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Presentation: Panel: Sequential Data

Track: Sequential Data: Natural Language, Time Series, and Sound

Location: Cyril Magnin I

Duration: 2:40pm - 3:20pm

Day of week: Wednesday

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Abstract

In this panel, we will discuss how the different fields within sequential data processing can benefit from each other, what the future trends are that we expect and take your questions.

Speaker: Joel Grus

Senior Research Engineer @allen_ai

Joel Grus is a research engineer at the Allen Institute for Artificial Intelligence in Seattle, where he works on AllenNLP, a deep learning framework for AI researchers. He wrote the beloved O'Reilly book Data Science from Scratch and the beloved blog post "Fizz Buzz in Tensorflow". In his spare time he does stand-up comedy and makes livecoding videos. Oh, and he doesn't like notebooks.

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Speaker: Keunwoo Choi

Research Scientist @Spotify

Keunwoo Choi is currently a Research Scientist at Spotify working with deep learning. Before working at spotify he worked for Naver Labs Corp and the Electronics and Telecommunications Research Institute. He has worked with music signal and deep learning, music information retrieval, technical translation, and various digital audio processing projects. Keunwoo received his Master of Science in Electrical Engineering and Computer Science from Seoul National University and his PhD from Queen Mary University of London.

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Speaker: Emmanuel Ameisen

Head of AI @InsightDataSci

Emmanuel Ameisen is the Head of AI at Insight Data Science. Emmanuel has years of experience going from product ideation to effective implementations. At Insight, he has led over a hundred AI projects from ideation to finished product in a variety of domains including Computer Vision, Natural Language Processing, and Speech Processing. Previously, he implemented and scaled out predictive analytics and machine learning solutions for Local Motion and Zipcar. Emmanuel holds master’s degrees in artificial intelligence, computer engineering, and management from three of France’s top schools.

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Speaker: Sean Taylor

Core Statistics Team @Facebook

Sean J. Taylor is a computational social scientist on Facebook's Core Data Science team specializing in field experiments, statistical modeling, and causal inference. His research interests include social influence processes and combining human expertise with machine learning. Sean received his Ph.D. in information systems at NYU’s Stern School of Business and holds a B.S. in economics from The University of Pennsylvania.

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2019 Tracks

  • Predictive Data Pipelines & Architectures

    Case Study focused look at end to end predictive pipelines from places like Salesforce, Uber, Linkedin, & Netflix

  • Sequential Data: Natural Language, Time Series, and Sound

    Techniques, practices, and approaches around time series and sequential data. Expect topics including image recognition, NLP/NLU, preprocess, & crunching of related algorithms.

  • ML in Action

    Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud