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

Time

Tuesday
June 3

Wednesday
June 4

Thursday
June 5

Friday
June 6

Saturday
June 7

Sunday
June 8

Monday
June 9
7:00
Arrivals

Breakfast

Breakfast

Breakfast

Breakfast

Breakfast

MPI
7:30

       
Estrellas folks:
 
8:00

        this is the last meal at centerpointe.  
8:30

           
9:00

Welcome Remarks



Richard Moore (SIAM)
Free Time
 
9:30

Mentor Presentation 1



Presentations    
10:00

Snacks Snacks Snacks Snacks
Estrellas folks:
 
10:30

Mentor Presentation 2



Presentations check out before 11:30!  
11:00

 



     
11:30


Voting





Group Photo
   
12:00

Lunch (Centerpointe) Lunch (Centerpointe) Lunch (Centerpointe) Lunch (Centerpointe) Lunch on your own; commute  
12:30

        to Claremont; free time; etc.  
13:00

Group Assignments




Free Time
   
13:30







 
Check in to CGU
 
14:00







  housing before 3:00  
14:30







     
15:00

Snacks Snacks Snacks      
15:30







     
16:00







     
16:30







     
17:00







     
17:30







     
18:00
Dinner/Free Time

Dinner/Free Time

Dinner/Free Time

Dinner/Free Time

Dinner/Free Time
   
18:30









   
19:00









   
19:30









   

 

Key

  Building 3, Room 2137
 

  Meals
 

  Group work in Building 8;
  Rooms 248, 249, 250
 

  Free Time

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.