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Third Joint Research Workshop

发布时间:2017-11-22 来源:本站原创 作者:本站编辑   浏览次数:


Third Joint Research Workshop between Southwest University and Deakin University



Southwest University · Chongqing ·China

Nov. 23, 2017

 



Introduction

 

      Southwest University, China and Deakin University, Australia will sponsor a Research Workshop to enhance collaboration between researchers in the School of Computer and Information Sciences (CIS) at SWU and the School of Information Technology (SIT) / Faculty of Science, Engineering and Built Environment at Deakin. This Workshop will focus on a theme of Agriculture Big Data. The Workshop will be held at SWU in Chongqing on November 23. There will be 12 presentations (6 from SWU and 6 from Deakin) on The Workshop.


Workshop Schedule

Date

Time

Program

Site

Nov. 23

Thursday

10:00 -10:20

Opening Speech

Teaching Building 25, Room 0114

 

10:20-10:45

Title:  Overview of Projects in   the Cyber Security SRC related to big data analysis

Speaker:  Prof.   Wanlei Zhou

10:45-11:10

TitleQuantifying   patterns in scientists’ activities with “big data” in science

Speaker:  Prof. Tao Jia

11:10-11:35

Title: CeRRF Overview and Relevant Project Examples

Speaker:  Dr. David   Halliwell

11:35-12:00

Title:   Big Data to Boost Agricultural Rural Modernization

Speaker:   Mr. Huining Pei

12:00-14:00

Lunch Time

14:00-14:25

TitleBig Water   Savings with Big Data – an irrigation perspective

Speaker:  A/Prof. John Hornnbuckle


 

Data

Time

Program

Site

Nov. 23

Thursday

14:25-14:50

Title: Design of Intelligent Agriculture Meteorological   Refine Service System for Future

Speaker: Dr.  Zizi Luo


14:50-15:15

TitleOpportunities   to accelerate agriculture’s adaptation to climate change using big data

Speaker:   A/Prof. Robert Faggian

Teaching Building 25,   Room 0114

 

15:15-15:40

TitleThe development and application of Omics data in silkworm

Speaker:   A/Prof. Minjin Han

15:40-16:00

Break

16:00-16:25

Title   Wrangle the beast - Challenges in data wrangling   and integration for big data

Speaker:  Dr Iman Avazpour

16:25-16:50

TitleDevelopment   and Technology Application of Agricultural Big Data

Speaker: Prof. Guocai   Yang

16:50-17:15

Title: Efficient experimental optimization for advanced manufacturing

Speaker:  Dr. Cheng   Li

17:15-17:40

Title: Deep Learning Meets Agriculture

Speaker:  Dr. Song   Wu


 


 

Title: Overview of Projects in the Cyber Security SRC related to big data analysis

Speaker: Prof. Wanlei Zhou.

 

BIO: Prof. Wanlei Zhou is currently the Alfred Deakin Professor, Chair of Information Technology, Associate Dean (International Research Engagement) of Faculty of Science, Engineering and Built Environment, and Research Director of the Strategic Research Centre in Cyber Security in Deakin University.

 

 

Title: Quantifying patterns in scientists’ activities with “big data” in science

Speaker: Prof. Tao Jia

 

BIO: Dr. Tao Jia obtained his Master in industrial and system engineering and Ph.D. in physics at Virginia Tech (USA). He worked as the postdoctoral researcher at Northeastern University (USA) from 2011 to 2013 and at Rensselaer Polytechnic Institute (USA) from 2013 to 2015. In September 2015, he worked as the professor at Southwest University (China). Dr. Jia's research interest focuses on the computational and analytical understanding of complex systems, including complex networks, social systems and biological systems. As the first author, he has published on top journals such as Nature Human Behaviour (1 paper, also as the co-corresponding author), Nature Communications (1 paper) and Physical Review Letters (2 papers, also as the co-corresponding author). He is awarded the Chinese Government Award for Outstanding Self-Financed Students Abroad in 2011 and the 1000 Young Talents Plan by the Chinese Government in 2015.

 

Abstract: Modern science increasingly constitutes a complex system: apparently complicated, involving strong interactions between different disciplines, within which a growing number of global scientists collaborate and compete with each other. The availability of large-scale datasets that capture major activities in science, such as publications, patents as well as citations, has created an unprecedented opportunity to explore in a quantitative manner the patterns of scientific activities. In this talk, I will introduce our recent works in which we quantify the research interest of individual scientists using the research subjects of their publications. We reveal patterns in the change of research interest in one’s career, allowing us further propose a statistical model that successfully reproduces empirical observations. Using the same framework, we also quantitatively measure the correlation between a scientist’s change of research interest and the change of performance at a large scale. Finally, we find the existence of both “homophily” and “heterophily” effect in scientific collaboration. Our works uncover and demonstrate a high degree of regularity underlying scientific research and individual careers.

 

 

Title: CeRRF Overview and Relevant Project Examples

Speaker: Dr. David Halliwell.

 

BIO: Dr. David Halliwell is Director of the Centre for Regional and Rural Futures (CeRRF) and Director Research Partnerships for the Science, Engineering and Built Environment (SEBE) Faculty at Deakin University. David has a PhD in Phosphorus Chemistry and 20 years of industry relevant research experience in the agriculture and water sectors. David has significant experience in engaging with industry and other external partners on collaborative research projects.

 

 

Title: Big Data to Boost Agricultural Rural Modernization

Speaker: Mr. Huining Pei

 

BIO: Mr. Huining Pei has been the chairman & G.M of Chongqing Agricultural Products Group Market Investment and construction CO., LTD., chairman & G.M of Chongqing Agricultural Products Group CO., LTD., and G.M of Chongqing Native Products Trading Center. He has promoted the establishment of Chongqing Rural Big Data Investment CO., LTD. and assumed the position of President. 

 

Abstract: The big data is rapidly developing into a new generation of information technology and service industry that finds new knowledge, create new value and enhance new capabilities. It has become the basic strategic resource of the country, and is becoming a new impetus for China's economic transformation and development, new opportunities to reshape national competitive advantage, and new ways to improve the governance capacity of the government. Agricultural countryside is one of the important fields of big data generation and application, which is the basis and important component of big data development in China. With the advancing informationization and agricultural modernization, agriculture and rural areas is big data and agriculture industry comprehensive depth fusion, has gradually become the locator of agricultural production, agricultural market navigation lights, and the baton of agricultural management, has increasingly become the nervous system of intelligent agriculture and promoting agricultural modernization at the core of the key elements.

 

 

Title: Big Water Savings with Big Data – an irrigation perspective

Speaker: Associate Professor John Hornnbuckle.

 

BIO: Associate Professor John Hornnbuckle is the Research Leader for Irrigation Science within Deakin Universities Centre for Regional and Rural Futures and manages the Irrigation Research laboratory in Griffith, NSW. His current research is focused on developing and investigating irrigation design and water management tools and technologies for improving water use efficiency, maximizing production and minimizing the environmental footprint of irrigation across a range of scales. He is the project leader for the IrriSAT Satellite Irrigation Water Management tools https://irrisat-cloud.appspot.com/# which are being used extensively for irrigation water management and benchmarking irrigation productivity in Australia and overseas. He currently leads a team working on a range of national and international projects focused on improving irrigation through the use of emerging technologies such as remote sensing using satellites and drones, IoT, informatics and on-ground sensor networks.

 

Title: Design of Intelligent Agriculture Meteorological Refine Service System for Future

Speaker: Dr. Zizi Luo

 

BIO: Dr. Zizi Luo is a senior engineer at Chongqing Meteorological Bureau.

 

Abstract: There are some insufficient issues existing in the current mainstream agrometeorology service mode in China, including inaccurate products, inconvenient service media, and shortage of participation from the local civil servants and agriculture experts, and lack of capabilities to serve farmers especially agricultural enterprises with precise, customized and professional products. Solving these issues is essentially important for the hilly areas in China, since there are more demand for such products and services due to the hilly environment. Therefore, we designed a new agrometeorology service mode named Precise & Intelligent Service Mode for Agrometeorology (PISA). This mode conveniently and quickly collects the personalized service demand from farmers, but also designs a series of systems and tools attracting all the relevant professional workers and agriculture experts to work together collaboratively and efficiently. Thus, this mode enables them serve those farmers with precise, intelligent and professional services without more personnel input. We applied this mode in Chongqing and result showed that it satisfied the demand of serving farmers with precise, intelligent and professional products and services in the hilly areas.

 

 

Title: Opportunities to accelerate agriculture’s adaptation to climate change using big data

Speaker: Associate Professor  Robert Faggian

 

BIO: Dr. Robert Faggian is Associate Professor of Climate Change Adaptation within Deakin University’s Centre for Regional and Rural Futures (CeRRF). He also holds honorary positions at the University of Melbourne (Melbourne School of Engineering and Faculty of Veterinary and Agricultural Sciences) and is an Associate member of the Centre for Disaster Management and Public Safety and the Melbourne Sustainable Society Institute. Dr. Faggian has an interdisciplinary research background with an emphasis on systems‐related issues (climate change, regional development, water resource management, food and fibre production). His research focus is determining the potential impacts of climate change on agriculture at the regional level, the implications for sustainable regional development and the use of strategic planning approaches to build adaptive capacity and resilience in regional and rural communities.

 

 

Title: The development and application of Omics data in silkworm

Speaker: Associate Professor Minjin Han

 

BIO: Dr. Minjin Han obtained his PH.D. in Biochemical and Molecular Biology at Southwest University, China. He worked as postdoctoral researcher at Chongqing University, China. In July 2015, he worked as assistant professor at Southwest University. Dr. Han’s long term research interest is to understand (1) what are the characteristics and functions of the transposons in the domesticated silkworm genome?  (2) Why are there so many transposons in the domesticated silkworm genome? In the past ten years, He has published more than 10 papers related transposons on the international journals including DNA research, Genome Biology and Evolution, Database-Oxford, BMC genomics and Mobile DNA.

 

Abstract: The domesticated silkworm, Bombyx mori, is a model insect for the order Lepidoptera and has importantly economic value for silk production. The draft genome sequences of the domesticated silkworm were completed by Southwest University and The Beijing Genomics Institute (BGI) in 2004, which was the first animal whose genome sequencing was completed independently by Chinese scientists. Over the last decade, the biological data of the domesticated silkworm has entered the Omics era, due to the rapid progress of high-throughput sequencing projects. The omics include Genome, Epigenome, Transcriptome, Proteome, Metabolome, Gut microbiomes and so on. As a result, the research fields in evolutionary biology, comparative genomics, functional genomics, variety breeding and improvement for silkworm have made rapid progress and important breakthroughs based on the omics data. Here, the development history and application of silkworm omics will be shared in this report.

 

 

Title: Wrangle the beast - Challenges in data wrangling and integration for big data

Speaker: Dr. Iman Avazpour.

 

BIO: Dr. Iman Avazpour received his PhD in Information Communication Technology from Swinburne university in 2014. He has been a postdoctoral fellow at Swinburne university and Deakin for two years working with industry on Data wrangling, Harmonisation and Visualisation problems. He is currently Lecturer in Software Engineering at Deakin university. His research interests are Data Integration, Visualisation, and Model Driven Engineering.

 

 

Title: Development and Technology Application of Agricultural Big Data

Speaker: Prof. Guocai Yang

 

BIO: Dr. Guocai Yang, Professor, supervisor of postgraduate. He is mainly engaged in intelligent software, internet of things, big data, agricultural information technology and other aspects of research and teaching. He is the deputy director of the Chongqing Research Center for Agricultural and Rural Informatization Engineering Technology, and vice president of computer network education branch of National Institute of computer education. He has successively presided over and studied more than 20 projects including the State, Ministry of Education, Chongqing Municipality and schools as well as various horizontal topics. He has won two provincial and ministerial level scientific and technological progress prize, and has been awarded third prize of Young Teachers Teaching of Fok Ying Tung Education Foundation, additionally, has published research papers and teaching researches more than 50. As an editor or deputy editor, he has compiled six provincial and ministerial planning textbooks and even accessed to CSQA and CSTE qualification certificate.

 

Abstract: The rapid development of the information age shows the important social and economic value of agricultural big data. Agricultural big data has become a new type of agricultural resources, and is leading and driving the steady progress of modern agricultural construction and agricultural supply side structural reform. Firmly grasping the opportunities of agricultural big data era development, and realizing the rapid transformation of traditional agriculture to modern agriculture, which is an urgent problem to be solved in the academic circles. The report mainly bases on the rapid development of agricultural data in the information age, sharing the team's practical technologies and applications about agricultural big data in recent years, as well as the current research in agriculture big data. Finally, the report has an outlook at the development direction of agricultural big data.

 

Title: Efficient experimental optimization for advanced manufacturing

Speaker: Dr. Cheng Li.

 

BIO: Dr. Cheng Li is currently a postdoctoral research fellow (ARC Laureate) in Centre of Pattern recognition and data analytics in Deakin University. He completed his PhD at Deakin University in 2015 and master degree at Huazhong University of Science and Technology in 2010. His research interests lie in the area of machine learning and pattern recognition, particularly Bayesian optimization.

 

 

Title: Deep Learning Meets Agriculture

Speaker: Dr. Song Wu

 

BIO: Song Wu received the B.S. degree and the M.S. degree of computer science from the Southwest University, China, in 2009 and 2012, respectively. He received his doctorate from Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Netherlands. He is currently working in the College of Computer and Information Science of Southwest University. His current research interests include machine learning, big data and deep learning based computer vision.

 

Abstract: More and more of today's farmers are embracing advanced technology to decrease their overall labor and become more efficient. Farmers are getting help from researchers and scientists who have turned the keen eye of AI toward agriculture, using deep learning applications to not only predict crop outputs but also to monitor environment changes around crops and help detect crop diseases before one spreads. In agriculture, specific information and patterns across vast amounts of images from drones or satellites can be used by deep learning to understand better how crops are growing globally and over time.