The SCIPE CyberInfrastructure Research Experience Opportunity (CI-REO) is a semester-long, mentored research program that supports undergraduate students through exposure to Research Cyberinfrastructure projects with researchers and cyberinfrastructure professionals during the academic year at 10 hours per week on a stipend. Research mentors are currently based on UH Manoa, UH Hilo and the University of Guam. Students not on based on UH Manoa, UH Hilo or the University of Guam will be offered remote options for participation.

Cyberinfrastructure (CI) refers to the integrated computing systems, data storage and management facilities, advanced instruments, visualization environments and the people who operate and manage them. They are all linked together by software and high-performance networks to improve research productivity and enable breakthroughs not otherwise possible.

It is highly recommended that applicants read the following information before applying to the program:

In your application, please indicate your interest in one or more currently available research projects. At this time, the program does not include a project focused on cybersecurity.

List of currently available research projects:

1. Estimating bird call density of any species of Hawaiʻian bird using Google Perch Bird Vocalization classifier: https://lohelab.org/ Project requires student to be physically at Hilo to participate, with an interest in ecology and conservation in Hawaiʻi. In the LOHE lab at UH Hilo, Jetstream2 is leveraged to run Google Perch Bird Vocalization classifier. This allows us to estimate bird call density of any species of Hawaiʻian bird from months of continuous passive recordings. Students can run validations on the Perch output, with the ultimate goal of estimate changes in bird abundance across the landscape over time. Research mentor: Dr. Patrick Hart.
   
2. Developing natural language processing models for Hawaiʻian: The student will build neural network models to support the revitalization of the Hawaiian language. This involves collecting data from Hawaiian books, newspapers, and other existing sources, and training and evaluating machine learning models for tasks such as language modeling and machine translation. The ideal student for this project would have strong programming skills in Python and prior experience with machine learning and neural networks (PyTorch). Knowledge of the Hawaiian language is desired but not absolutely necessary. Research mentor: Dr. Winston Wu.
   
3. Developing an LLM for astronomy, specifically to predict stellar parameters from spectra. The ideal student for this project would be a junior or senior with strong programming skills in Python and prior experience with machine learning and neural networks (PyTorch). The student would possess domain knowledge in astronomy. This model would take in spectra at different resolutions from different telescopes, e.g. Gaia, TESS, Gemini, and would be applied to classifying the properties of newly discovered stars and identifying new exoplanets. This is a collaboration with Dr. Cicero Lu at Gemini Observatory who is an expert in exoplanets and star formation. The student would gain experience training and fine-tuning large language models, and running computational experiments with multiple GPUs using Koa. Due to the large amount of data required, this project may also need Koa Research Storage. Research mentor: Dr. Winston Wu.
   
4. AI for zero trust architecture (ZTA) Basic fluency with Python is required. Experience using Jupyter, Python and AI/ML are desirable. Project involves 1) collecting appropriate data then 2) training and 3) evaluating an ensemble of classifiers of attributes of devices, users and workflows with the objective of producing a binary meta classifier over (trusted|untrusted) suitable for use as a component of a ZTA Policy Decision Point (PDP). The student will pursue the above research objective using available, on-premise CI that offers Jupyter notebooks with GPUs. Research mentor: Dr. Curt Dodds.
   
5. Building a data management & processing platform for scientific data, as the traditional platforms are not optimized for those data, especially when those data are associated with AI techniques. The ideal student would have (1) programming experience in Python or C++/C, and (2) experience in Machine Learning or Database Systems development. The proposed platform will be optimized specifically for AI applications on scientific data, which will improve the application data processing efficiency and save cost and leverage existing techniques. This project relies on cyberinfrastructure resources heavily. The main development is conducted on Koa and Jetstream2 currently, and the data is also stored there. Research mentor: Dr. Yifan Wang.
   
6. Volcano Data Analysis and AI-Driven Modeling using CyberInfrastructure Resources This project uses advanced AI and autonomous multi-agent systems to analyze large multimodal datasets from Hawaiian volcano monitoring stations, including seismic, gas emission, satellite, and thermal data. Students will leverage Jetstream2 and Koa resources to build collaborative agentic workflows for eruption forecasting, deploy computer vision agents for imagery analysis, and utilize LLM APIs (e.g., OpenAI, Anthropic) to autonomously synthesize scientific field reports and hazard bulletins. The work also involves creating interactive dashboards where AI agents dynamically retrieve and correlate predictive outputs with visualizations for hazard communication. The ideal student has strong Python programming skills, experience with deep learning libraries or LLM APIs (e.g., PyTorch, Hugging Face, OpenAI/Anthropic), and an interest in earth sciences and AI. Familiarity with GIS or geospatial data is a plus. Research mentor: Dr. Sukhwa Hong.
   
7. Extending the Hawai’i Climate Atlases: https://www.hawaii.edu/climate-data-portal/hawaii-climate-atlases/
   The Hawaiʻi Climate Atlases are a predecessor to the current Hawaiʻi Climate Data Portal (HCDP). These worked with several static datasets and are being modernized. This project is being developed using TypeScript, a typed superset of JavaScript using React as a framework. Currently, the climate atlas for rainfall data has been created. This needs to be extended to several other variables using this interface as a template. Research mentor: Jared Mclean
   
8. Hawai’i Climate Data Portal (HCDP) and Mesonet: https://www.hawaii.edu/climate-data-portal/hawaii-mesonet/
   The student will work closely with a team of climate scientists and cyberinfrastructure experts to develop creative ways to identify “bad” data in meteorological data from the Hawaiʻi Mesonet (HM), assist with software updates and data management on the Hawaii Climate Data Portal (HCDP), assist with front end web-design, and work collaboratively to develop interactive decision support tools that leverage Hawaiʻi Mesonet and HCDP data. Research mentor: Dr. Ryan Longman.
   
9. Protein-Protein Interaction Modeling Pipeline: This project builds a modular cyberinfrastructure pipeline for discovering, ranking, and structurally modeling protein-protein interactions. The student will first develop a literature and database mining component that queries NCBI, PubMed, and pathway analysis tools to identify protein pairs with experimental evidence of interaction but no existing structural model of the complex. Interactions will be ranked by the strength of experimental evidence (e.g., co-immunoprecipitation with mass spectrometry, yeast two-hybrid, antibody-based co-IP). The student will then use HPC resources including Koa to run AlphaFold Multimer and protein-protein docking software (e.g., HADDOCK, ClusPro) to predict binding interfaces and generate ranked interaction poses. Crystal structures from the PDB will be incorporated where available. Results will be deposited into a curated database for review. Basic fluency in Python is required. Experience with structural biology databases, molecular visualization tools (e.g., PyMOL, ChimeraX), and HPC job scheduling is desirable. The student will gain experience with large-scale computational biology workflows, HPC resource management, and AI-driven structural prediction on research cyberinfrastructure. Research mentor: Dr. Alex Stokes.
   
10. Intelligent Data Exploring Assistant (IDEA):
    This project gives an undergraduate researcher hands-on experience exploring multi-agent AI orchestration for coding tasks, working within the Intelligent Data Exploring Assistant (IDEA), an open-source agentic framework being developed at UH Mānoa to help oceanographers analyze complex datasets through natural language. The student will investigate how specialized AI agents — for data retrieval, code generation, analysis, and validation — can be composed into reliable pipelines using frameworks like LangGraph, benchmarking orchestration strategies against real oceanographic coding problems on UH’s Koa and Jetstream2 systems. Through a full research investigation — forming hypotheses, implementing and testing agentic workflows in Python, and benchmarking results — the student will gain experience in cutting-edge, time-relevant AI research while directly contributing to IDEA’s ongoing development. No prior oceanography background is required; Python fluency is recommended. Research mentor: Dr. Roderick Tabalba.
    
11. High Performance Computing (HPC) at Guam:
    Learn alongside mentors at the University of Guam to deepen your understanding of HPC workflows and infrastructure. Research mentor: Mr. Jed Henson.
    
12. Cyberinfrastructure-enabled workflows for learning and decision making at Guam:
    This project focuses on developing cyberinfrastructure-enabled workflows for learning and decision making under limited information. Many real-world systems, including environmental, ecological, and infrastructure systems, are only partially observed, resulting in incomplete, sparse, or noisy data. The student will learn how advanced cyberinfrastructure resources can be used to support data analysis, computational modeling, and uncertainty quantification. Students considering future graduate study in data science, applied mathematics, computer science, artificial intelligence, or related quantitative fields may find this experience especially valuable. Prior experience with Python, R, GitHub, scientific computing tools, research presentations, or poster presentations is beneficial but not required. Students should be willing to learn computational tools, engage in independent learning, and participate in regular mentor meetings. The most successful participants are typically proactive, responsive, and willing to communicate their progress, questions, and challenges throughout the research experience. Activities may include (1) accessing and utilizing cyberinfrastructure resources such as ACCESS allocations, TACC systems, Tapis, and cloud-based computing environments, (2) developing computational workflows for data preprocessing, visualization, and analysis, (3) implementing and testing state estimation methods, including Kalman filtering and state-space modeling, on partially observed datasets and (4) supporting reproducible research through computational notebooks, documentation, and workflow development. Research mentor: Dr. Jaeyong Choi.
    

Access this link to learn more about Research Cyberinfrastructure resources and services available to students and instructors.

Complementary to the mentored research component, students will meet weekly for workshops offered through the Hawaiʻi Data Science Institute, professional development and student cohort activities.

Spring 2027 Program Dates

January 11 to May 5, 2027

Eligibility Requirements

• Undergraduate students currently enrolled at the University of Hawaiʻi & the University of Guam, interested in advanced computing, integrated computing systems, data storage and management facilities, advanced instruments and visualization environments.
• All majors are eligible to apply.
• Basic fluency in Python is recommended.
• Students enrolled at the University of Hawaiʻi & the University of Guam should use their university- affiliated email accounts to apply.
• To be eligible to participate and receive the stipend per NSF regulations, students must be citizens of the United States, U.S. nationals (as defined in section 101(a) of the Immigration and Nationality Act), or U.S. permanent residents. All participants will be required to provide documentation of tax compliance prior to receipt of the stipend.

Stipend

$3,200 for full session participation

Application Deadline

November 30, 2026

Questions?

Email: datasci@hawaii.edu