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Data is everywhere these days, and more is being produced at an exponential rate. Much of this so-called "big data" can be considered junk until it is analysed by sophisticated computational algorithms to reveal its compelling story. This is the focus of Dr Matloob Khushi's research.
"Big data comes in all shapes and sizes, including videos, images, financial data, medical and genomic data. My research looks at developing novel methods of analysing and managing it to unveil patterns that were not visible by applying previously known methods.
"The applications are endless, including in health, agriculture, banking, education and robotics. It's very exciting to me that the effective analysis of these ubiquitous large datasets holds the potential to improve people's lives.
"For example, by analysing and learning from big data, we can design a health-assisting gadget that can alert someone when they need to see a doctor, or an artificial intelligence-based financial agent that can advise people on where and how to invest.
"Many research laboratories already study their own internally produced datasets, but my research focuses on the integrated analysis of publicly available large datasets from cross-laboratory sources, which can potentially reveal some very interesting patterns.
"For example, my research in this area has unveiled previously unknown molecular interactions between oestrogen and progesterone receptors in regulating breast cancer, interactions that were later experimentally confirmed by another lab.
"One of my future aims is to develop novel efficient machine learning methods to analyse and manage large datasets of any nature.
"I joined the University of Sydney as a data scientist in 2008 while also completing my PhD here. After a post-doctoral fellowship and a lot of further research, I joined the School of Computer Science in 2017.
"The University of Sydney is home to many world-leading researchers, and I consider myself fortunate to be surrounded and inspired by such people. I hope to contribute further towards the University's world-leading position in research."
Data is everywhere these days, and more is being produced at an exponential rate. Much of this so-called "big data" can be considered junk until it is analysed by sophisticated computational algorithms to reveal its compelling story. This is the focus of Dr Matloob Khushi's research.
"Big data comes in all shapes and sizes, including videos, images, financial data, medical and genomic data. My research looks at developing novel methods of analysing and managing it to unveil patterns that were not visible by applying previously known methods.
"The applications are endless, including in health, agriculture, banking, education and robotics. It's very exciting to me that the effective analysis of these ubiquitous large datasets holds the potential to improve people's lives.
"For example, by analysing and learning from big data, we can design a health-assisting gadget that can alert someone when they need to see a doctor, or an artificial intelligence-based financial agent that can advise people on where and how to invest.
"Many research laboratories already study their own internally produced datasets, but my research focuses on the integrated analysis of publicly available large datasets from cross-laboratory sources, which can potentially reveal some very interesting patterns.
"For example, my research in this area has unveiled previously unknown molecular interactions between oestrogen and progesterone receptors in regulating breast cancer, interactions that were later experimentally confirmed by another lab.
"One of my future aims is to develop novel efficient machine learning methods to analyse and manage large datasets of any nature.
"I joined the University of Sydney as a data scientist in 2008 while also completing my PhD here. After a post-doctoral fellowship and a lot of further research, I joined the School of Computer Science in 2017.
"The University of Sydney is home to many world-leading researchers, and I consider myself fortunate to be surrounded and inspired by such people. I hope to contribute further towards the University's world-leading position in research."
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PROCEEDINGS OF THE 17TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, WSDM 2024pp.529-537, (2024)
IEEE journal of biomedical and health informaticsno. 99 (2023): 1-10
COMPANION OF THE WORLD WIDE WEB CONFERENCE, WWW 2023pp.995-1003, (2023)
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMSno. 5 (2023): 2325-2334
WSDMpp.706-714, (2023)
23RD IEEE INTERNATIONAL CONFERENCE ON DATA MINING, ICDM 2023pp.1055-1060, (2023)
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