WiDS Taipei
 
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WOMEN IN DATA SCIENCE
TAIPEI CONFERENCE 2019

 
 

WiDS Taipei is an independent event organized by conference ambassadors and volunteers to coincide with the annual Global Women in Data Science (WiDS) Conference held at Stanford University and an estimated 150+ locations worldwide. All genders are invited to attend WiDS regional events, which features outstanding women doing outstanding work.

The Global WiDS Conference aims to inspire and educate data scientists worldwide, regardless of gender, and to support women in the field. This one-day technical conference provides an opportunity to hear about the latest data science related research and applications in a number of domains, and connect with others in the field.

WiDS Taipei Conference & Workshop

This year, we are excited to launch the second conference event at Taipei.  WiDS Taipei Conference will take place on 31th March 2018. In addition to the conference, we partner with Google Taiwan to host 2 free hands-on Machine Learning Crash Course workshops for all levels of leaners to join and network. Data science is a collaborative effort and thus we welcome people of different backgrounds and experiences!

Date
Workshop 1: 2019 March 17, 9 AM - 5 PM
Workshop 2: 2019 March 24, 9 AM - 5 PM
Conference: 2019 March 31, 9 AM - 6 PM

Venue
Workshop: National Taipei University
Taipei Campus

Conference: National Taiwan University
Liberal Education Classroom Bldg.

 
 

Event Schedule & Registration

 

Machine learning Crash course
Concept

09:00-17:00
March 17, 2019

Machine learning crash course
coding

09:00-17:00
March 24, 2019

Wids Taipei conference
 

09:00 - 18:00
March 31, 2019

 
 

* 2019 workshops are free events.
We will charge a deposit during registration and will refund the money after the successful attendance.

 
 

Early Bird & Student ticket

Don’t want to miss the event?  Get the Early Bird ticket and enjoy a disount price! Early Bird ticket are limited.

WiDS Taipei also offers discounted Student ticket.

 

Speakers & Panelists

 
 
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Sharon Chai

Analytics Lead, Marketing Effectiveness
Athleta (Gap Inc.)

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Elvena Fong

Health Data Analytics Program Manager
Center for Health Systems Innovation at Oklahoma State University

Ann Chen   Machine Learning Researche, KKBOX

Megan Sun

Data & Analytics Project Manager
OLX Group

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Bastiane Huang

Product Manager
Osaro

 
 
 
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Guiguan Lin

Technical Project Manager
GallopWave

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Mosky Liu

Python Charmer
Pinkoi

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Galit Shmueli

Distinguished Professor
National Tsing Hua University

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Bianca Chen

Principal Consultant

MasterCard
Applied Predictive Technology

 
 
 
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Tammy Yang

Co-Founder & Data Scientist
DT42

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Irene Chen

Senior Cloud Service Solution Product Manager
iKala

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Mei-hau pan

Data Scientist
Pinkoi

 

 

2019 Conference Full Schedule

 

 
 

09:30 - 09:40

Welcome Adress & Opening Remarks

 

 
 

09:40 - 10:40

(R 102)

Using Health Data to Drive Changes in Rural Health Care

Elvena Fong
Health Data Analytics Program Manager, The Center for Health Systems Innovation (CHSI) at Oklahoma State University

Electronic health record (EHR) data offers vast potential for changing the way that health care is delivered. However, because of the messy nature of the data, there are many data-cleaning issues that must be overcome prior to using this data. Come and learn about Oklahoma State University’s Center for Health Systems Innovation (CHSI), our vision to transform rural health care, how we’ve resolved some of the challenges with using EHR data, and some of our innovations based on health data.

 
 

 
 

10:50 - 11:50

(R 102)

Redefining Robots & Demystify Next Generation AI Enabled Robotics

Bastiane Huang
Product Manager, Osaro

Machine learning has made it possible to shift from manually programming robots to allowing machines to learn and adapt to changes in the environment. We will discuss how AI-enabled robots are used in warehouse automation and how we can use warehouse robotics as an example for other industries such as manufacturing and food assembly. We will describe recent progress in deep reinforcement learning and imitation learning, and discuss the requirements and challenges of various industrial problems, both pipelined and end-to-end systems. Our talk also covers the technology Osaro developed to address the challenges in industrial robotics.

 
 

 
 

10:50 - 11:50

(R 103)

Measurement & Omni-Channel Marketing to Grow Social Enterprise

Sharon Chai
Analytics Lead, Marketing Effectiveness, Athleta (Gap Inc.)

Why is it important to measure marketing efforts? What are the best practices and processes in theory to ensure that marketing is truly effective? How do you handle the challenges of a real-world retail environment and what are practical steps to optimize marketing spend? Sharon will share her perspective on bringing data to omni-channel marketing at Athleta, a premium fitness and lifestyle brand that is committed to igniting a community of active, healthy, confident women and girls who empower each other to reach their limitless potential. Come listen to hear how her journey from literature to data science and a growth-oriented mindset help her combine the art and science of marketing.

 
 

Lunch Break


 
 

13:00 - 14:00

(R 102)

Tactics for Empowering A Product with Artificial Intelligence

Guiguan Lin
Technical Project Manager, GallopWave

With the advancement of technology, we are connected to artificial intelligence in our daily lives - and indeed it will grow at a quicker pace in the near future. There are some amazing cases AI are used behind the scenes to improve user experience. However, AI is not the panacea and cannot be easily adopted. In this talk, I will share the tactics for applying AI into a product in different phases: from ideation, development, launch, to enhancement.

 
 

 
 

13:00 - 14:00

(R 103)

My Data Science Journey, from Physics to Business World

Tammy Yang
Founder, DT42

Introduce how high-energy physics is related to data science, how to recruit good data scientists from physics world, and how I use data science skills in business and management.

 
 

 
 

14:10 - 15:10

(R 102)

Value of Data in Business Experiments

Bianca Chen
Principal Consultant, MasterCard Applied Predictive Technology

In the current world, collecting and saving data is much easier than before. Therefore, how to rapidly analyze and utilize the data to generate value, prove hypothesis and facilitate daily business decisions become key. During this hour I will share how our company (APT) specializes in Test and Learn, a testing cycle and a platform that enables companies to maximize learning through business experiments.

 
 

 
 

14:10 - 15:10

(R 103)

Hypothesis Testing With Python: True Difference or Noise?

Mosky Liu
Python Charmer, Pinkoi

In an experiment, the averages of the control group and the experimental group are 169.61 and 169.88. Is the experimental group better than the control group? Or is the difference just due to the noise? In this talk, I will introduce statistical hypothesis testing to rule out the noise. Moreover, Python-based visualization, calculation, and simulation of various topics will be demonstrated, including α, β, effect size, sample size, inverse α (false discovery rate), and inverse β (false omission rate).

 
 

Coffee Break


 

15:30 - 16:30

(R 102)

Behavioral Big Data & Healthcare Research

Galit Shmueli
Distinguished Professor, The Institute of Service Science, National Tsing Hua University

Behavioral big data (BBD) refers to very large and rich multidimensional data sets on human and social behaviors, actions, and interactions, which have become available to companies, governments, and researchers. A growing number of data science researchers and practitioners acquire and analyze BBD for the purpose of extracting knowledge and scientific discoveries related to healthcare. However, the relationships between the researcher, data, subjects, and research questions differ in the BBD context compared to traditional behavioral data or inanimate big data. Also, the landscape of risks and harm to human subjects has greatly changed, as evidenced by the new data protection regulations (such as GDPR and revised IRB) that came into effect in 2018. Data scientists using health BBD face not only methodological and technical challenges but also ethical and moral dilemmas. These issues also affect industry-academia collaborations. In this talk I will discuss several dilemmas, challenges, and trade-offs related to acquiring and analyzing BBD for healthcare research.

 
 

 
 

15:30 - 16:30

(R 103)

A Data Project Is Born - From Initiative, Resource to Impact

Megan Sun
Data & Analytics Project Manager, OLX Group

A data project is an end-to-end journey from data engineering, analyst, BI, product to business. It is never only finished by data scientists. How to start a data project and make all data related people work for your project? How to get all the people resources ready? What kind of difficulties will you face while implementing? Let’s find the answer with 3 stories - within a team, cross-functional and even across countries.

 
 

16:40 - 17:20

Roundtable + Networking Session

Panelists: Irene Chen, Mei-Hua Pan, Guiguan Lin, Tammy Yang, Bianca Chen, Mosky Liu, Galit Shmueli, Megan Sun

 
 

17:30 - 17:45

Closing Remarks

 
 

Conference & Workshop Organizing Team

 
 
 

Sponsors & Partners

 
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