Harvard’s SM in Applied Mathematics pushes mathematicians beyond theory into modeling-driven discoveries across finance, biology, and climate science. A core of PDEs, stochastic processes, and optimization merges with machine learning and high-performance computing electives. Students collaborate in the Institute for Applied Computational Science, publishing in top journals and deploying code on cluster GPUs.
Agent-based model of urban traffic and ride-sharing dynamics
Gradient-free optimization for wind-farm layout
Stochastic differential-equation model of stock-market bubbles
Numerical solver comparing implicit vs. explicit schemes for ice-sheet flow
Neural-ODE approach to infectious-disease forecasting
Bayesian calibration of climate-carbon feedback parameters
Data-driven reduced-order models for plasma turbulence
Optimization of vaccine distribution networks under uncertainty
Open-source Python library for fractional PDE operators
Capstone on fairness metrics in algorithmic trading strategies
GPU-accelerated solver for seismic wave-propagation inversion
Workshop teaching high-schoolers applied-math visualization tools
Interactive dashboard demonstrating chaos in Lorenz systems
Thesis on game-theoretic analysis of renewable-energy auctions
Visualization of parameter sensitivity in epidemiological models
Translate elegant math into impactful solutions with Harvard’s applied-math community.
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