Seminar and Conference News

Vacancy of Research Scientist in Data Science

posted Nov 6, 2018, 8:57 PM by Yanchang Zhao   [ updated Nov 6, 2018, 8:58 PM ]

Research Scientist in Data Science - 2 Positions at Data61, CSIRO. 

Application close on 28 November 2018. 


See details at https://jobs.csiro.au/job/Melbourne%2C-VIC-Senior-Research-Scientist-in-Data-Science/493000300/


Seminar: Smart Data Analytics for Traffic Congestion Management

posted Nov 1, 2018, 5:51 PM by Yanchang Zhao   [ updated Nov 1, 2018, 5:51 PM ]

Seminar: Smart Data Analytics for Traffic Congestion Management, 

Speaker: Dr. Adriana-Simona Mihaita, Data61, CSIRO. 

Location: Black Mountain, Canberra, 

Time and Date: 2-4pm Friday 16 Nov

RSVP: https://www.meetup.com/en-AU/CanberraDataSci/events/256024965/

AusDM 2018 Program now available

posted Nov 1, 2018, 5:20 PM by Yanchang Zhao   [ updated Nov 1, 2018, 5:21 PM ]

AusDM 2018 Student Challenge on postcode grouping

posted Sep 17, 2018, 8:16 PM by Yanchang Zhao   [ updated Sep 17, 2018, 8:17 PM ]

AusDM 2018 Student Challenge on postcode grouping. Open to students with Australian universities. See details at http://ausdm18.ausdm.org/student-competition/

CFP: AusDM 2018 - final extension to 17 Aug

posted Aug 2, 2018, 4:04 PM by Yanchang Zhao   [ updated Aug 2, 2018, 4:06 PM ]

The 2018 Australasian Data Mining Conference (AusDM 2018) has extended its submission deadline to 17 August. This is the final chance to get in.

 

The conference proceedings will be published in Springers Communication in Computer and Information Science.

 

Conference URL: http://ausdm18.ausdm.org

AusDM 2018 submission deadline extended to 3 August

posted Jul 16, 2018, 10:35 PM by Yanchang Zhao   [ updated Jul 16, 2018, 10:35 PM ]

The 16th Australasian Data Mining Conference (AusDM 2018)
Bathurst, Australia,
28-30 November 2018 
URL: 
http://ausdm18.ausdm.org/

The Australasian Data Mining Conference has established itself as the premier Australasian meeting for both practitioners and researchers in data mining. It is devoted to the art and science of intelligent analysis of (usually big) data sets for meaningful (and previously unknown) insights. This conference will enable the sharing and learning of research and progress in the local context and new breakthroughs in data mining algorithms and their applications across all industries.

Since AusDM02 the conference has showcased research in data mining, providing a forum for presenting and discussing the latest research and developments. Built on this tradition, AusDM18 will facilitate the cross-disciplinary exchange of ideas, experience and potential research directions. 

This year AusDM is proud to announce that conference proceedings will be published in Springers Communication in Computer and Information Science.


Publication and topics

We are calling for papers, both research and applications, and from both academia and industry, for presentation at the conference. Accepted papers will be published in the AusDM 2018 proceedings by Springer. Some selected papers will be invited for submission with extension in a special edition of a Springer journal. More details are available at our website.


Keynote speakers

Professor Junbin Gao, The University of Sydney
Professor Geoff Webb, Monash University


Important Dates

Paper Submission: extended to 3 August 2018
Authors Notified: Monday 1 October 2018
Camera Ready Submission: Monday 15 October 2018
Preliminary Program Available: Wednesday 31 October 2018
Early Bird Cut-Off Date for Authors: Friday 2 November 2018
Conference Dates: Wednesday 28 - Friday 30 November 2018


Join us on LinkedIn: 
https://www.linkedin.com/groups/4907891

Seminar: Real-Time Analytics and Petabyte-Scale Open Data Science, Canberra, Tuesday 26 June 2018

posted May 28, 2018, 4:47 PM by Yanchang Zhao   [ updated May 28, 2018, 4:47 PM ]

This is a joint event with the Statistical Society of Australia (SSA) Canberra Branch. There will be two talks starting at 5:45pm at the event. Each talk will be of 30m presentation plus 15m Q&A. There will also be pre-talk drinks and nibbles from 5pm to 5:45pm, kindly sponsored by M&T Resources.


RSVP: https://www.meetup.com/CanberraDataSci/events/251057018/

Date: Tuesday 26 June 2018
Venue: ANU campus
Agenda:
5:00-5:45pm Pre-talk drinks and nibbles, Allan Barton Forum, 2nd floor CBE Building, ANU
5:45-6:30pm Talk #1 - Solution for real-time analytics embedded in business process, by Richard Gao, Australian Government
6:30-7:15pm Talk #2 - Machine Learning and analytics using an open data science approach, by Rishu Saxena, Cloudera

Talk #1:

Topic: Solution for real-time analytics embedded in business process - Orchestra of elements to deliver real-time analytics as a critical part of business process
Speaker: Richard Gao, Australian Government
Abstract: The talk will presents three evolution stages of decision-making process: 1) time consuming non-automatic decision making process, including data collection, analysis, report and decision making, 2) automatic decision making process as part of general business process (e.g. Operational Decision Manager), and 3) modern data science-driven business process, including machine learning algorithm development and real-time algorithm deployment systems as new feature.
Bio: Mr. Richard Gao, is a lead data scientist in the Commonwealth Australia, previously worked as a data analyst and statistician across multiple Commonwealth Departments for over 10 years. Before that, he worked as an R&D manager with Huawei Technologies and Math teacher with a local Chinese university. He holds Master of Applied Statistics, MBA and Bachelor of Applied Mathematics.

Talk #2:

Topic: Machine Learning and analytics using an open data science approach
Speaker: Rishu Saxena, Cloudera
Abstract: Data Science in an enterprise environment with strong security restrictions, mean that leveraging the latest publicly available algorithms, libraries and approaches is difficult. In this talk we will propose an approach to leverage petabyte-scale open data science, while still maintaining security and resource controls.
Bio: Rishu is System Engineer with Cloudera focusing on helping customers to leverage machine learning. Rishu has previously worked with public, research and private sector organisations to help define the vision, strategy, technical solutions and delivery approach for big data hubs, advanced analytics and visualisation. Rishu is currently studying a Masters of Data Science at Sydney University.

Big Data Innovation Summit & Machine Learning Innovation Summit, San Francisco

posted Mar 15, 2018, 3:21 PM by Yanchang Zhao   [ updated Mar 15, 2018, 3:22 PM ]

Understand how you can articulate your current skills/responsibilities and take the career path that you want. Read this article from our expert Hugh Williams, ML Data Science Manager at Uber: http://bit.ly/2oLOued

 

Would you like to learn more about data, NLP, AI, Deep Learning?

 

Join two San Francisco events:

 

* Big Data Innovation Summit, April 12 & 13http://bit.ly/2oKPkYE 

> Visualize, explore data and generate insights to make better decisions

> Maximize your delivery of valuable insights with an effective Data Intelligence program

> Leverage AWS components to build and test multiple attribution models at scale and find AWS patterns

 

 

* Machine Learning Innovation Summit, May 9 & 10http://bit.ly/2HV92JX

> Master how advanced analytics can help you make better decisions on promotions

> Discover how ML can highlight the most relevant products and increase customer satisfaction

> Learn how ML can provide you with real-time recommendations using resources efficiently and operating on a massive scale

 

 

Enjoy an exclusive $200 off all two-day passes with the code RDM200 when you register! For more information on the events and group rates, please contact Lewis: Lewis@theiegroup.com  

 

PS: Learn more about NLP and how Uber is using it. Know how to adopt ML by reading this 

interview with Hugh Williams, ML Data Science Manager at Uber: http://bit.ly/2IsBlj8

New E-learning Course by Prof. Dr. Bart Baesens: Profit-Driven Business Analytics!

posted Jan 10, 2018, 2:53 AM by Yanchang Zhao   [ updated Jan 10, 2018, 2:56 AM ]

The e-learning course on profit-driven business analytics presents a toolbox of advanced analytical approaches that support cost-optimal decision making. They are advanced in that they are tailored for use in a business setting, where it is crucial to account for the costs and benefits that are related to decision making based on the output of analytical models. We call such approaches profit-driven analytics and they extend and reinforce the abilities of traditional analytics. The profit-driven perspective towards analytics that is advanced in this course contrasts with a traditional statistical perspective, which ignores the costs and benefits related to decision making based on analytical models.

In the course, we discuss both profit-driven descriptive and predictive analytics, and as well introduce uplift modeling as a stepping stone toward developing prescriptive analytical models. We also discuss a range of profit-driven evaluation measures for assessing the performance of analytical models from a business perspective. Finally, we conclude by looking into the economic impact of adopting analytics and zoom into some practical concerns related to the development, implementation and operation of analytics within an organization.

The E-learning course consists of more than 7 hours of movies, each 5 minutes on average.  Quizzes are included to facilitate the understanding of the material. Upon registration, you will get an access code which gives you unlimited access to all course material (movies, quizzes, scripts, ...) during 1 year. The course focusses on the concepts and modeling methodologies and not on the SAS software.  To access the course material, you only need a laptop, iPad, iPhone with a web browser. No SAS software is needed.  See this link for more details.  

Course URL: https://support.sas.com/edu/schedules.html?id=5962&ctry=BE&utm_source=TWITTER&utm_medium=social-sprinklr&utm_content=1084173631

Bart Baesens

Professor Bart Baesens is a professor of Big Data & Analytics at KU Leuven (Belgium), and a lecturer at the University of Southampton (United Kingdom).  He wrote more than 200 scientific papers on Big Data & Analytics, Machine Learning, Credit Risk Modeling and Fraud Detection and has won various awards for his research (e.g. Goodeve medal for best JORS paper, best EJOR 2013 and 2016 paper).  His findings have been published in well-known international journals and presented at international top conferences.  He is the author of 6 books Analytics in a Big Data World, Fraud Analytics using Descriptive, Predictive and Social Network Techniques, Credit Risk Management: Basic Concepts, Credit Risk Analytics and Profit Driven Business Analytics.  He also teaches E-learning courses on Advanced Analytics in a Big Data World, Fraud Analytics, Social Network Analytics and Credit Risk Modeling.  His research is summarized at www.dataminingapps.com.  He regularly tutors, advises and provides consulting support to international firms with respect to their  big data, analytics and credit risk management strategy. 

Wouter Verbeke

Wouter Verbeke, Ph.D., is an assistant professor at Vrije Universiteit Brussel (Belgium). His research is mainly situated in the field of predictive, prescriptive and network analytics, and is driven by real-life business problems including applications in customer relationship, credit risk, fraud, supply chain and human resources management. In 2014, he won the EURO award for best article published in the European Journal of Operational Research in the category Innovative Applications of O.R. His research is summarized at www.data-lab.be

CFP: AusDM 2018, Bathurst, Australia, 28-30 Nov 2018

posted Nov 26, 2017, 2:45 PM by Yanchang Zhao   [ updated Nov 26, 2017, 2:48 PM ]

16th Australasian Data Mining Conference (AusDM 2018)
Bathurst, Australia,
28-30 November 2018 

The Australasian Data Mining Conference has established itself as the premier Australasian meeting for both practitioners and researchers in data mining. It is devoted to the art and science of intelligent analysis of (usually big) data sets for meaningful (and previously unknown) insights. This conference will enable the sharing and learning of research and progress in the local context and new breakthroughs in data mining algorithms and their applications across all industries.

Since AusDM'02 the conference has showcased research in data mining, providing a forum for presenting and discussing the latest research and developments. Built on this tradition, AusDM'18 will facilitate the cross-disciplinary exchange of ideas, experience and potential research directions. Specifically, the conference seeks to showcase: Research Prototypes; Industry Case Studies; Practical Analytics Technology; and Research Student Projects. AusDM'18 will be a meeting place for pushing forward the frontiers of data mining in academia and industry.

Publication and topics

We are calling for papers, both research and applications, and from both academia and industry, for presentation at the conference. All papers will go through double-blind, peer-review by a panel of international experts. Accepted papers will be published in the AusDM 2018 proceedings by Springer. Some selected papers will be invited for submission with extension in a special edition of a Springer journal. Please note that we require that at least one author for each accepted paper will register for the conference and present their work. One full registration will cover at most two papers.

AusDM invites contributions addressing current research in data mining and knowledge discovery as well as experiences, novel applications and future challenges. Topics of interest include, but are not restricted to:

- Applications of Data Mining and Case Studies - Big Data Analytics
- Biomedical and Health Data Mining
- Business Analytics
- Computational Aspects of Data Mining
- Data Integration, Matching and Linkage
- Data Mining Education
- Data Mining in Security and Surveillance
- Data Preparation, Cleaning and Preprocessing 
- Data Stream Mining
- Implementations of Data Mining in Industry
- Integrating Domain Knowledge
- Knowledge Discovery and Presentation
- Link, Tree, Graph, Network and Process Mining 
- Multimedia Data Mining
- Mobile Data Mining
- New Data Mining Algorithms
- Privacy-preserving Data Mining
- Spatial and Temporal Data Mining 
- Text Mining
- Web and Social Network Mining

Keynote speakers

As is tradition for AusDM we have lined up an excellent keynote speaker program. Each speaker is a well known researcher and/or practitioner in data mining and related disciplines. The keynote program provides an opportunity to hear from some of the world's leaders on what the technology offers and where it is heading.

Submission of papers

We invite three types of submissions for AusDM 2018:

- Academic submissions: Regular academic submissions can be made in Research Track reporting on research progress, with a paper length up to 12 pages. For academic submissions we will use a double-blind review process, i.e. paper submissions must NOT include author names or affiliations (and also not acknowledgements referring to funding bodies). Self-citing references should also be removed from the submitted papers (they can be added on after the review) for the double blind reviewing purpose.

- Industry submissions: Submissions can be made in the Application Track to report on specific data mining implementations and experiences in governments and industry projects. Submissions in this category can be up to 12 pages. These submissions do not need to be double-blinded. A special committee made of industry representatives will assess industry submissions.

- Industry Showcase submissions: Submission from industry and government on an analytics solution that has raised profits, reduced costs and/or achieved other important policy and/or business outcomes can be made in this track with a one page Abstract only.
Paper submissions are required to follow the general format specified for papers. LaTeX styles and Word templates will be available while LaTeX will be the recommended typesetting package.

The electronic submissions must be in PDF only, and made through the AusDM'18 Submission Page.

Important Dates

Paper Submission Closed: Friday 20 July 2018
Authors Notified: Monday 1 October 2018
Camera Ready Submission: Monday 15 October 2018
Preliminary Program Available: Wednesday 31 October 2018
Early Bird Cut-Off Date for Authors: Friday 2 November 2018
Conference Dates: Wednesday 28 - Friday 30 November 2018



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