10.30am – Monday 18 February
Dr. Amanda Barnard is a Chief Research Scientist within Data61 at CSIRO. She received her Ph.D. (Physics) in from RMIT in 2003, followed by a Distinguished Postdoctoral Fellow in the Center for Nanoscale Materials at Argonne National Laboratory (USA), and the prestigious senior research position as Violette & Samuel Glasstone Fellow at the University of Oxford (UK) with an Extraordinary Research Fellowship at The Queen’s College. She joined CSIRO as an Australian Research Council Queen Elizabeth II Fellow in 2009, and then as an OCE Science Leader, where she lead research developing structure/property relationships using computational physics and chemistry, machine learning, deep learning and AI. Dr Barnard is a member of the Nature Index Panel (NPG), and has previously served as an Associate Editor for Science Advances (AAAS), and is currently a member of the Editorial Advisory Board for Nanoscale (RSC), the Senior Editorial Board for the Journal of Physics: Materials (IOP) and the International Executive Board of Nano Futures (IOP). She is the Chair of the National Computational Merit Allocation Scheme for Australia (awarding $10 million in resources annually) and a Fellow of both the Australian Institute of Physics (FAIP) and the Royal Society of Chemistry (FRSC). For her work she has previously won 12 national and international awards, including the 2009 Young Scientist Prize in Computational Physics from the International Union of Pure and Applied Physics, the 2009 Malcolm McIntosh Award from the Prime Minister of Australia for the Physical Scientist of the Year, the 2010 Frederick White Prize from the Australian Academy of Sciences, the 2014 ACS Nano Lectureship (Asia/Pacific) from the American Chemical Society, and the 2014 Feynman Prize in Nanotechnology (Theory) from the Foresight Institute, being the first woman to do so in the history of the award.
Keynote: Dimension reduction in the data-driven design of materials
The fundamental aim of materials research is to identify features of materials that can be tuned to control how the material performs under specific conditions. The combination of computational materials science with machine learning provides a powerful way of relating structural features with functional properties, but uncovering these hidden connections is difficult, particularly when the data set is small, with a high dimensionality and with high variance (as they typically are in materials research). Fortunately the strategic use of dimension reduction methods can alleviate these problems; identifying which features are actually important, without the need for domain biases. In this presentation we will explore the differences between materials simulation and materials informatics, and use some dimension reduction machine learning methods to predict the charge transfer properties of a set of carbon nanostructures based on their surface characteristics. Once the key structural features have been identified, we will use statistical methods to predict ensemble properties and investigate the impact of tuning these features on the properties of the sample as a whole.
9.30am – Wednesday 20 February
Barbara Chapman is a Professor of Applied Mathematics and Statistics, and of Computer Science, at Stony Brook University, where she is affiliated with the Institute for Advanced Computational Science. She also directs Computer Science and Mathematics Research at Brookhaven National Laboratory. Barbara performs research on parallel programming interfaces and the related implementation technology, and has been involved in several efforts to develop community standards for parallel programming, including OpenMP, OpenACC and OpenSHMEM. Her research group has created an open source compiler, OpenUH, that enabled practical experimentation with proposed enhancements to application programming interfaces and a reference implementation of the library-based OpenSHMEM standard. Dr. Chapman has co-authored over 200 papers and two books. She obtained her B.Sc. Hons in Mathematics at the University of Canterbury and her Ph.D. in Computer Science from Queen’s University of Belfast.
Keynote: OpenMP For Exascale
Today’s High Performance Computing architectures exhibit significant compute power within each node of the machine, often achieved via the inclusion of one or more accelerators that are attached to CPUs. As a result, it has become essential that large-scale applications make effective use of intra-node as well as inter-node parallelism. In the U.S. Department of Energy’s Exascale Computing Project, several different approaches are being developed to support this requirement. Of these, the most widely adopted so far is OpenMP, a directive-based parallel programming interface supported by many compilers for Fortran, C and C++. In this presentation we discuss the challenges of intra-node programming and how OpenMP attempts to meet them
11.30am – Monday 18 February
Professor Gary Evans is the Chief Science Advisor for the Ministry of Business, Innovation and Employment (MBIE) and is a member of the Ferrier Research Institute at Victoria University of Wellington. His research involves designing and synthesising enzyme inhibitors for treating disease. He invented Ulodesine which completed Phase II clinical trials for the treatment of gout. Currently his work is focussed on the development of new antibiotic and antiviral drugs. Dr. Evans did his PhD at the University of Otago, a postdoc at Oxford University, and then worked in the biotechnology sector within the United Kingdom. He was appointed a Member of the NZ Order of Merit in 2014, and has received several awards, including the 2014 Janssen Best Innovation Award and the 2011 MacDiarmid Medal from the Royal Society of New Zealand.
Keynote: The New Zealand Science System: Challenges and opportunities for eResearch
The NZ government funded close to $1.6 billion of research in 2018 and of that the Ministry of Business, Innovation and Employment funded around $1.2 billion either directly or through their agents. In this talk I will discuss the R&D funding system in the context of the 2015 National Statement of Science Investment, the MBIE Science advisory system and the opportunities for funding eResearch.
8.30am – Tuesday 19 February
Tahu Kukutai (Ngāti Tiipa, Maniapoto, Te Aupōuri) is Professor of Demography at the National Institute of Demographic and Economic Analysis, University of Waikato. Tahu specialises in Māori and indigenous demographic research and has written extensively on official statistics (including census methodologies), Māori population change and Māori identity. She has undertaken research with and for government agencies, hapū, iwi and NGOs. Tahu is a founding member of the Māori Data Sovereignty Network Te Mana Raraunga and is Vice President of the Population Association of New Zealand. She is co-editor (with John Taylor) of Indigenous Data Sovereignty: Toward an Agenda (free download on ANU Press website). She was previously a journalist.
Keynote: Indigenous data sovereignty: Challenges and opportunities in Aotearoa NZ
Data is the 21st century’s most valuable resource. Aotearoa NZ is a world leader in linking administrative data, and an early adopter of data-driven policy-making but has yet to develop innovative models of data governance and ethics, value creation and benefit-sharing. Many of the assumptions underpinning Aotearoa NZ’s data ecosystems rest on Anglo-European legal concepts (e.g. individual privacy and ownership) which translate poorly into the big and open data environment. What is needed is a radically different way of conceptualising rights that relate to massive quantities of data. Indigenous data sovereignty (IDSov) marks an important departure from current theory and practice. At the heart of IDSov is the right of indigenous peoples and nations to control the collection, ownership, and application of data about their people, territories, lifeways and natural resources. This talk provides an overview of developments in IDSov with a specific focus on the opportunities and challenges in Aotearoa NZ.
9.30am – Tuesday 19 February
Ruby Mendenhall is an Associate Professor in Sociology and African American Studies at the University of Illinois, Urbana-Champaign. She is also the Assistant Dean for Diversity and Democratization of Health Innovation at the Carle Illinois College of Medicine. Mendenhall uses mixed methods research to examine how living in racially segregated neighborhoods with high levels of violence affects Black mothers’ mental and physical health. She also studies how racial microaggressions affect students of color health and sense of belonging on predominantly white campuses. She uses advanced computing to recover Black women’s lost history. Her research has appeared in academic journals such as Public Health, Social Forces, Social Science Research, Demography, Housing Policy Debate, The Review of Black Political Economy, The Black Scholar, and Social Service Review.
Keynote: Using Big Data to Recover Black Women’s Lost History
Throughout history, Black women’s lived experiences have often been invisible and erased. Therefore, it is important to combat the erasure of Black women and move toward a correction and claiming of their space within the digitized record. This presentation will discuss a study that employs latent dirichlet allocation (LDA) algorithms and comparative text mining to search 800,000 periodicals in JSTOR (Journal Storage) and HathiTrust from 1746 to 2014 to identify the types of conversations that emerge about Black women’s shared experience over time and the resulting knowledge that developed. This presentation will also discuss the potential for seamless creativity and the need to de-mystify advance computing tools across the social sciences and humanities.