Time: 08/21/2017 Full day
Location: MCEC 219, Melbourne Convention and Exhibition Centre
9:00 am Opening Remarks by Chair
9:00 am - 10:00 am Invited Talk 1: Prof. Dacheng Tao (University of Sydney)
Title: Recent Progress in Artificial Intelligence
Abstract: Since the concept of Turing machine has been first proposed in 1936, the capability of machines to perform intelligent tasks went on growing exponentially. Artificial Intelligence (AI), as an essential accelerator, pursues the target of making machines as intelligent as human beings. It has already reformed how we live, work, learning, discover and communicate. In this talk, I will review our recent progress on AI by introducing some representative advancements from algorithms to applications, and illustrate the stairs for its realization from perceiving to learning, reasoning and behaving. To push AI from the narrow to the general, many challenges lie ahead. I will bring some examples out into the open, and shed lights on our future target. Today, we teach machines how to be intelligent as ourselves. Tomorrow, they will be our partners to get into our daily life.
Bio: Dacheng Tao is Professor of Computer Science and ARC Future Fellow in the School of Information Technologies and the Faculty of Engineering and Information Technologies at The University of Sydney. He was Professor of Computer Science and Director of the Centre for Artificial Intelligence in the University of Technology Sydney. He mainly applies statistics and mathematics to Artificial Intelligence and Data Science. His research interests spread across computer vision, data science, image processing, machine learning, and video surveillance. His research results have expounded in one monograph and 500+ publications at top journals and conferences, such as IEEE T-PAMI, T-NNLS, T-IP, JMLR, IJCV, IJCAI, AAAI, NIPS, ICML, CVPR, ICCV, ECCV, ICDM; and ACM SIGKDD, with several best paper awards, such as the best theory/algorithm paper runner up award in IEEE ICDM’07, the best student paper award in IEEE ICDM’13, and the 2014 ICDM 10-year highest-impact paper award. He received the 2015 Australian Scopus-Eureka Prize, the 2015 ACS Gold Disruptor Award and the 2015 UTS Vice-Chancellor’s Medal for Exceptional Research. He is a Fellow of the IEEE, OSA, IAPR and SPIE.
10:00 am - 10:30 am Coffee Break A
10:30 am - 11:30 am Invited Talk 2: Prof. Geoff Webb (Monash University)
Title: Highly scalable graphical modeling
Abstract: Association discovery is a fundamental data mining task. The primary statistical approach to association discovery between variables is log-linear analysis. Classical approaches to log-linear analysis do not scale beyond about ten variables. By melding the state-of-the-art in statistics, graphical modeling, and data mining research, we have developed efficient and effective algorithms for log-linear analysis, performing in seconds log-linear analysis of datasets with thousands of variables and providing a powerful statistically-sound method for creating compact models of complex high-dimensional multivariate distributions. These techniques directly generalise to all main approaches to learning graphical models, providing exact methods with unparalleled scalability.
Bio: Geoff Webb is Director of the Monash University Center for Data Science and a Technical Advisor to BigML Inc, who have incorporated his best of class association discovery software, Magnum Opus, as a core component of their cloud based Machine Learning service. He was editor in chief of the premier data mining journal, Data Mining and Knowledge Discovery from 2005 to 2014 and Program Committee Chair of the two top data mining conferences, ACM SIGKDD and IEEE ICDM, as well as General Chair of ICDM. His primary research areas are machine learning, data mining, user modelling and computational structural biology. Many of his learning algorithms are included in the widely-used Weka machine learning workbench. He is an IEEE Fellow and has received the 2013 IEEE ICDM Service Award, a 2014 Australian Research Council Discovery Outstanding Researcher Award, the 2016 Australian Computer Society ICT Researcher of the Year Award and the 2016 Australasian Artificial Intelligence Distinguished Research Contributions Award.
11:30 am - 12:30 am Spotlight Presentations: 6 accepted short abstracts (5 minutes talk + 2 minutes Q&A for each paper)
12:30 pm - 2:30 pm Lunch Break (on yourself)
2:30 pm - 3:30 pm Invited Talk 3: Prof. James Bailey (University of Melbourne)
Title: Assessing correlations: Adjustment, correction and randomization
Abstract: Assessing correlations is a core task in the analysis of biomedical data (e.g. for feature ranking). Although correlation measurement is a well studied area, popular classic methods such as mutual information have some limitations: i) lack of a constant baseline value (average value between random partitions of a dataset) and ii) susceptibility to selection bias, iii) difficulty in assessing correlations between continuous features. These issues may lead to inappropriate assessment of dependency strength and misleading feature rankings for classification. In this talk, we discuss the desirability of employing a statistical correction for chance for mutual information and review enhancements that we have proposed to address these limitations - the adjusted mutual information, the standardized mutual information and the randomized information coefficient.
Bio: James Bailey is a Professor in the School of Computing and Information Systems at the University of Melbourne and was previously an Australian Research Council Future Fellow. He is an internationally leading researcher in data science with extensive experience in applying machine learning techniques to tackle challenges in health. In partnership with clinical collaborators, he has developed and deployed machine learning algorithms for real time prediction of hospital medical emergencies, as well as intelligent algorithms for delivering automated feedback to medical students engaged in simulated surgical training.
3:30 pm - 4:30 pm Coffee Break B & Poster Session
4:30 pm - 5:00 pm Best Paper Award Ceremony & Closing Session
Location: MCEC 219, Melbourne Convention and Exhibition Centre
9:00 am Opening Remarks by Chair
9:00 am - 10:00 am Invited Talk 1: Prof. Dacheng Tao (University of Sydney)
Title: Recent Progress in Artificial Intelligence
Abstract: Since the concept of Turing machine has been first proposed in 1936, the capability of machines to perform intelligent tasks went on growing exponentially. Artificial Intelligence (AI), as an essential accelerator, pursues the target of making machines as intelligent as human beings. It has already reformed how we live, work, learning, discover and communicate. In this talk, I will review our recent progress on AI by introducing some representative advancements from algorithms to applications, and illustrate the stairs for its realization from perceiving to learning, reasoning and behaving. To push AI from the narrow to the general, many challenges lie ahead. I will bring some examples out into the open, and shed lights on our future target. Today, we teach machines how to be intelligent as ourselves. Tomorrow, they will be our partners to get into our daily life.
Bio: Dacheng Tao is Professor of Computer Science and ARC Future Fellow in the School of Information Technologies and the Faculty of Engineering and Information Technologies at The University of Sydney. He was Professor of Computer Science and Director of the Centre for Artificial Intelligence in the University of Technology Sydney. He mainly applies statistics and mathematics to Artificial Intelligence and Data Science. His research interests spread across computer vision, data science, image processing, machine learning, and video surveillance. His research results have expounded in one monograph and 500+ publications at top journals and conferences, such as IEEE T-PAMI, T-NNLS, T-IP, JMLR, IJCV, IJCAI, AAAI, NIPS, ICML, CVPR, ICCV, ECCV, ICDM; and ACM SIGKDD, with several best paper awards, such as the best theory/algorithm paper runner up award in IEEE ICDM’07, the best student paper award in IEEE ICDM’13, and the 2014 ICDM 10-year highest-impact paper award. He received the 2015 Australian Scopus-Eureka Prize, the 2015 ACS Gold Disruptor Award and the 2015 UTS Vice-Chancellor’s Medal for Exceptional Research. He is a Fellow of the IEEE, OSA, IAPR and SPIE.
10:00 am - 10:30 am Coffee Break A
10:30 am - 11:30 am Invited Talk 2: Prof. Geoff Webb (Monash University)
Title: Highly scalable graphical modeling
Abstract: Association discovery is a fundamental data mining task. The primary statistical approach to association discovery between variables is log-linear analysis. Classical approaches to log-linear analysis do not scale beyond about ten variables. By melding the state-of-the-art in statistics, graphical modeling, and data mining research, we have developed efficient and effective algorithms for log-linear analysis, performing in seconds log-linear analysis of datasets with thousands of variables and providing a powerful statistically-sound method for creating compact models of complex high-dimensional multivariate distributions. These techniques directly generalise to all main approaches to learning graphical models, providing exact methods with unparalleled scalability.
Bio: Geoff Webb is Director of the Monash University Center for Data Science and a Technical Advisor to BigML Inc, who have incorporated his best of class association discovery software, Magnum Opus, as a core component of their cloud based Machine Learning service. He was editor in chief of the premier data mining journal, Data Mining and Knowledge Discovery from 2005 to 2014 and Program Committee Chair of the two top data mining conferences, ACM SIGKDD and IEEE ICDM, as well as General Chair of ICDM. His primary research areas are machine learning, data mining, user modelling and computational structural biology. Many of his learning algorithms are included in the widely-used Weka machine learning workbench. He is an IEEE Fellow and has received the 2013 IEEE ICDM Service Award, a 2014 Australian Research Council Discovery Outstanding Researcher Award, the 2016 Australian Computer Society ICT Researcher of the Year Award and the 2016 Australasian Artificial Intelligence Distinguished Research Contributions Award.
11:30 am - 12:30 am Spotlight Presentations: 6 accepted short abstracts (5 minutes talk + 2 minutes Q&A for each paper)
12:30 pm - 2:30 pm Lunch Break (on yourself)
2:30 pm - 3:30 pm Invited Talk 3: Prof. James Bailey (University of Melbourne)
Title: Assessing correlations: Adjustment, correction and randomization
Abstract: Assessing correlations is a core task in the analysis of biomedical data (e.g. for feature ranking). Although correlation measurement is a well studied area, popular classic methods such as mutual information have some limitations: i) lack of a constant baseline value (average value between random partitions of a dataset) and ii) susceptibility to selection bias, iii) difficulty in assessing correlations between continuous features. These issues may lead to inappropriate assessment of dependency strength and misleading feature rankings for classification. In this talk, we discuss the desirability of employing a statistical correction for chance for mutual information and review enhancements that we have proposed to address these limitations - the adjusted mutual information, the standardized mutual information and the randomized information coefficient.
Bio: James Bailey is a Professor in the School of Computing and Information Systems at the University of Melbourne and was previously an Australian Research Council Future Fellow. He is an internationally leading researcher in data science with extensive experience in applying machine learning techniques to tackle challenges in health. In partnership with clinical collaborators, he has developed and deployed machine learning algorithms for real time prediction of hospital medical emergencies, as well as intelligent algorithms for delivering automated feedback to medical students engaged in simulated surgical training.
3:30 pm - 4:30 pm Coffee Break B & Poster Session
4:30 pm - 5:00 pm Best Paper Award Ceremony & Closing Session