1st Singapore ACM SIGKDD Symposium

On Friday afternoon 27 July 2018, a number of esteemed data science thought leaders gathered at Hotel Jen Orchardgateway in the first-ever, invitation-only Singapore ACM SIGKDD Symposium.

It was an insightful session, and the attendees got to know one another, shared their current focus, exchanged ideas, and began a discussion towards a common vision.  We were very fortunate to have a diverse representation from academia, government, and industry.  There is also a consensus to pursue further such activities in the future that forge a stronger connection among the data science community in Singapore.

Look out for coming events in this space!


Aloysius Lim (in green, SIGKDD Chapter Membership Chairperson & Director of AI Products, Eureka AI) welcomed arriving attendees


Hady Lauw (SIGKDD Chapter Chair & Associate Professor, Singapore Management University) opened the session by introducing the Singapore ACM SIGKDD Chapter


Ee-Peng Lim (Professor, Singapore Management University) on Smart Systems for Citizens


Koo Sengmeng (Deputy Director of Strategic Alliances, AI Singapore) introduced AI Singapore


Robby Tan (Assistant Professor, National University of Singapore) on Computer Vision: Bad Weather, Motion and Human Image Analysis


Gyorgy Lajtai (Chief Executive Officer, Lynx Analytics) on How Neural Networks Help the Science of Loyalty


David Hardoon (Chief Data Officer, Monetary Authority of Singapore) on AI for Finance


Koo Ping Shung (Co-Founder, DataScience SG) introduced DataScience SG


João Gomes (Data Scientist and Director of Customer Success, DataRobot) on Automated Machine Learning: Enabling the AI-Driven Enterprise


Ying Li (SIGKDD Chapter Treasurer & Chief Scientist, Eureka AI) led an open discussion on data science issues


Xiaoli Li (Head of Data Analytics Department, Institute for Infocomm Research) on PU Learning and Imbalanced Learning


Jing Jiang (Associate Professor, Singapore Management University) on Recent Trends in Natural Language Processing


Arijit Khan (Assistant Professor, Nanyang Technological University) on Expressibility of Vertex-Centric, Distributed Graph Processing Paradigm


Bing Tian Dai (in blue, Assistant Professor, Singapore Management University) discussed his work on AI for Pedagogy


Ng See Kiong (Director of Translational Research, Institute for Data Science) on KDD Challenges & Opportunities


Zhu Feida (Associate Professor, Singapore Management University) on Data of the People, By the People, For the People




KDD.SG Tutorial organized by SIGKDD Singapore, DataScience SG & SMU SIS

Update on 9 June 2018: See the coverage of the event here.

Download the flyer for this tutorial and share it with your friends!

Image Classification Using Convolutional Neural Networks
with Applications to Facial Expression Recognition and Visual Sentiment Analysis

June 9, 2018 | 9.30am to 12pm | SOE/SOSS Seminar Rm B1-1 (Rm B127), SMU


With the prevalence of smartphones, everyone is now a casual photographer, leading to the abundance of images on the social media.  One useful machine learning task is to categorize an image into one of several classes. In this tutorial, we will cover how to build such a classifier using a deep learning technique called Convolutional Neural Networks or CNN.  While the task is generally applicable, this tutorial will focus on two applications relevant to user sentiments and preferences. One application is facial emotion recognition, classifying whether a photo indicates emotion such as happiness and sadness.  Another application is visual sentiment analysis, classifying whether an image found in a review signifies high or low rating.


Hady W. Lauw is an Assistant Professor of Information Systems at Singapore Management University (SMU), as well as NRF Fellow of the Singapore National Research Foundation.  Formerly, he served as postdoctoral researcher at Microsoft Research in Silicon Valley, as well as scientist at A*STAR’s Institute for Infocomm Research. He received his PhD from Nanyang Technological University on A*STAR Graduate Scholarship.  At SMU, he leads the Preferred.AI research project, whose research activities span data mining and machine learning, focusing on preference analytics and recommender systems. More information may be found at http://www.hadylauw.com.

Quoc-Tuan Truong is a PhD candidate in School of Information Systems, Singapore Management University (SMU). His research interests include machine learning, text mining and social network data analytics, with a focus on mining user preferences from review text and images. Quoc-Tuan received his Bachelor degree from University of Engineering and Technology, Vietnam National University, Hanoi. He was one of the ten Vietnamese recipients of the prestigious Honda Y-E-S (Young Engineer and Scientist’s) Award in 2016.  More information may be found at http://www.qttruong.info.

Date/Time Saturday 9 June 2018, beginning with coffee and tea at 9.30am

Venue SOE/SOSS Seminar Rm B1-1 (Rm B127), Basement 1, School of Economics/School of Social Sciences, Singapore Management University (Campus Map)

Prerequisite  You should be proficient in programming; familiarity with Python is a plus.  Prior knowledge of neural networks would be immensely helpful.

Preparation  Bring your own laptop.  There is guest wifi connectivity with limited speeds on site.  You are also welcome to bring your own internet connectivity. We will inform confirmed registrants of the data and packages to be downloaded a week beforehand.

RSVP on our Meetup.

See you there!

Industry Session at ADMA’17

Towards our objective of bridging the data science communities in academia and industry, SIGKDD Singapore and ADMA2017 jointly organized the Industry Session in the Advanced Data Mining and Applications 2017 conference (ADMA’17), which took place on Nov 6, 2017 in NTU Alumni House at Marina Square.

The session featured the following talks by five accomplished professionals at the forefront of data science developments in Singapore.

Data Mining-based Cost Optimisation for Electricity Retailers
by Dr Phua Chun Wei Clifton, Director of Analytics, NCS Group

Advancing Ensembles for Population Data Mining
by Dr Graham Williams, Director of Data Science Asia/Pacific, Microsoft

Personalised News and Video Recommendation System at LinkSure
by Dr Rubing Duan, Director of Big Data Department, LinkSure

Innovation Opportunities in the Logistics Industry
by Timothy Kooi, Innovation Leader, DHL’s Innovation Center

AI in Healthcare – Opportunities and Challenges from a Health System’s Perspective
by Sijia Wang, Team Lead from Health Insights Department, Integrated Health Information Systems (IHiS)

We thank all the speakers for the informative and insightful talks.


Dr Clifton Phua on Data Mining-based Cost Optimization for Electricity Retailers


Dr Graham Williams on Advancing Ensembles for Population Data Mining


Sijia Wang on AI in Healthcare


Inaugural Tutorial Attended by 120

The first KDD.SG tutorial jointly organized by SIGKDD Singapore and MAS was successfully held on September 14, 2017 at the MAS Theatrette.

We extend our special thanks to the tutorial speaker, Dr. Graham Williams, who delivered an informative hands-on session on machine learning with R.

The event could not have happened without the generous support by the MAS team, namely: Zhi Rong Chng, Su Fen Lee, and David Hardoon.

We are particularly grateful for the strong support from the community.  120 attendees came from various organizations in Singapore, including the universities (e.g., NUS, NTU, SMU), government agencies (e.g., MAS), as well as companies (e.g., KPMG, SingTel, UOB, PwC, DHL, DBS, SAS).

We look forward to organizing more events and activities towards building a stronger data science community in Singapore.


Dr. Graham Williams (Director of Data Science, Microsoft Asia, Singapore) delivered the tutorial on Machine Learning in R


Dr. Hady Lauw (Chair) introduced the Singapore ACM SIGKDD Chapter


Tutorial attendees in the MAS Theatrette


Inaugural KDD.SG Tutorial Jointly Organized with MAS

Download the flyer for this tutorial and share it with your friends!

Hands-On Machine Learning in R – Getting Started

September 14, 2017 | 6pm to 8.30pm | MAS Building

Graham Williams

Director of Data Science, Microsoft Asia, Singapore
Graham joined Microsoft recently after over 30 years as researcher, developer and educator in Artificial Intelligence, Machine Learning, Data Mining, Analytics and Data Science. He was previously lead Data Scientist for the Australian Government’s Center of Excellence in Data Analytics, Director of Data Science at the Australian Taxation Office, and Principal Research Scientist with CSIRO Australia. Over those 30 years he has also been an active contributor to the Open Source ecosystem with open source software projects across Emacs, Linux, Data Science, and R. Graham has authored many books, papers, Internet resources and software packages for data scientists. His latest book released in July 2017 introduces the Essentials of Data Science through a programming-by-example based approach to knowledge discovery in R. He maintains an extensive collection of R and Data Science resources on https://togaware.com.


Machine learning underlies much of today’s technological advances driving enterprises who are embracing data and information to become knowledge-driven organisations. R is a popular ecosystem for Knowledge Discovery, Machine Learning and Data Science. It has grown over the past 20 years from a specialist tool for a handful of statisticians to an estimated 4 million users today. It provides a platform for building and deploying machine learning based models.  This tutorial will introduce, with a hands-on focus, the R ecosystem for Machine Learning and Artificial Intelligence. We will understand some of the most widely used machine learning algorithms, including decision trees, and explore how we build models R. The concepts of ensembles will also be introduced as the state-of-the-art classification learner. The tutorial will focus on hands-on learning so that all attendees will gain the experience to then continue with building such models on their own. A particular focus is on the use of readily repeatable template scripts for our tasks.


Date/Time Thursday 14 September 2017, beginning with a light reception at 6pm

Venue MAS Theatrette, Level 16, Looking Glass, Level 11, MAS Building, 10 Shenton Way, Singapore 079117

RSVP on https://goo.gl/forms/duN9oipf3WGGgn2s1. Provide the info for entry into the venue. Due to limited capacity, we will send you a confirmation email if your registration is accepted.  Attendance will be recorded.

Preparation Bring your own laptop with your own internet connectivity (or Wireless@SG available on-site) for use of the X2Go application which you should also install. If you have no network connectivity then be sure to install R and Rattle and related packages prior to the session.

Call for Tutorials Interested in conducting your own tutorial?  Also check out our Call for Tutorials!