Cutting through the Big Data Hype

This conference aims to create a space for knowledge-sharing among data scientists applying machine learning techniques in their respective industries.


Engage in academic and yet industry-focused discussions. We explore how academic advances in machine learning have been tailored to address business problems and disrupt industries. We encourage creative solutions for transforming data and modifying algorithms to adapt to real-world constraints.


We open the discourse on challenging trade-offs when considering AI solutions. Regulatory compliance may require trade-offs in accuracy to allow for more interpretability and trust in machine learning models. Organisations have to constantly weigh the cost of privacy concerns against acquiring more data, risk short-term losses for enhancing long-term AI development, and sacrifice algorithmic accuracy to meet efficiency requirements.


We delve into specific use cases, discuss the practicality and suitability of various approaches, as well as the deficiencies and challenges encountered. The talks will challenge data scientists to innovate on machine learning techniques as well as develop a more holistic perspective on creating AI solutions.

Cutting through the Big Data Hype

This conference aims to create a space for knowledge-sharing among data scientists applying machine learning techniques in their respective industries.


Engage in academic and yet industry-focused discussions. We explore how academic advances in machine learning have been tailored to address business problems and disrupt industries. We encourage creative solutions for transforming data and modifying algorithms to adapt to real-world constraints.


We open the discourse on challenging trade-offs when considering AI solutions. Regulatory compliance may require trade-offs in accuracy to allow for more interpretability and trust in machine learning models. Organisations have to constantly weigh the cost of privacy concerns against acquiring more data, risk short-term losses for enhancing long-term AI development, and sacrifice algorithmic accuracy to meet efficiency requirements.


We delve into specific use cases, discuss the practicality and suitability of various approaches, as well as the deficiencies and challenges encountered. The talks will challenge data scientists to innovate on machine learning techniques as well as develop a more holistic perspective on creating AI solutions.

Cutting through the Big Data Hype

This conference aims to create a space for knowledge-sharing among data scientists applying machine learning techniques in their respective industries.


Engage in academic and yet industry-focused discussions. We explore how academic advances in machine learning have been tailored to address business problems and disrupt industries. We encourage creative solutions for transforming data and modifying algorithms to adapt to real-world constraints.


We open the discourse on challenging trade-offs when considering AI solutions. Regulatory compliance may require trade-offs in accuracy to allow for more interpretability and trust in machine learning models. Organisations have to constantly weigh the cost of privacy concerns against acquiring more data, risk short-term losses for enhancing long-term AI development, and sacrifice algorithmic accuracy to meet efficiency requirements.


We delve into specific use cases, discuss the practicality and suitability of various approaches, as well as the deficiencies and challenges encountered. The talks will challenge data scientists to innovate on machine learning techniques as well as develop a more holistic perspective on creating AI solutions.

Our Speakers


Foong Chee Mun

Chief AI Officer
MoneyLion

Roger Nash

Head of Data and Insights
Seek Asia

Ernest Chiew

Data Scientist
MoneyLion

Mohammad Tariq Hussain

Principal Data Scientist
Digi Telecommunications

Dr. Chan Chee Seng

Associate Professor
University of Malaya

Tan Wern Yi

Data Scientist
Digi Telecommunications

Sina Meraji

AI Product Manager
Supahands

Jenna Yang

Lead Data Scientist
MoneyLion

Asif Muhammad Iqbal

Head of Data Science
Digi Telecommunications

Shirley Zhu

Data Scientist
MoneyLion

Wong Chin Lin

Data Scientist
Seek Asia

Foong Chee Hong

Director of Data Science and Analytics
MoneyLion

Amir Othman

Editor and Analyst
Petak Ajaib

Khairul Nazran

Associate Analyst, Data Science Analytics
Bank Negara Malaysia

Ghazal Ghalebandi

Data Scientist
Seek Asia

More about the speakers More about the speakers

KL Data Science Conference 2019

KL Data Science Conference 2019



Explore the possibilities of machine learning advances designed to engage business challenges and disrupt industries.

*Limited seats available.

Contact Us

venue-location@2x

SEEK Asia

20, Menara AIA Cap Square,
No. 10, Jalan Munshi Abdullah, 50100 Kuala Lumpur.
Tel: +6(03) 2718 6868

Partners