Applying AI & Machine Learning To Media, Advertising & Entertainment
SEPTEMBER 22-25
Sponsored by
The data science salon is a unique vertical focused conference which grew into a diverse community of senior data science, machine learning and other technical specialists. We gather face-to-face and virtually to educate each other, illuminate best practices and innovate new solutions in a casual atmosphere.
Featured speakers
KEN ROMANO
Product Director at Associated Press
Denver Serrao
Sr. Software Development Engineer at WP Engine
ILYA KATSOV
Head of Data Science at GridDynamics
ANNE BAUER
Director, Data Science at The New York Times
AMIT BHATTACHARYYA
Head of Data Science at VOX Media
Asaf Somekh
Co-Founder & CEO at Iguazio
Daniel Meehan
CEO & Founder of Padsquad
Yves Bergquist
Program Director, AI & Neuroscience in Media Entertainment Technology Center at USC
Wes Shockley
Senior Manager, Audience & Analytics at Meredith Corporation
Aaron Richter
Senior Data Scientist at Saturn Cloud
Bob Bress
Head of Data Science at Freewheel, A Comcast Company
Steve Sobel
Global Industry Leader - Communications, Media & Entertainment at Databricks
Daryl Kang
Lead Data Scientist at Forbes
Roger Magoulas
Senior Content Advisor at Formulated.by
Alyssa Zeisler
Research & Development Chief at The Wall Street Journal
Amy Yu
VP, Product Strategy and Data Science Viacom
Sophia Tee
Senior Manager, Data Science Verizon
ALLY TUBIS
Senior Director, Customer Modeling at Disney Streaming Services
Dominick Rocco
Data Scientist at PhData
RYAN C. MCCABE
Senior Data Scientist at Spotify
Kim Martin
Data Science Manager at Netflix
Ilke Demir
Senior Research Scientist at Intel Corporation
Puneet Gangrade
Data Science Team Lead at MightyHive
Bradley Deutsch
Data Science Director at Known
Nathan Burke
Lead Data Science Engineer at Salesforce
Veysel Kocaman
Senior Data Scientist & ML Engineer at John Snow Labs
Himanshu Swamy
Manager - Data Science at Publicis Sapient
About
Data Science Salon unites the brightest leaders in the media, advertising, and entertainment across the nation in data science fields. We gather face-to-face and virtually to educate each other, illuminate best practices, and innovate new solutions. Data Science Salon | Media, Advertising & Entertainment is the only industry conference that brings together specialists in the media and entertainment data science field to educate each other, illuminate best practices, and innovate new solutions in a casual atmosphere. Get the most current state of current industry trends and innovations in media, advertising, entertainment through DSS podcasts, exclusive content, Webinars and live Trainings. DSS also has an extensive on-demand video library of presentations from the top industry experts.
A SAMPLE OF TOPICS COVERED
Enhancing the Viewer Experience with Machine Learning & AI
Content Personalization and Monetization
Personalization at Scale with AI
Cloud Automation and Machine Learning
Audience Targeting and Segmentation (across platforms)
Data and AI for emerging platforms
Data Governance
and More!
Sessions
(All times are US Eastern Time)
Tuesday, September 22 – Media
12:00 pm – 12:25 pm – Introduction & Guided Meditation
12:25 pm – 12:40 pm – 15 minute break
12:40 pm – 1:10 pm – Data science and fact-based journalism during times of crisis
Ken Romano – Product Director at Associated Press
1:50 pm – 2:15 pm – Personalization and Fraud Detection using Event Sequences Analysis
2:10 pm – 2:25 pm – 15 minute break
2:30 pm – 2:55 pm – Experimenting with Recommendations at The New York Times
3:00 pm – 3:30 pm – Content-Driven Advertising using First Party Data
3:35 pm – 4:00 pm – Predicting Ad Performance in Real Time Based on Multi-Variant Data
Asaf Somekh – Co-Founder & CEO at Iguazio & Daniel Meehan, CEO & Founder of Padsquad
4:05 pm – 4:25 pm – Computational Storytelling: Inside Hollywood’s Secret AI Lab
Yves Bergquist – Program Director, AI & Neuroscience in Media Entertainment Technology Center at USC
4:30 pm – 4:45 pm – Wrap up
Wednesday, September 23 – Media & Entertainment
12:00 pm – 12:10 pm – Introduction & Housekeeping
12:10 pm – 12:35 pm – Changing Media Habits : Data-Driven Editorial Strategy at Meredith
Wes Shockley – Senior Manager – Audience and Analytics at Meredith Corporation
12:40 pm – 1:05 pm – Is your Python too slow? Hardware and software for accelerating data science
Aaron Richter – Senior Data Scientist at Saturn Cloud
1:10 pm – 1:40 pm – Media Applications of Operations Research and Optimization Methods
Bob Bress – Head of Data Science at Freewheel, A Comcast Company
1:45 pm – 2:15 pm – Accelerating big data solutions for media use cases
Steve Sobel – Global Industry Leader – Communications, Media & Entertainment at Databricks
2:05 pm – 2:25 pm – 20 minute break
2:35 pm – 2:55 pm – Summarizing News Clusters Through Text Vectorization
3:00 pm – 3:45 pm – Panel Discussion: The Crisis In Media. Facts vs. Fiction in the new Always On World
Sophia Tee – Senior Manager, Data Science at VerizonAlyssa Zeisler – Research & Development Chief at The Wall Street Journal
3:45 pm – 3:55 pm – Wrap up
5.00 – 5.45 – Cooking Demonstration
Thursday, September 24 – Entertainment
11:30 am – 12:30 pm – Virtual Coffee Chat
12:30 pm – 12:40 pm – Introduction & Housekeeping
Ally Tubis – Senior Director, Customer Modeling at Disney Streaming Services
1:15 pm – 1:45 pm – The Future of Filmmaking: AI for Volumetric Capture
Ilke Demir – Senior Research Scientist at Intel Corporation
1:50 pm – 2:10 pm – MLOps in Practice: Experiment Tracking & Hyperparameter Tuning
Dominick Rocco – Data Scientist at PhData
2:10 pm – 2:25 pm – 15 minute break
2:25 pm – 2:55 pm – Automated Content Marketing Messaging
Ryan C. McCabe – Senior Data Scientist at Spotify
Friday, September 25 – Advertising
12:00 pm – 1:00 pm – Networking
1:00 pm – 1:10 pm – Introduction & Housekeeping
1:15 pm – 1:45 pm – Optimizing Media Frequency Using Google Ads Data Hub and Differential Privacy
1:50 pm – 2:10 pm – Data Science in Modern Marketing: Uniting Planning, Creative, and Execution
2:10 pm – 2:25 pm – 15 minute break
2:25 pm – 2:55 pm – Model Cards: A Transparency Tool for AI-Driven Marketing
Nathan Burke – Lead Data Science Engineer at Salesforce
Workshops
(All times are US Eastern Time)
Tuesday, September 29
2.00 pm – 3.00 pm Operationalizing ML Models with Verta MLOps
Conrado Miranda, CTO at Verta
- What is MLOps and how does it relate to DevOps and SDLC practices
- How to take models from training to staging via ModelDB and the Model Registry
- How to package, configure, and deploy models
- How to monitor models running in production
Wednesday, September 30
12.00 pm – 1.00 pm Natural language understanding at scale with Spark NLP
Veysel Kocaman – Senior Data Scientist and ML Engineer at John Snow Labs
Natural language processing is a key component in many data science systems that must understand or reason about text. Common use cases include question answering, paraphrasing or summarization, sentiment analysis, natural language BI, language modeling, and disambiguation. Building such systems usually requires combining three types of software libraries: NLP annotation frameworks, machine learning frameworks, and deep learning frameworks.
Thursday, October 1
12.00 pm – 2.00 pm Optimize content at scale using State-of-Art NLP techniques in cloud environment
NLP, also known as computational linguistics, is the combination of AI and linguistics that allows us to talk to machines as if they were human. This session is about an important concept used in the current state of the art applications in Speech Recognition and Natural Language Processing – viz Sequence to Sequence modelling. This will convert an input sequence into an output sequence. Just to give you a sneak peek of the potential application of seq2seq model can be speech recognition, machine translation, question answering, Neural Machine Translation (NMT), and image caption generation. This workshop will showcase on how to build a language model that we’ll focus on using recurrent neural network which captures the entire context of the input sequence. Seq2seq models typically employ two Recurring Neural Networks (RNNs). The model is trained to map an input sequence to an output sequence which are not necessarily of the same length as each other. The basic structure of the model is a network of encoders and decoder, bidirectional self-attention layer. I will showcase thorough implementation of a content optimization system using NLP techniques along with scalable deployment of model within Cloud.
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