Research
Research is the ability to dive deep into complicated topics & derive unique insight
Here are some research projects I have undertaken
Principia Musica
A mathematical decomposition of music. Analogies are often drawn between music and mathematics, alluding to the underlying structure of music.
It then naturally follows that if we consider music as a high-dimensional numerical representation, we ought to be able to apply statistical/mathematical methods, perform inference and model music.
AEVB for LDA
A challenge of modern data science is the diversity of the data: data types like text & images often translate to large, sparse numerical representations - which are often impractical for computation using traditional statistical techniques
My honour thesis we explored the viability of scaling Variational Bayes (VB) with a deep autoencoder (AEVB) to learn latent topic distributions (LDA)
We published the paper at the FAIR (Forum for Artificial Intelligence Research) conference & I presented my work in December 2019
Evolutionary Computation
An Implementation of Metaheuristics: Genetic Algorithms, Simulated Annealing & Particle Swarm Optimization to find approximate solutions to a Knapsack problem.
These intelligent optimization algorithms effectively search any high-dimensional domain to find good approximate global maxima/minima. Though applied to knapsack (a discrete problem set) all techniques are easily modified to handle continuous search domains. In fact, these are applicable to any problem that can be posed mathematically.
Underlying philosophy: Given a sufficiently complicated problem set, write an algorithm at writes an algorithm.