Working together
with foundations
that care.
Combinatorics and Graph Theory are extremely important to model discrete systems, counting sequences and are an important basis for logistics modelling and the learning of Graph Neural Networks.
Differential Geometry enables the study of geometric objectics using calculus. So not only position or velocity but also shape, curvature and overall geometry of spaces. This can be highly used to derive embedding models for reducing dimensionality and therefore the complexity of problems and also learn the manifold structure of the loss function we are studying when trying to optimize a certain model based on that loss function.
Time Series, Financial Mathematics and Optimization in Finance give the tools for modelling financial instruments and derive high precision models for time series. Computational Complexity, Computability Theory and Probabilistic Methods in Computer Science are king for finding simple, efficient and fast models which take advantage of the probabilistic sampling algorithms to derive not only one but thousands of different learners that yield final models with higher confidence while enabling uncertainty calculation.