Honours and Masters project

Cloud Data centres are designed to support the business requirements of cloud clients. However, due to the complexities of data centre infrastructure and their software systems, cloud service providers often do not have access to quality data regarding their IT equipment. This hinders their ability to better optimise the quality of their services and system performance. A clear message from across the industry is that better data allows for better decision making and resource management.

Thanks to the widespread deployment of smart meters, high volumes of residential load data have been collected and made available to both consumers and utility companies. Smart meter data open up tremendous opportunities, and various analytical techniques have been developed to analyse smart meter data using machine learning. This project will provide a new angle toward energy data analytics and aims to discover the consumption patterns, lifestyle, and behavioural changes of consumers.

User reviews on app distribution platforms such as Google Play store and Apple App store are a valuable source of information, ideas, and requests from users. They reflect the needs and challenges users encounter including bugs, feature requests, and design. Recent research has shown that reviews can also serve as a proxy for understanding the values of the users and how users perceive that their values have been violated by the mobile app/mobile app developers. However, there are limited studies that show whether mobile app updates fix violations of the user's values and to what extent.…

Theory and applications in data analytics of time series became popular in the past few years due to the availability of data in various sources. This project aims to investigate and generalise Hybrid and Neural Network methods in time series to develop forecast algorithms. The methodology will be developed as a theoretical construct together with wide variety of applications.

Optimal policies derived from decision-theoretic models such as Markov Decision Processes (MDPs) often prescribe a single “best” action for every state. However, in real-world conservation contexts, managers rarely follow these prescriptions perfectly—due to uncertainty, limited trust, or operational constraints. This project explores how to make optimal policies more useful and interpretable by helping managers identify which states are critical to get right.

 Automation has affected employment at least as far back as Gutenberg, the introduction of the printing press and the effect on scribes and others. Such changes have occurred in the centuries since. In more recent times, we see electronic intelligence showing increasingly rapid advances, with examples including (e.g.) easily accessible, free, rapid and often somewhat reliable language translation. More recent advances include the increasing emergence of driverless cars.

Classical propositional logic (CPL) captures our basic understanding of the linguistic connectives “and”, “or” and “not”. It also provides a very good basis for digital circuits. But it does not account for more sophisticated linguistic notions such as “always”, “possibly”, “believed” or “knows”. Philosophers therefore invented many different non-classical logics which extend CPL with further operators for these notions.

Given a knowledge base describing the existing background constraints and assumptions about what is possible in the world as well as the prior experience of an autonomous agent on the one hand and probabilistic perception of the current state of the world of the autonomous agent, on the other hand, it is essential to devise and efficiently enumerate the most consistent world models that are likely to be valid under the prior knowledge in order to refine the agent’s up-to-date perception and take the most suitable actions.

In this project, we will conduct an empirical study to understand certain problems in software systems. 

The energy transition to net zero is in full swing! We at Monash University's Faculty of Information Technology (FIT) are in the unique position that we support the transition across an immensely broad range of topics: from model-predictive building control and community battery integration to wind farm optimisation and multi-decade investment planning, we support clever algorithms and data crunching to make decisions automatically and to let humans make informed decisions, too.