Agenda

Predictive Analytics World for Business Las Vegas 2020

May 31-June 4, 2020 – Caesars Palace, Las Vegas



TRACK TOPICS – The three tracks of the main two-day conference cover these topics:
BUSINESS
Operationalization, management, best practices
Track 1
TECH
Machine learning methods & advanced topics
Track 2
CASE STUDIES
Cross-industry business applications of machine learning
Track 3
TOPICS – The sessions across this two-day, three-track conference are grouped into the following four topics:
BUSINESS
Operationalization, management, best practices
Track 1
TECH
Machine learning methods & advanced topics
Track 2
CASE STUDIES
Cross-industry business applications of machine learning
Track 3
Session Levels:

Blue circle sessions are for All Levels
Red triangle sessions are Expert/Practitioner Level

Pre-Conference Workshops - Sunday, May 31st, 2020

8:30 am
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm

This one day workshop reviews major big data success stories that have transformed businesses and created new markets. Click workshop title above for the fully detailed description. 

Session description
Instructor
Marc Smith
Chief Social Scientist
Connected Action Consulting Group
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm

Gain experience driving R for predictive modeling across real examples and data sets. Survey the pertinent modeling packages.  Click workshop title above for the fully detailed description. 

Session description
Instructor
Robert MuenchenUniversity of Tennessee
Manager of Research Computing Support
University of Tennessee
4:30 pm
End of Sunday Pre-Conference Training Workshops

Pre-Conference Workshops - Monday, June 1st, 2020

8:30 am
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm

This one-day session surveys standard and advanced methods for predictive modeling (aka machine learning). Click workshop title above for the fully detailed description. 

Session description
Instructor
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm

This one-day introductory workshop dives deep. You will explore deep neural classification, LSTM time series analysis, convolutional image classification, advanced data clustering, bandit algorithms, and reinforcement learning. Click workshop title above for the fully detailed description. 

Session description
Instructor
James McCaffreyMicrosoft
Senior Scientist Engineer
Microsoft
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm

Python leads as a top machine learning solution – thanks largely to its extensive battery of powerful open source machine learning libraries. It’s also one of the most important, powerful programming languages in general. Click workshop title above for the fully detailed description. 

Session description
Instructor
Clinton BrownleyWhatsApp
Data Scientist
WhatsApp
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm

Machine learning improves operations only when its predictive models are deployed, integrated and acted upon – that is, only when you operationalize it.  Click workshop title above for the fully detailed description. 

Session description
Instructor
James TaylorDecision Management Solutions
CEO
Decision Management Solutions
4:30 pm
End of Monday Pre-Conference Training Workshops

Predictive Analytics World for Business - Las Vegas - Day 1 - Tuesday, June 2nd, 2020

8:00 am
Registration
Networking Breakfast
8:45 am
Eric SiegelPredictive Analytics World
Conference Founder
Predictive Analytics World
8:50 am
MACHINE LEARNING WEEK KEYNOTE
Lessons from: Lyft

In this keynote address, Gil Arditi will cover the areas of machine learning development at Lyft, talk about friction points in the model lifecycle – from prototyping and feature engineering to production deployment – and show how Lyft streamlined this process internally. He will also cover a step-by-step example of a model that was recently developed and taken to production.

Session description
Speaker
Gil ArditiLyft
Product Lead, Machine Learning
Lyft
9:15 am
MACHINE LEARNING WEEK KEYNOTE
Lessons from: Google

As principles purporting to guide the ethical development of Artificial Intelligence proliferate, there are questions on what they actually mean in practice. How are they interpreted? How are they applied? How can engineers and product managers be expected to grapple with questions that have puzzled philosophers since the dawn of civilization, like how to create more equitable and fair outcomes for everyone, and how to understand the impact on society of tools and technologies that haven't even been created yet. To help us understand how Google is wrestling with these questions and more, Jen Gennai, Head of Responsible Innovation at Google, will run through past, present and future learnings and challenges related to the creation and adoption of Google's AI Principles.

Session description
Speaker
Jen GennaiGoogle
Head of Responsible Innovation, Global Affairs
Google
9:40 am
The Session Description will be available shortly.
Session description
10:00 am
Exhibits & Morning Coffee Break
10:30 am
Track 1: BUSINESS - Operationalization, management and best practices
Digital decisioning
10:30 am - 10:50 am

Digital decisioning operationalizes machine learning and artificial intelligence so your systems act intelligently on your behalf, making precise, consistent, real-time decisions at every touch point. Digital decisioning applies machine learning and artificial intelligence at scale to automate the decisions essential for more profitable, more customer-centric, and more digital business operations. 

In this session, thought leader James Taylor will outline a proven approach to digital decisioning, by which large, established companies gain value from machine learning. He'll cover examples of large, established companies succeeding with digital decisioning to illustrate the key principles of the approach. Whether you are using machine learning to drive operational efficiency, improve customer satisfaction, manage risk, or reduce fraud, digital decisioning will help you succeed.

Session description
Speaker
James TaylorDecision Management Solutions
CEO
Decision Management Solutions
Project leadership
10:55 am - 11:15 am
Lessons from: Cisco

In a climate where 85% of data science projects fail, moving beyond the hype into the plateau of productivity necessitates understanding what differentiates successful from unsuccessful analytics initiatives. This talk takes an inside-out view of how analytics-driven organizations operate differently and provides insight into how you as a leader can harness their best practices.

Session description
Speaker
Jennifer RedmonCisco
Chief Data Evangelist
Cisco Systems, Inc
Track 2 - TECH - Machine learning methods & advanced topics
Model explainability/interpretability

As predictive modelers, we love advanced machine learning techniques such as neural networks, deep learning networks, and decision tree ensembles because the accuracy they can achieve can be significantly better than linear methods, such as linear and logistic regression. However, these methods do not offer the advantages of linear methods for model interpretation and diagnostics. Which variables are the most important in the model? How stable are the model parameters? How stable are the model predictions? 

This talk will describe the use of random permutation to uncover and describe model input and model prediction sensitivities. Techniques such as Breiman’s “permutation importance” and the use of bootstrap sampling to uncover sensitivities will be discussed and will be applied to models built from data drawn from customer analytics.

Session description
Speaker
Dean AbbottSmarterHQ
Co-Founder and Chief Data Scientist
SmarterHQ
Track 3 - CASE STUDIES - Cross-industry business applications of machine learning
Cross-enterprise applications
10:30 am - 10:50 am
Case study: Facebook

In this talk, we will explore production level machine learning application at Facebook scale and the lessons learnt in developing and deploying ML applications across Ranking & Recommendation, Computer Vision, and NLP domains. 

We will learn the challenges across Data, Feature processing, Model training and serving at Facebook scale.  The privacy and security implication and the continious integration / continious deployment consideration for ML applications.

Session description
Speaker
Mohamed FawzyFacebook
Senior Software Engineering Lead - AI Infra
Facebook
Cross-enterprise applications
10:55 am - 11:15 am
Case study: Walmart Labs and Sam's Club

Machine Learning is emerging as a key enabler in building next generation products. Machine learning accelerates the product lifecycle. In this session, Walmart Lab's Director of Product Management Vinod Suresh will explore two case studies, one where machine learning helped identify customer problems and the other where it helped scale a product from 1 to 100.

Session description
Speaker
Vinod SureshWalmart Labs
Director of Product Management
Walmart Labs
11:15 am
5-minute transition between sessions
11:20 am
Track 1 - BUSINESS - Operationalization, management and best practices
Gaining stakeholder buy-in
11:20 am - 11:40 am
Lessons from: FedEx

Lack of trust in artificial intelligence / machine learning is pervasive throughout society. And the greatest areas of distrust are associated with AI’s potential impact on jobs. This lack of trust contributes to the under-utilization of collaborative AI systems and severely reduces their possible benefits. In this talk, Clayton discusses how to develop and launch AI systems to ensure that knowledge workers trust and ultimately use the system’s insights and recommendations.

Session description
Speaker
Clayton ClouseFedEx
Senior Data Scientist
FedEx
Pitfalls and best practices
11:45 am - 12:05 pm

Success stories abound that extoll the use of machine learning and analytics in a wide variety of fields and on a wide swath of problems. But there are valuable lessons to be learned from what didn't work, whether a spectacular failure or just bumps in the road. In this talk, we describe real-world examples, some anonymous and most from the speaker's decades of analytical consulting. We will describe pitfalls, misconceptions, and uncertainties commonly encountered by companies in their path towards adoption of analytics that led to failure, lost time, and wasted investment.

Session description
Speaker
Track 2 - TECH - Machine learning methods & advanced topics
Machine learning automation
Case study: Facebook

Optimizing product features, promotion campaigns, and machine learning model hyperparameters is advantageous because effective configurations can improve outcomes, such as engagement, performance, and quality.  However, it is often challenging to identify superior configurations because the space of possible configurations is vast, and the resources available for searching the space is limited.  This talk introduces Ax, an open source platform for optimizing experiments using multi-armed bandit and Bayesian optimization, including how it has been successfully applied to a variety of product, infrastructure, and ML applications.  By automating the process of identifying effective configurations via adaptive experiments, Ax enables product managers, developers, and analysts to efficiently get the most out of their software.

Also sign up for Clinton Brownley's workshop:  Machine Learning with Python: A Hands-On Introduction

Session description
Speaker
Clinton BrownleyWhatsApp
Data Scientist
WhatsApp
Track 3 - CASE STUDIES - Cross-industry business applications of machine learning
Analytics in retail
11:20 am - 11:40 am
Case study: Walmart

All retailers want to know their target buyers better. However, understanding the past and present of their interactions simply isn’t enough these days and predictive analytics is the next step to better understanding their customers. 

In this session, topics of discussion will include, yet will not be limited to:

  • How ML can enable price optimization, product placement and assortment selection
  • Using machine learning algorithms effectively for generating suggestions for substitute and complimentary items
  • Using deep learning and reinforcement learning to improve order forecasting
  • Utilizing optimization algorithms to reduce store costs by optimizing replenishment cycle and safety stock
  • Scaling algorithms to generate recommendations for individual stores and to monitor their performance
Session description
Speaker
Hamza FarooqWalmart Labs
Principal Data Scientist
Walmart Labs
Marketing applications; reinforcement learning
11:45 am - 12:05 pm
Case study: Samsung

We developed a brand new reinforcement learning based approach to address one of the biggest challenges in email marketing - maximize engagement (CTR) and thus conversions. We have developed an agent that intelligently learns from previous campaigns and prescribes target population that better respond to a future campaigns. We refer to this agent as "PE" (Prescriptive Engine). Underneath the hood PE solves Multi-Arm Bandit problem/Exploration-Exploitation dilemma. In this session we will share details about the underpinnings of PE and how PE has been put into action at Samsung.

Session description
Speaker
Venkata PakkalaSamsung
Staff Data Scientist
Samsung
12:05 pm
Lunch
1:30 pm
SPECIAL PLENARY SESSION

The three most important analytic innovations I’ve seen in (35 years of) extracting useful information from data are: Ensemble models, Target Shuffling, and Awareness of Cognitive Biases. Ensembles are competing models that combine to (very often) be more accurate than the best of their components. They seem to defy the Occam’s Razor tradeoff between complexity and accuracy, yet have led to a new understanding of simplicity. Target Shuffling is a resampling method that corrects for “p-hacking” or the “vast search effect” where spurious correlations are uncovered by modern methods’ ability to try millions of hypotheses. Target shuffling reveals the true significance of a model, accurately assessing its out-of-sample precision. Lastly, the increased understanding of our Cognitive Biases, and how deeply flawed our reasoning can be, reveals how projects can be doomed unless we seek out — and heed — constructive critique from outside.

Session description
Speaker
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
2:15 pm
The Session Description will be available shortly.
Session description
2:35 pm
5-minute transition between sessions
2:40 pm
Track 1: BUSINESS - Operationalization, management and best practices
Cross-enterprise management
2:40 pm - 3:00 pm
Lessons from: Google

In this session, Rich will discuss how Google approaches using AI and machine learning within the enterprise. He'll share how Google staffs and structures AI and machine learning teams, specific processes and governance of enterprise projects, and examples of AI and machine learning use-cases. 

In addition to a high-level view, he'll deep dive into a number of these use-cases to understand them and their wider applicabilities.

Session description
Speaker
Richard DuttonGoogle
Head of Machine Learning for Corporate Engineering at Google
Google
Cross-enterprise management
3:05 pm - 3:25 pm
Lessons from: LinkedIn

In this presentation, we will share with the audience our work at AI at LinkedIn and cover what it takes to launch AI products at scale by way of a few examples, such as building a machine learning platform at scale for ML engineers at LinkedIn, and more.

Session description
Speaker
Priyanka GaribaLinkedIn
Head of Artificial Intelligence Technical Program Management
LinkedIn
Track 2 - TECH - Machine learning methods & advanced topics
Uplift modeling, marketing analytics
Case study: Sam's Club

Marketing isn't just any more about predicting who's more likely to buy a product, it's about identifying which customers are more likely to be persuaded by advertising. Traditional response models often target shoppers that would have bought a product anyways. Ib contrast, uplift modeling focuses on maximizing incremental sales by only targeting those customers that have a high likelihood of making a purchase if they receive an offer. 

This session will cover how Sam's Club leverages this highly effective machine learning technique, and what it takes to develop a culture of experimentation and build out a contact and response history from numerous tests, which serves as a foundation for the development of uplift models.

Session description
Speaker
Markus DmytrzakSam’s Club
Director, Advanced Analytics and Decision Sciences
Sam's Club
Track 3 - CASE STUDIES - Cross-industry business applications of machine learning
Product recommendations, analytics in the entertainment industry
2:40 pm - 3:00 pm
Case study: Fadango

Fandango is the ultimate digital network for all things movies serving more than 60 million unique visitors per month across the entire movie life cycle. In this talk we will discuss Fandango360, Fandango's B2B platform that allows studio marketers and partners reach fans of their upcoming movies with targeted interactions across popular social media and advertising platforms. We will highlight some of the technical, machine learning algorithmic and application-centric novelties behind this endeavour and will conclude the talk with some of the performance marketing results, insights and learning stemming from hundreds of theatrical marketing campaigns run on the platform.

Session description
Speaker
Reeto MookherjeeFandango
Vice President, Data, Analytics and Business Intelligence
Fandango
ML in AdTech
3:05 pm - 3:25 pm
Case study: 33Across and SOVRN

In the programmatic AdTech space, the trend of header bidding explosion has put the strain on both Supply Side Platforms (SSPs) and Demand Side Platforms (DSPs). In many cases, 95% of bid requests from SSPs do not get a bid response from DSPs and DSPs hate processing so much junk. The result is ever-increasing infrastructure (AWS) costs for both sides of the business.

In this session, attendees will learn how machine learning techniques can be applied to millions of rows of data to predict which bid requests are unlikely to receive a bid response, and how even a minor improvement of 0.4% in average bid response rates can result in incremental revenue of $100,000 or more.

Session description
Speaker
Allen YuLineate
Director of AI
Lineate
3:25 pm
Exhibits & Afternoon Break
3:55 pm
Track 1 - BUSINESS - Operationalization, management and best practices
Analytics strategy
3:55 pm - 4:15 pm
Lessons from: Freewheel, A Comcast Company

Data Science provides a company the  opportunity to create tremendous competitive advantages.  Often times, Data Science applications within a company's product suite can differentiate it from the industry at large.  This presentation covers some of the key strategies and concerns that a company must consider when pursuing the development of intellectual property for competitive advantage. Here we look at key considerations such as: what makes Data Science innovations patentable, when to consider patenting versus trade secrets, and how to identify areas where innovation will provide the most advantages for a company.

Session description
Speaker
Bob BressComcast Cable
Vice President of Analytics & Business Intelligence
Freewheel, A Comcast Company
Project leadership
4:20 pm - 4:40 pm

Many analytic models never get the data they need to be successful; many analytic models that do are never deployed successfully into operations; and, many deployed models never bring the value they promised to stakeholders.  In this talk, we give a framework for those leading analytic or AI projects or interested in leading these types of projects in the future that improves the odds for overcoming these and related challenges.  We also discuss the results of a survey about some of the common reasons analytic and AI projects fail that underpins the framework.

Session description
Speaker
Robert Grossman
Managing Partner
Analytic Strategy Partners LLC
Track 2 - TECH - Machine learning methods & advanced topics
Uplift modeling, marketing analytics
Case study: CVS

Usually, a campaign's incremental sales or profit margins are used to evaluate the performance of marketing programs. The incremental sale or margin is :  S/M(target group) - S/M(control group).  Here, we have developed a predictive model for

                                 max[S/M(target group) - S/M(control group)] 

by using personalization data and machine learning methods. The first step is to develop the predictive baseline model at individual  level:  

                               P[S/M(control group)] = P[probability]* P[amount/purchaser]

which is for the estimation of the control group. Similar models are built for the target group. The selection of the target population in the future campaign is based on the maximum of the difference.

Session description
Speaker
John GaoWorkHuman
Senior Manager
WorkHuman
Track 3 - CASE STUDIES - Cross-industry business applications of machine learning
Cybersecurity applications
3:55 pm - 4:15 pm
Case study: McAfee

The cybersecurity market is now forecast to reach USD 289.8B by 2026 but the proper application of machine learning, deep learning, and AI to handle 500,000 new unique threats per day is imperative to ensure you're not a target.  An appropriate development process examining the risks including Deep Fake (images, videos, and text), Model Hacking (Adversarial Machine Learning attacks) and Explainability of complex algorithms are key to building trust in the final security application.  Learn how to protect yourself against these risks as you develop your algorithms, and what steps you can take to minimize your vulnerability.

Session description
Speaker
Celeste FralickMcAfee
Chief Data Scientist, Senior Principal Engineer
McAfee
Building data science capacity
4:20 pm - 4:40 pm
Case study: Canada Energy Regulator

To create a foundation of expertise in data science, the National Energy Board of Canada (NEB), now the ‘Canada Energy Regulator,’ held cross–organizational data science workshops aimed at helping teams build capacity in data analytics, experimentation culture and evidence-based decision making. In this talk, industry expert Shingai Manjengwa of Fireside Analytics will cover the approach her firm implemented for the NEB, for both “hands–on” data science training, including computer programming, as well as an on–line version of the training that now has over 500 learners registered and a completion rate of 80%. This case study showcases a novel professional development and training approach to building these capacities. Find out how they did it and how you increase capacity in your organization.

Session description
Speaker
Shingai ManjengwaFireside Analytics
Chief Executive Officer
Fireside Analytics Inc.
4:40 pm
5-minute transition between sessions
4:45 pm
Track 1: BUSINESS - Operationalization, management and best practices
Management
4:45 pm - 5:05 pm
Lessons from: Xerox PARC

Machine learning and product management are two of the hottest things in tech right now, but putting them together requires reflection on the specific skills, techniques, attitudes and responsibilities for PMs when it comes to ML-driven products. Machine learning is not 'technology as usual' and so product managers need to adapt by a) understanding how the technology works, b) learning how to communicate an ML-centric value proposition to diverse stakeholders, c) adjusting how the MVP is defined and d) not becoming so enamored with the ML that the non-ML stuff is forgotten.

Session description
Speaker
Mark CramerPARC, a Xerox Company
Applied AI Product Management
Xerox at PARC
Analytics culture and leadership
5:10 pm - 5:30 pm
Lessons from: AppFolio

You may have heard the quote, "culture eats strategy for breakfast"; even the best strategies can fall flat without the right energy in execution. 

In this session, you'll hear about how AppFolio's Analytics & Research Community has inspired expanded leadership engagement, deeper cross-functional collaboration, and new ways of thinking across the business. Join us for this session if you'd like to learn more about how to build and nurture a fun and impactful Analytics culture, and avoid the sentiment of "just another meeting".

Session description
Speaker
Michael GaltressAppfolio
Director, Business Analytics & Insights
Appfolio
Track 2 - TECH - Machine learning methods & advanced topics
Uplift modeling, marketing analytics
Lessons from: Fidelity

Randomized experiments allow us to determine the overall treatment effect of a program (e.g. marketing, medical, social, education, political, economic). Uplift modeling takes a further step to identify individuals who are truly positively influenced by a treatment or intervention through machine learning and predictive modeling by uncovering heterogeneous treatment effects in available data. This technique enables us to identify the “persuadables” and thus optimize target selection in order to maximize treatment impact. This important subfield of data science or business analytics has gained tremendous attention in recent years in application areas such as personalized marketing, personalized medicine, political election, and healthcare programs with plenty of publications and presentations from both industry practitioners and academics across the world.

However, business and medical applications often involve more than one treatment. Additionally, there are often budget and quantity constraints involved. This talk will review current uplift modeling methodologies, extend predictive modeling to multiple treatment situations, bridge the gap between predictive analytics and prescriptive analytics by introducing the mathematical problem for treatment optimization, and propose various solutions to both deterministic and stochastic optimization problems. Examples from the retail industry will be used as an illustration. While the talk is geared towards marketing type applications (“personalized marketing”), the same methodologies can be readily applied in other fields such as insurance, medicine, education, political, and social programs.

Session description
Speaker
Victor LoFidelity Investments
AI and Data Science Center of Excellence Leader, Workplace Investing
Fidelity Investments
Track 3 - CASE STUDIES - Cross-industry business applications of machine learning
Customer feedback, text analytics
Case study: Google

Companies are frequently faced with large amounts of unstructured text data, like forum comments or product reviews. Important trends can emerge in these datasets, but it can be time-consuming to read through comments, and keyword matching frequently misses critical nuances. We'll discuss how we've approached this problem at Google using Natural Language Processing, with examples of the approach applied to open datasets. We'll explore how this fits into the ML project lifecycle, with examples of common pitfalls. Finally, we'll highlight how to use this technology as part of a "human in the loop" approach to supercharge your existing team members.

Session description
Speaker
Peter GrabowskiGoogle
Software Engineering Manager
Google
5:30 pm
Networking Reception
7:00 pm
End of first Conference Day

Predictive Analytics World for Business - Las Vegas - Day 2 - Wednesday, June 3rd, 2020

8:00 am
Registration
Networking Breakfast
8:45 am
KEYNOTE
Lessons from: GM

Drawing from his experience as the chief data and analytics officer at three different companies, A. Charles Thomas – now chief data and analytics officer at General Motors – will share insights and lessons learned from both sides of the unique, two-pronged role he plays at GM.

First, Charles' team leverages analytics to enhance GM's traditional businesses, such as selling vehicles, OnStar, Warranty, SiriusXM, and others. The team generates insights to drive billion-dollar improvements in functions such as manufacturing, HR, Marketing, and Digital.

Second, Charles' team also drives revenue from their unique access to tremendous quantities of vehicle data. This includes direct licensing of connected vehicle data (e.g. GPS data to traffic and parking apps, media, retail, and insurance companies), as well as using these data to create new businesses in insurance, fleet management, and others.

In this keynote address to both the PAW Business and PAW Industry 4.0 audiences, Charles will share his unique insider's vantage.

Session description
Speaker
A Charles ThomasGeneral Motors - GM
Chief Data & Analytics Officer
General Motors
9:30 am

Rexer Analytics has been surveying analytic professionals for over a decade. In 2020, over a thousand people from around the world participated in the 10th Data Science Survey. In this PAW session, Karl Rexer will present highlights of recent survey results and discuss trends from the past decade. 

Highlights will include:

  • Key algorithms
  • Deep learning adoption and key techniques  
  • Challenges of self-service analytics
  • Analytic software adoption
  • Job satisfaction & job prospects
Session description
Speaker
Karl RexerRexer Analytics
President
Rexer Analytics
9:40 am
The Session Description will be available shortly.
Session description
10:00 am
5-minute transition between sessions
10:05 am
Track 1 - BUSINESS - Operationalization, management and best practices
10:05 am - 10:25 am
The Session Description will be available shortly.
Session description
Speaker
Michael SimonCIA
Chief of Analytics
CIA
Presenting modeling results
10:30 am - 10:50 am
Lessons from: ConstantContact

Data Scientists can serve as thought partners and data translators to the rest of the business.  In this talk, we'll cover 4 case studies/models I've built throughout my career, and how this seemingly complex material can be clearly communicated.  Effective communication and demystifying these “black boxes” can lead to data scientists & stakeholders building strong relationships based on trust and understanding.

Session description
Speaker
Kristen KehrerData Moves Me
Founder
Data Moves Me
Track 2 - TECH - Machine learning methods & advanced topics
Model explainability
Case study: Paychex

The use of AI in decision making processes brings efficiency and data-driven results, but also risks.  Machine learning creates models which make predictions based upon patterns learned from past data.  The reasoning behind these decisions is not available to the users of the models, or recipients dealing with the consequences of the decisions. E.g., a sales person doesn't know why a business is a good lead, and credit doesn't know why credit is denied.  This is a case study on adding local explanations to machine learning algorithms, so that users will have greater confidence and insight into machine-driven decisions.

Session description
Speaker
Track 3 - CASE STUDIES - Cross-industry business applications of machine learning
ML for social good
10:05 am - 10:25 am

Predictive analytics has been contributing to address some of the world’s most challenging social problems. Predictions have been successfully driving decisions in many social domains such as crisis response and disease outbreak, economic empowerment and financial inclusion, health and hunger, student education and teacher productivity, security and justice, climate change and adaptation, and many others. 

In this talk, Prof. Bari of New York University and his team will highlight the societal benefits of predictive analytics’ algorithms that can create both business opportunities and social impacts to help both developed and developing economies.  The talk will answer questions such as: Can predictive analytics fight loneliness? can algorithms detect malnutrition? how do recommendation systems help refugees integrate? what are the capabilities of predictive analytics that can address climate change?  how can image recognition detect skin cancer through smart phone images? how can deep learning be applied detect road outages from satellite images after natural disasters? how can predictive analytics be leveraged to maximize student achievement? How can natural language processing be used to detect student distress before the school notices?... 

The talk will also address the ethics, risks, and limiting factors, and how to mitigate them to realize a social impact. Prof. Bari was formerly with the World Bank Group and has been holding the role of AI advisor for a leading international organization. Julian Smith is a master’s candidate and researchers in computer science at the courant institute of mathematical science of New York University. His research focuses on using predictive analytics to address climate change. 

Session description
Speaker
Anasse Bari Ph.D.New York University
Professor of Computer Science
New York University
Churn modeling
10:30 am - 10:50 am
Case study: Philips

Data science and predictive analytics play a key role in churn management by empowering companies to identify at-risk subscribers, as well as determining the incentive or intervention with the highest likelihood of leading to customer retention. 

In this talk, I will present the use case of Philips Lifeline in managing subscribers of our medical alert service. We identified at-risk cohorts by predicting their likelihood of churn and applied A-B testing of a cost-efficient intervention. Using predictive analytics to leverage the efficient use of our intervention, we reduced the churn rate by 50% in subscribers with a high predicted risk of churning.

Session description
Speaker
Enrique Gil Ph.D.Philips Research
Scientist
Philips
10:50 am
Exhibits & Morning Coffee Break
11:20 am
Track 1: BUSINESS - Operationalization, management and best practices
Algorithmic fairness; ethics
11:20 am - 11:40 am

AI is the new electricity. It is fundamentally and radically changing the fabric of our world.  But like any new technology, it has a dark side: machine bias. If unchecked, machine bias leads to biased products and services which may discriminate against some of your customers, citizens, or employees based on their race, gender, age, etc.  Bias is also bad for organizations: it can lead to missed opportunities, lost consumer confidence, reputational risk, etc. Machine bias, therefore, is organizational risk. This session will help you learn about machine biases: how they emerge, why they are dangerous, and how mitigate them.

Session description
Speaker
Natalia ModjeskaInfo-Tech Research Group
Director, Research, DnA (Data & Analytics)
Info-Tech Research Group
Algorithmic fairness; ethics
11:45 am - 12:05 pm
Case study: Canada Post

Each day, machine learning and AI (ML/AI) models make decisions that affect the lives of millions of people. As these models become more integrated with everyday decision making, organizations need to be increasingly vigilant of the risk created by potentially discriminatory algorithms.

As these algorithms can expose an organization to significant liability, Senior Management and Boards of Directors require assurance that the reputational, financial, and legal risks of implementing a biased ML/AI model are being sufficiently mitigated.

This session will discuss the concept of model fairness and the bias avoidance activities that data scientists must be able to demonstrate to auditors and regulatory bodies. 

Session description
Speaker
Allan SammyCanada Post
Director, Data Science and Audit Analytics
Canada Post
Track 2 - TECH - Machine learning methods & advanced topics
Modeling methods

While decision trees play a central role in contemporary data science applications, they are almost always used in large ensembles combining hundreds or even thousands of trees. The gradient boosting machine (which leverages hundreds to thousands of small trees) is perhaps the all-time most popular learning machine and made its recent reputation displaying superior performance across a number of Kaggle competitions; Random Forests (which leverages hundreds of large trees) is well known for its feature selection prowess especially in the context of huge numbers of potential predictors. Unfortunately, the excitement generated by the power of these modern learning machines has induced the field of data science to neglect the value and dramatic effectiveness of their direct predecessor, the single decision tree. In this presentation, we step back in time to review what made the single decision tree such a revolutionary analytical tool and we present a number of applications in which the single decision tree is more effective and more appropriate for the problem at hand than subsequent multi-tree methods. Examples are drawn from e-commerce consumer behavior, consumer insurance billing, detecting undesirable differences between two or populations, and the interpretation of complex models.

Session description
Track 3 - CASE STUDIES - Cross-industry business applications of machine learning
Churn modeling
11:20 am - 12:05 pm

Over 160 Billion digital breadcrumbs teach us about people's behavior online and offline every day. Harnessing these behavioral signals, we can model and predict behavior and changing states of need and consumption. In this case study, we predict consumers whose subscriptions are up for renewal and are highly likely to churn (cancel their subscription). Can we correctly identify them based on past digital behavioral patterns? Can we learn anything about them in order to understand their motivation for this behavioral change, and possibly change their minds? This fascinating journey through online behavioral data and its uses will answer these questions.

Session description
Speaker
Gilad BarashDstillery
VP of Analytics
Dstillery
Churn modeling
11:45 am - 12:05 pm
Case study: Wix

Wix is a freemium website builder with over 4 million active paid subscriptions. To retain customers, we built a model which predicts how likely each users is to churn. The model is live, and helping us save users. In this talk, Wix's Gil Reich will share this journey and their top lessons, including the how and why of establishing an end-to-end model, treatment, and feedback.

Session description
Speaker
Gil ReichWix
Data Developer
Wix
12:05 pm
Lunch
1:15 pm
KEYNOTE
Lessons from: Kennesaw State University

How many .edu addresses are in your inbox right now? As organizations pursue digital transformation strategies, challenges related to finding and retaining analytical talent, objectively assessing the relevance of new, and emerging technology and engaging in deep and meaningful innovation with eventual payback are common to all sectors of the economy. Deep, collaborative partnerships with universities can help mitigate many of these challenges. This is all the more true because data science itself has given rise to a new "entrepreneurial university" paradigm. Dr. Priestley is an academic Associate Dean, who worked for organizations like Accenture and VISA EU, and now manages corporate partnerships with the likes of Blue Cross Blue Shield, Emerson, Equifax, and GE, as well as fire departments and law enforcement. She will discuss the ways that organizations should be thinking about working with universities, but typically don't — including research, innovation, "externships," training options, recruitment, and other strategic relationships. After this session, you will never look at universities the same way again.

Session description
Speaker
Jennifer Lewis PriestleyKennesaw State University
Professor of Applied Statistics and Data Science
Kennesaw State University
2:00 pm
The Session Description will be available shortly.
Session description
2:10 pm
5-minute transition between sessions
2:15 pm

No one wants to go to jail. Making decisions based on machine learning models offers new and exciting ways to be convicted -- in court and in the court of public opinion. Models that encode bias and automate discrimination are illegal or at least unethical. Demonstrating the compliance of a model is a hurdle that regulated industries must cross for every model. Even models that seem fine to you and your regulator might sound awful when described in the media. Ethics and compliance are as important to your success as data quality, MLOps, and operationalization. This panel will discuss ethics, bias, regulatory compliance, explainability, and much more to help keep you and your models out of jail.

Session description
Moderator
James TaylorDecision Management Solutions
CEO
Decision Management Solutions
3:00 pm
Exhibits & Afternoon Break
3:30 pm
Track 1 - BUSINESS - Operationalization, management and best practices
Industry/University Partnerships
3:30 pm - 3:50 pm
Lessons from: Hanesbrand

As organizations transform into data and analytics centric enterprises, the talent pool is barraged with demands for talent to build innovative, agile data solutions. Strategic, intentional partnerships with universities can help you develop and acquire graduates with the skills to implement not only state-of-the-art data science but also compelling narratives from data, collaboration, and creativity. Join us to learn how HanesBrands' partnership with Elon University has yielded a pipeline of premier talent through research, national Champion® analytical challenges, on-campus internships, and customized sales online learning modules.

Session description
Speakers
Haya AjjanElon University
Associate Professor of MIS, Director of the Center for Organizational Analytics,
Elon University
Ben Martin Ph.D.Hanes
Chief Data Analytics Officer
Hanesbrands Inc
Team building
3:55 pm - 4:15 pm
Lessons from: MINDBODY, Inc

Want to take your analytics team to the next level? Do you start with getting the right talent, tools, or finally build that EDW you've been dreaming about? Learn how MINDBODY's Data Science department scaled up from a small collection of SQL reporting analysts to a successful centralized team of data analysts, engineers, and scientists that produce high-quality, and heavily adopted, analytical models for all areas of our global SaaS company.

Session description
Speaker
Charlie LewisMINDBODY
Senior Manager, Business Intelligence
MINDBODY, Inc
Track 2 - TECH - Machine learning methods & advanced topics
Data engineering
Case study: Google

Solving a data science problem is an iterative exercise. It requires running experiment after experiment —  trying new approaches with different parameters and lots of data. To manage this complexity, it is very helpful to have a platform to build reusable workflows that can be tracked.

Kubeflow Pipelines is a component of the Kubeflow open-source project, focused on building and deploying portable ML workflows on Docker containers. In this session, the audience will learn about KubeFlow Pipelines and how it can help improve reuse and reproducibility of the machine learning process.

Session description
Speaker
Karl WeinmeisterGoogle
Developer Advocacy Manager
Google
Track 3 - CASE STUDIES - Cross-industry business applications of machine learning
Workforce analytics
3:30 pm - 3:50 pm
Case study: Bristol-Myers Squibb

Although many investors may view mergers and acquisitions as attractive to an organization, for many employees, these transactions are viewed as threatening, unstable, and downright scary. In addition to lack of trust in leadership and a proper culture fit; reduction in force, down leveling, loss of certain company benefits are some of the typical fears of employees involved in M&A. As such, many employees take control of their “destiny,” and simply exit. This presentation is focused on how an organization facing acquisition can identify the drivers of employee attrition and implement proactive measures to prevent employees from leaving.

Session description
Speakers
Emma VazirabadiBristol-Myers Squibb
Associate Director of People Insights & HR Analytics
Bristol-Myers Squibb
Jason FelicianoBristol-Myers Squibb
Associate Director of HR Analytics
Bristol-Myers Squibb
Workforce analytics
3:55 pm - 4:15 pm
Case study: Mercer

Employer investments in employee health and wellness represent an increasingly expensive component of total rewards. The efficacy of these investments is commonly assessed in terms of their impact on measures of employee health, their direct impact on total labor costs and on the labor market competitiveness of the offer. Unfortunately, these outcomes do not reflect the full economic value of health and wellness investments. 

This session will demonstrate how applying advanced workforce analytics can help measure the actual workforce impact of these investments and their true economic value. The presenter will highlight examples drawn from actual client work where predictive modeling methods applied to integrated sets of health and HRIS workforce data have helped leaders to quantify the workforce impact of health and wellness programs and estimate their ROI. The modeling can also reveal how work environment affects the trajectory of health claims and risks. Finally, the session will speak to the modeling methods used and the importance of using a coherent economic lens to inform modeling strategy and interpretation of results.

Session description
Speaker
Haig NalbantianMercer
Senior Partner, Co-leader Mercer Workforce Sciences Institute
Mercer
4:15 pm
5-minute transition between sessions
4:20 pm
Track 1: BUSINESS - Operationalization, management and best practices
Data management and data preparation
4:20 pm - 4:40 pm
Lessons from: BP

Businesses, large and small, have various types of data clusters (data marts, data warehouses, data lakes, etc.) in their organizations storing different kinds of data their stakeholders care about. Making sense of those disparate sources of data and being able to link them at some semantic level is the key to unleash the hidden value in the data. This is a surprisingly challenging problem facing pretty much any enterprise and there are both technical and organizational (people) issues that need to be addressed to tackle the problem. In this talk, BP's Cetin Karakus will present a hybrid technical architecture that has emerged as a symbiosis of data warehousing and Big Data analytics technologies to enable creating ultra large scale data graphs from existing enterprise data clusters in a technically and organizationally scalable fashion through incremental/agile build-up efforts. This is a very pragmatic and practical approach to build semantic and predictive data analytics capabilities on top of existing data assets in a rapid and cost-effective way.

Session description
Speaker
Cetin Karakus
Global Head of Quant Technology and Analytics Core Strategies
BP
Pitfalls and best practices
4:45 pm - 5:05 pm
Lessons from: Caesars Entertainment

Seasoned analytics and data science leader will share his experience on why analytics/data science projects so often go wrong.  One of these is an exclusively short-term focus, which happens frequently, can be tempting due to an ease-of-measurement bias, but often leads to exactly the wrong conclusions.  Other traps can lead to biased conclusions as well while others lead to analysis projects that waste time and go nowhere actionable.  The session discusses how to avoid these traps and keep analytics focused on where it adds value and leads to true added business value.

Session description
Speaker
Josh FrankCaesars Entertainment
Vice President-Gaming Data Science & Fraud Analytics
Caesars Entertainment
Track 2 - TECH - Machine learning methods & advanced topics
Modeling methods
4:20 pm - 4:40 pm
Case study: Cisco

Segmentation and Profiling is a technique to understand how segments impact various business metrics. Typically, analysts do it using pivot tables and drill-downs to understand significant trends and trend changes. This is a laborious activity, especially if it needs to be extensive. But, by inventively using Association Rules Mining technique,  we can perform automatic segmentation and profiling and extract trends and trend changes over large data sets.  Changes in trend across time intervals can be automatically detected and reported in an extensive manner while saving time and effort.

Session description
Speaker
Kumaran PonnambalamCisco
Analytics Architect
Cisco Systems, Inc
Data preparation
4:45 pm - 5:05 pm
Case study: Dow Chemical

Most descriptions of data cleaning focus on data problems which can be determined upon inspecting the data, such as missing data and outliers. However, this cleaning exercise misses even more serious issues with data which may not be apparent. The chief cause of these latent data problems is that a modeling dataset has the last version of the data, instead of live data that a production model would see. This talk will give an overview of how data versions adversely impact modeling, as well as give advice on how to overcome these problems.

Session description
Speaker
Paul SpeakerThe Dow Chemical Company
Senior Data Scientist
The Dow Chemical Company
Track 3 - CASE STUDIES - Cross-industry business applications of machine learning
Consumer reimbursement claims
4:20 pm - 4:40 pm
Case study: GIVT (EU flight claims)

Under EU261 directive, airplane passengers have the right to be compensated if their flight is sufficiently delayed. GIVT helps passengers file such claims. Every claim needs to be verified as there are various conditions that can invalidate it: extreme weather, strikes, bird hit, etc. In this talk, I will describe a machine learning system which replaces manual verification of claims.

Session description
Speaker
Piotr Wygocki Ph.D.MIM Solutions
​Co-founder
MIM Solutions
Supply chain management
4:45 pm - 5:05 pm
Case study: ChannelAdvisor

"How much will I sell?" is a simple question with a complex answer.  Stocking the right amount of a product in a warehouse is like navigating between Scylla and Charybdis:  keep too much on hand and pay warehousing fees and the opportunity cost of holding; keep too little on hand and lose out on potential sales.

In this talk, we will present an overview of ChannelAdvisor's demand forecasting system for e-commerce brands and retailers, which helps customers understand sales forecasts, allowing them to manage their inventory more effectively.

Session description
Speaker
Kevin FeaselChannelAdvisor
Engineering Manager, Predictive Analytics
ChannelAdvisor
5:05 pm
End of second Conference Day

Post-Conference Workshops - Thursday, June 4th, 2020

8:30 am
Post-Conference Training Workshop

Full-day: 8:30am – 4:30pm

This one-day session reveals the subtle mistakes analytics practitioners often make when facing a new challenge (the “deadly dozen”), and clearly explains the advanced methods seasoned experts use to avoid those pitfalls and build accurate and reliable models.  Click workshop title above for the fully detailed description.

Session description
Instructor
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
Post-Conference Training Workshop

Full-day: 8:30am – 4:30pm

Gain the power to extract signals from big data on your own, without relying on data engineers and Hadoop specialists. Click workshop title above for the fully detailed description.

Session description
Instructor
James Casaletto
PhD Candidate
UC Santa Cruz Genomics Institute and former Senior Solutions Architect, MapR
Post-Conference Training Workshop

Full-day: 8:30am – 4:30pm

This workshop dives into the key ensemble approaches, including Bagging, Random Forests, and Stochastic Gradient Boosting. Click workshop title above for the fully detailed description.

Session description
Instructor
Dean AbbottSmarterHQ
Co-Founder and Chief Data Scientist
SmarterHQ
Post-Conference Training Workshop

Full-day: 8:30am – 4:30pm

During this workshop, you will gain hands-on experience deploying deep learning on Google’s TPUs (Tensor Processing Units) at this one-day workshop, scheduled the day immediately after the Deep Learning World and Predictive Analytics World two-day conferences.  Click workshop title above for the fully detailed description.

Session description
4:30 pm
End of Post-Conference Training Workshops
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