Annual Event

Information Resilience PhD School 2022

Thank you to all our delegates and speakers who participated in the CIRES inaugural Information Resilience PhD School!

12 September, 2022 - 14 September, 2022

UQ Global Change Institute, Building 20, Staff House Road, The University of Queensland

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Thank you to all our delegates and speakers who participated in the CIRES inaugural Information Resilience PhD School!

Congratulations to our Prize Winners

3MT first place (session 1a) Rm 275
Sandy Spiers (Curtin University) – Maintenance Optimisation for Network Assets
Comment: A well-packaged 3MT presentation that reveals an exciting PhD journey.

3MT first place (session 1b) Rm 273
Tessa Faulks (Monash University) – Improving Nitrogen Use Efficiency (NUE) from Urea through the Development of a Novel Coating
Comment: Nicely engaged with the audience and presented the research idea in a clear and logical way. The poster is a professional visual aid that allows the audience to easily follow along and sets out a solid structure.

3MT runner up (session 1a)
Nazym Baizhiyen (University of Adelaide) – Measuring and Monitoring Particle Size Distributions to Divert Low Value Waste
Comment: Good use of intuitive examples, which is tremendously helpful for people outside the domain to grab the core idea of research.

3MT runner up (session 1b)
Hechuan Wen (The University of Queensland) – Run-time Causal Inference under Domain Shift
Comment: Hechuan delivered the presentation very confidently. He used simple words to clarify the research motivation and highlight the innovation efficiently and effectively. Well done!

3MT people’s choice (session 1a)
Jinchun Du (Monash) “Ultrafast Euclidean Pathfinding using Hub Labeling”

3MT people’s choice (session 1b)
Clement Lartey (UniofSA) “Outlier detection in flotation sensor information

Poster Session first place
Yiheng Hu (University of New South Wales) – 3D Point Cloud Multiview Registration
Comment: Great layout and clear communication of the core technical novelty.

Poster Session runner up
Xin Zheng (Monash University) – Multi-relational Graph Neural Architecture Search
Comment: A clear, coherent, concise poster that motivates viewers to search for the paper to read.

Poster Session people’s choice
Yerniyaz Abildin (UofAdelaide) “Constraints and Quantifying Uncertainty on Resource Domain Boundaries”


Who should attend?  

Are you an Australian-based PhD candidate, or planning to commence a PhD in Australia? Is your research data oriented? Are you aiming to address any of the following challenges?

  • Low data quality, i.e., how to handle issues like missing entries, false information, lack of samples, class imbalance? 
  • Limited insights obtained from data, i.e., how can we maximize the value of “big data” with advanced techniques? 
  • Unknown reliability and trustworthiness of data analysis tools, i.e., is the automated decision process transparent and interpretable? 
  • Anything that hinders resilient application of your research, e.g., ethics, privacy, cyber security, generalisability, efficiency, adoption etc. 

If yes, then the Information Resilience PhD School is the right place for you!

The Information Resilience PhD School offers a 3-day program from 12 September to 14 September at the St Lucia Campus of The University of Queensland for current or prospective PhD students interested in exploring and understanding cutting-edge data science practices, pathways towards academic or industry careers, and prospects of next-generation research in the field. The Information Resilience PhD School brings research students from Australian universities together with leading national and international researchers to enhance and invigorate research education, offering an opportunity to share and discuss research with both peers and mentors.  

What does the Information Resilience PhD School deliver? 

Knowledge sharing and acquisition in data-driven research. A series of interactive activities including a keynote, tutorial, open discussion, and poster session will be hosted. Internationally renowned scholars will present techniques that enable, advance, and shape many research areas and applications today. These activities will cover the following topics (selectively listed): 

  • Data management and engineering 
  • Data Profiling 
  • Information retrieval and predictive analytics 
  • Data privacy and security

Mentorship from both academia and industry. The Information Resilience PhD School will feature industry leaders and world-class researchers sharing their experience in digital transformation, stories of data-driven R&D practices, and visions on next-generation information technologies. The dedicated activities include: 

  • Special Guest International Speakers & Topics
  • Roundtable sessions hosted by industry leaders and academics 
  • Mentoring and advising on research and professional development 
  • Experience sharing by recently graduated PhDs

Social networking. The Information Resilience PhD School offers opportunities to engage with guest speakers, build connections with peers with common research interests and establish interdisciplinary collaborations with experts across domains. Our social networking events include: 

  • Interactive poster and presentation sessions with awards 
  • Guided tour 
  • Banquet 

Important Dates 

  • 7 August 2022: Applications Due – closed
  • 15 August 2022: Successful Applicants Notified 
  • 12-14 September 2022: Information Resilience PhD School 2022

Link to Information Resilience PhD School Flyer

  • Dr Divesh Srivastava

    AT&T Labs Research, USA

    Keynote: Exploring and Analyzing Change – The Janus Project


    Abstract: Data change, all the time.  The Janus project seeks to address the Variability dimension of Big Data by modeling, exploring, and analyzing such change, providing valuable insights into the evolving real world and the ways in which data about it are collected and used. We start by identifying technical challenges that need to be addressed to realize the Janus vision. Towards this end, we have extracted and worked with the histories of various structured datasets, including DBLP, IMDB, open government data, and Wikipedia, for which a detailed history of every edit is available.  Our DBChEx (Database Change Explorer) prototype enables interactive exploration of data and schema changes, and we show how DBChEx can help users gain valuable insights by exploring two real-world datasets, IMDB and Wikipedia infoboxes.

    Based on an analysis of the history of 3.5M tables on the English Wikipedia for a total of 53.8M table versions, we then illustrate the rich history of structured Wikipedia data: we show that tables are created in certain locations, they change their shape, they move, they grow, they shrink, their data change, they vanish, and they re-appear; indeed, each table has a life of its own.  Finally, to help automatically interpret the useful knowledge harbored in the history of Wikipedia tables, we present recent results on two technical problems: (i) identifying Natural Keys, a particularly important piece of metadata, which serves as a primary key in tables over time and consists of attributes inherent to an entity, and (ii) matching tables, infoboxes and lists within a Wikipedia page across page revisions.  We solve these problems at scale and make the resulting curated datasets available to the community to facilitate future research. This is joint work with Tobias Bleifuß, Leon Bornemann, Dmitri Kalashnikov, and Felix Naumann.

    Bio: Divesh Srivastava is the Head of Database Research at AT&T. He is a Fellow of the ACM, the President of the VLDB Endowment, co-chair of the ACM Publications Board, and on the Board of Directors of the Computing Research Association. He has served as PC co-chair of many international conferences including SIGMOD 2021, VLDB 2020 (Industrial), SIGMOD 2020 (Industrial), and ICDE 2019. He has presented keynote talks at several international conferences, and his research interests and publications span a variety of topics in data management. He received his Ph.D. from the University of Wisconsin, Madison, USA, and his Bachelor of Technology from the Indian Institute of Technology, Bombay, India.

  • Dr Miao Xu

    The University of Queensland

    Keynote: Weakly Supervised Machine Learning: What, Why and How

    Abstract: Typical machine learning techniques usually assume data are fully-supervised, i.e., the labels provided are perfect. In practice, data can be weakly supervised in a variety of different forms since the labels provided can be imperfect, incomplete, or inaccurate. The first part of the talk will give a brief description of weakly supervised machine learning. Several different problem settings such as partial labels, complimentary labels, and noisy labels will be introduced.  In the second part of the talk, a powerful method named co-teaching will be elaborated on by giving the principle to reduce the impact the noisy labels and the empirical studies on benchmark datasets.
    Bio: Dr Miao Xu is a lecturer at the University of Queensland and a visiting scientist at RIKEN Japan. Her research focuses on machine learning from imperfect data and has got multiple publications in top-tier international conferences and journals. She got the CAAI Outstanding Dissertation Award in 2019.

  • Professor Felix Naumann

    Hasso Plattner Institute (HPI), Germany

    Tutorial: Data Profiling – Extracting Metadata from Tables


    Abstract: Data profiling comprises a broad range of methods to efficiently extract various metadata from a given dataset, including data types and value patterns, keys and foreign keys, and various other data dependencies. This research area has recently thrived, due to (i) its simple problem statements, such as “discover all key candidates”, paired with the high computational complexity of the problems, (ii) the manifold opportunities for algorithmic improvements, such as apriori-inspired pruning or data sampling, and (iii) the various application areas for data profiling results, such as query optimization and data cleaning. Accordingly, the tutorial-style talk will be divided into three parts. After a motivation and overview of the field covering some basic data structures and methods, we will regard several concrete dependency discovery algorithms. Finally, we will highlight several application areas for data profiling results, including information integration, data cleaning, and query optimization.

    Bio: Felix Naumann studied mathematics, economy, and computer sciences at the University of Technology in Berlin and completed his PhD thesis in the area of data quality at Humboldt University of Berlin in 2000. After a PostDoc position at the IBM Almaden Research Center working on data integration topics, he became assistant professor for information integration, again at the Humboldt-University of Berlin in 2003. Since 2006 he holds the chair for Information Systems at the Hasso Plattner Institute (HPI) at the University of Potsdam in Germany. He has been visiting researcher at QCRI, AT&T Research, IBM Research, and SAP. His research interests include data profiling, data quality and cleansing, and data integration, recorded in over 200 scientific publications. Next to numerous PC memberships for international conferences, he has organized several conferences in various roles, including VLDB 2021 as PC co-chair, and he is trustee of the VLDB Endowment. More details are via this link.

  • Professor Wenjie Zhang

    University of New South Wales

    Tutorial: Heterogeneous Graph Processing – Applications, Challenges & Solutions


    Wenjie Zhang is a Professor, ARC Future Fellow, Head of the Data and Knowledge Research Group (https://unswdb.github.io/) and Deputy Head of School (Research) in the School of Computer Science and Engineering, the University of New South Wales, Australia. Her research interests include spatial-temporal data analysis, uncertain data analysis and graph data processing. Since 2008, she has published more than 160 papers in top venues such as TKDE, TODS, VLDBJ, SIGMOD, VLDB, and ICDE. She received the Australian Research Council Future Fellowship (FT3) in 2021 and Discovery Early Career Researcher Award in 2011. In 2019, she received the Chris Wallace Award for her significant contributions to large-scale graph data processing. Her research has been well supported by 12 Australian Research Council grants and several industry funded projects. Wenjie is an Associate Editor for IEEE Transactions on Knowledge and Data Engineering and The VLDB Journal. The full list of her publications (and presentation slides) : http://www.cse.unsw.edu.au/~zhangw/

  • Associate Professor Gianluca Demartini

    The University of Queenslad

    Tutorial Practical Session: Crowd Sourcing


    Dr. Gianluca Demartini is an Associate Professor in Data Science at the University of Queensland, Australia. His main research interests include Information Retrieval, Semantic Web, and Human Computation. His research is currently funded by the Australian Research Council, Facebook, and Google. He received Best Paper awards at the AAAI Conference on Human Computation and Crowdsourcing (HCOMP) in 2018, at the European Conference on Information Retrieval (ECIR) in 2016 and 2020, and the Best Demo award at the International Semantic Web Conference (ISWC) in 2011. He has published more than 150 peer-reviewed scientific publications including papers at major venues such as WWW, ACM SIGIR, VLDBJ, ISWC, and ACM CHI. He is an ACM Senior Member, ACM Distinguished Speaker, TEDx speaker, and CIKM 2021 General co-chair.

    Before joining The University of Queensland, he was Lecturer at the University of Sheffield in UK, post-doctoral researcher at the eXascale Infolab at the University of Fribourg in Switzerland, visiting researcher at UC Berkeley, junior researcher at the L3S Research Center in Germany, and intern at Yahoo! Research in Spain. In 2011, he obtained a Ph.D. in Computer Science at the Leibniz University of Hanover focusing on Semantic Search.

  • Dr Lei Han

    The University of Queensland

    Tutorial Practical Session: Crowd Sourcing


    Lei Han is currently a postdoctoral research fellow with The University of Queensland (UQ), Brisbane, Australia. His main research interests include human computation, crowdsourcing, user behavior, data mining and analysis, and information retrieval. Before joining UQ in 2018, he had been working in the software sector for eight years. He is currently working on understanding the human factors in crowdsourced data, and on building human-in-the-loop machine learning algorithms for data quality discovery.(Based on document published on 14 February 2022).

  • Dr Fred Roosta-Khorasani

    The University of Queensland

    Speaker: The ups and downs of a PhD Journey


    Fred is an ARC DECRA Fellow and Lecturer in the School of Mathematics and Physics, The University of Queensland. He is also an Associate Investigator in the ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) and Distinguished Research Scholar at the International Computer Science Institute (ICSI) Berkeley, USA.

  • Junliang Yu

    The University of Queensland

    Speaker: The ups and downs of a PhD Journey


    Junliang Yu is currently undertaking his PhD at The University of Queensland and aims to submit his thesis by the end of 2022.  He has published over 10 peer-reviewed papers in the most prestigious conferences including the Conference on Knowledge Discovery and Data Mining (KDD), World Wide Web (WWW), IEEE’s International Conference on Data Mining (ICDM), AAAI, the Conference on Information and Knowledge Management (CIKM), and journals including IEEE’s Transactions on Knowledge and Data Engineering (TDKE) and the International Journal on Very Large Data Bases (VLDBJ). Junliang’s research interests are data mining and machine learning, with a particular focus on recommender systems, tiny machine learning, and self-supervised learning.

  • Associate Professor Hassan Khosravi

    The University of Queensland

    Facilitator: Interactive session on Data Literacy


    Dr Hassan Khosravi is a CIRES Chief Investigator and Theme Leader, and Senior Lecturer in Data Science and Learning Analytics at The University of Queensland. As a computer scientist by training, he is passionate about the role of artificial intelligence in the future of education. In his research, he draws on theoretical insights from learning science and exemplary techniques from the fields of human-computer interaction, learning analytics and explainable AI to design, implement, validate and deliver technological solutions that contribute to the delivery of learner-centred, data-driven learning at scale. His past research and publications have addressed a number of diverse topics such as learning graphical models, statistical-relational learning, social network analysis, cybersecurity and game theory.

  • Shane Downey

    Allianz Partners Australia

    Panel: Research Careers in Industry and Government – with CIRES Partner Investigators


    Shane Downey is the General Manager, Enterprise Data Management at Allianz Partners Australia. He has made significant contributions to a variety of organisations over a career spanning 20+ years, including Government, banking and insurance, finance, wealth management, retail, manufacturing, and health. Shane is also a part-time academic researcher in the field of data quality with The University of Queensland, Mater Health and Mater Research.

  • Associate Professor Kristen Gibbons

    Queensland Health

    Panel: Research Careers in Industry and Government – with CIRES Partner Investigators


    Kristen Gibbons is a Senior EpidemiologistMater Research, and with the Child Health Research Centre, The University of Queensland.

  • Dr Maciej Trzaskowski

    Max Kelsen

    Panel: Research Careers in Industry and Government – with CIRES Partner Investigators


    Dr Maciej Trzaskowski is Head of Research at Max Kelsen providing supervision to 10 researchers and machine learning engineers, as well as 3 PhD students and 2 postdocs. He also holds a position of a visiting scientist at QIMR Berghofer Medical Research Institute. He previously held the title of The British Academy Fellow aiming to improve utilisation of polygenic risk prediction in large population samples. He also underwent extensive training in application of a wide variety of Artificial Intelligence techniques extending beyond Machine Learning at the Department of Mathematics and Engineering, King’s College London as well as at University of Washington, Seattle. When in academia, he was a member of the Human Genome Organisation and a member of HUGO Trainee Committee, Behavioural Genetics Association, as well as an Associate Member and consultant for InLab quantitative and molecular genetics.

  • Dr Rocky Chen

    The University of Queensland

    Welcome and PhD School Chair

    Dr Rocky Tong Chen is the Chair of the 2023 Information Resilience PhD School, and CIRES Chief Investigator. He is currently a Lecturer in Business Analytics with Data Science Group at The University of Queensland. Before that, he received his PhD degree in Computer Science from The University of Queensland in 2020. His research work has been published on top venues like SIGIR, SIGKDD, ICDE, WWW, ICDM, IJCAI, AAAI, CIKM, TOIS, TKDE, etc., where his research interests include data mining, machine learning, recommender systems, and predictive analytics.

  • Professor Shazia Sadiq

    The University of Queensland

    Opening address: Introduction to CIRES and the PhD School

    Professor Shazia Sadiq is the CIRES Centre Director and a research and education leader in data science at The University of Queensland. Her research track record has focused on overcoming challenges that stem from disparate IT systems and result in information silos, and she has developed new methods that to tackle these challenges through integrated solutions for information quality and effective use. Shazia is passionate about the positive impact emerging technologies from data science, machine learning and artificial intelligence can have on our future. She advocates for the responsible and ethical technology developments and believe strongly that these developments require trans-disciplinary collaborations between research, industry, government and community.

Registration is now closed!


To be considered for a place at the 2022 Information Resilience PhD School, please submit the online application by 7 AUGUST. The Information Resilience PhD School is an in-person event and open only to Australian based students. Capacity is limited to 50 attendees only. Attendees will be selected based on the relevance of their research backgrounds and interests to School topics.

You will receive notification after 15 AUGUST. If accepted, you will also receive additional information about attending the PhD School. There is NO registration charge to attend the Information Resilience PhD School.

ONLINE APPLICATION: https://cires.org.au/cires-phd-school-2022



Up to 25 CIRES Travel Grants are available for Australian based students who live outside Brisbane, to subsidise travel and accommodation costs to attend the PhD School. The grants are worth up to a maximum of $1,000 and would be reimbursed based on receipts. You may indicate your interest to be considered for a CIRES Travel Grant via the online application form.

Link to Information Resilience PhD School Flyer


PROGRAM Dr Rocky Chen  tong.chen@uq.edu.au 


ARC Training Centre for Information Resilience [CIRES]
The University of Queensland
Brisbane Qld 4072 Australia

2022 Presentations on YouTube

2022 PhD School Photo Gallery

Watch the 2022 Highlights Video:

Check back to this page for links to:

  • Speaker videos
  • Poster Gallery
  • 3MT presentations


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