Computer models are an essential tool of modern society. Whether it is for designing airplanes, predicting dominant virus strains in a pandemic or estimating how different policies will impact the CO2 concentration in the next 5 decades, our society makes abundant use of models. While some models can be built from first principles, the majority of artificial intelligence (AI) models are learned from data.
AI models have achieved unprecedented results in the past decade. But this success comes at a cost: it is unsustainable. In fact, the computational power needed to learn large models has doubled every 3.4 months since 2012. In 2019, learning a single model could emit as much carbon as five cars in their lifetimes. This ever-increasing need for computational power is driven by the large amounts of model parameters that can only be reliably learned from both large-scale and high-dimensional data.
The research in this PhD project will be on developing a new theory to make learning models from data sustainable. The key idea of this theory is to significantly compress model parameters with a novel technique: tensor networks. By exploiting correlations tensor networks can capture relevant information such that only a fraction of the original model parameters is required. The focus of this PhD project will be on developing theory for learning kernel machines (support vector machines, Gaussian processes, …) with tensor networks and by using a Bayesian inference approach.
You will join the Delft Tensor AI Lab (DeTAIL) where a team of enthusiastic researchers is developing new tensor theory for applications in machine learning, control and biomedical signal processing.
The department Delft Center for Systems and Control (DCSC) of the faculty Mechanical, Maritime and Materials Engineering, coordinates the education and research activities in systems and control at Delft University of Technology. The Centers' research mission is to conduct fundamental research in systems dynamics and control, involving dynamic modelling, advanced control theory, optimisation and signal analysis. The research is motivated by advanced technology development in physical imaging systems, renewable energy, robotics and transportation systems.
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