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Event Schedule

  • Conference in Melbourne : 06 - 07 March, 2019


Day 1: Machine Learning & Deep Learning Day (06th March, 2019)


X Topic Abstract

The talk will present how innovation combined with data analytics can lead to breakthrough technologies. The talk will illustrate this using some examples. An outlook at future trends and directions created by Artificial Intelligence (AI) will be presented to allow the listener to imagine possible futures.

Speaker Profile

Prof. Alexe Bojovschi, #1 Amazon Best Seller of "INNOAPHORISMS - A spark each day - EMPOWER INNOVATION" in Business, Management & Leadership, Decision-Making & Problem Solving category in 5 countries including USA, Australia, Canada, France and Germany, is an inventor and innovator. His innovations are used by his own companies, IntAiB, iiRNet and iDataMachine and other organizations with whom he worked including IBM, American Air Force Research Laboratory, Australian Defence Science and Technology, Monash University, RMIT University, Victoria Partnership for Advanced Computing, Swinburne University of Technology and Aston University. He holds 8 US and international patents and has published many technical papers. He is a pioneer in deriving technology and business insights from BigData. Alexe developed accelerated learning, creativity and innovation techniques and courses delivered at IntAIB and RMIT University. Alexe is working currently with The University of Melbourne, IND-Technology and Amity University on innovation projects.


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This talk will explore how Long Short Term Memory (LSTM) recurrent neural networks combined with python can use artificial intelligence to provide, invalid, unexpected or random data as inputs for testing software and finding security vulnerabilities


X Topic Abstract

Deep learning is one the hottest topic in recent years, with ever increasing layers so is the complexity, and hence computation power required for training. To increase the efficiency of the network optimization for deep learning become increasing in demand. In this talk we will introduce several different optimization techniques and walkthrough some examples on how to best apply these techniques.


X Topic Abstract

1. The progress of computer vision.
2. Different approach in different areas and industries
3. Things we did in our project and how we utilizes computer vision to innovate the industry.


X Topic Abstract

By learning from businesses' abundant historical data, predictive analytics provide something beyond standard business reports and sales forecasts: Actionable predictions for each customer. This talk will provide delegates with the techniques, tips, and pointers crucial in running a successful predictive analytics and data mining initiative. Learn how to strategically position and tactically deploy predictive analytics and data mining at your organization, bridge the prevalent gap between technical understanding and practical use, and create an effective predictive model to aid in building unique competitive advantages for marketing.

Speaker Profile

Dr William Yeoh is the Director for IBM Centre of Excellence in Business Analytics at Deakin University Australia. His main research interests include Business Intelligence and Analytics, AI, Data Mining, Information Quality, Enterprise Systems, Cloud Computing & Crowdsourcing. His research are supported by various funding bodies and have appeared in high-tier journals and top conferences. Dr Yeoh has won numerous awards including the 2017 ICT Educator of the Year Gold Award (awarded by the Australian Computer Society ACS - the Australia's premier ICT professional association), internationally-competitive IBM Faculty Award that rarely bestowed on Australian-based academics, Deakin’s Vice-Chancellor Award, and Faculty Excellence in Research Award.


X Topic Abstract

In this presentation, I will explore the different marketing problems that Deep Learning is already being applied to. Delve into a case study to illustrate the process whereby an organisation without a team of Machine Learning engineers can harness the capability of this technology to automate human perception tasks. Finally, I finish by touching on the current capabilities and limitations of Deep Learning, highlighting the types of issue organisations face when attempting to get a project underway.

I will walk you through the details of the presentation. I use a lot of fascinating use cases, and just a little bit of data theory, to help answer crucial Digital and AI questions and assess the best course of action for complex organisations who are on their path to apply digital thinking to the real world customer issues, so that they can create unique and measurable digital experiences.

Audience Takeaway :
AI marketing use cases, voice-based search strategy, chat-bot strategy, marketing automation.

Speaker Profile

Neel Bhattacharya is an experienced leader in digital marketing analytics, AI automation and disruptive technologies with a focus on delivering sustainable competitive advantage and profitability. She has over 15 years of deep customer life-cycle management experience across a number of industries, in the ANZ, Asia and North American regions.

She has driven digital marketing and AI enabled marketing optimisation practices at companies such as IBM, Symantec and most recently Infosys Consulting. Currently, she is a Management Consultant for Infosys Consulting where she is responsible for educating, advocating and driving disruptive technologies, digital analytics and optimisation best practices for her clients. Neel brings to the table a proven ability to identify and capture strategic market opportunities through an omni-channel approach. As an active member of the digital marketing community, she is also a regular speaker at various industry events. She holds a Masters in Communication Management from University of Delhi and a Diploma in Creative Writing from the Chicago State University, USA.


X Topic Abstract

That sounds great. I hope I can go better with large scale web crawling using python. I can explain the way it works and the positive and negative aspects involved in it. Can be able to discuss further about Crawl rates, Right Data and other important aspects of it. Security measures and legal aspects involved in it.

If possible I can be able to show some examples from my previous projects involving large scale web crawling.


X Topic Abstract

A look into the how machine learning can create value internally. Value being a combination of quantitative and qualitative returns, rather than implementing new tech and hoping for positive things afterwards.

schedule 09:00AM - 09:15AM Registration / Conference Overview
Nitesh Naveen, Partner & Managing Consultants - Digital Transformation, 1.21GWS
schedule 09:15AM - 10:00AM AI Driven Innovation and Innovation-Driven AI - Click Here for More Info
Alexe Bojovschi, Innovation Mentor, University of Melbourne
schedule 10:00AM - 10:45AM May the Fuzz be With You - Click Here for More Info
Heidi Thorpe, CEO & Founder, Cat with a Monocle
schedule 10:45AM - 11:15AM Tea Break
schedule 11:15AM - 12:00PM Optimization for Deep Learning - Click Here for More Info
Louis Liu, Data Scientist, ANZ
schedule 12:00PM - 12:45PM Computer Vision in Real Estate Industry - Click Here for More Info
YiFei Wang, Director, Happy Hackers Pty Ltd
schedule 12:45PM - 01:45PM Lunch Break
schedule 01:45PM - 02:30PM Predictive Analytics Using Data and Insights to Transform Marketing and Personalization - Click Here for More Info
Dr. William Yeoh, Director, IBM Centre of Excellence in Business Analytics, Deakin University
schedule 02:30PM - 03:15PM Disruptive Technologies: AI Advances That Will Transform Marketing - Click Here for More Info
Neel Bhattacharya, Sr. Management Consultant, Infosys
schedule 03:15PM - 03:45PM Tea Break
schedule 03:45PM - 04:30PM Let's Talk About the Web Spiders/Web Crawling With Python - Click Here for More Info
Neelakanteswar Patnaik, Freelance Web Designer
schedule 04:30PM - 05:15PM Ideas for Creating Value with Machine Learning - Click Here for More Info
Mark Sango, Director Digital Services, Softvision Australia

Day 2: Data Visualisation Day (07th March, 2019)


X Topic Abstract

Innovation is the driving idea behind a majority of current emerging technology projects. As these technologies become more mainstream, a more proactive and balanced approach to business value and innovation is what will create truly impactful projects. This talk covers true innovation: innovation within constraints. Using lessons learnt in enterprise-level projects that can be used at any level of project implementation.

Speaker Profile

Jeremiah is focused on creating machine learning, natural language, and deep learning projects valuable and relatable. He has received numerous industry awards, recently recognised for industry contribution as one of the Top 25 Analytics Professionals in Australia for 2018 by the IAPA.

X Topic Abstract

A case study on how data visualization of qualitative and quantitative data analyzed to make government departments understand the key aspects of the 2018 Election. Organizations deal with a huge volume of structured and unstructured data every day. Effective Word Cloud visualization, visualizing the co-relation and how different variables are connected, providing the key to understand the data and the connection between them are an integral part of presenting data in a convincing way. This highlights the key aspects of data, which helps people to make important decisions.


X Topic Abstract

Using data science in retail has become a necessity to keep up with the competition. Retailers have to realize that big data technologies can help them focus their resources efficiently, make smarter decisions, and improve performance. New sources of data, from transaction data to trajectory data and social media data, present more information about customers and open new opportunities for retail organizations to achieve unprecedented value and competitive advantage in a fast-growing industry space.

By deploying the right tools and the right processes, data scientists can infer what and when customers want. This talk helps you to better understand the value of big data analytics in the retail industry. We will take a look at six use cases of data scientist in retails, which are currently in production in big retail companies.

Speaker Profile

A Ph.D. qualified data scientist with 5 years of commercial experience and a strong mathematical background who has won several national and international mathematics awards. Amin takes advantage of his mathematical ability in mining and analyzing data to bring value to businesses. With years of professional experiences, Amin has been working on different types of data including time-series, spatio-temporal data (trajectories & GPS), card transaction data, graphs (networks), customer data, bank data, text logs, and sensor data. He Joined Urbis early 2018 to work on card transaction data and analyze and enhance retail performances.


X Topic Abstract

It is interesting to define intelligence initially. Then identifying what is data science (DS). Then move towards business centric benefits. Introduce what my involvement in Data Science. Inter-disciplinary approaches, changing DS landscape. The finally defining the professional models that uphold and reinvigorate data science.

Speaker Profile

Champ Mendis is the Chief Data Scientist at Triple A Super. He also work as an adjunct lecturer for Charles Sturt University, Chair, Computer Society Chapter, Australia IEEE VIC/TAS Section. He has more than 10 years of experience working in Data Science, Artificial Intelligence & Machine Learning and worked for organizations such as Colmar Brunton, University of Melbourne, ACTU, DST (formerly DSTO), University of Sydney, ACTU and ARRB. He holds a PhD in Computing and Information Systems from University of Melbourne and was a member of one of the best research groups in Data Science in Australia

X Topic Abstract

It is interesting to define intelligence initially. Then identifying what is data science (DS). Then move towards business centric benefits. Introduce what my involvement

Speaker Profile

X Topic Abstract

Data Science has proven to be an effective tool in extracting valuable insights from data, and today, more than ever over the last two decades, organisations are investing in their Data Science capabilities. There is still, however, seems to be a present gap between deriving insights and scientific optimal decision making, especially for projects with complex structures and rules. As such,a next logical step to evolve the way organisations benefit from data is to empower them to make the optimal decisions based on the data-driven insights. This would call for optimal decision sciences, such as Operations Research, Mathematical Programming and Optimisation. With the insights, inferences and learnings derived from data with the help of Data Science as the input to a properly built Operation Research approach including mathematical programs/models tailored to the problems, specifications and limitations in hand, powerful solutions can be created to help the organisations fill the gap between deriving insights and optimal decision making.

Speaker Profile

With a background in Mathematics and Computer Science, Dr. Omid Karr has more than 10 years of experience in design and development of Data Science solutions across various industries. Starting his professional career as a Consultant in the financial sector for almost 3 years, he then pursued his career by undertaking PhD studies in Australia. During the PhD studies and after graduation, he continued cutting-edge research and development in a consultancy capacity on Data Science (including ML, DL and AI) and Optimisation. Since 2015, Dr. Karr works with Roster Right, a technology-based management consulting start-up, as the Principal Scientist, where he has co-developed a number of patented scientific engines and has worked on and successfully delivered numerous Data Science and Optimisation projects across multiple industry sectors.

X Topic Abstract

In data science we often interested in the relationships between hundreds of different variables. Being able to represent these relationships visually can often yield significant insights. This presents a challenge for data visualisation since we cannot visualise more than three spatial dimensions as humans. We can split the approaches to deal with this problem into two broad categories. Firstly, there are techniques aimed at reducing the number of variables, we call these dimensionality reduction techniques. Secondly, there are techniques aimed at increasing the number of dimensions we can visualise such as heat maps, pairwise plots and using colour to represent extra dimensions. In this talk we will provide an overview of these techniques.

Speaker Profile

My educational background is in pure mathematics. After my studies I created my company, De Roza Education and research that focuses on tuition and the development of educational material. I wrote software in C++ to automate the production of education material. I soon realised that it is extremely difficult to develop certain types of material such as reading comprehension using only standard programming techniques.

schedule 09:00AM – 09:15AM Registration / Conference Overview
Nitesh Naveen, Partner & Managing Consultants - Digital Transformation, 1.21GWS
schedule 09:15AM - 10:00AM Balancing Innovation With Business Value in Data Science Projects - Click Here for More Info
Jeremiah Mannings, Managing data Scientist| Australia | Co-founder & Head of Tech, Evolved Projects, Capgemini
schedule 10:00AM – 10:45AM Identify Different Types of Data Visualisations and How to Use Them Efficiently
schedule 10:45AM - 11:15AM Tea Break
schedule 11:15AM – 12:00PM Data in Context: Science Insights through Visualization - Click Here for More Info
Khitish Mohanty, Data Scientist, Department of Premier and Cabinet (Vic)
schedule 12:00PM – 01:00PM Lunch Break
schedule 01:00PM - 01:30PM How Data Science Brings Value to Retails - Click Here for More Info
Amin Sadri, Data Scientist, Urbis
schedule 01:30PM - 02:00PM Data Science: Theory to Practice - Click Here for More Info
Champ Mendis, Chief Data Scientist, Triple A Super
schedule 02:00PM – 02:30PM Practice using data visualization methodology with a real data set to uncover business insights
Manish Lalwani, Data analytics and Visualization Manager, nbn™ Australia|Accenture
schedule 02:30PM – 03:15PM Upcoming Trends In Data Science - Click Here for More Info
Omid Karr, Principal Scientist - Data Science & Optimisation, Roster Right
schedule 03:15PM - 03:45PM Tea Break
schedule 03:45PM – 04:30PM Data Visualisation in Higher Dimensions - Click Here for More Info
Shaun De Roza, Data Science Consultant, General Assembly

Conference Ticket Price & Plan

Any One Day

AUD 300

Till 06 March, 2019

Conference ticket

Tea break

Both Days

AUD 600

Till 06 March, 2019

Conference ticket

Tea break