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Scaling Data Science with WIDS 2017

I attended the second Annual WIDS conference this year which was an amazing one day event held at Stanford. It had various a lot of interesting sessions from distinguished speakers from the Academics as well from the Industry. This event provided me a great opportunity to hear about the latest data science related research being conducted, and how data science is being leveraged to solve challenging issues and interesting problems from healthcare to the oil industry, from statistics modeling to utilizing Machine Learning for Social good. The event also inspired me to perform research on unexplored areas of Data science where I can utilize my potential as well as connect with potential mentors, collaborators, and others in the field.

What made the event more remarkable is that apart from the 500 attendees present at the event from industry and academic, we were also joined by women participating through 75+ regional events worldwide. It clearly showed the deep interest in Data Science by women across the globe.

Here are some short summaries of some of the talks I attended:

· Keynote by Diane Greene, Google Cloud

She gave an interesting story of her life and her work at different companies all through the years (She co-founded VMware!). As the leader of Google Cloud Business, she gave an interesting insight on where Google in applying Machine Learning and coming up with good predictions.

· Designing Visualizations: A Sytematic Approach — Miriah Meyer, University of Utah

Miriah described the process of understanding the usefulness of data — “Data Counseling” as she coined it. She spoke of how important it is to operationalize real-world problems into actionable tasks, referencing the data.

· Using Machine Learning to determine how many people have been killed in Syria -Megan Price, Human Rights Data Analysis Group

It was quite interesting to know how the non-profit group used methods from Statistics and Computer Science to quantify mass violence. Her talk focused on quantifying uniqueness in victims killed in the ongoing conflict in Syria using open source tools in Python and R.

· Mission-oriented Research: Data Science Supporting National Security- Deborah Frincke, National Security Agency (NSA)

Deborah led the Afternoon Keynote with a very fascinating presentation on the agency’s research programs and provided key-insights on the challenges NSA faces with Data Science like Adversial Machine Learning. She talked about how predictive analytics is used to provide decision makers with timely and accurate forecasts of global events. Nether less to say, everyone was on the edge of the seats to know where the NSA was getting their data from and how they were evaluating it!

· What Machine Learning can do for Healthcare, and what Healthcare can do for Machine Learning — Finale Doshi- Velez, Harvard University

I was very excited to listen to her talk as I had recently published my work in ML for Healthcare at NIPS and has seen her work at that conference too. Finale showed us her groups’ work in learning time series and sequential decision-making models. She stressed on the problems associated with data collection in Healthcare industry, how the data received can be of low quality, from biased sources and the need for robust data for better understanding and solving problems in Healthcare.

· Beware what you ask for: The Secret life of Predictive Models — Claudia Perlich

Claudia gave an eye-popping presentation on Predictive modeling and gave different examples showing how models sometimes behave differently from what we expect them to do. She gave an important message that we should pay attention to predictions made by model and when they are ready to be released ‘into the wild’.

Apart from these talks, there were many other Technical Vision Talks, Breakout sessions as well as Career Panel led by leading panelists from the industry.

This conference provided a good discussion on the current state of the art in machine learning algorithms, tools and architectures for intelligent systems. There are a lot of exciting things that are starting to happen around Data science, ML, Cognitive sciences and technology. This conference helped me understand the major areas and directions engineers are working on. I was truly made aware of how broad data science as a field was. I am super inspired now to keep learning and working towards making a difference!

As a Speaker remarked “Focus on your strengths…. There has never been a better time to be a woman in Data Science”

Happy Learning!

If you missed the #WIDS2017 talks by the speakers, you can catch them at