is intended to build the research capacity of the faculty and students at our HSIs; for this reason, junior investigators are especially encouraged to apply as lead on their own projects! Furthermore, the funding mechanism only allows for one faculty member per institution per project to be funded as the intention is to build one-to-one faculty researcher connections across our CAHSI institutions.

I didn’t submit an abstract. Can I still be involved in the ideation process or be a potential collaborator on a research project? Yes!

Those tenured and tenure-track faculty members in active CAHSI computing departments without an abstract are still encouraged to attend the information sessions to learn more about the overall process and the research projects that are seeking collaborators. An additional way to make yourself known to investigators seeking collaborators is to create a CAHSI Expertise Connector profile .

What are the reporting requirements if my project is funded? A mid-term report and a final report are required, however, the reporting load is intended to be less burdensome than that of an NSF award. Funded researchers are expected to report on accomplishments, participants, obstacles, research findings and completed activities, and broader impacts.

Awardees will be provided reporting templates at the time of award notification.

The window for abstract submissions is now closed CAHSI-Google IRP Awardees Paul Gazzillo, University of Central Florida and Suzanne Rivoire, Sonoma State University; Finding and Fixing Security Misconfigurations through DevOps-Aware Testing and Verification (second year of funding) Chris Kanich, University of Illinois at Chicago and Magdalini Eirinaki, San Jose State University; Cloudsweeper: Leveraging Language Models to Personalize Sensitive Archive Search (second year of funding) Taoran Ji, Texas A&M University, Corpus Christi and Boxiang Dong, Montclair State University; Bias Detection and Mitigation in Large Language Model-Driven Education Tools Yue Dong, University of California, Riverside and Tingting Chen, California State Polytechnic University, Pomona; Adversarial Robustness Auditing for Lightweight Mobility Scooter Rider Evaluation Systems Dongsheng Luo, Florida International University and Qi Zhu, University of Houston- Victoria; Compound Flood Forecasting and Reasoning for Gulf Coast Regions Xin Zhang, San Diego State University and Ian Kash, University of Illinois at Chicago; Fairness-Aware Multi-Agent Imitation Learning for Equitable Urban Intelligence Yanzhao Wu, Florida International University and Mingyan Xiao, California State Polytechnic University, Pomona; Privacy-Preserving Large Language Models: Mitigating Membership Inference Attacks in Healthcare Juan Medina, University of Puerto Rico, Mayagüez and Iyadunni Adenuga, Kean University; Multimodal Human-Machine Interface for a Team-Based Driving Approach in Highly Automated Vehicles Yunhe Feng, University of North Texas and Bin Hu, Kean University; Enhancing Differential Privacy for User-End Large Language Model Prompts with a Three-Stage Framework Cornelia Caragea, University of Illinois at Chicago and Hongmin Li, California State University, East Bay; Robust and Well-Calibrated Semi-Supervised Models for Disaster Response Toward Out-of-Distribution Aware Time Series Data Mining Yifeng Gao, University of Texas Rio Grande Valley Yao Qin, University of California, Santa Barbara Building a Trustworthy Deep Learning Model for Urban-Scene Image Segmentation: Robustness and Uncertainty Analysis Yuyin Zhou, University of California, Santa Cruz Responsible Artificial Intelligence Enhancing Moral Decision-Making in Large Language Models Heena Rathore, Texas State University Sahar Hooshmand, California State University- Dominguez Hills Mitigating Bias in Class-Imbalanced Image Synthesis Models Meng Tang, University of California, Merced Gongbo Liang, Texas A&M University, San Antonio Responsible Design and Development of a Validated AI Video Description Rater Ilmi Yoon, San Francisco State University Vassilis Athitsos, University of Texas at Arlington Mitigating Socio-Economic Data Bias in Central Valley Air Quality Mapping with Community Agriculture Data Shijia Pan, University of California, Merced Kathy Kanemoto, Merced College CAHSI-Google IRP Renewal Request: A Unified Framework of Fairness-aware Optimization through Randomization Jing Yuan, University of North Texas Jiayin Wang, Montclair State University Towards the Safety and Security Gap of Integrating LLMs into Software Bohzen Liu, Texas A&M University- Corpus Christi Young Lee, Texas A&M University- San Antonio Protect On-Device AI Security with Multi-Enclave Architecture and Autoencoder Yongzhi Wang, Texas A&M University- Corpus Christi Avdesh Mishra, Texas A&M University- Kingsville Toward Automatic and Sound Memory Safety Bug Repair Xiaoguang Wang, University of Illinois Chicago Zechun Cao, Texas A&M University, San Antonio Cloudsweeper: Leveraging Language Models to Personalize Sensitive Archive Search Chris Kanich, University of Illinois Chicago Magdalini Eirinaki, San Jose State University Towards Safer Model Ecosystem Hyeran Jeon, University of California, Merced Moayed Daneshyari, California State University, East Bay Finding and Fixing Security Misconfigurations through DevOps-Aware Testing and Verification Paul Gazzillo, University of Central Florida Suzanne Rivoire, Sonoma State University CAHSI-GOOGLE IRP RENEWAL REQUEST: Fuzzing the Physical World to Identify Environmental Threats to AI-Based Autonomous Systems Alvaro Cardenas, University of California, Santa Cruz Younghee Park, San Jose State University CAHSI-Google IRP Renewal Request: Establishing Trust and Resiliency in Industrial Cyber Physical Systems Deepak Tosh, University of Texas at El Paso Paso Christian Servin, El Paso Community College