The MS in Applied Mathematics and Computational Science at the University of Pennsylvania bridges rigorous mathematics with practical computational applications. These thesis ideas are ideal for students looking to model complex systems, optimize algorithms, and simulate real-world phenomena.
Numerical Methods for Solving High-Dimensional PDEs
Optimization Algorithms in Large-Scale Machine Learning Models
Sparse Matrix Techniques in Computational Fluid Dynamics
Mathematical Modeling of Epidemic Spread in Urban Areas
Simulation of Neural Activity Using Finite Element Analysis
Quantum Algorithms for Combinatorial Optimization
Stochastic Modeling in Climate Risk Analysis
GPU Acceleration of Iterative Solvers for Sparse Systems
Topology-Based Analysis in Complex Network Graphs
Mathematical Foundations of Deep Learning Architectures
Bioinformatics Algorithms for Gene Expression Clustering
Multi-Scale Modeling of Materials under Stress
Computational Geometry in 3D Object Reconstruction
Sensitivity Analysis in Nonlinear Dynamical Systems
Mathematical Optimization in Supply Chain Design
Collexa mentors students in scientific computing, model verification, algorithm development, and technical writing for applied mathematics research at Penn.
Whether it's Machine Learning, Data Science, or Web Development, Collexa is here to support your academic journey.
"Collexa transformed my academic experience with their expert support and guidance."
Computer Science Student
Reach out to us for personalized academic assistance and take the next step towards success.
