Roadmap

Time: Monday 3:00 pm CT

Location: Zoom (Ether)

Current schedule (2021 Spring)

2016 schedule

2017sp schedule

2017fa schedule

2018sp schedule

2019sp schedule

2019fa schedule

2020sp schedule

2020fa schedule

Speaker Guidelines

TL;DR Do unto others as you would have them do unto you.

At minimum, an AIG presenter should prepare a few slides and an example code.

1. The slides should explain: - what the algorithm is - how a minimum example works - why the algorithm might be practically useful
2. A code that demonstrates the simplest problem the algorithm solves.
3. Make a pull-request (PR) to the website repository to make your presentation and code eternal. Follow instructions in the README.md file.

The presentation should be 30-50 min if given without interruptions. Interactive elements are encouraged. e.g. a Jupyter notebook demo. with tweakable parameters given by the audience.

Ideas for presentations

• Machine learning.
  • Back propogation.
  • Clustering.
  • Boltzmann machine.
• Control theory and signal processing.
  • Model reduction.
  • Kalman filter.
  • Hidden markov model.
  • Proportional-integral (PI) controller.
• Stochastic algorithms.
  • The Metropolis approach to sampling and alternatives
  • Global balance (pentalty method).
  • Quasi-random numbers.
  • Ant Colony Optimization
  • Parallel tempering.
  • Stochastic hill climbing.
  • Bayesian networks.
  • Simulated annealing
  • Evolutionary, and genetics algorithms.
  • Particle Swarm Optimization
  • Belief propogation
  • Gibbs sampling
• Encryption.
  • Symmetric-key, Public-key (RSA) cryptography
  • Cryptanalysis (breaking encryption).
  • Hashing.
• Optimzation.
  • Global Newton methods
    • line search
    • trust region
    • iterative solution of linear equations
    • matrix free
  • Generalized minimal residual method (GMRES)
    • preconditioning
    • additive Schwarts
    • Algebraic and geometric multi-grid
    • Block Jacobi
  • Quadratic optimization.
  • Convex optmization.
  • Steepest descent, Conjugate gradiant, Quasi-Newton, ….
  • Noisy optimization.
  • Compilers (fortan).
  • Simplex method.
• Linear Algebra.
  • Random matrix theory.
  • QR / SVD. principle component analysis
  • Diagnolization, inversion.
  • Lanczos
  • Fast Fourier transforms (FFT).
• Numerical solutions to differential equations.
  • Finite differences
  • Finite elements
  • Finite volumes
  • Spectral elements
  • PDE solvers (additional problems from multivariate)
  • Energy conserving or time-reversal invariant versions.
  • Runge-Kutta and family.
• Data compression.
  • Image compression techniques (one or more).
  • Compressed sensing (probabilistic approach and connections to stat mech).
  • Compressed sensing (l-1 technique).
• Image Processing
  • Image recognition
  • Automatic focus
• Visualization
  • Marching Cubes
• Quantum computing.
  • Quantum annealing.
  • Quantum error correction.
  • Quantum encryption.
  • Quantum stabilizers.
  • Grover algorithm.
  • Shor algorithm.
• Parallelism.
  • Parallel linear algebra.
  • OpenMP and MPI
  • GPU, Cuda, …
• Computer networks/the internet.
  • Google search bar, page rank.
  • The Internet protocal suite.
  • Packet switching vs. cell-based switching.
  • Mobile networks.
  • Error detection and correction, Hamming codes.
  • Internet security.
  • Network routing.
• Classic CS algorithms
  • Quicksort, Graph theory, …
  • Cellular atomata.
  • Theory of computation (Turing, finite state machine, definition of language, regular expressions).
  • complexity theory.