Dynamic Talks: San Francisco "Contextual recommendations and next-best acti...

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Microsoft Reactor

680 Folsom Street


San Francisco, CA 94107

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Come join us at the second event of our free technical meetup series, "Dynamic Talks", in San Francisco!

Topic: Contextual recommendation and next best action models

The first talk will be given by Ilya Katsov, Head of Practice, Industrial AI at Grid Dynamics, and will be about decision automation in marketing systems using reinforcement learning. The second talk will be given by Pallav Agrawal, Director of Data Science at Levi's, and will be about realtime contextual product recommendations that scale and generate revenue. The third talk will be given by Madhura Dudhgaonkar, Senior Director, Machine Learning Products at Workday, and will be about machine learning services - how to begin, and when do you start scaling? Come enjoy a night of technical talks and networking opportunities at the Microsoft Reactor space in San Francisco. We hope to see you there!


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

[6:20pm - 7:00pm]: First talk will be presented by Ilya Katsov on "Decision Automation in Marketing Systems using Reinforcement Learning", followed by a Q&A

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

[7:10pm - 7:50pm]: Second talk will be presented by Pallav Agrawal on "Realtime Contextual Product Recommendations...that scale and generate revenue", followed by a Q&A

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

[8:00pm - 8:40pm] Third talk will be presented by Madhura Dudhgaonkar on "ML Services - How do you begin and when do you start scaling?", followed by a Q&A

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

Ilya Katsov's talk details:

Title: "Decision Automation in Marketing Systems using Reinforcement Learning"

Abstract: In this talk, we will discuss automatic decision-making and AI techniques for customer relationship management. First, we will present a methodology that helps to develop highly automated promotion and loyalty management systems. Next, we will walk through practical examples of how predictive models can be used to characterize customer intent, and how optimization and reinforcement learning techniques can be used to build next best action models that incorporate targeting, budgeting, and pricing decisions.

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).

Pallav Agrawal's talk details:

Title: "Realtime Contextual Product Recommendations…that scale and generate revenue"

Abstract: Recommendation systems are all around us. E-commerce companies like Amazon recommend products we are likely to buy based on our browsing behavior. Netflix suggests what shows we should watch based on our binging habits. Spotify builds a personalized playlist we would enjoy listening to, based on their understanding of what musical genre we are into.

In this talk, we will explore recent advances in the area of product recommendations in both research and practice. We will see how machine learning, design thinking and solid data engineering principles are combined to create an engaging customer experience that positively impacts the bottom line.

We will look at how we use various deep learning architectures to obtain image and text embeddings that supplement user and product based features to generate product recommendations that align closely with a consumer’s aesthetic preferences.

The talk would be of interest to data scientists, data engineers, product managers, UX designers and anyone interested in machine learning.

About Pallav Agrawal:

During the daytime, Pallav works as a Data Scientist and tries to extract meaningful signals from the noisy world we live in. As the moon rises and evening sets in, all bets are off and one might find Pallav on his bike riding through the Berkeley hills in bright colored lycra or performing never-before-scenes of Dramedy with his Improv troupe.

Pallav is a part-time Human Centered Design Thinking coach and has helped non-profits and early-age startups develop clarity on their mission and recognize growth areas. He moved to the Bay Area in 2010, and somehow managed to acquire a Masters in Structural Engineering after spending two years actually learning how to think.

He is an avid follower of Seth Godin, Ken Robinson, and Nicholas Taleb, and is currently looking at ways to explain algorithms through cute, anthropomorphized animals.

Madhura Dudhgaonkar's talk details:

Title: ML Services - How do you begin and when do you start scaling?

Abstract: So you have heard all the hype around how Machine Learning is going to change the world. But within your business context, where do you start? How do you get leadership buy-in for investment? And how and when you start scaling your ML Services?

In this session, you will walk away with an actionable framework to bootstrap and scale a machine learning services team. You will see the framework in action through an actual 0 to 1 product journey involving deep learning where we delivered value in record speed in-spite of not having a dataset when we started. You will get practical tips on how to make decisions about when and how to scale your capability to scale ML Services and platform. Some of the tips are pretty counterintuitive and revealed themselves with our experience of productizing ML services over the last 5+ years. (Using a diverse range of technologies - Vision, Language, Graph, Anomaly Detection, Search Relevance, Personalization)

About Madhura Dudhgaonkar:

Madhura Dudhgaonkar is a Machine Learning Product Leader at Workday who is passionate about leveraging technology to make our lives better. Madhura’s career journey goes from being a hands-on engineer to leading large product organizations across SUN Microsystems, Adobe, and now Workday. Her background covers building both consumer and enterprise products - the latest involving multiple 0 to 1 product journeys leveraging Machine Learning. She is considered a thought leader in building ML products, and is frequently invited to speak at AI conferences.

Madhura holds a Master’s degree in math and computer science. She is also passionate about creating a future where talent shines and grows regardless of where they come from and what they look like. She drives diversity and inclusion work via leading a Women at Workday chapter. When not obsessing over technology, she can be found outdoors, running, hiking or snowboarding.

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Microsoft Reactor

680 Folsom Street


San Francisco, CA 94107

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