title: Agile methods for agile universities

authors: Twidale, Michael B.

abstract: We explore a term, Agile, that is being used in various workplace settings, including the management of universities. The term may have several related but slightly different meanings. Agile is often used in the context of facilitating more creative problem-solving and advocating for the adoption, design, tailoring and continual updating of more innovative organizational processes. We consider a particular set of meanings of the term from the world of software development. Agile methods were created to address certain problems with the software development process. Many of those problems have interesting analogues in the context of universities, so a reflection on agile methods may be a useful heuristic for generating ideas for enabling universities to be more creative.

text: Agile methods for agile universities We explore a term, Agile, that is being used in various workplace settings, including the management of universities. The term may have several related but slightly different meanings. Agile is often used in the context of facilitating more creative problem-solving and advocating for the adoption, design, tailoring and continual updating of more innovative organizational processes. We consider a particular set of meanings of the term from the world of software development. Agile methods were created to address certain problems with the software development process. Many of those problems have interesting analogues in the context of universities, so a reflection on agile methods may be a useful heuristic for generating ideas for enabling universities to be more creative.

year: 2013

title: High-throughput machine learning algorithms

authors: Mitchell, Rory

subjects: thesis with publication

abstract: The field of machine learning has become strongly compute driven, such that emerging research and applications require larger amounts of specialised hardware or smarter algorithms to advance beyond the state-of-the-art. This thesis develops specialised techniques and algorithms for a subset of computationally difficult machine learning problems. The applications under investigation are quantile approximation in the limited-memory data streaming setting, interpretability of decision tree ensembles, efficient sampling methods in the space of permutations, and the generation of large numbers of pseudorandom permutations. These specific applications are investigated as they represent significant bottlenecks in real-world machine learning pipelines, where improvements to throughput have significant impact on the outcomes of machine learning projects in both industry and research. To address these bottlenecks, we discuss both theoretical improvements, such as improved convergence rates, and hardware/software related improvements, such as optimised algorithm design for high throughput hardware accelerators. 

Some contributions include: the evaluation of bin-packing methods for efficiently scheduling small batches of dependent computations to GPU hardware execution units, numerically stable reduction operators for higher-order statistical moments, and memory bandwidth optimisation for GPU shuffling. Additionally, we apply theory of the symmetric group of permutations in reproducing kernel Hilbert spaces, resulting in improved analysis of Monte Carlo methods for Shapley value estimation and new, computationally more efficient algorithms based on kernel herding and Bayesian quadrature. We also utilise reproducing kernels over permutations to develop a novel statistical test for the hypothesis that a sample of permutations is drawn from a uniform distribution.

The techniques discussed lie at the intersection of machine learning, high-performance computing, and applied mathematics. Much of the above work resulted in open source software used in real applications, including the GPUTreeShap library [38], shuffling primitives for the Thrust parallel computing library [2], extensions to the Shap package [31], and extensions to the XGBoost library [6].

text: High-throughput machine learning algorithms The field of machine learning has become strongly compute driven, such that emerging research and applications require larger amounts of specialised hardware or smarter algorithms to advance beyond the state-of-the-art. This thesis develops specialised techniques and algorithms for a subset of computationally difficult machine learning problems. The applications under investigation are quantile approximation in the limited-memory data streaming setting, interpretability of decision tree ensembles, efficient sampling methods in the space of permutations, and the generation of large numbers of pseudorandom permutations. These specific applications are investigated as they represent significant bottlenecks in real-world machine learning pipelines, where improvements to throughput have significant impact on the outcomes of machine learning projects in both industry and research. To address these bottlenecks, we discuss both theoretical improvements, such as improved convergence rates, and hardware/software related improvements, such as optimised algorithm design for high throughput hardware accelerators. 

Some contributions include: the evaluation of bin-packing methods for efficiently scheduling small batches of dependent computations to GPU hardware execution units, numerically stable reduction operators for higher-order statistical moments, and memory bandwidth optimisation for GPU shuffling. Additionally, we apply theory of the symmetric group of permutations in reproducing kernel Hilbert spaces, resulting in improved analysis of Monte Carlo methods for Shapley value estimation and new, computationally more efficient algorithms based on kernel herding and Bayesian quadrature. We also utilise reproducing kernels over permutations to develop a novel statistical test for the hypothesis that a sample of permutations is drawn from a uniform distribution.

The techniques discussed lie at the intersection of machine learning, high-performance computing, and applied mathematics. Much of the above work resulted in open source software used in real applications, including the GPUTreeShap library [38], shuffling primitives for the Thrust parallel computing library [2], extensions to the Shap package [31], and extensions to the XGBoost library [6].

year: 2021

title: The Digital Divide in New Zealand: The position of Māori and Pacific peoples

authors: Gibson, John

subjects: Māori digital access

abstract: Information and communication technology plays an ever-growing role in economic, political and social life and those who are unable to access such technologies are increasingly disadvantaged. Hence, what has come to be known as the ‘digital divide’ is of considerable significance. Here, indicators of that divide as it affects Māori and Pacific Islands peoples in Aotearoa/New Zealand are presented and discussed.

text: The Digital Divide in New Zealand: The position of Māori and Pacific peoples Information and communication technology plays an ever-growing role in economic, political and social life and those who are unable to access such technologies are increasingly disadvantaged. Hence, what has come to be known as the ‘digital divide’ is of considerable significance. Here, indicators of that divide as it affects Māori and Pacific Islands peoples in Aotearoa/New Zealand are presented and discussed.

year: 2002-09