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!

Machine learning Crash course
Concept

09:00-17:00
March 17, 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.

Welcome Adress & Opening Remarks

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

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.

14:10 - 15:10

(R 102)

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).

15:30 - 16:30

(R 103)