Skip to navigation Skip to main content

Registration and breakfast

Machine Learning

Registration and breakfast

Main Stage: Chair's Opening Remarks

Alex Weber Award-winning host, motivational comedian speaker .

Main Stage Opening Keynote: Virtual Beings Not Virtual Assistants

Edward Saatchi CEO Fable Studio

Main Stage Panel: Spotlight on social media

- Best practice when dealing with large data sets
- How to upscale large ML models
- Understanding data ethics and privacy and how it might affect you in the future

Jeffrey Tang Data Scientist - Team Investigations Tech Lead Twitter
Peipei Wang User Experience Designer LinkedIn
Evren Eryurek PhD Director, Product Management Google

Main Stage: Modern Day Technology

Dan Devone TV Personality NBC Sports

Morning coffee and networking break

Building an online dating recommender system: lessons learned in practicality

One of the most relevant but overlooked challenges in adding machine learning capabilities to an existing product is to minimize disruptions to the tech stack and codebase. In this talk, Shanshan will describe how a small team at Hinge built a game-changing machine learning-based online dating recommender in a matter of weeks by solving this challenge. Moreover, she will discuss how they designed the system with maintainability and scalability in mind.

Shanshan Ding Director of Data Science Hinge

How Bloomberg Media uses Artificial Intelligence

Bloomberg.com consistently ranks among the top 10 most visited news and financial websites on the internet with around 60 million unique visitors a month. We use data to achieve a wide variety of business goals, including driving user engagement, increasing ad revenue, improving conversions for subscriptions, helping subscriber retention, and more.
In this talk, we will provide a broad overview of the different ways we use AI techniques and data to help us achieve these business goals. We will discuss how these techniques translate to our end-user experience, sometimes in very subtle (almost transparent) ways, and help move the needle. We will also present a case study where we used Machine Learning to personalize introductory offers to drive more subscriptions to our website, without cannibalizing revenue.

Dhaval Shah Director of Engineering Bloomberg

Understanding customers and crafting elegant experience for them

In an ever-changing marketplace and industry where new products are constantly emerging, data can play a big role in helping us learn and respond in a timely way to shifts in user interests, behaviors, and needs. Design can be informed by data. Design can also bring deeper meaning
to data. How might data experts work with User Experience Designers in the practice of designing experiments and collecting data, with the common goal of understanding customers and crafting elegant experience for them?

Peipei Wang User Experience Designer LinkedIn

Networking lunch

With innovative launchpad presentations from:
Nico Rode, TIBCO
Wade Tibke, Sigma Computing
Michael McNair, Codey.ai & 55B Labs

Panel: Bridging the data skills gap and preparing for the future

Tal Ben Yakar Senior Data Scientist Uber
Zheqing Zhu Machine Learning Tech Lead Facebook
Jorge Zuloaga Senior Director of Data Science Big Squid

Spotting digital doppelgängers - behavioral clustering of users at scale

Be it for customer segmentation for ad targeting or understanding user behavior to devise product strategy, there is immense value in being able to group hundred of millions of users into a countable number of easily relatable user profiles / clusters. This talk will cover the various unsupervised machine learning techniques available for such a task along with the pros and cons of each technique in practice. In addition, we will cover ways of evaluating the quality of clusters produced by these unsupervised techniques

Jeffrey Tang Data Scientist - Team Investigations Tech Lead Twitter

Deep learning in online commerce

In this talk, we will discuss how recent advancements in artificial intelligence are transforming online retail. Deep learning-based image analysis and natural language processing open exciting new horizons for product discovery, ranking and recommendations. We will share our experience building practical and profitable solutions for online retailers using latest ML technologies and platforms.

Eugene Steinberg Technical Fellow and Principal Architect Grid Dynamics

Afternoon coffee and networking break

Machine Learning and Artificial Intelligence at Marketing@Uber

Uber is part of the logistical fabric of more than 700 cities around the world. Whether it’s a ride, a sandwich, or a package, we use technology to give people what they want, when they want it.

Mario Vinasco Marketing Analytics Manager Uber

Models as a Service for real-time decisioning

This talk will lay down, step by step, the critical aspects of building a well-managed ML flow pipeline that requires validation, versioning, auditing, and model risk governance. We discuss the benefits of breaking the barriers of a monolithic ML use case by using a service-based approach consisting of features, models, and rules.

Join us to have an insight into the technology behind the scenes that accepts a raw serialized model built using popular libraries like H2O, SciKit Learn or Tensorflow, or even plain python source models and serve them via REST/gRPC which makes it easy for the models to integrate into business applications and services that need predictions

Niraj Tank Senior Manager, Software Engineering Capital One
Sumit Daryani Manager, Software Engineering Capital One

Chair's closing remarks

Stefan Byrd-Krueger Chief Analytics Officer ParsonsTKO

Networking drinks reception

Networking drinks reception ends at 6.00pm.

Registration and Breakfast

Machine Learning

Registration and breakfast

Chair's opening remarks

Stefan Byrd-Krueger Chief Analytics Officer ParsonsTKO

Enable machine learning for everyone - An ML case study for abandoned online cart data

Recent development on the machine learning packages and DevOps platforms makes it possible of mass participation for maximum business value. In this talk, we will review those developments and present a case study (Abandoned Cart) as part of T-Mobile’s effort to scale up Machine Learning along with retrospective thoughts on lessons and experiences.

Yunhang Chen Principal Engineer T-Mobile

Panel: Lessons learned from building scalable machine learning projects

Abhishek Sethi Machine Learning Scientist Amazon
Johnson D'Souza Senior Machine Learning Engineer Course Hero
Yunhang Chen Principal Engineer T-Mobile

Morning coffee and networking break

Artificial Intelligence: Building the Future of E-Commerce

In this talk, we will explore the applications of different facets of AI (machine learning, deep learning, NLP and computer vision) in online retail and e-commerce. We will walk through practical examples of how AI can be used to personalize customer experience and revolutionize your customer journey through personalized marketing campaigns, personalized recommendations, supply chain, CRM, reranked search and more. Kamelia will share her thoughts on building successful AI teams and an experiment-driven culture that will reshape your business by enhancing customer experience through AI.

Kamelia Aryafar Chief Algorithms Officer Overstock.com

Computer vision from space

Planet operates the largest constellation of satellites in human history, capturing 1.5 million images of Earth on a daily basis. These images are funneled into their analytic engine, serving intelligence products that are revolutionizing how many industries measure global activity. In this talk, Jesus will present how raw pixels from space are transformed into API feeds of analytic information. He will describe some specific applications, like deforestation monitoring, ship detection and mapping of building and road footprints all over the world. In addition, he will share some lessons learned in the development of machine learning models and datasets.

Jesus Martinez-Manso Engineering Manager, Machine Learning Planet

Machine Leaning in Education

This session will cover key technical challenges in processing academic documents and how they differ from general purpose NLP techniques applied to solve non-structured text use-cases.
In addition we will discuss various Machine Learning solutions to augment academic documents with rich meta data information extracted from these documents.

Johnson D'Souza Senior Machine Learning Engineer Course Hero

Networking lunch

With innovative launchpad presentations from:
Danielle Deibler, Marvelous.ai
Ted Benson, Instabase
Pat Giblin, 451 degrees

Experimentation-driven customer experience

Kuntal offers use cases, lessons learned and business impacts made using customer experience data and experimentation. She shares how combining quantitative data with unstructured quantitative data could provide powerful insights into user behavior that can be leveraged for improving business KPIs. Her team is using AI to support thousands of employees at PayPal and fueling innovation and data driven culture

Kuntal Maya Goradia Director of Analytics PayPal

Human in the loop Machine Learning

Fraud is an adversarial problem where attackers are constantly working against the system to identify and exploit any loopholes that might be present in the system. Unlike most machine learning applications where labels can be automatically inferred by the system, the underlying signatures of fraud attacks are often novel and varied. This makes it critical for humans to work together with the machines and enable the human in the loop system to better discover blind spots and identify novel fraud attacks. In this talk with Affirm’s Head of Data Science, Nitesh Kumar will explain the human-in-the-loop approach and how this model can fundamentally drive machine learning based fraud systems to be more efficient. Additionally, he will discuss how machines can enable and guide human intervention through active learning.

Nitesh Kumar Head of Data Science Affirm

Afternoon coffee & networking break

Main stage: DATAx Start-up Showcase

Hear from the most innovative start-ups in San Francisco and how they will change the data landscape in the next few years.

Alex Weber Award-winning host, motivational comedian speaker .