We will evolve the national-scale Sage platform to build a new pathfinding testbed that links real-time AI-enabled sensors with existing NSF HPC and networking resources to provide unique cyber-infrastructure supporting research exploring new AI techniques, algorithms, and AI-driven climate understanding. The future national-scale computing continuum can use the testbed to gain critical insights and advance AI, as well as the coupled complex adaptive systems that makeup Earth&rsquo;s biosphere. 
The UIC team, led by Michael E. Papka, will collaboratively work with Northwestern University and other participating institutions to lead the following: 
i. Low-Cost Sage Node Development: UIC will oversee a team of undergraduate students who will research and develop low-cost Sage nodes for use in classroom settings. These nodes will be designed to provide accessible and affordable access to the Sage platform for educational purposes. Deliverables include designing and developing low-cost Sage nodes, documentation, and evaluating their performance and usability in classroom settings. 
ii. Jupyter Notebook Examples for Education and Outreach: The UIC team will lead the development of educational examples using Jupyter notebooks to support the project&rsquo;s education and outreach objectives. These examples will demonstrate the capabilities of the Sage platform and provide accessible learning resources for students and educators interested in AI techniques, algorithms, and AI-driven climate understanding. Deliverables include the creation of Jupyter notebook examples, user guides, and instructional materials to support their use in educational settings, as well as reports detailing the impact and effectiveness of these resources in achieving the project&rsquo;s education and outreach objectives.

NAIRR Pilot: A National-Scale Testbed Supporting Artificial Intelligence Research Spanning the Computing Continuum

Searching for scientific documents is a pervasive task among researchers. While most academic search engines, such as Google Scholar, search over text data, image data provides relevant information to complement text-based queries. For instance, in biomedical and biology research, images show experimental results and provide cues about the methods followed. This work shows the importance of combining text and image-based features to aid researchers in finding relevant documents in a COVID-19 collection. We derived a taxonomy to represent image content based on their acquisition method (e.g., captured in a microscope), integrated this information by leveraging deep learning image classifiers, and presented the result in hybrid image+text surrogates. Our design improved the user experience during document retrieval.

The system architecture. The online components include: web interface, application server for data retrieval, and Apache Lucene search engine. The offline components are responsible for labeling, model training and document preprocessing.

Enhancing biomedical search interfaces with images

The development and construction of facilities that allow for efficient data sharing while also providing computational resources for data analysis is becoming a crucial activity across all fields of science. In many research areas, the generation of large-scale, sophisticated simulations on HPC platforms is now as important a part of science as are the associated experimental and theoretical studies. The simulations can be computationally expensive, and often only a limited number of codes exist that can take full advantage of large-scale HPC systems. Therefore, sharing simulation results within the community to enable comparisons to experimental data and further data exploration in search of new insights is essential to push science forward. 
EVL graduate research assistants work with Argonne National Laboratory scientists on the following tasks: 
• Active development of the data portal to support releasing cosmological simulation data to the community. 
• Test data portal and develop tests to increase the portal&rsquo;s robustness. 
• Develop of documentation and tutorials on the use of data portal.

Data Sharing and Analysis Across Leadership Computing Facilities

Advanced radiotherapy treatments for Oropharyngeal cancer have improved patient survival rates, although they lead to understudied long-term symptoms. By analyzing three-dimensional dose distributions to vital organs via custom unsupervised machine learning, we identify correlations with long-term patient-reported symptoms. Our innovative approach surpasses existing clinical guidelines, offering actionable insights for optimizing treatment plans and minimizing post-treatment side effects. 
This work by visual computing researchers at EVL is in collaboration with clinicians at MD Anderson Cancer Center from Texas and data mining specialists at University of Iowa, and it is supported by funding from the US National Institutes of Health (NIH awards NCI-R01-CA258827 and NLM-R01-LM012527), and US National Science Foundation (NSF awards CDSE-1854815 and CNS-1828265).

Overview of the methods used for each symptom of interest.

Multi-Organ Spatial Stratification of 3-D Dose Distributions Improves Risk Prediction of Long-Term Self-Reported Severe Symptoms in Oropharyngeal Cancer Patients Receiving Radiotherapy: Development of a Pre-Treatment Decision Support Tool

We present an interactive analysis system to support sequential rule mining (SRM) model builders who work in cancer symptom research. SRM is a promising unsupervised machine learning method for predicting longitudinal patterns in temporal data which, however, can output many repetitive patterns that are difficult to interpret without the assistance of visual analytics. Our system facilitates mechanistic knowledge discovery of treatment-related toxicities (i.e., "roses have thorns") in large-scale cohort data. 
This work was developed by visual computing researchers at EVL in collaboration with data mining specialists at the University of Iowa, and clinicians at the MD Anderson Cancer Center in Texas, and it is supported by funding from the US National Institutes of Health (NIH awards NCI-R01-CA258827 and NLM-R01-LM012527), and US National Science Foundation (NSF awards CDSE-1854815 and CNS-1828265).

Research - 2023

NAIRR Pilot: A National-Scale Testbed Supporting Artificial Intelligence Research Spanning the Computing Continuum

Enhancing biomedical search interfaces with images

Data Sharing and Analysis Across Leadership Computing Facilities

Multi-Organ Spatial Stratification of 3-D Dose Distributions Improves Risk Prediction of Long-Term Self-Reported Severe Symptoms in Oropharyngeal Cancer Patients Receiving Radiotherapy: Development of a Pre-Treatment Decision Support Tool

Roses Have Thorns: Understanding the Downside of Oncological Care Delivery Through Visual Analytics and Sequential Rule Mining

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