Agenda

Predictive Analytics World for Industry 4.0 Las Vegas 2019

June 16-20, 2019 – Caesars Palace, Las Vegas



This page shows the agenda for PAW Industry 4.0. Click here to view the full 7-track agenda for the five co-located conferences at Mega-PAW (PAW Business, PAW Financial, PAW Healthcare, PAW Industry 4.0, and Deep Learning World).

Session Levels:

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

Pre-Conference Workshops - Sunday, June 16th, 2019

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. 

Leader
Marc Smith
Chief Social Scientist
Connected Action Consulting Group
2:00 pm
Pre-Conference Training Workshop

Two and a half hour afternoon workshop:

This 2.5 hour workshop launches your tenure as a user of R, the well-known open-source platform for data analysis. Click workshop title above for the fully detailed description.

Leader
Brennan LodgeGoldman Sachs
Data Scientist VP
Goldman Sachs
4:30 pm
End of Pre-Conference Training Workshops
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Pre-Conference Workshops - Monday, June 17th, 2019

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.

Leader
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
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.

Leader
Brennan LodgeGoldman Sachs
Data Scientist VP
Goldman Sachs
Pre-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.

Leader
Dean AbbottSmarterHQ
Co-Founder and Chief Data Scientist
SmarterHQ
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.

Leader
James McCaffreyMicrosoft
Senior Scientist Engineer
Microsoft
4:30 pm
End of Pre-Conference Training Workshops
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Day 1 - Tuesday, June 18th, 2019

8:00 am
Registration & Networking Breakfast
8:45 am
Eric SiegelPredictive Analytics World
Conference Founder
Predictive Analytics World
8:50 am
MEGA-PAW SUPER-PLENARY KEYNOTE

A veteran applying deep learning at the likes of Apple, Samsung, Bosch, GE, and Stanford, Mohammad Shokoohi-Yekta kicks off Mega-PAW 2019 by addressing these Big Questions about deep learning and where it's headed

  • Late-breaking developments applying deep learning in retail, financial services, healthcare, IoT, and autonomous and semi-autonomous vehicles
  • Why time series data is The New Big Data and how deep learning leverages this booming, fundamental source of data
  • What's coming next and whether deep learning is destined to replace traditional machine learning methods and render them outdated
Session description
Speaker
Mohammad Shokoohi-YektaApple
Senior Data Scientist
Apple
9:15 am
MEGA-PAW SUPER-PLENARY KEYNOTE

In the United States, between 1500 and 3000 infants and children die due to abuse and neglect each year. Children age 0-3 years are at the greatest risk. The children who survive abuse, neglect and chronic adversity in early childhood often suffer a lifetime of well-documented physical, mental, educational, and social health problems. The cost of child maltreatment to American society is estimated at $124 - 585 billion annually.

A distinctive characteristic of the infants and young children most vulnerable to maltreatment is their lack of visibility to the professionals. Indeed, approximately half of infants and children who die from child maltreatment are not known to child protection agencies before their deaths occur.

Early detection and intervention may reduce the severity and frequency of outcomes associated with child maltreatment, including death.

In this talk, Dr. Daley will discuss the work of the nonprofit, Predict-Align-Prevent, which implements geospatial machine learning to predict the location of child maltreatment events, strategic planning to optimize the spatial allocation of prevention resources, and longitudinal measurements of population health and safety metrics to determine the effectiveness of prevention programming.  Her goal is to discover the combination of prevention services, supports, and infrastructure that reliably prevents child abuse and neglect.  

Session description
Speaker
Dyann Daley MDPredict Align Prevent
Founder and CEO
Predict-Align-Prevent
9:40 am
Sponsored Session
The Session Description will be available shortly.
Session description
10:00 am
Exhibits & Morning Coffee Break
10:30 am
KEYNOTE
Case Study: Intel

AI is framed by models, sensors and technologies.  These often ignore the human who must deal with and trust AI outputs.  How do we translate the mental models and senses that humans deploy daily into algorithms that take us from data to inference to action?  With the explosion of sensors at the edge, how do we actually make sense at the edge?  This presentation draws from a recent Intel study of over 250 people in manufacturing and its supporting ecosystem to explore what it takes to accelerate the adoption of Industry 4.0 in a systems of systems approach.

Session description
Speaker
Irene PetrickIntel
Senior Director of Industrial Innovation
Intel Corporation
11:15 am
5-minute transition between sessions
11:20 am

Many organizations are faced with the challenge of how to analyze their sensitive data without hosting it on any public cloud. This talk will focus on companies who collect data from their factory operations and are interested in predicting mechanical failures. The audience will get an overview of how to formulate their business problem, perform feature engineering and build a predictive maintenance model using R/Python.

Session description
Speaker
Jaya MathewMicrosoft
Senior Data Scientist
Microsoft
12:05 pm
Lunch
1:30 pm

Field issue (malfunction) incidents are costly for the manufacturer’s service department. A normal telematics system has difficulty in capturing useful information even with pre-set triggers. In this session, Yong Sun will discuss how a machine learning, deep learning based predictive software/hardware system has been implemented to solve these challenges by 1) identifying when a fault will happen 2) diagnosing the root cause on the spot based on time series data analysis. Yong Sun will cover a novel technique for addressing a lack of training data for the neural network based root cause analysis.

Session description
Speaker
Yong SunIsuzu
Supervisor
Isuzu Technical Center of America
2:15 pm
Sponsored Session
The Session Description will be available shortly.
Session description
Sponsored by
diwo
2:35 pm
5-minute transition between sessions
2:40 pm
Case study: Autoloop, LLC

We will discuss the approach used to learn topics and their place in a multi level hierarchy on hundreds of millions of text records. These methods are generalizable beyond the domain in which they were applied. We used a combination of supervised and unsupervised machine learning methods, which we will discuss at more length including the technologies, algorithms, and results.

Session description
Speaker
Wes MadrigalAutoLoop
Machine Learning Engineer
AutoLoop
3:25 pm
Exhibits & Afternoon Break
3:55 pm
Case study: Eaton
3:55 pm - 4:15 pm

Industry 4.0 can suffer from a real-world application problem when industrial and manufacturing companies only view IoT as a solution during large-scale plant upgrades or new construction. This case study presents how a manufacturing company has been able to generate energy cost savings in the scale of $MM through targeted deployment of sensors and IoT connected equipment into existing, large, (and sometimes very old and dirty) machinery and factories. By lowering the threshold of what projects are considered worthy of an IoT investment, what was previously considered run-of-the-mill operations can suddenly provide insightful information and an exciting ROI.

Session description
Speakers
Steve MillerEaton
Vice President, Controls and Protection Division
Eaton
Lyle SprinkleEaton
Director: Meters, Relays & IoT Solutions
Eaton
KEYNOTE
4:20 pm - 4:40 pm

IOT is untappable. But there is nothing the hive mind can't do. Particularly with a good algorithm.

Session description
Speaker
Steven RamirezBeyond the Arc
CEO
Beyond the Arc
4:40 pm
5-minute transition between sessions
4:45 pm
Case study: Optoro
4:45 pm - 5:05 pm

Optoro’s three core data culture problems were the following:

• Fear of Data
• Inconsistent Use of Vocabulary and Metrics
• Data Mistrust

This presentation will outline the strategies that we used to combat these issues. This on-going endeavor is yielding benefits to Optoro, including
• Increased alignment on company goals
• Improved ease of communication between teams and with Senior Management
• Consistency across external messaging

Session description
Speaker
Vanessa LamOptoro
Manager, Business Intelligence
Optoro
5:10 pm - 5:30 pm

The session helps participants understand the role of data, the importance of a data strategy in an organization, the types of business analytics to execute, and ten practical steps to develop a data strategy to improve business processes. We will explore how to conduct research to identify opportunities to minimize threats, manage risks, and improve performance. 

The session will use the dataFonomics® (A Data to Information Economics Framework) methodology as a platform for developing a Data Strategy. Attendees will be given an overview of the framework, steps, practical knowledge on data analysis and how to develop a data strategy.

Session description
Speaker
Nicole Antoinette SmithOhio University
Lecturer
Ohio University
5:30 pm
Networking Reception
7:00 pm
End of first Conference Day

Day 2 - Wednesday, June 19th, 2019

8:00 am
Registration & Networking Breakfast
8:55 am
KEYNOTE
Case study: UPS

Turning data into a business advantage through optimization is the goal of most organizations. UPS has been on a twenty year journey to achieve this goal and has seen cost improvements reaching $1B annually. At the same time, UPS has been able to offer new products and services backed by data and analytics. 

Session description
Speaker
Jack LevisUPS
Senior Director, Process Management
UPS
9:40 am
Sponsored Session
The Session Description will be available shortly.
Session description
10:05 am
Case study: Uber

In today’s digital age, users expect a fast, reliable mobile experience. Degradations (also referred to as regressions) in mobile app performance affect not only user experience, but even hurt business metrics. However, existing mobile app release pipelines lack the necessary infrastructure to detect regressions in a mobile app's performance before it is rolled out to the world. At Uber, we are building a state-of-the-art mobile regression detection pipeline, with the goal to detect regressions as small as 1%. Our approach includes both technological innovation as well as employing machine learning along with statistical testing techniques to improve the sensitivity of the regression experiments.

Session description
Speakers
Ritesh AgrawalUber
Senior Data Scientist
Uber
Anando SenUber
Product Manager
Uber
10:50 am
Exhibits & Morning Coffee Break
11:20 am
KEYNOTE
Case study: GM

At GM we are committed to have a world with zero crashes, zero emissions and zero congestion. The causes of crashes are directly correlated to human intelligence, that is, learning from experience, adapting to new situations, and using knowledge to prevent accidents.

Accident Detection and Avoidance Systems (ADAS) are a big step taken by car manufacturers to prevent the accidents. The effects of ADAS on the human attention and perception differ depending on the road conditions as well as areas (i.e., rural vs urban roads).

In a pair of case studies, we examine ADAS systems and insurance underwriting risk.

Session description
Speakers
Meltem Ballan Ph.DGeneral Motors - GM
Chief Data & Analytics Office
General Motors
Robert WelbornGeneral Motors - GM
Head of Data Science, Connected Car
General Motors
12:05 pm
Lunch
1:10 pm
Special plenary session

The core Bayesian idea, when learning from data, is to inject information — however slight — from outside the data.  In real-world applications, meta-information is clearly needed — such as domain knowledge about the problem being addressed, what to optimize, what variables mean, their valid ranges, etc.  But even when estimating basic features (such as rates of rare events), even vague prior information can be very valuable. This key idea has been re-discovered in many fields, from the James-Stein estimator in mathematics and Ridge or Lasso Regression in machine learning, to Shrinkage in bio-statistics and “Optimal Brain Surgery” in neural networks.  It’s so effective — as I’ll illustrate for a simple technique useful for wide data, such as in text mining — that the Bayesian tribe has grown from being the oppressed minority to where we just may all be Bayesians now.

Session description
Panelist
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
2:00 pm
Sponsored Session
The Session Description will be available shortly.
Session description
2:10 pm
5-minute transition between sessions
2:15 pm

The companies getting the most value from advanced analytics spend much more of their time and money embedding analytics into their core workflows than others. The most successful, in fact, spend more than half their analytics budget not to build analytics, but to deploy and operationalize it. Companies that don’t complete this last mile, those that stop once they have completed the core analytics, see their analytic investments go to waste. Join this expert panel to hear what you can do to make sure you can embed analytics in your front line and maximize the return on your analytics investment.

Session description
Moderator
James TaylorDecision Management Solutions
CEO
Decision Management Solutions
Panelists
Ryohei Fujimaki
CEO
dotData
Krishna Kallakuridiwo
President, diwo – Cognitive Decision Making Platform
diwo
3:00 pm
Exhibits & Afternoon Break
3:30 pm
Case study: Vistra Energy

Vistra Energy is one of the largest energy companies in the United States, owning both power generation and retail operations in extremely competitive markets throughout the country. We combine the big data opportunities we have available as a utility with advanced analytics to provide premium customer services that differentiate us from other utility providers. We will present three case studies demonstrating how we are able to leverage our firehose of 15-minute interval IOT device energy usage data from over 1.5 million customers with advanced modeling techniques to provide added value to our customers and increase brand loyalty.

Session description
Speaker
Greg StevensVistra Energy
Manager, Advanced Analytics
Vistra Energy
4:15 pm
5-minute transition between sessions
4:20 pm
4:20 pm - 4:40 pm

Acronyms abound in the area of predictive analytics and machine learning is no exception. The discipline  of predictive analytics has been used by businesses since the end of World War II. But machine learning has been at the core of this activity  since its very early business applications. In these very early days, the "machine"  itself and not the human was identifying the predictive algorithms that would optimize a given business solution albeit in a more simplistic manner.  With the advent of Big Data, this concept of machine learning  has now expanded to more complex forms

Session description
Speaker
Richard BoireEnvironics Analytics
Senior Vice President
Environics Analytics
4:45 pm - 5:05 pm

Due to a continuing influx of IoT devices, industrial businesses today are collecting more data than ever before. Yet only 8% of businesses are using more than 25% of the data they collect. In this session, IoT expert Dave McCarthy will describe how data science services can help organizations optimize IoT data and achieve deployments that support predictive capabilities. He will walk attendees through how they can leverage data science services to verify their optimal IoT use case and operationalize their data, enabling the speed, connectivity, and accuracy they need for condition-based maintenance, data-driven diagnostics, predictive failure and more.

Session description
Speaker
Dave McCarthyBSquare
Vice President, IoT Solutions
BSquare
5:05 pm
End of second Conference Day
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Post-Conference Workshops - Thursday, June 20th, 2019

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.

Leader
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.

Leader
James Casaletto
Senior Solutions Architect
MapR Technologies
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) – held the day immediately after the Deep Learning World and Predictive Analytics World two-day conferences. Click workshop title above for the fully detailed description.

Leader
Martin GornerGoogle
Developer Relations
Google
4:30 pm
End of Post-Conference Training Workshops
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