2025 Graduate Student Mathematical Modeling Camp

Program Format

GSMMC is a four-day summer training experience focused on graduate- and advanced undergraduate-level training and career development. Students work in teams to model and analyze problems posed by experienced faculty mentors, developing the type of modeling and problem-solving skills prized by industrial researchers while in a guided setting.

Location

All activities of the GSMMC will take place on the campus of California State Polytechnic University Pomona (Cal Poly Pomona) in Pomona, CA from June 4 – 7, 2025.

GSMMC 2025 Director

Manuchehr Aminian, Department of Mathematics and Statistics, Cal Poly Pomona

Program Schedule

GSMMC 2025 Mentors

• Henry Boateng, Associate Professor, San Francisco State University
  Title: Randomized Sketching in Randomized Least-Squares Applications
  Abstract: Randomized least-squares methods employ random sketch matrices to embed the problem in a lower dimensional space. We will explore the efficacy of two Johnson-Lindenstrauss (JL) sketch matrices in randomized least-squares algorithms. We assess the algorithms based on their performance on applications to cancer detection and handwriting digits.
• Hangjie Ji, Assistant Professor, North Carolina State University
  Title: Physics-based and data-driven modeling of lava flows
  Abstract: Lava flows pose significant threats to human communities and infrastructure, particularly as population density increases near active volcanoes. Each year, volcanic eruptions and lava flows cause millions of dollars in damage. Lava erupting from a volcanic vent behaves as an extremely viscous liquid. As it cools, the outer surface solidifies into a crust, which progressively slows down and eventually halts the flow. Understanding lava flow dynamics is essential for hazard assessments and the development of hazard maps during active eruptions. This project will explore both physics-based and data-driven approaches to modeling lava flow dynamics. The physics-based model is expected to account for factors such as gravity, surface topography, and heat transfer. Students will also be encouraged to develop reduced-order models using satellite images of past lava flows. Through this project, students will gain familiarity with data-driven reduced-order modeling techniques, ODEs/PDEs, asymptotic analysis, and numerical simulation.
• Sooie-Hoe Loke, Associate Professor, Central Washington University
  Title: Risk Sharing in Insurance
  Abstract: Risk sharing lies at the heart of many insurance mechanisms, including reinsurance, mutual insurance, and decentralized models, where the primary goal is to distribute losses among multiple agents. Students will engage with a range of tools commonly used in the study of risk sharing, such as optimization techniques, probabilistic modeling, and network theory. The project begins with an investigation of classical frameworks and culminates in the design and analysis of original risk sharing networks, with applications to real-world data. Emphasis will be placed on identifying fair and efficient allocations of risk and on interpreting the structural properties that emerge from network-based formulations.