All posts by Data Scientist

Data Analysis on Aviation Accidents

By | Booz Allen, Data Science, Kaggle | No Comments

Hey there! My name is Katherine Larson and I joined on as a Data Scientist in July 2016, though I had been interning with the firm since 2014. Since my first internship with Booz Allen, it’s been embedded in my head that data is the key to everything. All the trends in the data hold meaning, but it’s up to us to discover what that meaning is through data science techniques. Read More

To Some It’s a Competition; To Me It’s Personal

By | Booz Allen, Data Science, Kaggle | No Comments

Vegetarians don’t understand what I am about to tell you.  I know they like to tell you that veggie-burgers can be just as good; but anyone with a true addiction to the great North American bovine knows it is simply false.  So here it goes: my father has not had a cheeseburger in 18 months.  On the law of averages in this country that would make him a carnivorous outlier.  But Bernie is no ordinary carnivore.  Dad is a man who enjoys his burgers so much that a table of raucous companions would come to silence on the rare occasion he would order any another dish at a restaurant.  But he has not had a burger in 18 months.  The sad fact is that cancer not only takes the people we love, it can also take a way of life.   Read More

Turning Machine Intelligence Against Cancer

By | Booz Allen, Data Science, Kaggle | No Comments
In the U.S., cancer will strike two in every five people in their lifetimes. But it affects all of us.

That’s why, in 2015, the office of the Vice President announced the Cancer Moonshot. It’s an audacious effort to make a decade’s worth of progress in cancer prevention, diagnosis, and treatment in just five years.

Beginning today, the 2017 Data Science Bowl will pursue one of the Cancer Moonshot’s key goals: unleashing the power of data against this deadly disease. Presented by Booz Allen and Kaggle, the competition will convene the data science and medical communities to develop cancer detection algorithms, and help end the disease as we know it. Read More

How Data Science Can Help Cure Cancer

By | Booz Allen, Data Science | No Comments

I will never forget that call.image001

“Kelly has cancer,” my dad said softly.

Knees weak, I sat down on the bed. I didn’t know if my sister was going to live. And, despite us having spent decades doing everything together, she’d have to fight this battle on her own. I’m not the only one who’s heard that kind of call. The moment I experienced was not singular to me, it is one that is repeated over 12.7 million times each year – with over half of those ultimately not surviving. Read More

Winning the 2nd Annual Data Science Bowl: Hedge Funds to Heart Disease

By | Booz Allen, Data Science | No Comments

Tencia Lee, a Math graduate and hedge fund trader, partnered with Qi Liu, a PhD in Physics also with a hedge fund background, to devise the winning algorithm in this year’s Data Science Bowl. They spent more than 100 hours each in evenings and on weekends building and testing algorithms. Working in parallel, Lee and Liu built and trialled hundreds of algorithms to read the heart scans. Their efforts paid off, with the largest prize in the competition, among 993 data scientist contestants in the Data Science Bowl. In this blog, Tencia Lee reveals the work behind the win. Read More

Leading and Winning Team Submissions Analysis

By | Booz Allen, Data Science | No Comments

Can we determine clinical applicability?

This year’s competition was intended to catalyze a change in cardiac diagnostics, so connecting the competition participants and the medical community is an essential part of the DSB. I have done some preliminary analysis of the Data Science Bowl’s (DSB) top 4 team submissions. The goal is to present the results in terms that are meaningful to the medical research community. In doing so I hope to spark a dialog between the communities. Read More

Segmentation and LV localization Based Approaches

By | Booz Allen, NVIDIA | No Comments
In our last blog post we described an end-to-end deep learning solution to this challenge. By “end-to-end” we mean that the raw pixels constituting a SAX study for an individual patient were fed into a convolutional neural network (ConvNet) and predicted left ventricle (LV) systolic and diastolic CDFs came out the other end – the only other processing that took place was the zero mean unit variance (ZMUV) pre-processing of the images. Whilst this approach to the problem is elegant in its simplicity, it is also a very challenging function for a neural network to learn. This is because there is no explicit training signal for the area of the left ventricle that should be measured from each image, just the whole volume for the SAX study. Read More

Building and Working on a Dispersed Team

By | Booz Allen, NVIDIA | No Comments

This year is the first time that Booz Allen and NVIDIA have partnered to enter a team into the Data Science Bowl. Our goal for this combined team was to share some of our successes and challenges along the way, as well as to provide insight into how to approach this type of competition. We’ve been able to post updates about our progress, respond to questions on the Kaggle forums, and help other teams find new ways of looking at the problem. Of course, we’re also hoping that by combining our talent and resources we will be able to come up with a top solution – even if we’re not eligible for the prize money. Read More