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DataScience: Elevate | Livestream Event – Register Today

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.


Join 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

View the full agenda.

Featuring speakers and panelists from:  speakers4.jpg

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

Livestream Registration Here!




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.


moran-hansen.jpgMorgan Hansen
Director of Data Science, ALG, a TRUECar Company
Speaker: Finding the Good, Bad, and Ugly Predictions You’re Making EverydayBuilding 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.jpgTom 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: The funny side of data science: Using humor to predict successful dates
Everyone knows that eHarmony studies marriages to find out what features of individuals and couples predict the best relationships.  What you may not know, however, is that eHarmony has also spent years using complex machine-learning algorithms to predict which couples are most likely to talk to each other online so that we can help our users actually get to that first date.  Now, eHarmony is aiming our research at what makes for a great first date, and our first phase is focusing on that funniest part of your personality: Your sense of humor.
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.
Wesley Kerr
Senior Data Scientist, Riot Games
Wesley is a Senior Data Scientist at Riot Games where he develops and deploys data-powered products for League of Legends.  He has helped ship a product which provides six unique discounted offers for every player of League of Legends, and more recently he has been embedded with the Player Behavior team, where he develops machine learning algorithms to better understand and detect unsportsmanlike behavior in League of Legends.  Previously, he was a Senior Software Engineer at Google working in Google Research and as part of the app understanding team within Google Play.  He graduated from the University of Arizona with a Ph.D. in Computer Science with an emphasis on Artificial Intelligence.  His research interests include developing new algorithms to improve game experiences by leveraging machine learning techniques in game environments.


Aurora LePort
Data Scientist, Verizon Wireless
Aurora LePort, Ph.D. is a Data Scientist at Verizon Wireless in Irvine, CA where she builds predictive models using various machine and deep learning techniques to forecast customer behavior. Her findings enable the Marketing, Finance and Customer Care teams to build better experiences for Verizon’s customers. Previously, she worked as Head of Research at GrandPad Inc., a startup producing a simple and secure tablet computer that digitally connects seniors to family and friends. She combined evidence-based therapy with senior-friendly technology to facilitate the social and cognitive fitness of its elder users. Dr. LePort completed The Data Incubator’s Data Science Fellowship in 2016 and received her Ph.D. in Neurobiology and Behavior from UC, Irvine in 2014. At UCI she successfully pioneered the study of Highly Superior Autobiographical Memory using a combination of MRI, behavioral & cognitive tests.


Andrea Trevino
Lead Data Scientist,
Andrea is a lead data scientist at 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 leads a team of data scientists at Quora in Mountain View, CA. Her team leverages data-driven research and machine learning to optimize Quora’s content recommendation system, which delivers personalized knowledge to hundreds of millions of users. Previously, she was a lead data scientist at Quora, where she focused on using data insights to identify opportunities in user activation and retention channels. She graduated from Harvard with a degree in Physics. At Harvard, she conducted biophysics research that constructed Markov chain and biased random walk models to understand protein RNA interaction and energy barriers.

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