Academic Mentors by Year:
Rachel Adler
Developing an mHealth app for Cancer Survivors to Encourage Physical Activity and Cognition
The number of cancer survivors in the US is already 18 million and is expected to rise to 22.5 million by 2032. Approximately 40% of cancer survivors experience long-term physical, cognitive, psychological, and social consequences of cancer and its treatment, which can lead to significant disability. Research shows that including physical activity and cognitive interventions can improve health outcomes and overall quality of life.
This research project focuses on designing and developing a custom mobile health (mHealth) application for cancer survivors. The app will be accessible via mobile and web platforms and will include features such as physical activity and cognitive-motor exercises, a generative AI based conversational agent, gamification elements, and integration with wearable fitness trackers.
Sever Tipei
Music on High-Performance Computers
The project centers on DISSCO, software for composition, sound design and music notation/printing developed at UIUC Computer Music Project, NCSA and Argonne National Laboratory. A parallel version was also developed at the San Diego Supercomputer Center with support from XSEDE (Extreme Science and Engineering Discovery Environment). Written in C++, DISSCO presently runs on the NCSA Delta system with support from ACCESS and uses both CPU and CUDA platforms.
DISSCO has a directed graph structure and uses stochastic distributions, sieves (part of Number Theory), Markov chains and elements of Information Theory to produce musical compositions. Efforts are presently directed toward moving the existing GUI from using gtkmm to Qt, adding new features, refining a system for the notation of music as well as to the realization of an Evolving Entity, a composition whose aspects change when computed recursively over long periods of time thus mirroring the way living organisms are transformed in time (Artificial Life).
Due to the fact that DISSCO is a “black box” that does not allow the user to interfere during computations and that the computer makes decisions not controlled by the user, it shares features with AI type of projects. Further developments are considered in this area.
Papers on DISSCO, co-authored with SPIN interns, have been presented at international conferences and compositions realized with DISSCO have been featured in concerts in the US, Europe, Asia and Australia.
Skills needed:
- proficiency in C++ programming
- familiarity with Linux Operating System
- familiarity with music notation preferred but not required.
More information: About DISSCO, About Notation, About Evolving Entity
Michael Miller
Exploring Quantum Sound and Music
This project seeks to explore the state of the art using quantum computing concepts to create sound and music. We will explore information available from previous conferences and seek out new developments. We will then explore what is needed to create a framework/interface for users to experiment with and look toward having a platform available to deploy on quantum resources when acquired by NCSA.
Soham Pal
Empirical Characterization of QAOA Energy Landscapes for Machine Learning-Guided Parameter Initialization
This NCSA SPIN project addresses parameter sensitivity and the well-documented challenge of vanishing gradients (“barren plateaus”) in the Quantum Approximate Optimization Algorithm (QAOA). The project aims to execute a systematic empirical characterization of QAOA energy landscapes. The intern will utilize NCSA’s DeltaAI infrastructure alongside the NVIDIA CUDA-Q platform to simulate QAOA execution across multiple graph families.
Using CUDA-Q’s GPU-accelerated simulation backends and Python graph libraries, the student will develop a parallel simulation workflow to analyze thousands of unique graph instances. The workflow will involve extracting structural and spectral features from these graphs to train an interpretable machine learning model, such as XGBoost paired with SHAP analysis, to predict optimal initial variational parameters. This data-driven warm-starting approach will be systematically benchmarked against standard classical initialization baselines to evaluate its effectiveness in finding optimal optimization pathways.
The primary deliverable for this project will be a public, DOI-citable QAOA landscape benchmark dataset hosted on Zenodo, alongside the mandatory student presentation at the NCSA SPIN Showcase. For the student, the internship provides practical experience in hybrid classical-quantum workflows and high-performance computing infrastructure.
Angela C. Lyons
Eyes on the Storm: AI-Driven Mapping of Extreme Weather Impacts on Illinois Communities, Agriculture, and Insurance Risk
Extreme weather events – floods, droughts, severe storms, and heat waves – are reshaping agricultural productivity, economic stability, and community well-being across Illinois. Yet the data needed to understand these impacts at fine spatial and temporal resolution remains scattered and underused. This project brings together satellite and remote sensing imagery with geo-coded transaction and mobility data to build a unified, AI-ready dataset for studying how extreme weather shapes human and economic outcomes across the state.
Undergraduate research assistants will help construct this dataset and apply machine learning and deep learning models to examine how extreme weather events affect human well-being, agricultural output, and local economic development — with an eye toward what these patterns mean for the insurance industry and agricultural policy. Students will work across the full research lifecycle: sourcing and cleaning satellite and geospatial data, merging it with socioeconomic and business-activity data, building and testing predictive models, and interpreting results for real-world decision-making.
This is a hands-on opportunity to gain experience in remote sensing, geospatial machine learning, and applied data science, while contributing to research with direct relevance to catastrophe risk modeling, disaster preparedness, and agricultural and insurance policy. Training will be provided in Python-based geospatial and machine learning workflows. Students will work closely with a research team based at NCSA and the Department of Agricultural and Consumer Economics.
Preferred Qualifications:
• Undergraduate student in data science, computer science, electrical and computer engineering, statistics, or related fields interested in climate risk, agricultural economics, or applied AI/ML
• Proficiency in Python and/or R
• Basic knowledge of machine learning techniques and/or geospatial analysis
• Experience with satellite/remote sensory data and GIS tools (e.g., QGIS, ArcGIS) is a plus
• Ability to create maps, dashboards, and visual analytics
• Familiarity with version control systems (e.g., Git/GitHub)
• Strong problem-solving skills and attention to detail
Taras Pogorelov
Modeling of the complex environment of the cell
The cell environment is complex, crowded, and is difficult to capture for sufficient timescales with modern computational approaches. The Pogorelov Lab at Illinois uses the specialized supercomputer Anton to model cell-like environment for hundreds of microseconds. We develop computational analysis tools and workflows to mine this large and unique data. We work in close collaboration with experimental labs to cross-validate computational and experimental data. Modeling approaches include classical molecular dynamics and data analysis. This project includes development of workflows for analysis of cell signaling, protein-protein and protein-metabolite interactions, and water dynamics that are vital to the life of the cell. The qualified student should have experience with R/Python programming, use of Linux environment, and of NAMD, Gromacs, MDAnalysis, and VMD software packages.
Mohamad Alipour
Drone-Based Remote Sensing and Autonomous Inspection for Infrastructure, Agriculture, and Hazards
This project focuses on drone-based remote sensing for a wide range of real-world applications, including building and bridge inspections, agricultural sensing, and natural hazard reconnaissance. The student will gain hands-on experience operating drones and collecting high-quality aerial data while engaging in research activities that integrate drone path planning, AI-based feature detection, and autonomous flight programming. The project emphasizes designing intelligent flight strategies to efficiently capture critical information, developing and applying machine learning methods to detect structural, environmental, and agricultural features from drone imagery and sensor data, and implementing autonomous flight workflows for repeatable and scalable data collection. Through this experience, the student will be exposed to the full pipeline of drone-enabled sensing—from mission planning and data acquisition to intelligent analysis—while contributing to research that advances the use of drones for infrastructure monitoring, precision agriculture, and rapid response to natural hazards.