From stochastic differential equations (SDEs) modeling option pricing to GPU-accelerated finite-element solvers for biomedical flows, USC’s Applied & Computational Mathematics program melds theory with high-performance computing (HPC). Students master Python, C++, and Julia packages, deploy cloud-native JupyterHub clusters, and employ ML libraries like TensorFlow to uncover latent patterns in climate datasets. A required industry practicum exposes majors to fintech risk labs and bioinformatics startups, sharpening communication skills for cross-disciplinary collaboration (CDC).
GPU-based Navier–Stokes solver benchmarking CUDA vs. HIP
Graph-theoretic model of power-grid resilience under cyber-attacks
Variational auto-encoder (VAE) for seismic-waveform denoising
Bayesian SIR model predicting pandemic peaks with mobility data
Cryptographic lattice-based homomorphic encryption demo
Topology optimization of 3-D truss using evolutionary algorithms (EA)
Fractional PDE simulation for anomalous diffusion in batteries
Quantum annealing heuristic for portfolio diversification
Reinforcement-learning adaptive mesh refinement (AMR-RL)
Sparse regression discovering governing equations of turbulence
Neural operator accelerating tsunami early-warning models
Algebraic multigrid comparison across heterogeneous CPUs
Markov-chain Monte Carlo (MCMC) risk model for insurance floods
Tensor-network compression of climate-model state vectors
Cloud-native container orchestration for parallel interval arithmetic
Causal graph uncovering policy effects on housing markets
Interactive dashboard explaining chaotic Lorenz attractor to K-12
Transform data-heavy problems into clear numerical solutions.
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