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IT and computer science
Today's changing world needs technology leadership that can identify opportunities and create solutions – trusted leaders who work across sectors and industries to deliver security and sustainability.
The Faculty of Engineering and Information Technology (FEIT) has a long history of tackling big challenges to benefit society. This has been our mission for more than 160 years. Confronted with new global challenges, complex issues like climate change and digital disruption need our attention – with a contemporary approach to IT solutions.
In the face of these challenges, we’re using research to create more impact than ever before.
As a graduate researcher, you’ll find a wide range of research topics available. We employ more than 450 academic staff, all experts in their field. This means you’ll find deep knowledge and expertise in your chosen area – mentors who think creatively about complex issues, and who’ll help you achieve your research goals.
Diversity is important to us. We promote an inclusive culture, with strong female representation and an increasing number of Indigenous students and staff. More than half of the Faculty's students come from overseas.
Together, we’re driven by a passion to keep finding solutions to the biggest challenges of our time.
Explore our research
As a graduate researcher in FEIT, you can pursue exciting opportunities in a range of projects .
Our priority research disciplines are:
- AI, data science and robotic: Artificial intelligence, big data and robotics are disrupting the world. No one will remain untouched by this evolving technology. FEIT is collaborating across the University to pursue multidisciplinary research opportunities. This includes working with the Melbourne Centre for Data Science in the Faculty of Science.
- Smart and sustainable development: At a global level, we need more efficient and sustainable use of resources. FEIT research focuses on energy, water distribution, food production and smart infrastructure.
- Health technologies: Expertly designed technologies can ha ve significant impact on global health and wellbeing. FEIT is working with health professionals and patients to deliver healthier communities.
- Defence technologies: our increasingly complex world requires excellent security and cybersecurity technologies. FEIT is a national leader in defence research.
We also offer research innovation programs by sector. Work on real-life business challenges with our industry partners in these areas:
- Transport : creating more sustainable transport systems and more liveable cities.
- Defence : keeping Australians safe in a complex security landscape.
- Water, environment and agriculture : securing vital natural resources for future generations.
- Sustainable mining : improving sustainability in the mining sector.
- Food and agribusiness : assisting food businesses with new products and processes.
- Infrastructure : meeting the infrastructure needs of rapidly growing urban populations.
- Energy : understanding how to transition to a clean energy system.
- Cybersecurity : detecting cyberattacks and governing cyber operations.
Depending on your research theme, you’ll be aligned with one of three schools within FEIT:
- School of Computing and Information Systems
- School of Chemical and Biomedical Engineering
- School of Electrical, Mechanical and Infrastructure Engineering
Research centres and institutes
FEIT is home to more than 20 major research centres and institutes . These organisations represent joint ventures between universities, industry and government bodies in Australia. Each centre offers its own research opportunities.
Learn how we're making a difference
- We’re exploring the flaws and ethics of AI . The Biometric Mirror raises awareness about the social implications of unrestricted AI. It takes your photo, then analyses it to identify your demographic and personality characteristics.
- We’re using your smart phone as your therapist . Technology is changing how we interact. It’s now being used to offer digital mental healthcare.
- We’re predicting traffic in real-time . Sophisticated AI can predict traffic congestion up to three hours in advance, to help commuters and freight companies plan their journeys and support traffic signal optimisation.
- We’re designing ‘sight’ for computers . Computer vision research is developing algorithms that can efficiently recognise objects and surroundings, for use in applications like autonomous vehicles.
Collaborate with other disciplines
We work across disciplines to address big, bold questions. Collaborating with researchers from other disciplines leads to creativity and innovation. Depending on the topic, you might work with experts in design, economics or health.
Interdisciplinary initiatives and institutes that relate to engineering include:
- Creativity and Wellbeing Hallmark Research Initiative
- Centre for Artificial Intelligence and Digital Ethics
- ARC Training Centre in Cognitive Computing for Medical Technologies
Graduate researchers also have access to many other interdisciplinary research opportunities across the University, including PhD Programs .
Partner with an overseas institution
Our international joint PhD opportunities allow you to access expertise, training and resources from two institutions, and spend a minimum of 12 months studying overseas.
Some of our joint PhD projects have included:
- A study into machine learning for second language acquisition , with the Hebrew University of Jerusalem (Israel).
- A study into trustworthy and insightful algorithms for industrial decision making , with KU Leuven (Belgium).
Explore more fully-funded joint PhD projects .
Work in a stimulating environment
Melbourne connect.
Melbourne Connect is a purpose-built, innovation precinct at our Parkville campus. It encourages collaboration between the disciplines of science, technology, engineering and mathematics, and redefines how businesses, researchers, governments and entrepreneurs work together to drive digital solutions.
- Search for a supervisor in your field of research
- Learn more about completing a Doctor of Philosophy - Engineering and IT.
- Learn more about completing a Master of Philosophy - Engineering and IT .
- Find out more about how to apply .
- Explore the School of Computing and Information Systems website to learn more about engineering and IT research.
- Read about the latest research findings IT and Computer Science .
Information Technology and Computer Science
Study information technology & computer science
Register to receive information on graduate study, scholarships, key dates, upcoming events and what it's like to study with us.
Revolutionise business, entertainment and health with IT.
Explore how the latest advancements in AI and cybersecurity are impacting the world by pairing creative thinking and practical application with science and engineering. Benefit from a curriculum designed by world-leading experts and aligned with the industry.
Access internships and industry projects, and develop the technical and professional skills that will keep you agile in a constantly evolving industry. Whether it’s big data, cloud computing, software engineering or bioinformatics, you’ll be equipped to work in all types of settings – from your own start-up to multinationals, and not-for-profits.
Our programs
Master of information technology.
Create technical solutions and drive success in business, government, health, entertainment and society. Choose your specialisation and an elective track to hone your expertise in a field that matches your interests. 2 years full time or 4 years part time, on campus (Parkville).
Master of Information Systems
Prepare yourself for a dynamic career in IT management and digital business. Explore topics such as database systems, organisational processes, app development, consulting, business analysis, emerging technologies, IT strategy and governance. 2 years full time or 4 years part time, on campus (Parkville).
Master of Software Engineering
Acquire the best practice knowledge of every stage of the software development cycle from design and engineering to deployment. You can specialise in ‘Artificial Intelligence’, ‘Business’, ‘Cyber Security’, ‘Distributed Computing’ or ‘Human Computer Interaction’. 6 years part time or 3 years full time, on campus (Parkville).
Master of Computer Science
Gain specialist skills in at least one area of knowledge systems, programming languages and distributed computing, information systems, mathematics/statistics, spatial information science or linguistics. 2 years full time or 4 years part time, on campus (Parkville).
Master of Data Science
Tailor the course to suit your interest in a specialist area of data science. Gain the technological and analytical abilities that are vital for managing and interpreting large, complex collections of data. 2 years full time or 4 years part time, on campus (Parkville).
Other courses
Gain new skills in programming, designing online solutions and developing web applications. 1 year full time or 2 years part time, on campus (Parkville).
Gain programming/maths experience with the equivalent of a major in Computer Science at undergraduate level. 6 months full time or 1 year part time, on campus (Parkville).
The course provides a pathway to the Master of Computer Science and provides the knowledge of undergraduate level computer science. 1 year full time or 2 years part time, on campus (Parkville).
An ideal starting point for those interested in joining the booming data science industry and who do not have a background in computer science or statistics. 1 year full time or 2 years part time, on campus (Parkville).
Why study with us?
- The University of Melbourne is ranked # 1 in Australia for Computer Science and Information Systems
- The School of Computing and Information Systems is an international research leader in computer science, information systems and software engineering. This world class research is the basis of the course curriculum.
- Ranked #8 for graduate employability
Times Higher Education World University Rankings 2022 / QS Graduate Employability 2022
Study Engineering and IT: Information Session
Monday 20 May, Melbourne Connect Learn more about studying engineering and IT at the University of Melbourne. Join us for an in-person information session and Q&A.
Looking for personalised advice?
Find out more about our graduate degrees and get support with your application. Speak to our expert staff online, via phone or at an upcoming event. You can also register to learn more about your course options and opportunities via email.
I chose to study the Master of Information Systems because of its flexibility and it gives me the opportunity to pursue many different career paths. The course work helps students grasp the complexity of real-world applications of information systems in a variety of industries and since I’m interested in UI and UX, I wanted the opportunity to choose electives within the course so I could explore my interests. April Xu Master of Information Systems
School of Computing and Information Systems
Artificial intelligence.
Artificial intelligence research is a particular strength in the School of Computing and Information Systems. Our researchers address many different approaches to AI, encompassing deep learning, data mining, machine learning, natural language processing, and agent-based systems. Our theories and techniques can be applied to a wide range of practical problems, including cyber-security, health, finance and government.
Melbourne is also host to the IBM and ARC Training Centre for Cognitive Computing for Medical Technologies , which is furthering our research and translating into solving critical needs in the domains of medicine and health.
Group leader
Research Themes
Artificial intelligence assurance lab.
Our research explores ethical, regulatory and legal issues relating to Artificial Intelligence. We validate AI technologies with respect to quality, safety, privacy, and reliability.
AI and Autonomy Lab
Intelligent systems can act (semi-)autonomously, working alongside human experts to analyse and help solve complex challenges. Designing good agents needs to balance the ability of agents to work effectively with other agents and human experts to have the maximum real-world impact.
Natural Language Processing
Natural Language Processing (NLP) is a field of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language. We develop algorithms and computational models to analyze and process large volumes of natural language data in order to extract meaning and insights from text, speech, and other forms of human communication.
- Digital Health
Digital health refers to the use of data and technology, such as mobile apps, wearables, and other digital tools, to improve healthcare delivery, patient outcomes, and overall health and wellness. We work in several areas of digital health and integrate data, analytics, and communication technologies to help healthcare providers and patients manage and prevent diseases, track health metrics, and improve healthcare access and efficiency.
| First Name | Last name | Position | Profile | |
|---|---|---|---|---|
| Naveed | Akhtar | Senior Research Fellow, Decra | ||
| Uwe | Aickelin | Professor and Head of School | ||
| Basim | Azam | Research Fellow | ||
| James | Bailey | Professor | ||
| Tim | Baldwin | Professor | ||
| Khuyagbaatar | Batsuren | Research Assistant in Natural Language Processing | ||
| Michelle | Blom | Senior Lecturer | ||
| Renata | Borovica-Gajic | Lecturer | ||
| Nestor | Cabello | Research Fellow in Artificial Intelligence | ||
| Daniel | Capurro | Senior Lecturer | ||
| Yanchuan | Chang | Postdoctoral Research Fellow | ||
| Brian | Chapman | Associate Professor | ||
| Simon | Coghlan | Senior Lecturer in Digital Ethics | ||
| Trevor | Cohn | Professor | ||
| Michael | Conway | Senior Lecturer | ||
| Andrew | Cullen | Research Fellow | ||
| Simon | D'Alfonso | Lecturer | ||
| Ting | Dang | Senior Lecturer | ||
| Tom | Drummond | Melbourne Connect Chair of Digital Innovation for Society | ||
| Kris | Ehinger | Senior Lecturer | ||
| Lea | Frermann | Lecturer | ||
| Nicholas | Geard | Associate Professor | ||
| Mohammad | Golzarijalal | Research Fellow - Modelling & Optimisation | ||
| Caren | Han | Senior Lecturer in Digital Innovation | ||
| Eun-Jung | Holden | Professor of Digital Innovation | ||
| Jean | Honorio | Senior Lecturer in Machine Learning | ||
| Eduard | Hovy | Professor in Digital Innovation | ||
| Curtis | Huang | Research Fellow | ||
| Shanika | Karunasekera | Professor | ||
| Kemal | Kurniawan | Research Fellow | ||
| Christine | de Kock | Associate Lecturer in NLP / Human Language Technology | ||
| Michael | Kirley | Professor | ||
| Lars | Kulik | Professor | ||
| Kemal | Kurniawan | Research Fellow | ||
| Jey Han | Lau | Lecturer | ||
| Chris | Leckie | Professor | ||
| Sze | Leung | Research Assistant | ||
| Jinhao | Li | Research Assistant | ||
| Chunhua | Liu | Research Fellow in Empirical Software Engineering | ||
| Feng | Liu | Lecturer | ||
| Nir | Lipovetzky | Senior Lecturer | ||
| Ling | Luo | Lecturer | ||
| Mansoureh | Maadi | Research Fellow - Digital, The ARC Digital Bioprocess Development Hub | ||
| Neil | Marchant | Research Fellow | ||
| Julian | Marx | Lecturer | ||
| Sarah | Monazam Erfani | Arc Decra Fellow | ||
| Bastian | Oetomo | Postdoctoral Research Fellow | ||
| Yulia | Otmakhova | Research Fellow, NLP/Media Bias | ||
| Giulio | Passerotti | Research Fellow | ||
| Adrian | Pearce | Professor | ||
| Batugahage Kushani Anuradha | Perera | Postdoctoral Fellow in Social Media Analytics | ||
| Jianzhong | Qi | Senior Lecturer | ||
| Sarita | Rosenstock | Lecturer | ||
| Ben | Rubinstein | Professor | ||
| Simone | Schmidt | Research Associate in Digital Mental Health | ||
| Long | Song | Research Fellow | ||
| Fangziyun | Tong | Research Assistant in Digital Mental Health | ||
| Douglas Eduardo | Valente Pires | Associate Professor | ||
| Ekaterina | Vylomova | Lecturer | ||
| Joseph | West | Research Fellow | ||
| Zesheng | Ye | Postdoctoral Research Fellow in Trustworthy Machine Learning | ||
| Cameron | Zachreson | Research Fellow | ||
| Ying | Zhao | Research Fellow |
Artificial Intelligence Graduate Researchers
| Given | Family name | Profile | Thesis Title |
|---|---|---|---|
| David | Adams | Automatic Insights: Database management in the era of data deluge | |
| Abeer | Alshehri | Explanation of goal recognition systems | |
| Hanan | Alsouly | Dynamic many-objective optimisation using evolutionary algorithms | |
| Jiayang | Ao | Explainable computer vision algorithms | |
| Vincent | Barbosa Vaz | Predict+optimise through time | |
| Lyndon | Benke | Deceptive planning in multiagent environments | |
| Uri | Berger | Interactive multimodal language acquisition | |
| Maria | Bulychev | TBA | |
| Daniel | Cabrera Lozoya | Augmenting clinical mental health practice with machine learning models and digital phenotyping insights | |
| Chengyi | Cai | Foundation-model reuse via model reprogramming | |
| Meng | Cai | Enhancing Transformer Models: A Study of Information Bottleneck Theory for Efficient Representation Learning | |
| Nathaniel | Carpenter | Towards a therapist's AI - Building a real-time support system for clinicians engaging in therapeutic conversations via online chat sessions | |
| Wachiraphan | Charoenwet | Pragmatic use of CodeQL in the context of code review | |
| Liuliu | Chen | Understanding Internalized Stigma in People with Mental Health Conditions on Social Media: Manifestations, Influencing Factors, and Impacts | |
| Ming-Bin | Chen | Interactive agents for combating misinformation | |
| Shaona | Cheng | Agitation States Computing for Tailored Music Recommendation | |
| Damian | Curran | Towards a unified narrative representation for computational understanding of narrative text | |
| Sayantan | Dasgupta | Energy-Aware Machine Learning | |
| Dinuka | de Zoysa | Discovering Insights through Intent Prediction and Guided Exploration | |
| Roben | Delos Reyes | Modelling the impact of pandemics on health care services | |
| Nadeeshan Dananjaya Kumara | Dissanayake Mudiyanselage | Intelligent Intersection Management for Improved Traffic Safety | |
| Songlin | Du | Collaborative Human-ML Decision Making for Medical Diagnostics: an ICP-based approach | |
| Yilin | Geng | Character-centric Story Understanding and Generation | |
| Chenhao | Gu | Reconstructing Social Influence Networks and Tracking Opinion Dynamics on Social Media | |
| Christopher | Guest | TBA | |
| Matteo | Guida | Towards Multilingual Models of Framing Detection in News Coverage of Contemporary Polarizing Issues | |
| Yiyi | Guo | Transfer learning driven weather prediction | |
| Tom | Harris | Multiscale modelling of infectious disease systems | |
| Afsaneh | Hasanebrahimi | Robust Anomaly Detection | |
| Haitian | He | Video anomaly detection in crowded scenes at multi-timescale | |
| Mitra | Heidari | Modelling and optimization of bioprocess systems | |
| Nimeshika Udayangani | Hewa Dehigahawattage | A Pixel-level Unsupervised Approach to Image Anomaly Detection | |
| Markus | Hiller | Constraining learning methods through geometry and causal reasoning | |
| Guang | Hu | Explainable agency using epistemic planning | |
| Sukai | Huang | Texted based reinforcement learning agent | |
| Zhuoqun (Calvin) | Huang | Adaptive data analysis for principled data reuse | |
| Demian Aaron | Inostroza Amestica | Explaining cross-lingual variation in grammatical case systems through Information-theoretical approaches | |
| Dasun Oshada | Jayasinghe | Real time digital twins for intelligent transportation systems | |
| Fan | Jiang | Retriever-augmented Approaches for Natural Language Processing | |
| Weiwei | Jiang | Developing interactive systems using Near-Infrared Spectroscopy | |
| Yanbei | Jiang | Abstract Visual Reasoning with Text Explanation | |
| Anirudh | Joshi | Determining argument quality and reasoning proficiency using deep neural networks | |
| Zaher | Joukhadar | Autonomous and robust AI in low-resource settings | |
| Jemima | Kang | Using NLP to determine semantic drift in mental health discourse | |
| Saumya Hansanie | Karunadhika | Development of a Novel Algorithm for Motif Discovery and Anomaly Detection in Medical Time Series Data from Ventilators | |
| Dimuthu Lakmal | Kariyawasan Jalath Thanthrige | A Multi-Modal Approach For Fake News Detection On Social Media | |
| Oliver | Kim | Task planning in mobile robots using common sense as a generative model | |
| Fajri | Koto | Neural Language Model for Abstractive Text Summarisation | |
| Mojgan | Kouhounestani | Modification of the temporal data embedded in electronic medical records dataset targeting cancer prediction using machine learning methods | |
| Robert | Langtry | Machine learning for automated generation of validated infra-red signature models | |
| Thao | Le | Explaining the Confidence in AI-Assisted Decision Making | |
| Thi Xuan May | Le | Shapelet Transformer for Time Series and Its Applications in Healthcare | |
| Chao | Lei | Automated Monitoring of Green Infrastructure in Melbourne | |
| Craig | Lewis | Entrepreneurial initiatives and team collaboration: Effects of modes of behaviour on start-up team effectiveness and success | |
| Haopeng | Li | Multimodal Video Understanding Based on Deep Learning | |
| Jinhao | Li | Deep hypothesis testing and its applications in rule-based recommendations | |
| Miao | Li | Neural Multi-document Modelling and Abstractive Summarization | |
| Muxing | Li | Trustworthiness in Using Foundation Models | |
| Yuhao | Li | Robust and explainable medical data mining and its application | |
| Zuqing | Li | Query Constraint-Aware Tabular Data Generation | |
| Ran | Liang | Spatio-temporal Traffic Prediction Using Deep Learning Models | |
| Zheng Wei | Lim | Cross-lingual Psycholinguistics with NLP methods | |
| Hong Yi | Lin | Using Neural Machine Translation Approaches to Automate Code Improvements for Code Reviews | |
| Chang | Liu | Personalized Outlier Detection for Time Series Using XGBoost and Deep Learning Algorithms | |
| Shijie | Liu | Enhancing adversarial defence via robust statistics and certified robustness | |
| Yuansan | Liu | Robust AI for guidance of a cochlear implant procedure | |
| Xueqi | Ma | Adversarial Learning based on Manifold Learning and Graph-based learning | |
| Aso | Mahmudi | Developing Advanced Processing Tools for Under-Resourced Languages Focusing on Morphological Features | |
| Isura | Manchanayaka | Identifying Coordination and Influential People in Social Media and Their Intentions | |
| Kevin | McDonald | Enhancing reinforcement learning algorithms for team-based systems | |
| Raphaël | Merx | Enhancing Machine Translation for Medical Education: A Study on Nursing Students in Timor-Leste | |
| Behzad | Moradi | Enhanced Bilevel Optimization via Machine Learning | |
| Suhail | Najeeb | Explaining Vision Transformers for Small Object Detection | |
| Abir | Naskar | Identifying Adolescent Risk-taking Behaviours from Clinical Text | |
| Thye Shan | Ng | Modular Multimodal Architectures for Plug-and-Play Applications | |
| Paul | Ou | Data-Driven Methods for Modelling, Optimising and Validating Digital Bioprocess Development | |
| Zhihao (Hardy) | Pei | Reinforcement Learning for robust decision-making under deep uncertainty | |
| Anh | Pham | Measuring and improving the usability of agent-based models for infectious disease modelling | |
| Tien Dung | Pham | A Robust Datamining Approach to Cell Culture Media Optimisation | |
| Marzieh | Parvaneh Akhteh Khaneh | Designing online learning environment based on peer feedback | |
| Priyanka | Pillai | Understanding the impact of population heterogeneity on the computational modelling of STIs | |
| Pagnarasmey | Pit | Unlearnable Text and Protecting Digital Data from Exploitation | |
| Pagnarith | Pit | Macro-economic policy investigation through agent-based modelling | |
| Yiyuan (Gracie) | Pu | Literature-based discovery for Alzheimer's disease | |
| Viktoria | Schram | Performance Prediction for Low Resource Scenarios | |
| Rinu Ann | Sebastian | Explainable computer vision to improve human-machine interaction | |
| Prabodi | Senevirathna | Quantifying and mitigating machine-learning induced overdiagnosis in sepsis | |
| Xinling | Shen | Dynamic Indoor Evacuation Planning in an Emergency | |
| Zewei | Shi | Multimodal Large Language Model for Digital Trust | |
| Jiajia | Song | What Makes AI Planning Hard? From Complexity Analysis to Algorithm Design | |
| Yige | Song | Student learning profile - The exploration, interpretation and feedback provision on students’ online learning behaviours | |
| Xinyu | Su | Traffic prediction using graph neural networks | |
| Fengze | Sun | Effective and Efficient Algorithms for Region Similarity Queries | |
| Sing Chee | Tan | Improving Rapid Response Systems through Predictive Analytics | |
| Gisela | Vallejo | A Fair Plan Towards Mitigating Bias and Misinformation | |
| Sameera | Vithanage | Towards Understanding User Perceptions of Fake News on Social Media | |
| Hung Thinh | Truong | Evidence extraction from the clinical trials literature | |
| Archana | Vadakattu | Dynamic learning in cognitive agent architectures | |
| Chen | Wang | Inverse Reinforcement Learning Approach for intent inference in adversarial environments | |
| Chenyang | Wang | Extending matrix profile with soft comparison operators and weighting functions for time series anomaly detection | |
| Dalin | Wang | Image captioning with conditional-GAN | |
| Jinxi | Wang | Experimental Design and Performance Optimisation of Monoclonal Antibody Production in Bioreactors under Insufficient Data Based on Machine Learning | |
| Jun | Wang | Adversarial machine learning for machine translation | |
| Tingxuan | Wang | Modelling 3D Shape of Sands from X-ray Computed Tomography Images | |
| Yuxiang | Wang | NeuralDB: Unleashing the Power of Neural Networks in Database Management Systems for Intelligent Data Processing | |
| Helani | Wickramaarachchi | An efficient, autonomous reward shaping algorithm with evolutionary game theory: applied to the field of multi-agent systems | |
| Zhuohan | Xie | Hierarchically structured neural narrative generation | |
| Rui | Towards Explainable Fact Checking | ||
| Aotao (John) | Xu | A computational analysis of conceptual combination through time | |
| Diana | Yang | Robust data mining | |
| Jie | Yang | Save you from irrational decisions: a responsive agent affected by user emotions | |
| Jinrui | Yang | Fairness and Bias in Natural Language Processing | |
| Shuo | Yang | Multimodal Commonsense Knowledge Distillation for Visual Question Answering | |
| Aryan | Yazdan Parast | Improving robustness of machine perception by intervention | |
| Meng Abigail | Yuan | Modalities and measurement of systems for information discovery | |
| Canaan | Yung | Adversarial attacks on generative AI models | |
| Jiacheng | Zhang | Enhancing trustworthiness and robustness of machine learning models in adversarial environments | |
| Tianyi | Zhang | Digital phenotyping (personal sensing) for mental health and well-being | |
| Zhenkai | Zhang | To solve the current problems of NeRF and to explore the application of NeRF in robotic vision or automatic drive | |
| Johnson | Zhou | Applied Deep Learning Control for Neurological Disease Models | |
| Zhijian | Zhou | Data-adaptive Non-parametric Hypothesis Testing | |
| Anqi | Zhu | Action recognition with explanation | |
| Rongxin | Zhu | Automatic summarization for multi-party conversation | |
| Farzaneh | Zirak | Using Eye-Tracking Technology to Improve Visual Data Exploratory Tools Performance |
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Make your own research contribution with the Doctor of Philosophy (Engineering and IT) at Australia’s leading university*. Build your expertise in a specialist area and be supported by experienced supervisors and advisory committees to create significant change in society.
The School of Computing and Information Systems is an international research leader in computer science, information systems and software engineering. We are focused on delivering impact in the following key areas: Research themes. Artificial intelligence.
We are working on novel and practical solutions to improve the security and privacy of large real-world systems. Our research includes both attack and defense approaches that work on different layers, from web APIs, ML models, core software libraries to micro-architecture, firmware and hardware.
Explore the ways in which you can undertake research in IT and Computer Science at the University of Melbourne and read through the unique benefits.
Master of Computer Science. Gain specialist skills in at least one area of knowledge systems, programming languages and distributed computing, information systems, mathematics/statistics, spatial information science or linguistics. 2 years full time or 4 years part time, on campus (Parkville).
Our researchers address many different approaches to AI, encompassing deep learning, data mining, machine learning, natural language processing, and agent-based systems. Our theories and techniques can be applied to a wide range of practical problems, including cyber-security, health, finance and government.