Women in data science – WiDS 2018

The Stanford Women in Data Science conference 2018  is starting on March 6th at 1am Hong-Kong time

Live Broadcast

We encourage everyone to follow the broadcast here 

You can tweet using the hashtag #WiDS2018Q


The program can be found here, we reproduce it here for convenience in HK time zone

1:00-1:10am: Opening Remarks: Margot Gerritsen, Senior Associate Dean and Director of ICME, Stanford University
1:10-1:30am: Welcome Address: Maria Klawe, President, Harvey Mudd College
1:30-2:05am: Keynote Address: Leda Braga, CEO, Systematica Investments
2:05-2:10am Regional Event Check-in
2:10-2:50am: Technical Vision Talks:
     2:10-2:30am Mala Anand, EVP, President, SAP Leonardo Data Analytics
     2:30-2:50am Lada Adamic, Research Scientist Manager, Facebook
2:50-3:10am: Morning break
3:10-3:15am: WiDS Datathon Winners Announced
3:15-3:55am: Technical Vision Talks:
     3:15-3:35am: Nathalie Henry Riche, Researcher, Microsoft Research
     3:35-3:55am: Daniela Witten, Associate Professor of Statistics and Biostatistics, University of Washington
3:55am-4:30am: Keynote Address: Latanya Sweeney, Professor of Government and Technology in Residence, Harvard University
4:30-6:00am:  Lunch and Breakouts (NO LIVESTREAM)
6:00-6:35am: Keynote Address: Jia Li, Head of Cloud R&D, Cloud AI, Google
6:35-7:15am Technical Vision Talks:
     6:35-6:55am: Bhavani Thuraisingham,
Professor of Computer Science and Executive
Director of Cyber Research and Education Institute, University of Texas at Dallas
     6:55-7:15am: Elena Grewal, Head of Data Science, Airbnb
7:15-7:30am  Afternoon break 

7:30-7:35am Regional event check-in
7:35-8:15am Career Panel moderated by Margot Gerritsen
Bhavani Thuraisingham 
 Professor of Computer Science and Executive
Director of Cyber Research and Education Institute, University of Texas at Dallas
     Ziya Ma,  Vice President of Software and Services Group and Director of Big Data Technologies, Intel Corporation
     Elena Grewal Head of Data Science, Airbnb
     Jennifer Prendki, Head of Data Science, Atlassian
8:15-8:55am: Technical Vision Talks
     8:15-8:35am: Risa Wechsler, Associate Professor of Physics, Stanford University
     8:35-8:55am: Dawn Woodard, Senior Data Science Manager of Maps, Uber
8:55-9:00am: Closing Remarks


November Unhackathon

Our 3rd event !

Once again a small crowd of Data Scientists has been courageous enough to fight their impulse for just chilling out in the wonderful sunday’s weather in HongKong and instead came to hone their skills on 2 topics :

  • An exploration of HKEX data and its links to HK financial markets
  • A study of the very hyped cryptocurrencies

Crypto-currencies correlation

This topic stemmed from the follow-up of the previous “Coindex” subject.
The study of correlation should give an idea of how much diversification would be important in a portfolio or index of crypto-currencies, in other words, how much an index would provide a sense of the true performance of the currencies in the crypto world.

Here the focus has been given to a classical-flavored study of correlation among the currencies available on Poloniex Exchange on sep 16th, 2017.
First of all a joyplot retrieved the shapes of return distributions for many currencies :
ridge_plot.jpegSome currencies such as OMG (OmiseGo) and CVC (Civic) are too new and then have a short historics that meks them not at all normally distributed, and are then considered as outliers and removed from the scope.

Then we came up with proper correlation calculations


And we can get a 36% global average correlation (average of all 1 to 1 correlations), hinting that diversification could be an important driver of portfolio efficiency.

If we graph this measure along time, we see that the correlation tends to increase along time, suggesting that there is some re-correlation of crypto markets.

Next step might be to understand why this re-correlation happens.

The complete analysis, including the used data, can be found on github.


Our first event: Unhackathon at the Hive


What is an Unhackathon anyway?

Data Science Hong Kong was set up to as a way for people interested in data science to network and share ideas. We have an active public Slack group where people regularly share articles and discuss all things tech and data science. The group has organised a number of informal meetups before but we wanted to a start a regular event based around coding and presenting, and not just on talking and networking.

There are many IT, tech and data science events in Hong Kong but they are infrequent and often serve primarily as a marketing or recruitment tool. Not satisfied with the state of tech events in Hong Kong, we set out to create an event that was started from the bottom up and would focus on who knew the most and not who spoke the loudest, which is inviting to beginners but not to those uninterested in technical details.

We have therefore started a regular unhackathon. This is our term for a hackathon where the agenda would be set by participants and people would have fun coding together, instead of being a competition. It’s a way to improve your skills and share projects you are passionate about with the community.

Our first event gets under way

Our first gathering was made possible by The Hive. They were very keen on supporting the data science community in Hong Kong and let us use the MakerHive in Kennedy Town which was a fantastic venue for our first event.

The event started with the floor being opened to pitches. After signing up for a slot by putting up a post-it, pitchers were given 5 minutes to convince others to work on their project.


There were many great ideas and teams were formed around those that attracted enough interest. Discussions were soon under way on what each team wanted to achieve by the end of the day.


Of course, being a hackathon, there was coding, coding and more coding!


As it became time for lunch, teams headed out to Kennedy Town center to find a restaurant. Any loss of coding output was more than made up for by the opportunity that people got to better know their teammates. Real data scientists don’t skip lunch!

Presentation time

4 hours and much coding later the deadline for presentations loomed. All the teams gladly accepted a 20 minute grace period to put the final touches on their work.


Some of the projects presented were :

  • Address mapping in Hong Kong
  • Twitter topic analysis
  • Crypto-currency analysis
    This team aimed at building an index of cryptocurrencies similar to usual financial market indices, to be used as a benchmark of refined to explore portfolio strategies.


  • Facial Expression Recognition using Keras
    内嵌图片 3
    The team of 3 used a MNIST convolutional neural network model and retrained it on facial expression data from Kaggle, with 55% accuracy over 7 categories

Everyone had made great progress on their projects and a common theme across presentations was that so much more could have been accomplished with just a bit more time. It’s good then that we already have started planning for our next event in September!

Just because the event is over does not mean the coding stops! If you enjoyed the project you worked on or more importantly enjoyed the people you worked with then do continue collaborating and share with us what you did at our next event!

If this event seems interesting then please contact us by email, social media or join our slack group. We’ll keep you updated there about any future events.

Data Science Hong Kong