Event: Learn Data Science from Experts at Google and Netflix Register now

DataScience: Elevate is a half-day event dedicated to data science best practices and featuring subject matter experts from Google, Netflix, Live Nation, and more.


July 27th, 2017 | Live Stream | 9:00am - 1:30pm PST

About Elevate

Join DataScience.com and other industry leaders for Elevate, a half-day event featuring presentations, panels, and networking sessions focused on elevating data science work.

Elevate was created to share best practices for building big data pipelines, using machine learning and artificial intelligence to solve business problems, and scaling the work of data science teams.

  • When: Thursday, July 27th, 2017
  • Time: 9:00 a.m. - 1:30 p.m. PST

Featuring speakers and panelists from:  

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Do you work with data science, machine learning or artificial intelligence in the Los Angeles area? Register for the live event!

Register For The Live stream

Jay Yonamine
Head of Data Science, Global Patents, Google
Speaker: Expert-Support Data Science
In industry, a dichotomy has emerged between two types of data science applications: fully automated (i.e., recommender engines and ad placement algorithms) and expert support (i.e., data-driven tools to guide expert decision making). While clear best practices and success stories have emerged around fully automated systems, expert-support tools have received far less attention. This talk will introduce the concept of expert-support data science, highlight its importance, and provide best practices to apply within your organization.
John Carnahan
CDO and EVP of Data Science and Engineering, Live Nation

Speaker: Pricing Tickets into the Hands of Fans
For over 40 years, artists and venues have depended on Ticketmaster to get tickets into the hands of fans. In the primary market, tickets are often priced below market leaving a large arbitrage opportunity in the secondary market. The conventional view is that resale trading in the secondary market provides a more efficient marketplace where exchanges better reflect the demand and supply of tickets than the primary market. A global pricing strategy, however, has little to do with primary versus secondary markets and instead requires a marketplace that balances both short and long term concerns of artists and fans. In this presentation, I will describe the approaches that Ticketmaster has taken to sell tickets, using Contextual Bandit and the Verified Fan program, as a single globally-optimized strategy.

Morgan Hansen
Director of Data Science, ALG, a TRUECar Company

Speaker: Finding the Good, Bad, and Ugly Predictions You’re Making Everyday
Building a machine learning model that minimizes prediction error is a core skill for predictive data scientists. Picking the right cost function is usually easy enough, but understanding how well your model is predicting all the nooks and crannies of a diverse data set can help surface trends and groupings that may have gone unnoticed. Visualizing your predictions — either segmented manually or by algorithmically defined categories — can help you identify segments of your prediction population that may need some TLC, find latent variables, or even surface potential business risks.


Tom Kershaw
Chief Product and Technology Officer, Rubicon Project, Inc.

Speaker: Big Data in Ad Tech
For 10+ years, the online advertising industry has been heavily dependent on machine learning algorithms to optimize auction dynamics. Buyers and sellers have relied on complex analyses of petabytes of data to set prices, floors, and other rules that govern the billions of auctions per day that fuel programmatic advertising. The increasing need to optimize results has been in conflict with the need for transparency. Buyers and sellers are questioning the "black box" nature of machine learning. Tom's talk will explore the history of algorithmic optimization in advertising and how to look for a happy medium between complexity and simplicity, as well as between optimization and transparency.


Steve Carter
Chief Scientist, eHarmony
Speaker info coming soon.
F. William High
Senior Data Scientist, Netflix

Will is a Senior Data Scientist at Netflix in Los Angeles, where he builds novel demand and consumption models for the movies and TV shows Netflix streams to its subscribers globally using big data and machine-learning methods. He serves as a Data Ambassador for DataKind, bringing state-of-the-art data science practices to bear on the problems facing non-profits in the health, education, and water sectors since 2013. Previously, he worked in the online digital advertising domain in New York. Will received a Ph.D. from Harvard and a B.A. from Berkeley and has conducted research at Caltech and the University of Chicago, where he specialized in gravitational lensing studies of dark matter and dark energy in the fields of astrophysics and cosmology.

Andrea Trevino
Lead Data Scientist, DataScience.com

Andrea is a lead data scientist at DataScience.com where she designs solutions for business needs across a range of verticals (e.g., adtech, digital media, eCommerce, gaming, IoT/sensor-based systems). Previously, she worked at Boys Town National Research Hospital to improve the efficiency of clinical tests and study the role of bilingualism in perception.  She received her Ph.D. in electrical and computer engineering from the University of Illinois Urbana-Champaign. In her research, she applied data science concepts to understand how acoustic features contribute and interact in human speech recognition. She also conducted research at MIT Lincoln Laboratories, where she used a machine learning approach to understand how biomarkers interact with major depressive disorder.

Lili Jiang
Data Science Manager, Quora
Lili is a Data Science Manager at Quora.  Her team focuses on feed recommendation and new user growth. Prior to Quora, she graduated from Harvard University with a degree in Physics. 

Agenda coming soon