Agenda

Predictive Analytics World for Industry 4.0 Las Vegas 2020

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


Click here to view the full 8-track agenda for the five co-located conferences at Machine Learning Week (PAW Business, PAW Financial, PAW Healthcare, PAW Industry 4.0, and Deep Learning World).

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
CloseSelected Tags:

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
CloseSelected Tags:

Predictive Analytics World for Industry 4.0 - 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
KEYNOTE

One of, if not THE, biggest impediments to Industrial firms realizing the efficiencies promised by "Industry 4.0" remains the access to quality, (near) real-time data. Ignoring the Purdue model, industrial firms should utilize cutting-edge cyber security tools to connect critical assets directly with professionals having the skills necessary to deploy advanced analytics solutions, optimizing machines and processes. Terry Miller, from Siemens, will evaluate a case study utilizing this architecture to capture and predict valve "stiction" in a Wastewater treatment plant flow loop.

Session description
Speaker
Terry MillerSiemens
Global Digital Strategy and Business Development
Siemens
11:15 am
5-minute transition between sessions
11:20 am

TEMPA - TExt Mining with Predictive Analytics, for Engineering - An approach that allows users to face any unplanned outage in any engineering asset. The asset could be a gas turbine, an aircraft engine, an MRI machine, a locomotive, or a wind turbine. These assets normally provide both descriptive (text) and measured (numerical) output as data - which, when combined properly, have the potential to provide highly actionable insights. TEMPA enables this. This talk would focus on proven methods to automatically extract events from both textual information and operational data, monetize these insights improving profit, as applied in General Electric.

Session description
Speaker
Rajagopalan ChandrasekharanGE Global Research
Senior Engineer
General Electric
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

Data Science in general and Deep Learning in particular continue to reshape the future of the Energy sector across various segments. From exploration, development and production to downstream and new energies business, measurable value of digitalization has been observed in both efficiencies and savings. Deep Learning is one of the key underlying enablers for creating competitive advantage. This presentation provides an overview of some of use case applications and lessons learned from establishing a platform that progress ideas to embedded business enablers.

Session description
Speaker
Mohamed SidahmedShell Oil Company
Machine Learning and AI Manager
Shell
3:25 pm
Exhibits & Afternoon Break
3:55 pm
KEYNOTE

It is a rare opportunity for us to live in a time in which important new fields as Big Data Analytics (BDA) and the Internet of Things (IoT) are being born, maturing, and working together to advance technological progress. The following presentation will outline new opportunities and challenges for predictive analytics when applied to the field of Industrial IoT. It will also discuss various drivers and inhibitors that need to be considered, as well as successful strategies for offering predictive analytics for IoT.

Session description
Speaker
Andrei KhurshudovCaterpillar
Director, Advanced Analytics
Caterpillar Digital
4:40 pm
5-minute transition between sessions
4:45 pm
KEYNOTE
The Keynote Description will be available shortly.
Session description
Speaker
Steven RamirezBeyond the Arc
CEO
Beyond the Arc
5:30 pm
Networking Reception
7:00 pm
End of first Conference Day

Predictive Analytics World for Industry 4.0 - 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
9:40 am
The Session Description will be available shortly.
Session description
10:00 am
5-minute transition between sessions
10:05 am

Advances in technology and an abundance of data have made machine learning a key component in the fight against evolving fraud. Find the best solution suited for a business’ specific needs requires investigating the types of machine learning models in use, the datasets that trained them, the combination of data being leveraged by the models and their approach to obtaining truth data. 

In this talk, the key concepts and approaches to machine learning, including supervised and unsupervised learning techniques and their applications in fraud domain will be explained. Also, real examples of Paypal fraud prevention tool will be provided.

Session description
Speaker
Samira GolsefidPayPal
Principal Data Scientist
PayPal Inc.
10:50 am
Exhibits & Morning Coffee Break
11:20 am

Advances in additive manufacturing (AM) has allowed metal components to be fabricated quicker, and more cost-effectively than traditional metallurgical processes. For laser powder bed fusion (LPBF) AM, the area beneath the laser turns gaseous (vapor depression) while the area immediately surrounding turns to a liquid (melt pool). Once solidified they form the material microstructure, which determines the performance, lifespan, and physical features of the build. With in-situ data collected by ultrafast x-ray imaging, machine learning can be used to predict these geometries. Additionally, recommendations can be made for the underlying process parameters to determine optimal settings for future build characteristics.

Session description
Speaker
Andy RamlatchanNASA
Senior Computer Scientist
NASA Langley Research Center
12:05 pm
Lunch
1:15 pm

Manufacturing has long embraced predictive analytics for process control, but has been slow to adopt active process control in which a black box makes end-to-end decisions for entire process lines. We explore several reasons for this development and compare predict analytics to modern AI based decision making systems, through reinforcement deep learning models. We show that the two are not mutually exclusive and combined can offer a method to overcome the intrinsic limitations of native AI for effective deployment and usage.

Session description
Speaker
Vadim Pinskiy PhD
VP of R&D
Nanotronics
2:00 pm
The Session Description will be available shortly.
Session description
2:10 pm
5-minute transition between sessions
2:15 pm
KEYNOTE

Organizations are routinely faced with the challenge of how to analyze their IoT data. 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 the entire process starting with how to formulate their business problem, perform feature engineering and build a predictive maintenance model using Python using both tradition/Deep learning techniques.

Session description
Speaker
Jaya MathewMicrosoft
Senior Data Scientist
Microsoft
3:00 pm
Exhibits & Afternoon Break
3:30 pm

Research driven companies have a long ranging, unique and amazing data record. A constant challenge is to find relevant data for development scientists and customers to help them solving production problems. Typical approaches span from enterprise scale data lakes to sophisticated numerical simulations. This talk gives insights into use cases that transform a long established manufacturing company into a data-driven supplier.

Session description
Speaker
Martin ElstnerCovestro
Expert Chemoinformatics
Covestro
4:15 pm
5-minute transition between sessions
4:20 pm

There has been an exponential increase in the number of electronic sensors, which now are driving every industry - right from Manufacturing to Aerospace. These sensors are capable of producing a lot of data. However, the sensors are known to be notorious for their jitters. These jitters often result in false alarms which could be troublesome for industrialists. In the session, we will take a deep dive into the state of the art approach. Auto-Encoders are a great product of Deep Learning Neural Networks, if used correctly they have the potential to save lot of cost on the asset.

Session description
Speaker
Rohit KewalramaniKPIT Technologies
Data Scientist
KPIT Technologies
5:05 pm
End of second Conference Day
CloseSelected Tags:

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
CloseSelected Tags:
Share This

Get Predictive Analytics World news and event information delivered straight to your inbox.