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Dynamic Talks: San Francisco "Advanced Machine Learning for Customer Intell...

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Microsoft Reactor

680 Folsom Street

#145

San Francisco, CA 94107

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Come join us at the next event of our free technical meetup series, "Dynamic Talks", in San Francisco! Dynamic Talks is an ongoing meetup series featuring technical talks from some of the leading experts in tech in major cities around the US. Enjoy talks about the most innovative subjects in AI, ML, voice platforms, the Cloud, and search. Enjoy a night of technical talks and networking opportunities at the Microsoft Reactor space in San Francisco. We hope to see you there!

Topic: "Advanced Machine Learning for Customer Intelligence"

Agenda

[6:00 pm - 6:20 pm]: Guests arrive, food and drinks are served

[6:20 pm - 7:00 pm]: The first talk will be by Jason Guaci on "Moving from Data Scientist Descent to Reasoning Systems," followed by a Q&A

[7:00 pm - 7:10 pm]: Networking break

[7:10 pm - 7:50 pm]: The second talk will be by Ilya Katsov on "New Frontiers in Marketing Data Science and Personalization"," followed by a Q&A

[7:50 pm - 8:00 pm]: Networking break

[8:00 pm - 8:40 pm] The third talk will be by Srivatsan Ramanujam on "ML driven sales and marketing for the enterprise," followed by a Q&A

[8:40 pm - 9:00 pm] More networking time, closing remarks and the event concludes

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Talk Details:

Jason Gauci talk details:

Title: "Moving from Data Scientist Descent to Reasoning Systems."

Abstract: From the user interface to the content ranking, nearly every software product has to reason about how to create the most value for an end user. Reasoning systems complement supervised learning to power some of the world's most popular websites, yet reasoning lags behind supervised learning in adoption and understanding. As a result, engineers and data scientists have to spend an extraordinary amount of time tuning policies and running black-box optimization. In this talk, we'll cover machine learning for reasoning at a broad level and dive into ReAgent, an open source platform for reinforcement learning, contextual bandits, and other reasoning methods.

About Jason Gauci:

Jason Gauci leads the Applied Reinforcement Learning team @ Facebook AI. Jason has 13 years of experience building machine learning systems at Apple, Google Research, and Lockheed Martin Applied Research, and has a PhD in computer science from UCF with a focus on Neuroevolution.

Ilya Katsov talk details:

Title: "New Frontiers in Marketing Data Science and Personalization"

Abstract: In this talk, we will discuss emerging techniques for customer behavior modeling and personalization. First, we will review traditional modeling methods such as look-alike and collaborative filtering, and discuss their limitations. Next, we will walk throuhg several case studies and show how advanced methods can address some of the challenges. In particular, we will show how sequence-to-sequence models help to learn better semantic representation of customers, how reinforcement learning and advanced econometrics help to make better marketing decisions, and how deep learning methods can help to improve personalization models.

About Ilya Katsov:

Ilya joined Grid Dynamics in 2009, and since then has been leading engagements with a number of major retail and technology companies, focusing primarily on Big Data, Machine Learning, and Economic Modeling. He is currently managing the Industrial AI consulting practice that helps clients become successful AI adopters and deliver innovative AI solutions. He is the author of several scientific articles and international patents, and also authored a book, “Introduction to Algorithmic Marketing: Artificial Intelligence for Marketing Operations” (2017).

Srivatsan Ramanujam talk details:

Title: "ML driven sales and marketing for the enterprise"

Abstract: Sales and marketing teams in enterprises have too many leads to pursue but have limited time and budget at their disposal. To build a strong sales pipeline, marketers should target their prospects with the right content to engage their interests and nurture them before handing them off to their sales teams. Prioritizing the right deals for sales team requires effective strategies for scoring leads and accurately forecasting opportunities help them identify issues early to meet their targets. In this talk, we will look under the hood of the machine learning pipelines in Salesforce Einstein that help sales and marketing teams win more deals. Specifically, we'll look at the problem of scoring prospects based on their engagement so that marketers know when they are ready to buy. Next, we will share our journey on model interpretability in providing actionable insights with our predictions. Finally, we will describe how we generate scores and insights for all customers through a model tournament, so that enterprises and small businesses alike can reap the benefits of machine learning.

About Srivatsan Ramanujam:

Srivatsan Ramanujam is a Director of Software Engineering at Salesforce where he currently leads engineering for machine learning products in Salesforce Einstein. Previously at Salesforce, Srivatsan lead the Customer Intelligence data science team, solving problems in customer growth, retention and platform adoption. Prior to Salesforce he led data science projects for customers in multiple industry verticals at Pivotal (VMWare) and was a lead engineer for NLP products at Sony Mobile and ML products at Sony PlayStation. Srivatsan earned a Masters in Computer Science from the University of Texas at Austin.

Parking Information:

Parking is available in the Moscone parking garage at 255 3rd Street, San Francisco, CA 94103, or street parking. Alternatively, use public transport​ when possible.

About Data Points:

Data science and engineering community focused on enterprise applications of AI/ML and practical usages of deep learning, reinforcement learning, natural language processing, advanced optimization, and computer vision in enterprise operations including marketing, supply chain management, security, and logistics.

  • Twitter: @Data_Points_GD
  • Linkedin: Data Points GD
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Date and Time

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Microsoft Reactor

680 Folsom Street

#145

San Francisco, CA 94107

View Map

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