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How Machines Understand Text and Using that for Clinical Trials, Customer Service, Sales and More

Overview and Case Studies of Natural Language Processing (NLP) and Machine Learning

We have a great session planned that will use two case studies of Natural Language Processing and Machine Learning to explore opportunities that businesses have with understanding text. Come join us for a moderated session led by Dr. Jon Morra as he guides us through in depth use cases of NLP and their applications in predictive analytics. The session will consist of two presentations by leading experts in the fields of NLP and machine learning, and will be followed by a moderated Q and A session.

RapportBoost is an LA-based artificial intelligence company that is focused on optimizing chat agent behavior for large brands that interact directly with their customers. During its two-year quest to develop its proprietary software platform, the company has explored countless natural language and machine learning models to develop an engine capable of separating the signal from all of the noise present in chat data. The results are chats that more effectively engage customers and produce a measurable increase in customer retention, satisfaction, and conversion rates. Dr. Michael Housman will share some of the learnings from this journey and some of the challenges that are next on the horizon.

Deep 6 AI, an LA-based company, got its start by beating out some of the biggest names in analytics in a U.S. government-sponsored contest, leading to a contract with the U.S. intelligence community. After several successful years serving the government space, Deep 6 AI shifted its focus to healthcare exclusively. Moving from one complex data environment to another required the company to evolve its solution from NLP specific to the intelligence community to the healthcare domain, which has tightly controlled hierarchical ontologies. The solution: Deep 6 AI turned to the National Institutes of Health (NIH) and Unified Medical Language System (UMLS) to provide the terminology and medical concepts while building its own NLP pipeline to do the extraction. In this session, we will discuss differences in techniques and tools across industries and how that impacts text parsing, matching, optimization, and more.

Using the two case studies and generalizing from there, we will cover key topics:

• Technologies available to understand text such as Tensor Flow. What they do, their limits, alternatives, where to start.

• Key challenges, especially as you start working on new opportunities.

• Getting over the cold start problem and how to find existing sources of data to help bootstrap the learning processes..

• Key differences in text from different sources (including transcribed text) and the impact that has on solutions.

This is going to be a high impact event that will give you a good understanding of the landscape of natural language processing and machine learning and two great case studies of their use.

Michael Housman, Co-Founder and Chief Scientific Officer, RapportBoost.AI

Michael Housman is the Co-Founder and Chief Scientific Officer at RapportBoost.AI, an artificial intelligence company that uses data and analytics to help large brands communicate more effectively with their customers through chat and messaging. Prior to RapportBoost.AI, he spent 10 years building analytics platforms for early-stage technology companies and engaged in complementary research that was profiled by such media outlets as The New York Times, Wall Street Journal, The Economist, and The Atlantic. Dr. Housman received his A.M. and Ph.D. in Applied Economics and Managerial Science from The Wharton School of the University of Pennsylvania and his A.B. from Harvard University.

Brian Dolan, Chief Scientist + Co-founder, Deep6

Brian Dolan is well-known in the data community as a leading mathematician, data scientist and analyst with more than 20 years of hands-on experience. Prior to co-founding Deep 6 AI, Brian consulted and led enterprise analytic projects from design to implementation for clients like EMC, SuperValu, Northern Trust, Zions National Bank, Havas Media, T-Mobile, the U.S. Intelligence Community, and Research In Motion. Formerly Chief Scientist at Greenplum (acquired by EMC), Brian also headed up data scientist teams at Yahoo! Inc. and served as Director of Research Analytics at FOX/MySpace. He is also co-author of the seminal “MAD Skills: New Analysis Practices for Big Data,” which has been cited more than 400 times in the big data/machine learning industry. Brian has a Master of Arts in Pure Mathematics as well as a Master of Science in Bio-Mathematics from UCLA.

Jon Morra, VP Data Science, ZEFR

Jon Morra is the Vice President of Data Science at ZEFR. IN this role he leads a team of data scientists responsible for creating data-driven models. Jon and his team are focused on understanding video content better in order to drive value for ZEFR’s customers. Previously, Jon was the Director of Data Science as eHarmony where he helped support a variety of machine learning initiatives including matching, pricing, churn modeling, and fraud prevention. Jon holds a B.S. in Biomedical Engineering from Johns Hopkins and a Ph.D. also in Biomedical Engineering from UCLA.

The LA CTO Fourm is generously sponsored by Amazon AWS.

This event is also sponsored by Homeier Law.

Venue: Red Bull Media House, 1740 Stewart Street, Santa Monica

Parking: You can park for free in the gated Red Bull Media House parking lot. From Stewart Street, pull in to the gate and tell the security com that you're here for the event. There will be a valet in front of the building.