Full Mega-PAW 7-Track Agenda – Detailed Session Descriptions

Predictive Analytics World

June 3-7, 2018 – Caesars Palace, Las Vegas


This page shows the full 7-track agenda for the five co-located conferences at Mega-PAW. Mega Pass registration is required for full access. To view the agenda for one individual conference, click here: PAW Business, PAW Financial, PAW Healthcare, PAW Manufacturing, or Deep Learning World.

Session Levels:

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

Predictive Analytics World - Las Vegas - Day 1 - Tuesday, June 5th, 2018

8:00 am
Registration & Networking Breakfast
Registration & Networking Breakfast
Registration & Networking Breakfast
Registration & Networking Breakfast
Registration & Networking Breakfast
8:45 am
Room: Augustus II
PAW Business
Eric SiegelPredictive Analytics World
Conference Founder
Predictive Analytics World
PAW Financial
Eric SiegelPredictive Analytics World
Conference Founder
Predictive Analytics World
PAW Healthcare Opening Remarks from the Conference Series Founder
Eric SiegelPredictive Analytics World
Conference Founder
Predictive Analytics World
Room: Augustus II
PAW Manufacturing Opening Remarks from the Conference Series Founder
Eric SiegelPredictive Analytics World
Conference Founder
Predictive Analytics World
Room: Augustus II
Deep Learning World Opening Remarks from the Conference Series Founder
Eric SiegelPredictive Analytics World
Conference Founder
Predictive Analytics World
8:50 am
Room: Augustus II
PAW Business KEYNOTE

Applied deep learning has fast become a standard tool for many industry machine learning applications. New advances in neural network techniques have opened the doors to solving problems at scale that were out of reach until recently.  Because of these advances, applications such as in image recognition for self-driving cars, medical image classification, text translation, and fake news detection are both tractable and often the industry standard.  In this keynote, Mike Tamir, who heads the data science teams at Uber ATG—the self-driving cars division—reveals how two key application areas of deep learning signal the broad importance of this emerging technology.

Session description
Speaker
Mike TamirUber
Head of Data Science, Advanced Technologies Group
Uber
PAW Financial KEYNOTE

Applied deep learning has fast become a standard tool for many industry machine learning applications. New advances in neural network techniques have opened the doors to solving problems at scale that were out of reach until recently.  Because of these advances, applications such as in image recognition for self-driving cars, medical image classification, text translation, and fake news detection are both tractable and often the industry standard.  In this keynote, Mike Tamir, who heads the data science teams at Uber ATG—the self-driving cars division—reveals how two key application areas of deep learning signal the broad importance of this emerging technology.

Session description
Speaker
Mike TamirUber
Head of Data Science, Advanced Technologies Group
Uber
Room: Augustus II
PAW Healthcare KEYNOTE

Applied deep learning has fast become a standard tool for many industry machine learning applications. New advances in neural network techniques have opened the doors to solving problems at scale that were out of reach until recently.  Because of these advances, applications such as in image recognition for self-driving cars, medical image classification, text translation, and fake news detection are both tractable and often the industry standard.  In this keynote, Mike Tamir, who heads the data science teams at Uber ATG—the self-driving cars division—reveals how two key application areas of deep learning signal the broad importance of this emerging technology.

Session description
Speaker
Mike TamirUber
Head of Data Science, Advanced Technologies Group
Uber
Room: Augustus II
PAW Manufacturing KEYNOTE

Applied deep learning has fast become a standard tool for many industry machine learning applications. New advances in neural network techniques have opened the doors to solving problems at scale that were out of reach until recently.  Because of these advances, applications such as in image recognition for self-driving cars, medical image classification, text translation, and fake news detection are both tractable and often the industry standard.  In this keynote, Mike Tamir, who heads the data science teams at Uber ATG—the self-driving cars division—reveals how two key application areas of deep learning signal the broad importance of this emerging technology.

Session description
Speaker
Mike TamirUber
Head of Data Science, Advanced Technologies Group
Uber
Room: Augustus II
Deep Learning World KEYNOTE
Case Study: Uber

Applied deep learning has fast become a standard tool for many industry machine learning applications. New advances in neural network techniques have opened the doors to solving problems at scale that were out of reach until recently.  Because of these advances, applications such as in image recognition for self-driving cars, medical image classification, text translation, and fake news detection are both tractable and often the industry standard.  In this keynote, Mike Tamir, who heads the data science teams at Uber ATG—the self-driving cars division—reveals how two key application areas of deep learning signal the broad importance of this emerging technology.

Session description
Speaker
Mike TamirUber
Head of Data Science, Advanced Technologies Group
Uber
9:15 am
Room: Augustus II
PAW Business KEYNOTE

Data science, if judged as a separate science, exceeds its sisters in truth, breadth, and utility.  DS finds truth better than any other science; the crisis in replicability of results in the sciences today is largely due to bad data analysis, performed by amateurs.  As for breadth, a data scientist can contribute mightily to a new field with only minor cooperation from a domain expert, whereas the reverse is not so easy.  And for utility, data science can fit empirical behavior to provide a useful model where good theory doesn’t yet exist.  That is, it can predict “what” is likely even when “why” is beyond reach.

But only if we do it right!  The most vital data scientist skill is recognizing analytic hazards.  With that, we become indispensable.

Session description
Speaker
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
PAW Financial KEYNOTE

Data science, if judged as a separate science, exceeds its sisters in truth, breadth, and utility.  DS finds truth better than any other science; the crisis in replicability of results in the sciences today is largely due to bad data analysis, performed by amateurs.  As for breadth, a data scientist can contribute mightily to a new field with only minor cooperation from a domain expert, whereas the reverse is not so easy.  And for utility, data science can fit empirical behavior to provide a useful model where good theory doesn’t yet exist.  That is, it can predict “what” is likely even when “why” is beyond reach.

But only if we do it right!  The most vital data scientist skill is recognizing analytic hazards.  With that, we become indispensable.

Session description
Speaker
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
Room: Augustus II
PAW Healthcare KEYNOTE

Data science, if judged as a separate science, exceeds its sisters in truth, breadth, and utility.  DS finds truth better than any other science; the crisis in replicability of results in the sciences today is largely due to bad data analysis, performed by amateurs.  As for breadth, a data scientist can contribute mightily to a new field with only minor cooperation from a domain expert, whereas the reverse is not so easy.  And for utility, data science can fit empirical behavior to provide a useful model where good theory doesn’t yet exist.  That is, it can predict “what” is likely even when “why” is beyond reach.

But only if we do it right!  The most vital data scientist skill is recognizing analytic hazards.  With that, we become indispensable.

Session description
Speaker
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
Room: Augustus II
PAW Manufacturing KEYNOTE

Data science, if judged as a separate science, exceeds its sisters in truth, breadth, and utility.  DS finds truth better than any other science; the crisis in replicability of results in the sciences today is largely due to bad data analysis, performed by amateurs.  As for breadth, a data scientist can contribute mightily to a new field with only minor cooperation from a domain expert, whereas the reverse is not so easy.  And for utility, data science can fit empirical behavior to provide a useful model where good theory doesn’t yet exist.  That is, it can predict “what” is likely even when “why” is beyond reach.

But only if we do it right!  The most vital data scientist skill is recognizing analytic hazards.  With that, we become indispensable.

Session description
Speaker
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
Room: Augustus II
Deep Learning World KEYNOTE

Data science, if judged as a separate science, exceeds its sisters in truth, breadth, and utility.  DS finds truth better than any other science; the crisis in replicability of results in the sciences today is largely due to bad data analysis, performed by amateurs.  As for breadth, a data scientist can contribute mightily to a new field with only minor cooperation from a domain expert, whereas the reverse is not so easy.  And for utility, data science can fit empirical behavior to provide a useful model where good theory doesn’t yet exist.  That is, it can predict “what” is likely even when “why” is beyond reach.

But only if we do it right!  The most vital data scientist skill is recognizing analytic hazards.  With that, we become indispensable.

Session description
Speaker
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
9:40 am
Room: Augustus II
PAW Business Platinum Sponsored Session

DataRobot will demonstrate how innovations in Machine Learning & Artificial Intelligence can easily be leveraged to address a variety of opportunities and challenges across industries. Today, companies now have the ability to use machine learning & AI as a competitive advantage in their market, reduce operational costs, create new revenue

streams, and drive higher customer satisfaction and loyalty. DataRobot's presentation will outline how scaling AI across your enterprise will deliver top & bottom line-benefits...create truly an "AI-Driven Enterprise".

Session description
Sponsored by
DataRobot
Speaker
David RussellDataRobot
RVP of Sales - West Region
DataRobot
PAW Financial Platinum Sponsored Session

DataRobot will demonstrate how innovations in Machine Learning & Artificial Intelligence can easily be leveraged to address a variety of opportunities and challenges across industries. Today, companies now have the ability to use machine learning & AI as a competitive advantage in their market, reduce operational costs, create new revenue streams, and drive higher customer satisfaction and loyalty. DataRobot's presentation will outline how scaling AI across your enterprise will deliver top & bottom line-benefits...create truly an "AI-Driven Enterprise".

Session description
Sponsored by
DataRobot
Speaker
David RussellDataRobot
RVP of Sales - West Region
DataRobot
Room: Augustus II
PAW Healthcare Platinum Sponsored Session

DataRobot will demonstrate how innovations in Machine Learning & Artificial Intelligence can easily be leveraged to address a variety of opportunities and challenges across industries. Today, companies now have the ability to use machine learning & AI as a competitive advantage in their market, reduce operational costs, create new revenue streams, and drive higher customer satisfaction and loyalty. DataRobot's presentation will outline how scaling AI across your enterprise will deliver top & bottom line-benefits...create truly an "AI-Driven Enterprise".

Session description
Sponsored by
DataRobot
Speaker
David RussellDataRobot
RVP of Sales - West Region
DataRobot
Room: Augustus II
PAW Manufacturing Platinum Sponsored Session

DataRobot will demonstrate how innovations in Machine Learning & Artificial Intelligence can easily be leveraged to address a variety of opportunities and challenges across industries. Today, companies now have the ability to use machine learning & AI as a competitive advantage in their market, reduce operational costs, create new revenue streams, and drive higher customer satisfaction and loyalty. DataRobot's presentation will outline how scaling AI across your enterprise will deliver top & bottom line-benefits...create truly an "AI-Driven Enterprise".

Session description
Sponsored by
DataRobot
Speaker
David RussellDataRobot
RVP of Sales - West Region
DataRobot
Room: Augustus II
Deep Learning World Platinum Sponsored Session

DataRobot will demonstrate how innovations in Machine Learning & Artificial Intelligence can easily be leveraged to address a variety of opportunities and challenges across industries. Today, companies now have the ability to use machine learning & AI as a competitive advantage in their market, reduce operational costs, create new revenue streams, and drive higher customer satisfaction and loyalty. DataRobot's presentation will outline how scaling AI across your enterprise will deliver top & bottom line-benefits...create truly an "AI-Driven Enterprise".

Session description
Sponsored by
DataRobot
Speaker
David RussellDataRobot
RVP of Sales - West Region
DataRobot
10:00 am
Exhibits & Morning Coffee Break
Exhibits & Morning Coffee Break
Exhibits & Morning Coffee Break
Exhibits & Morning Coffee Break
Exhibits & Morning Coffee Break
10:30 am
Room: Pompeian III
PAW Business Track 1: BUSINESS - Analytics operationalization & management

Operationalizing predictive models

This PAW track – "BUSINESS - Analytics operationalization & management" – is kicked off by James Taylor of Decision Management Solutions, an industry leader in the operationalization of predictive models.

Many organizations still struggle to get a business return on their investment on advanced analytics. The biggest barrier? An inability to integrate analytics, especially predictive analytics, into frontline systems and business processes. Work with a number of global companies has revealed three critical success factors. By adopting a more decision-centric approach to analytics, changing the way requirements and business understanding are defined, and considering advanced analytics as one of a set of decision-making technologies, organizations can tie their analytics investments to business results and deliver the business value they are looking for.

Session description
Speaker
James TaylorDecision Management Solutions
CEO
Decision Management Solutions
Room: Augustus II
PAW Business Track 2: WORKFORCE - Retaining & optimizing HR with analytics

Workforce optimization and retention

Drawing on a unique database consisting of workforce (HRIS) data from multiple organizations, Mercer's Workforce Analytics team has applied predictive modeling to examine to what extent systematic differences exist between what millennials actually value in employment as compared to older employees. The results challenge "popular press" stereotypes about millennials at work. By identifying the predictive antecedents of employee turnover, i.e., what employees do, as opposed to relying on survey results, i.e., what millennials say they value, this research provides insight into actual behavior in a way that has practical relevance to the design of effective employee value propositions (EVPs) and total rewards.

Session description
Speakers
Haig NalbantianMercer
Senior Partner, Co-leader Mercer Workforce Sciences Institute
Mercer
Tauseef RahmanMercer
Principal, Workforce Strategy & Analytics
Mercer
Room: Augustus I
PAW Business Track 3: CASE STUDIES - Manifold business applications of machine learning
Infrastructure planning; telecom
10:30 - 10:50 am
Case Study: Charter Communications (Spectrum)

Charter Communications (Spectrum) is the second largest cable service provider in the nation, and every investment to expand fiber network is based on maximizing the ROI. Charter makes an extensive use of Predictive Analytics based on Logistic Regression, Survival Analysis, GIS analysis to determine the best go-to prospects and assign sales territories to maximize return.

The marketing team has been able to improve campaigns' performance by over 30%, and the network construction team has been able to identify thousands of new buildings with the best ROI (with only 5% rejection rate) which were deemed unreasonable till now.

Session description
Speaker
Nishant SharmaCharter Spectrum
Director of Predictive Analytics
Charter Communications
Predictive Analytics World for Business
Track 3: CASE STUDIES - Manifold business applications of machine learning
TV ad targeting
10:55 - 11:15 am
Case Study: Turner

Turner has developed a machine learning algorithm that is capable of predicting granular audience targets. This innovation, called Competitive Audience Estimation, enabled advertisers/agencies to buy and execute audience targeted marketing campaigns on linear TV. This talk will overview the methodology and attained results. 

Session description
Speaker
Wes ChaarTurner
Senior Vice President of Analytics, Data and Decision Sciences
Turner Broadcasting System, Inc.
Room: Sicily
PAW Financial
Fraud detection
Case Study: FICO

The Financial industry has been one of the most successful to adopt machine learning. FICO's fraud detection systems played a pivotal role in establishing a long lasting victory for supervised neural networks 25 years ago in the credit card fraud detection space. Since then, we have continually improved behavior and unsupervised machine learning techniques, including methods from collaborative filtering and density-based models. Also important is explainable AI, which has been a part of fraud models for the last 20 years. We will discuss new innovations which are having dramatic impact on the financial systems for detecting and preventing fraud, abuse and money laundering.

Session description
Speaker
Scott ZoldiFICO
Chief Analytics Officer
FICO
Room: Emperors II
PAW Healthcare

Come join us to learn how one of UnityPoint's affiliates revamped their re-admissions reduction strategy using predictive modeling across the care continuum to reduce risk-adjusted re-admissions by 40% over three years. Under the hood are predictive models that forecast hospital length of stay, 30 day readmission risk, a heat map depicting likelihood of readmission each day post discharge, and follow up visit no show risk at the patient level each morning. We'll share the integrated tool, development details, and how use has spread organically across our health system.

Session description
Speakers
Ben ClevelandUnityPoint Health
Data Scientist
UnityPoint Health
Rhiannon Harms
Executive Director, Analytics
UnityPoint Health
Room: Pompeian IV
PAW Manufacturing
Case study: Siemens

Anomaly detection has utility in many industries, and its potential to save costs and risk is huge when applied to maintenance prediction. But widespread sharing of techniques for predicting major episodes without a classic pattern of historical data has been limited by the lack of public data. In this paper, we use new, publicly available IOT data from motor sensors along with a variety of techniques to create predictive models. We also explore the effectiveness of Image Mining to further enhance our models. A roadmap for use on ANY type of sensor or IOT data is also discussed. Examples will be used throughout based on public data and open source software so that everyone can benefit from this work.

Session description
Speaker
Phil WintersCIAgenda
Author and Thought Leader
CIAgenda
Room: Pisa
Deep Learning World KEYNOTE

Groundbreaking theory, big data, and compute power — with this trifecta, the extraordinary advent of deep learning seems almost inevitable. It propels machine learning to new heights across many industries. As we ride this wave of progress, still in acceleration, we come to a new class of challenges akin to those of traditional machine learning — but now the stakes are higher. In this keynote, DLW Founding Chair Luba Gloukhova will cover great challenges deep learning has already overcome and those that still remain.

Session description
Speaker
Luba GloukhovaStanford Graduate School of Business
Research Analytics Consultant
Stanford Graduate School of Business
11:15 am
5-minute transition between sessions
5-minute transition between sessions
5-minute transition between sessions
5-minute transition between sessions
5-minute transition between sessions
11:20 am
Room: Pompeian III
PAW Business Track 1: BUSINESS - Analytics operationalization & management
Operationalizing analytics
11:20 - 11:40 am
Case Study: Northwestern Mutual

Getting models and analytic products ‘over the line’ and implemented or operationalized can be challenging due to factors often outside the direct control of a modeling or analytics team.  I’ll share some of the obstacles that we’ve met in our journey maturing as an Enterprise Data and Analytics organization.  Some are business and organizational challenges, others are technology related. At Northwestern Mutual, we’ve successfully navigated a number of these challenges and deployed models and other analytic products for mission critical business functions.  I’ll talk through some of the challenges and share our approach to overcoming them.

Session description
Speaker
Dave PahlNorthwestern Mutual
Sr. Director - Enterprise Data and Analytics
Northwestern Mutual
Predictive Analytics World for Business
Track 1: BUSINESS - Analytics operationalization & management
Analytics management
11:45 am - 12:05 pm
Case Study: Quicken Loans

Data Science’s massive potential can only be unleashed if it’s aligned with, and empowered by, senior leadership in an organization. Even the most sophisticated, transformative and well-executed projects can be for naught if the right people don’t trust how it works, its value or haven’t developed a culture that values Data Science. But while senior leaders speak business, Data Science teams often speak in math or code, and increasingly do so as the field evolves. How can Data Science teams bridge this gap to elevate their place and impact within an organization? In this talk I will share the story of Quicken Loans’ Data Science team and how some simple but insightful best practices can build the trust, relationships and understanding with senior leaders to transform organizations.

Session description
Speaker
James CarsonQuicken Loans
Data Science Team Leader
Quicken Loans
Room: Augustus II
PAW Business
The Session Description will be available shortly.
Session description
Predictive Analytics World for Business
Workforce optimization
11:20 am - 12:05 pm
Case Study: Atlassian

Companies worldwide make massive investments to make their employees more productive. This endeavor has become more critical than ever as access to a qualified workforce is becoming increasingly challenging. While engaging users with relevant content is certainly a desired outcome, most product implementations still focus on optimizing click-through rate or engagement rather than productivity. As a workforce and management software market leader, Atlassian holds a unique position to determine how project documentation and team communication actually impact the throughput of employees. This talk addresses the intriguing correlations between the usage patterns observed in workforce collaboration tools, and the productivity of the user.

Session description
Speaker
Daniel ChungAtlassian
Data Scientist
Atlassian
Room: Augustus I
PAW Business Track 3: CASE STUDIES - Manifold business applications of machine learning
Student registration acquisition modeling
11:20 - 11:40 am
Case Study: American Career College

Our overarching goal was to determine the underlying phenomena that create market demand for educational programs. The central question was: Is it the type of program, or the lead to enrollment process, or some identifiable behavior of the leads, or lastly would the demographic attributes turn out to be the chief drivers of enrollment. Rather than model the likelihood of enrollment with all the available variables, we methodically determined the product or school program variables that made them attractive; then the process of enrollment and finally to the behavioral characteristics of prospective students.  

Session description
Speaker
Peter Karpiuk
Consultant
Roundrock Management
Predictive Analytics World for Business
Track 3: CASE STUDIES - Manifold business applications of machine learning
Student registration acquisition modeling
11:45 am - 12:05 pm
Case Study: Becker College

Predictive modeling has gained popularity in studying college enrollment due to fierce competition in higher education. To make informed decisions and allocate limited resources to improve enrollment, predictive modeling has been applied to challenge and change the traditional recruitment process. This session is intended for two learning outcomes: Participants who are not familiar with predictive modeling will learn how to lay out a plan to collect and build a comprehensive data infrastructure and conduct predictive modeling. Participants who have run predictive modeling will learn how to critically examine the quality of their predictive analyses.

Session description
Speaker
Feyzi BagirovHarrisburg University
Data Science Advisor at Metadata.io and Analytics Instructor, Harrisburg University of Science and Technology
Room: Sicily
PAW Financial
Disaster recovery forecasting
11:20 - 11:40 am
Case Study: Queensland Reconstruction Authority (Australia)

This case study details how reconstruction costs following major weather events can be rapidly estimated by predicting damage to infrastructure at a granular geographic level. There is an increasing severity of major weather events impacting communities and infrastructure coupled with rising reconstruction costs. In Australia, funding models for disaster recovery relief are changing. To help get communities back on their feet, reconstruction funding is required sooner following such events, meaning public agencies need to find efficiencies in the existing machinery of government processes for disaster recovery. Implications for financial institutions and insurers will also be covered.

Session description
Speakers
Kieran Dibb
Director of Technical Services
Queensland Reconstruction Authority (QRA)
Will DoddDeloitte
Director - Consulting
Deloitte
Predictive Analytics World for Financial
Algorithmic trading
11:45 am - 12:05 pm

We substantially increase investment returns in large stocks by comparing the current market context with prior contexts. The approach differs from simple auto-correlation of a few stock time-series. Contexts comprise over 5000 market and economic events. We key on the  unusualness of many events within a context, and show a new similarity measure to find linkages among large sets of past events, current events, and large stock investment opportunities. We benchmark the enhanced investment returns against well-known market indices. Large stock cumulative returns in 2016 were 18% compared to OEX benchmark of 9%; Large stock cumulative returns for 10yrs were 205% compared to OEX benchmark of 50%. This session explains our scientific work presented.

Session description
Speaker
Kevin PrattZZAlpha LTD.
Chief Scientist
ZZAlpha Ltd.
Room: Emperors II
PAW Healthcare

Healthcare reporting and analytics has traditionally treated free text notes as a black hole. The Advanced Analytics team at OSF Healthcare has been leveraging natural language processing (NLP) to address this opportunity space for more than a year. Projects have included the extraction of Ejection Fractions from Echocardiograms, concept analysis of patient satisfaction surveys and named entity recognition for PHI and ailment identification. This session will provide a practical overview of NLP approaches the team has found useful and provide tips on how an organization can get started mining intelligence out of its free text without requiring large scale investments.

Session description
Speakers
Chris Franciskovich
Manager of Advanced Analytics
OSF Healthcare System
Jason Weinberg
Data Scientist
OSF Healthcare
Room: Pompeian IV
PAW Manufacturing
Case study: Seagate Technology

The surge in IoT and cloud computing have led to a one-way centralized transmission of raw data from billions of installed sensors to centralized systems for storage and processing. As a result, challenges were raised whether the surplus amount of data can be processed securely in real-time while allowing data recovery for predictive analytics. Edge analytics will offer a significant step toward the solution of this growing concern. By deploying predictive analytics models on the edge, this presentation will reveal the impact of edge-based analytics on Seagate operations, such as: predictive maintenance, product quality check, security surveillance, and safety monitoring.

Session description
Speaker
Abbas ChokorSeagate Technology
Staff Data Scientist
Seagate Technology
Room: Pisa
Deep Learning World

Senior Scientist Engineer at Microsoft, James McCaffrey and Pranjal Daga, Data Scientist at Cisco, will field questions from the audience of deep learning practitioners about all things deep learning deployment, from initial research and development to championing within the organization to scaling up for production and maintaining.

Session description
Speakers
Pranjal DagaCisco
Data Scientist
Cisco
James McCaffreyMicrosoft
Senior Scientist Engineer
Microsoft
12:05 pm
Lunch
Room: Augustus I
PAW Business Lunch & Learn

As data stores grow and more emphasis is being placed on understanding the relationships between people, places, and things, interest in graph technology has exploded.

Unlike relational databases, which rely on costly joins to reconstitute connections, graphs emphasize the connections themselves, putting far more value on the data between the data.  Graphs link entities, along with their associated properties, directionally with edges that describe the relationships between them. And because it is possible to use multiple links, understanding temporal and spatial elements becomes far easier.

In this session, Brandy Freitas from Pitney Bowes, in conjunction with their partner ICC, will discuss graph databases and how they are charging analytics in a variety of industries. They will walk through the power of the graph, practical use cases, and take a dive into query building, machine learning, and native graph algorithms. With real life examples, including topics like sales prospecting and fraud detection, this session aims to get participants up to speed on the use of graph databases and showcase their importance in modern business decisions.

Session description
Sponsored by
Pitney Bowes
Speakers
Brandy FreitasPitney Bowes
Senior Data Scientist
Pitney Bowes
Lunch
Room: Emperors II
PAW Healthcare

The need for timely decisions in ever-changing, unique scenarios is carved out in the healthcare field, where leveraging AI is expected to become a $6.6 Billion market in the next couple years. diwo combines the knowledge at hand with its Cognitive Framework to provide pre-packaged, quantified decisions, allowing knowledge to turn into the best possible action. A use-case demonstrating diwo’s cognitive decision-making for Readmission will be presented.

Session description
Sponsored by
diwo
Speaker
Gaurav Joharidiwo
Principal Business Architect
diwo
Room: Pompeian IV
PAW Manufacturing

Even with the siloed nature of most manufacturing industries, vast cost-savings are to be had across several points of the manufacturing and distributing processes. With real-time predictions, diwo leverages its cognitive framework to coordinate analytics such as current supply and demand signals to quantify decisions based on these contextual insights. Decision-making is taken into the next realm beyond knowledge generation, enabling the optimization of cost-savings and decision-making.

Session description
Sponsored by
diwo
Speaker
Krishna Kallakuridiwo
President & Founder
diwo
 
12:25 pm
 
 
 
Lunch
Lunch
Lunch
1:30 pm
Room: Augustus II
PAW Business KEYNOTE
Enterprise-wide deployment
Case Study: Dell EMC

Looking for tips and pointers on how and where to use machine
learning? Look no further. In this session, Theresa Kushner - SVP of Dell EMC's
Performance Analytics Group - will cover best practices as they apply for
medium to large enterprises, including:

- Tips for multinationals to effectively apply predictive
analytics in their business – where, how and why?

- Hottest techniques in predictive analytics for enterprises

- Role of start-ups in boosting predictive analytics techniques;
applying external expertise

- How to effectively partner with your IT organization to get
the most out of machine learning

Session description
Speaker
Theresa KushnerDell EMC
Sr Vice President, Performance Analytics Group
Dell EMC
Room: Sicily
PAW Financial KEYNOTE

Machine Learning is gaining significant steam in many industries – especially financial services, where the adoption rate has been very high. Robotic and Intelligent Process Automation have been used extensively and have reached a maturity in operations and finance areas such as Risk, Compliance, Anti-Money Laundering and Know Your Customer (AML/KYC), Fraud Detection and Prevention, Loan Processing and Approvals and Governance. Financial services organizations currently are using AI surveillance tools to thwart financial crime, while others deployed machine learning for tax planning. Wealth Management leaders can now offer automated investing advice across multiple channels, and some insurers now use automated underwriting tools in their decision-making process. In this keynote presentation, State Street Vice President Radha Kuchibhotla will survey this wide range of industry movement and highlight the most important facets.

Session description
Speaker
Radha KuchibhotlaState Street
Vice President
State Street
 
 
 
1:50 pm
 
 
Room: Emperors II
PAW Healthcare KEYNOTE

A modern health enterprise where business and clinical decisions are powered by advanced analytics stands in stark contrast to the existing status quo across health and life sciences today. Existing approaches to informatics — based in descriptive views of limited data sources — are incapable of supporting the sophisticated insights needed to optimize the tradeoffs between health outcomes and costs, and between standardized medical treatment plans and more personalized care practices.  As discussed in the book Health Analytics: Gaining the Insights to Transform Health Care (Wiley, 2013), the health industry’s analytical lens must shift from the retrospective, presumptive, and population-oriented practices and policies commonly used today towards collaborative, data-driven, predictive, patient-centered, and real-time engagement-oriented processes.  Insight-driven health requires integrated perspectives across health outcomes, financial management, risk management, performance management, and behavioral medicine.  But the transition requires new business competencies, technical capabilities, and strong leadership.

Session description
Speaker
Jason BurkeUNC Health Care System
System VP & Chief Analytics Officer
UNC Health Care System
Room: Pompeian IV
PAW Manufacturing KEYNOTE

Data science, machine learning, and Artificial Intelligence are all relevant to the future of reliability practices in manufacturing and utilities. If your organization can predict failure-free performance, not only do you avoid extremely costly downtime, you reduce the potential harms that occur when critical systems unexpectedly fail. With the explosive growth of sensors and Internet of Things technology, and Big Data infrastructure to analyze data at massive scale, is the promise of AI within our grasp? 

In this keynote you'll learn: 

1) What are the differences between data science, machine learning, and AI? 

2) What are the use cases and success stories for AI in managing reliability for manufacturing and utilities? 

3) How can your organization get started on the path to AI development? 

Session description
Speaker
Steven RamirezBeyond the Arc
CEO
Beyond the Arc
Room: Pisa
Deep Learning World
IT
Case Study: Cisco

In the field of Automatic Speech Recognition (ASR), the state of the art for generic conversations have reached super human levels. However, things are not nearly as good in specialized knowledge domains: attempting to transcribe vendor-customer or intra-vendor conversations often results in high double-digit error rates. Considering the low performance of ASR on real data, it becomes imperative to look at the multimodal data of text and images to customize the Language Model, and audio to customize the Acoustic Model. This session will focus on discussing end-to-end Recurrent Neural Network architectures which can learn alphabets just through a sound spectrogram. 

Session description
Speaker
Pranjal DagaCisco
Data Scientist
Cisco
2:15 pm
Room: Augustus II
PAW Business Diamond Sponsored Presentation from Tellius

Despite enterprise investments in business intelligence and data science tools, the thirst for data and analytics is far exceeding the ability for organizations to deliver actionable insights from all their data. Most business users are stuck running static reports, while the data professionals who support them tackle the increasing backlog of ad hoc requests, struggling with operationalizing insights into the business workflow.
 
The new era of intelligent analytics applications will raise the adoption of analytics tools in the enterprise while closing the gap between BI and AI. Tellius will demonstrate how business users are empowered to ask questions through an easy-to-use, search-driven interface and effortlessly perform advanced analysis through automated discovery of insights powered by machine learning.

Session description
Sponsored by
Tellius
Speakers
Jason GranTellius
Lead Data Scientist
Tellius
Ajay KhannaTellius
Founder and CEO
Tellius
Room: Sicily
PAW Financial Diamond Sponsored Session

The word is all around that cognitive computing is poised to disrupt industries. You might wonder if it is real or just a meme. And if so, how does impact me or my business? The phrase ‘cognitive computing’ conjures up varying thoughts in our minds which could be exciting and scary at the same time. It’s about time that we delve deeper and develop a clear understanding of what “cognitive” buzzword is all about? How does cognitive decision-making differ from Analytics, AI, and Natural Language Processing? Financial services stand to gain most by cognitive decision-making as the greater understanding of customers, products, and the operating environment directly translates into business value. This presentation highlights the essentials of a cognitive decision-making system, such as DIWO, and will share thoughts on how such an approach can reshape the financial services industry.

Session description
Sponsored by
diwo
Speaker
Satyendra Ranadiwo
Chief Technology Officer
diwo
 
 
 
2:35 pm
5-minute transition between sessions
5-minute transition between sessions
5-minute transition between sessions
5-minute transition between sessions
5-minute transition between sessions
2:40 pm
Room: Pompeian III
PAW Business Track 1: BUSINESS - Analytics operationalization & management

Value-driven project strategies

Decisions are never made in a vacuum. They are the product of how decision-makers perceive their policy, political, business, and organizational environment. Shaping their decisions means shaping their environment. 

In this talk, Daniel Porter, Chief Analytics Officer of BlueLabs, will discuss his team’s approach for using data science to shape environments by influencing influencers. The approach pinpoints influencers across different industries and then dynamically scores them on their elasticity and level of influence within a given domain, so that organizations can target the most relevant influencers with ads and outreach, online and offline. 

Using examples from influencer outreach for major organizations, Porter will talk about how his team's approach to identifying and scoring influencers, from Fortune 100 executives to government officials, staff, major donors, and policy elites. Through a combination of data science tools and algorithms ranging from NLP classification to uplift modeling and non-proportional sampling methods, this approach provides organizations with an unparalleled ability to achieve their brand and policy objectives. 

Session description
Speaker
Daniel Porter
Co-Founder
BlueLabs
Room: Augustus II
PAW Business Track 2: WORKFORCE - Retaining & optimizing HR with analytics

Workforce optimization; sales analytics
Case Study: Shell International

In 2017, the HR Analytics team in Shell collaborated with Shell Global Commercial, and connected Sales and HR data to improve the way we hire, reward, and develop sales account managers. The research project, called Project Spark, developed an Individual Sales Performance Model to identify key drivers of Sales Performance. Statistical modelling techniques (e.g. general linear modelling, logistic regression analysis, multi-level modelling) were applied to understand the effects of, for example, Personality, Career Types, and Team Leadership on Sales Performance. Based on the insights, Global Commercial and HR are co-designing practical solutions to get the best out of our people.  

Session description
Speakers
Tashi ErdmannShell Oil Company
HR Analytics Manager
Shell International
Linda van LeeuwenShell Oil Company
HR Analytics Analyst
Shell International
Room: Augustus I
PAW Business Track 3: CASE STUDIES - Manifold business applications of machine learning
IP video player data analysis
2:40 - 3:00 pm
Case Study: Comcast Cable

Comcast data scientists need to use the vast resources of our in-house data lake in order to perform ML tasks such as anomaly detection. In the schema-onread model of data lakes, a data scientist first has to find datasets of interest, infer the meaning of each attribute, and clean up the data to enable joins across datasets and to ensure equivalent data (e.g., timestamp or geo location) is represented in the same format or vocabulary. Typically 80% of her time is spent on these tasks, before she can begin identifying features of interest and actually running the ML models.  

How does data governance make feature engineering easy?  Imagine a data lake in which datasets are represented via peer-reviewed Apache Avro schemas, using common sub-schemas, with the semantics and format of each attribute well-documented.  A metadata repository enables data discovery based on these schemas, and also stores end-to-end lineage  from streaming data capture through storage in the lake.  Governance also enforces schemas to be compatible when they evolve, so that data stored in different schema versions don’t need to be converted before use in feature engineering.  Also, by definition, the features are already defined in the datasets in a common Avro representation. Finally, it allows the data preparation for ML to become generic and therefore re-usable, e.g. extracting from any data set a time series of a single value (i.e. a feature) according to several dimensions.

Session description
Speakers
Gabriel CommeauComcast Cable
Software Engineer
Comcast Cable
Barbara EckmanComcast Cable
Principal Data Architect
Comcast Cable
Predictive Analytics World for Business
Track 3: CASE STUDIES - Manifold business applications of machine learning
Pricing
3:05 – 3:25 pm

The point of a business is to maximize profit. The best way is to always have the best price. But sometimes it makes sense to offer discounts to focus on the long term. This talk will focus on building and deploy such a tool, internationalization and dealing with a large number of Shiny users.

Session description
Speaker
Konrad PabianczykAppsilon Data Science
Key Account Manager
Appsilon Data Science
Room: Sicily
PAW Financial
Life insurance underwriting
2:40 - 3:00 pm
Case Study: Northwestern Mutual

Breaking technical requirements into small enough increments that allow data scientists to deliver something of value to stakeholders every few weeks is a difficult challenge.  In this session, we cover how Northwestern Mutual used an Agile Analytics approach to develop a system of predictive models that helps automate risk assessments in life insurance underwriting.  We briefly introduce aspects of Scrum and Kanban along with an overview of the Standard Methodology for Analytical Models.  The session concludes with an illustration on how to assess the incremental information gain from improved models within a framework of data monetization.

Session description
Speaker
Daniel FuhrmannNorthwestern Mutual
Senior Data Scientist
Northwestern Mutual
Predictive Analytics World for Financial
Insurance
3:05 – 3:25 pm

This session will provide an overview of how predictive analytics helps insurers understand the financial drivers of their performance in the Affordable Care Act (ACA, aka Obamacare). Attendees will learn about:

- The development and implementation of a model that simulated patients' Health and Human Services (HHS) 2014-2016 risk scores

- Large-scale collection of detailed EDGE data to uncover drivers of performance in the ACA markets

- Key insight from the model, including that sicker patients are not driving losses for health plans due to risk adjustment costs

- Takeaways for payers and providers seeking to manage risk and profitability in an unfamiliar environment

Session description
Speaker
Syed MehmudWakely Consulting Group
Principal & Senior Consulting Actuary, ASA
Wakely Consulting Group, Society of Actuaries
Room: Emperors II
PAW Healthcare

Pharmaceutical industry operations are a complex network of interrelated business entities. There is an opportunity for fraud at each stage in the manufacturer/supplier-to-prescriber-to-dispenser-to-consumer distribution chain, and there are many known fraud schemes. We focused on the prescriber-dispenser-consumer end of the chain, and in particular, on schemes involving controlled prescription drugs. 

Creating features from heuristics (domain experts) and deduction (graph analytics), which we employed in machine learning models, we developed a tool that can detect prescriber  fraud schemes such as collusions, negligent prescribing and pill mills.  Taya Fernandes, Drug Diversion & Data Mining Analyst, Medicaid Fraud Control Unit, Indiana, contributed to this presentation.  

Session description
Speaker
Jaya Tripathi
Principal Scientist
MITRE
Room: Pompeian IV
PAW Manufacturing
Case study: Leading Telco Company

Preventive maintenance is a known use case for IoT (Internet of Things) and has numerous applications in a variety of industries. Electronic equipment such as internet modems and combined internet/cable boxes are good examples for a preventive maintenance application in Telcos. Wired internet provides the main connection point for a variety of wired/wireless devices at home to the outside world for security, TV/radio, music, internet, within-home WiFi, VOIP, etc. Health of such devices are critical for a good customer service that directly impacts customer experience measured through NPS. Each one of these devices can generate a variety of health measures (variables) and report that periodically to central collection points for general analysis. There are variety of ways to ingest, collect, and analyze such data. Analysis could range from BI/visualization to simple models to predictive models. In this talk, I provide a real-world use case for millions of DOCSIS devices where each provides 10-20 physical measurements about its health. The data collection infrastructure is capable of ingesting hundreds of millions of data records daily. I show two practical scoring approaches for creating alerts for use by customer service staff including field personnel. Using such scores, one can quantify quality of service (QoS) in a single number based on physical measurements. The first approach uses expert knowledge to create an hourly additive score using engineering expert advice. The second approach leverages an hourly predictive model score trained on device health measurements to predict probability of a technical support call in the hours ahead. I explain how the second score can be used as a measure of customer irritation. In each case, high Scores indicate a severe deviation from normal operating conditions (healthy operation range) or high probability of potential customer irritation requiring attention from customer service/field. The methodology is generalizable to other equipment and devices in different settings and different industries.

Session description
Speaker
Khosrow HassibiR Systems, Inc.
Chief Data Scientist / Chief AI Officer AI and Analytics Center of Innovation
R Systems Inc.
Room: Pisa
Deep Learning World
Finance

High frequency trading (HFT) has been characterized as an arms race with 'Red Queen' characteristics. And yet, Machine Learning (ML) has barely impacted HFT. Indeed, its application raises more questions than it answers.  How do we integrate an agent's actions into a predictive model? If all agents use ML, then why trade?  This talk demonstrates a novel model for attributing strategy performance to predictive accuracy. Our model quantifies the extent to which an agent's action invalidate the prediction. Moreover, the model sheds light on why a zero-sum game is an unlikely outcome for ML agents.

Session description
Speaker
Matthew DixonIllinois Institute of Technology
Assistant Professor of Finance and Statistics
Illinois Institute of Technology
3:25 pm
Exhibits & Afternoon Break
Exhibits & Afternoon Break
Coffee & Exhibition Break
Exhibits & Afternoon Break
Exhibits & Afternoon Break
3:55 pm
Room: Pompeian III
PAW Business Track 1: BUSINESS - Analytics operationalization & management

Thought leadership; research & development

The opportunities and challenges created by the expanding size and definition of data has become the catalyst for many universities across the country to think differently. The evolving discipline of data science is changing the way universities approach education, research and funding. In particular, the way universities collaborate with the private sector for research and development has fundamental changed - at least when it comes to data science. And, importantly, this new private sector/university collaborative framework has, in turn, contributed to the evolution of Data Science for both parties.

In this session, Dr. Jennifer Lewis Priestley, director of one of the country's first Ph.D. program in Data Science, will talk about how the relationship between universities and private sector companies has been impacted by the evolution of data science.

Session description
Speaker
Jennifer Lewis PriestleyKennesaw State University
Professor of Applied Statistics and Data Science
Kennesaw State University
Room: Augustus II
PAW Business Track 2: WORKFORCE - Retaining & optimizing HR with analytics

Workforce optimization
3:55 - 4:15 pm
Case Study: Centric Consulting

Our consulting company, Centric, was struggling with low utilization - too many employees were not on billable projects. We designed and implemented a machine learning model to help us better understand the factors driving employee utilization. Although we had only a few hundred rows of data with which to work, the insights gained were used to redesign internal business processes, resulting in a 13% gain in utilization which in turn resulted in a nearly 20% improvement in company-wide profitability. In this session, we will cover the process we took from designing the machine learning model to implementing our findings in "real life," including tips, tricks, and lessons learned.

Session description
Speaker
Carmen FontanaCentric Consulting
Cloud & Artificial Intelligence Practice Lead
Centric Consulting
Predictive Analytics World for Business
Track 2: WORKFORCE - Retaining & optimizing HR with analytics

Workforce optimization
4:20 – 4:40 pm
Case Study: Baptist Health Medical Group

Engaging employees is critical for any organization.  It doesn't need to be difficult, and it doesn't need to be expensive.  In this case study, the Baptist Health Medical Group's Practice Optimization Team will demonstrate what it did (and what you can do) to measure employee engagement, and use statistics-based insights to take engagement to the next level. 

In this session, we will share our approach for: 

1) Asking the right questions,

2) Identifying engagement drivers, 

3) Communicating insights to leadership, and

4) Creating team-generated action plans. 

You already have the tools and budget to do what we did. We will show you how.

Session description
Speaker
Matt HayesBaptist Health Medical Group
Director, Practice Optimization
Baptist Health Medical Group
Room: Augustus I
PAW Business Track 3: CASE STUDIES - Manifold business applications of machine learning
Customer service analytics
Case Study: Google

At Google, we developed analytical methods to evaluate the hypothesis that Universal Agents, who handle support calls across several products, would outperform Laned Agents, who are specialized to support only a single product. Here in Google's gTech group, initially operating under the assumption that Universal Agents would have higher utilization than the more prevalent Laned Agents, Google increased the number thereof, thereby creating the opportunity to collect the data needed to make this comparison. After several months, we compared the two strategies. To do so, however, required some innovation, since many factors impact agent performance. To normalize for these factors and get a clean adjusted estimate of performance, we applied a form of mixed modeling (an advanced form of ANOVA). Our findings were counterintuitive: Universal Agents under-perform Laned agents in overall cost and measures of customer satisfaction. This project corrected many misconceptions and resulted in a relative increase in Laned Agents.

Session description
Speaker
Tarek SoukiehGoogle
Head of Analytics - YouTube Trust and Safety
Google
Room: Sicily
PAW Financial
Reinsurance
3:55 - 4:15 pm
Case Study: Maiden Re

One of the key needs of reinsurance providers who provide insurance to commercial auto businesses is on how reinsurance premia can be aligned with the risk profile of the loan portfolios. This session will focus on how Maiden Re developed an integrated insights portal that can profile customer risk by analyzing companies they underwrite, monitor loss ratios, and identify the best prospects to pursue, using predictive analytics techniques.

Session description
Speakers
John HenryMaiden Re
Head of Data Science
Maiden Re
Sriram KrishnamurthyTiger Analytics
VP, Data Science
Tiger Analytics
Predictive Analytics World for Financial
Thought leadership
4:20 – 4:40 pm

Big Data, increased automation of tools, and artificial intelligence all represent significant changes to the working environment of the data scientist. Nowhere is this more pertinent than in financial services and insurance, which have been the traditional bedrock of predictive analytics and data science. Numerous business examples from this sector will be explored, both within the area of consumer response as well as consumer risk. The discussion will focus on what has remained the same versus what has changed within the four stages of data science. Given the highly disruptive nature engulfing both financial services and insurance, what does this mean to the data scientist such that the role becomes an even more critical one during these turbulent times?

Session description
Speaker
Richard BoireEnvironics Analytics
Senior Vice President
Environics Analytics
Room: Emperors II
PAW Healthcare

People with kidney failure treated by dialysis exhibit higher hospitalization rates than those without kidney disease, resulting in higher morbidity and mortality rates. Fresenius Medical Care developed, tested, and deployed a predictive model (with over 200 clinical and non-clinical variables) to identify patients at risk of hospitalization in the next year. Interdisciplinary clinical teams performed interventions with the identified high-risk patients. To date, we have observed a 23% reduction in average yearly hospital admission rates compared to controls in a regional assessment. This presentation will discuss the creation, implementation, and challenges with large scale deployment of this predictive model.

Session description
Speakers
Andrew LongFresenius Medical Care
Data Scientist
Fresenius Medical Care North America
Len Usvyat
Vice President, Integrated Care Analytics
Fresenius Medical Care North America
Room: Pompeian IV
PAW Manufacturing

In a recent project, a predictive system managed cross-border, multi-modal material flows and delivered better supply chain performance than spreadsheet-based manual replenishment.  Reality checks using naive Bayesian predictions operated on an evolving supply chain data set to provide alerts and visualization of potential problems before they became real.  This improved customer satisfaction by increasing communication and on-time and full rates (OTIF).   Additionally, fewer change orders reduced shipment delays due to re-filing export paperwork with the government. 

This presentation will present the business situation, improvements, and user interface to provide operational insights to executives, supply chain experts, and users.  The data architecture, mathematical modeling techniques, and lessons learned will be presented to provide insights to data scientists and system architects.

Session description
Speaker
Gary NeightsElemica
Senior Director
Elemica
Room: Pisa
Deep Learning World
Cross-Industry
Case Study: Microsoft

Long short-term memory (LSTM) deep neural networks have revolutionized natural language and are the foundation of systems like Siri and Alexa. Because LSTM networks maintain state, they can model time series regression problems. In this lively and informal session, Dr. James McCaffrey from Microsoft Research will explain exactly what LSTM networks are, without using (many) Greek letters or annoying math jargon. You'll leave this session with a solid understanding of LSTM networks, know what they can and cannot do, and have all the information you need to implement an LSTM network or communicate with subject matter experts.

Session description
Speaker
James McCaffreyMicrosoft
Senior Scientist Engineer
Microsoft
4:40 pm
5-minute transition between sessions
5-minute transition between sessions
5-minute transition between sessions
5-minute transition between sessions
5-minute transition between sessions
4:45 pm
Room: Pompeian III
PAW Business Track 1: BUSINESS - Analytics operationalization & management

Value-driven project management

Analytics is hard and getting stakeholders and executives to act and use the results from analytics may be even harder. In this presentation we argue that to turn analytics into advantage requires: (1) choosing the right analytical target to go after, (2) building credibility for the insights, (3) offering the tools that turn the insights into decision alternatives. We showcase these factors via 5 actual business case studies (each of which proved to benefit greatly from their efforts).

Session description
Speaker
Marco Vriens
CEO
Kwantum LLC and University of La Crosse, WI
Room: Augustus II
PAW Business Track 2: WORKFORCE - Retaining & optimizing HR with analytics

Legality and ethics in operationalization

Predictive analytics tools hold great promise for harnessing the power of data to make empirically-based decisions with significant increases in efficiency and effectiveness. However, predictive analytics tools come under special scrutiny when used for high-stakes decision-making such as employment selection. Join us for an overview of foundational laws, theories of discrimination, and approaches for measuring adverse impact and validity in the use of predictive analytics for employment purposes. 

Session description
Speakers
Eric DunleavyDCI Consulting Group
Director of the Personnel Selection and Litigation Support Services Group
DCI Consulting Group
Kelly TrindelEEOC
Chief Analyst
Formerly at Equal Employment Opportunity Commission
Room: Augustus I
PAW Business Track 3: CASE STUDIES - Manifold business applications of machine learning
Customer service analytics
Case Study: IBM

IBM Technical Support handles over a million tickets per year and is committed to helping clients solve technical issues and creating optimal client experience. An always-on prediction system was recently developed to automatically forecast the daily client experience outcomes by ticket prior to ticket resolution. Support agents use it to prioritize their workload and take tailored actions to improve client experience of high risk tickets. In this session, I'll introduce the journey of developing the prediction system via iterations by combining classical statistical methods, machine learning algorithms, and human feedback to achieve significant improvement in model quality and user adoption.

Session description
Speaker
Dan YangIBM
Senior Data Scientist
IBM
Room: Sicily
PAW Financial
Enterprise deployment

In this digital age of banking, a large part of financial institutions' time and resources is spent in fraud analysis and credit monitoring, yet knowing and engaging the customer based on their preference and ways of interacting is the new normal. It's all about knowing how to help the customer improve their standing, and even predicting when they may have challenges or knowing which product they could benefit from in the future. Predictive analytics are being more relied upon to help agents and customer service representatives service and engage the customer more effectively. Successful machine learning-driven prediction and recommendations, coupled with ML-driven bots can inherently differentiate your products and services to your customer and create a lasting relationship. This session will discuss the pieces and parts behind using ML and bots to change the customer engagement model of your financial institution.

Session description
Speaker
Tracie Coker KambiesDeloitte
Principal | Retail Technology and Analytics
Deloitte
Room: Emperors II
PAW Healthcare

For children who have mental health conditions, starting therapies early is key for their future quality of life. Early and accurate screening tools are vital.

We present a clinically validated machine learning driven App to screen for autism. We give an overview of the development and optimization of two machine learning algorithms that use different media to identify autism, as well as the combination of their results, and the clinical validation of the algorithm's performance.  We also present an algorithm to simultaneously screen for multiple conditions. We show its preliminary expected performance when outcomes of ADHD or autism are possible.

Session description
Speaker
Ford GarbersonCognoa
Senior Data Scientist
Cognoa
Room: Pompeian IV
PAW Manufacturing

Steven Ramirez, Conference Chair, wraps up what we've learned at PAW Manufacturing.

Session description
Speaker
Steven RamirezBeyond the Arc
CEO
Beyond the Arc
 
5:30 pm
Networking Reception
Sponsored by
DataRobot
Room: Augustus II
PAW Business Track 2: WORKFORCE - Retaining & optimizing HR with analytics
Workforce analytics
5:30 PM to 5:50 PM
Case Study: Twitter

Attracting and retaining the right talent continues to be a struggle, especially in the Silicon Valley. Advances in statistical modeling have allowed us to predict and understand in depth and breadth why employees leave companies. This presentation will walk through some of the analytic techniques being used at Twitter, such as survival analysis, decision trees, etc. being used to predict employee attrition.

Session description
Speaker
Menghan ChenTwitter
People Data Scientist
Twitter
Networking Reception
Sponsored by
DataRobot
Networking Reception
Sponsored by
DataRobot
Networking Reception
Networking Reception
7:00 pm
Dinner with Strangers
End of first Conference Day
End of first Conference Day
End of first Conference Day
 

Predictive Analytics World - Las Vegas - Day 2 - Wednesday, June 6th, 2018

8:00 am
Registration & Networking Breakfast
Registration & Networking Breakfast
Registration & Networking Breakfast
Registration & Networking Breakfast
Registration & Networking Breakfast
8:45 am
Room: Augustus II
PAW Business KEYNOTE

What does a 90-year old company have to do with predictive analytics? Quite frankly, everything. Through predictive analytics services and solutions, Caterpillar is increasing the value of its equipment and commitment to its customers by helping them predict potential outcomes and make better business decisions. Morgan Vawter, Caterpillar’s Chief Analytics Director, will share how this legacy company is combining domain expertise with vast amounts of data and advanced mathematical techniques to help its customers build a better world through better fuel productivity, increased safety, planned downtime and other predictable efficiencies.

Session description
Speaker
Morgan VawterCaterpillar
Chief Analytics Director
Caterpillar
 
Room: Emperors II
PAW Healthcare
Jeff DealElder Research
Vice President of Operations
Elder Research
Room: Augustus II
PAW Manufacturing KEYNOTE

What does a 90-year old company have to do with predictive analytics? Quite frankly, everything. Through predictive analytics services and solutions, Caterpillar is increasing the value of its equipment and commitment to its customers by helping them predict potential outcomes and make better business decisions. Morgan Vawter, Caterpillar’s Chief Analytics Director, will share how this legacy company is combining domain expertise with vast amounts of data and advanced mathematical techniques to help its customers build a better world through better fuel productivity, increased safety, planned downtime and other predictable efficiencies.

Session description
Speaker
Morgan VawterCaterpillar
Chief Analytics Director
Caterpillar
 
8:50 am
 
 
Room: Emperors II
PAW Healthcare KEYNOTE

It is widely known that, while it delivers what is arguably the world’s best high technology care, the US healthcare system in general suffers from significant problems of equity, quality, safety and cost.  Beginning in 2007 the Institute of Medicine called from a “Learning Healthcare System” which depends heavily on informatics.  To provide that support requires that adoption of digital records which has now largely occurred; the ability of those record systems to share data which is the current challenge; and the analysis of that data to gain new medical knowledge that is fed back to the system as providers and patients make critical decision.

This talk will discuss the significance of and provide the basics of the new HL7 Fast Healthcare Interoperability Resource (FHIR) standard in both achieving interoperability and hosting practical approaches to providing analytic based tools to providers and patients.  It will feature actual examples of tools developed using these new approaches.

Session description
Speaker
Lucieanne Ide, MD, PhD
Founder & Chairman
Rimidi
 
 
9:00 am
 
 
 
 
Room: Pisa
Deep Learning World Founding Chair Opening Remarks
Luba GloukhovaStanford Graduate School of Business
Research Analytics Consultant
Stanford Graduate School of Business
9:05 am
 
Room: Sicily
PAW Financial KEYNOTE
Case Study: Northern Trust

In this keynote, Northern Trust Senior Vice President Andy Curtis will provide an overview on robotics, machine learning, and cognitive computing in the context of operation and process optimization. He will further discuss the benefits and challenges around implementing and maintaining machine learning-based systems.

Session description
Speaker
Andy CurtisNorthern Trust
Senior Vice President
Northern Trust
 
 
Room: Pisa
Deep Learning World KEYNOTE
IT
Case Study: Capital One

Domain generation algorithm malware makes callouts to unique web addresses to avoid detection by static rules engines. To counter this malware, we created an ensemble model that evaluates if domains are malicious. The ensemble consists of two deep learning models a convolutional neural network and a long short-term memory network. These deep networks are flexible enough to learn complex patterns and do not require manual feature engineering. Our system analyzes enterprise-scale network traffic in real time. This talk will discuss the machine learning algorithms that were used to build the model and the model-as-a-service architecture utilized for low-latency processing.

Session description
Speakers
Kate HighnamCapital One
Machine Learning Engineer
Capital One
Domenic PuzioKoto
Machine Learning Engineer
Koto
9:30 am
Room: Augustus II
PAW Business PLENARY SESSION

Rexer Analytics has been surveying analytic professionals for over a decade.  In 2017, over a thousand people from around the world participated in the 8th 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
 
 
Room: Augustus II
PAW Manufacturing PLENARY SESSION

Rexer Analytics has been surveying analytic professionals for over a decade.  In 2017, over a thousand people from around the world participated in the 8th 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
 
9:35 am
 
 
Room: Emperors II
PAW Healthcare Sponsor Presentation from DataRobot

Join DataRobot in this talk to learn how to apply Automated Machine Learning (AML) to help healthcare organizations drive ROI. We will discuss why AML is the future of ML/AI. We will share some common use cases that AML can help with and show a live product demo on a simple use case.

Session description
Sponsored by
DataRobot
Speaker
Cliff YangDataRobot
Customer Success Data Scientist
DataRobot
 
 
9:40 am
Room: Augustus II
PAW Business Diamond Sponsored Session

diwo (Data In, Wisdom Out) acts as a sixth sense, continuously working in the background to sense and quantify invisible opportunities and situations before they arise, so business users are empowered to act in time and make optimal decisions. Its uniquely developed framework harnesses cognitive computing and machine learning to proactively guide business decision making. diwo's business-first approach prioritizes business and user context, scalability, and value on day one. The new platform’s design tackles adoption issues common with other transformative initiatives. As diwo is vertical-agnostic, it is applicable to a wide range of specific business scenarios across industries. Its scalability allows for a variety of businesses to roll it out according to its own organization’s needs by integrating with existing data and analytics assets.

Session description
Sponsored by
diwo
Speaker
Krishna Kallakuridiwo
President & Founder
diwo
 
 
Room: Augustus II
PAW Manufacturing Sponsored Session

diwo (Data In, Wisdom Out) acts as a sixth sense, continuously working in the background to sense and quantify invisible opportunities and situations before they arise, so business users are empowered to act in time and make optimal decisions. Its uniquely developed framework harnesses cognitive computing and machine learning to proactively guide business decision making. diwo's business-first approach prioritizes business and user context, scalability, and value on day one. The new platform’s design tackles adoption issues common with other transformative initiatives. As diwo is vertical-agnostic, it is applicable to a wide range of specific business scenarios across industries. Its scalability allows for a variety of businesses to roll it out according to its own organization’s needs by integrating with existing data and analytics assets.

Session description
Sponsored by
diwo
Speaker
Krishna Kallakuridiwo
President & Founder
diwo
 
10:00 am
5-minute transition between sessions
5-minute transition between sessions
5-minute transition between sessions
5-minute transition between sessions
5-minute transition between sessions
10:05 am
Room: Pompeian III
PAW Business Track 1: BUSINESS - Analytics operationalization & management
Sourcing analytics staff
Case Study: Verizon

In a world where demand outpaces supply, finding and keeping analytics talent has become a real dilemma.  Identifying the right mix of business skills and analytics skills can feel like an impossible search.  With so many people looking for strong talent, it often becomes difficult to compete.  How do you attract the right skills to your team to ensure a strong analytics capability?  What types of levels, roles, and titles do you need?  What are some of the ways to ensure you retain your analytics talent?  This session will discuss different compositions of successful analytics teams, as well as titles, career paths, and tips to win at the salary game. 

Session description
Speaker
Anne G. RobinsonVerizon
Executive Director, Strategy, Analytics and Systems
Verizon
Room: Augustus II
PAW Business Track 2: TECH - Predictive modeling & machine learning methods
Model interpretation
Case Study: YMCA

As data science captures more attention from decision makers, translating the models from the language of the analyst into a language of the decision maker has become an important topic at conferences and in journals. It used to be that the focus on data storytelling was on visualization techniques. While this is important, as analyses become more complex, the task of interpreting the models likewise becomes more complex. Before we can decide on visualization techniques, we first need to uncover what to visualize. In this presentation, Mr. Abbott will describe ways to unravel complex descriptive and predictive models so they can be explained and visualized using machine learning models and resampling techniques. 

Session description
Speaker
Dean AbbottSmarterHQ
Co-Founder and Chief Data Scientist
SmarterHQ
Room: Augustus I
PAW Business Track 3: MARKETING - Analytics for customer acquisition & retention
Marketing analytics strategy
10:05 - 10:25 am
Case Study: John Hancock

By 2021, Digital Marketing spend will near $120B in US and will be 46% of overall marketing spend. People-based marketing is a foundation of improving targeting efficiency. Successful targeting requires a combination of sophisticated modeling, deployment and application of both think and learn and test and learn methods. In this presentation, we will go over some of the results and lessons learned by John Hancock in its Direct to Consumer (D2C) journey, that went from 0 - 60 in less than a year.

Session description
Speaker
Kirit KottamJohn Hancock Insurance
Data Science Director
John Hancock Insurance
Predictive Analytics World for Business
Track 3: MARKETING - Analytics for customer acquisition & retention
Response modeling for marketing
10:30 – 10:50 am
Case Study: Central Pacific Bank

In 2017, our bank implemented a machine learning propensity model in our direct consumer campaign. As a result we saw a 40% reduction in marketing cost and a 70% increase in response rate. Building a machine learning model is hard; convincing the organization to implement it is harder. In this session, we will present a success story of how we convinced the management committee to implement a machine learning model that appears to them like a black box. As a result, we saw a culture shift; the organization is now more willing to explore data science solution in other products.

Session description
Speakers
Ryuichi UmedaCentral Pacific Bank
Data Scientist
Central Pacific Bank
Haifeng WuCentral Pacific Bank
Data Scientist
Central Pacific Bank
Room: Sicily
PAW Financial
Credit risk in payroll processing
Case Study: Paychex

Many Paychex clients are new businesses, with little or no established credit record. The Paychex payroll business is essentially extending short credit to our clients from the time they submit a payroll, until the time the funds complete transfer. This is a case study on dealing with the issues around Credit Risk Modeling for these new businesses. 

Session description
Speaker
Rob RollestonPaychex
Manager, Data Science
Paychex
Room: Emperors II
PAW Healthcare

Successful care management (CM) programs begin with identifying the right people to manage. Identifying the wrong individuals can lead to a costly and inefficient allocation of limited CM resources. At CDPHP, we’ve developed a cloud-based framework for population health management using predictive models to identify members who would benefit from CM based on their prospective need for such services. Additionally, the models aid in triaging members to ensure they receive the resources best suited to their needs. Utilizing potential CM needs increases the opportunity to positively impact members’ health and limits false positives compared to traditional methods.

Session description
Speaker
Matthew Vielkind
Data Scientist
Capital District Physicians' Health Plan
Room: Pompeian IV
PAW Manufacturing

The Data Mining Cost Model (DMCoMo) attempts to provide a robust parametric approach for estimating the cost of a data mining project.  This presentation will provide a practical approach for applying parametric estimation concepts in analytics-based manufacturing organizations with specific focus on using project management techniques for estimating and deploying a data mining project.  Additional focus will be placed on how to leverage the results of a data mining project for putting a data mining process into production.  The presentation will also recommend further areas of application and research.

Session description
Speaker
David Perkins, CAP®, PMP®
Owner and Principal Consultant
D & G Analytics, LLC
Room: Pisa
Deep Learning World
Cross-Industry

Head of Lyft's Machine Learning Platform Gil Arditi and Seagate Technology Senior Data Scientist, Melanie Beck field questions from an audience of deep learning practitioners about all things deep learning deployment, from initial research and development to championing within the organization to scaling up for production and maintaining.

Session description
Speakers
Gil ArditiLyft
Product Lead, Machine Learning
Lyft
Melanie BeckSeagate Technology
Senior Data Scientist
Seagate Technology
10:50 am
Exhibits & Morning Coffee Break
Exhibits & Morning Coffee Break
Exhibits & Morning Coffee Break
Exhibits & Morning Coffee Break
Exhibits & Morning Coffee Break
11:20 am
Room: Pompeian III
PAW Business Track 1: BUSINESS - Analytics operationalization & management
Analytics leadership
Case study: CA Technologies

After 3 years of building a global analytics team for a 4B software company from bottoms up, we are now the go-to analytics shop for CA Technologies. We are considered a foundational piece of all transformational initiatives especially around sales, marketing and customer success. We’ve been featured in Forbes, Forrester and just received Information Magazine’s Analytics 50 Award. Hear about the key secrets and learnings from a successful analytics team leader that can help you reduce trial by error and avoid inevitable pitfalls. 

Session description
Speaker
Jennifer BerteroCA Technologies
VP, Business Analytics
CA Technologies
Room: Augustus II
PAW Business Track 2: TECH - Predictive modeling & machine learning methods
Machine learning automation; ensemble models
Case Study: Which? (UK consumer reports)

A predictive model used in production has an incredible amount of manual overhead. That restricts the “real work” that a data scientist can do. If you can identify and automate that 85/90% of the work, what can be achieved? In this presentation, the elements of “model process automation” will be broken down and discussed using open source software throughout to demonstrate the logic. A real world example will be shown with Which? (the UK equivalent of Consumer Reports and Consumer Advocates) using 29,000 pricing ensemble models that create, train, build, tune and score every day totally automatically. From this example, best practices will be discussed.

Session description
Speaker
Phil WintersCIAgenda
Author and Thought Leader
CIAgenda
Room: Augustus I
PAW Business Track 3: MARKETING - Analytics for customer acquisition & retention
Optimizing fan engagement
11:20 - 11:40 am
Case Study: Viacom

With a portfolio of brands ranging from Nickelodeon to Comedy Central, Viacom distributes and markets content that appeals to all demographics across the globe.  Still, finding the right digital audiences for tune-in marketing is a challenge. In this session, Viacom will share the tools and models they have built to create smarter audience targets, leveraging digital content consumption by TV viewers.

Session description
Speaker
Samantha LipsonViacom
Sr Dir. Measurement, Data Scientist & Data Engineer
Viacom
Predictive Analytics World for Business
Track 3: MARKETING - Analytics for customer acquisition & retention
Targeting marketing
11:45 am – 12:05 pm
Case Study: Caterpillar

Caterpillar is implementing predictive models to identify customers that are most likely to purchase heavy equipment.  In this session, a data scientist and an analytics manager from Caterpillar will discuss these models.

Session description
Speakers
Andy JacobCaterpillar
Data Scientist
Caterpillar
Will ScheckCaterpillar
Analytics Manager
Caterpillar
Room: Sicily
PAW Financial
Workers compensation
11:20 - 11:40 am

Injured workers being away from work constitutes one of the greatest claim cost drivers. In this case study, a Return-to-Work model is introduced to predict the likelihood that injured workers will stay away from work for longer durations. This model serves to target early interventions, such as proper treatments and treatments that assist in returning to work. Such measures reduce claim costs. We explore millions of claims and hundreds of millions of payment records across 2000+ of our clients, including myriad variables. Cutting-edge statistical and machine learning algorithms (GLM, Decision Tree, Random Forest, Gradient Boosting, Neural Network, etc.) are utilized, in combination with business knowledge.

Session description
Speaker
Mei Najim
CSPA, Advanced Analytics Consultant and Advisor
Advanced Analytics Consulting Services, LLC (formerly with Gallagher Bassett)
Predictive Analytics World for Financial
Regulatory management
11:45 am – 12:05 pm
Case Study: Enova International (CashNetUSA)

Regulatory changes are inevitable and often costly. The ability to adapt quickly to regulatory changes is crucial in today's competitive marketplace. In this session, we will show how using digital decisioning enabled our flagship brand, CashNetUSA, to not only quickly bring its ACH return rate in compliance with NACHA Rules Amendment of 15% but also improve its operational efficiency and customer experience. As a result, our projected loss of $18MM annually was reduced to $8.5MM.

Session description
Speaker
Joe DeCosmoEnova International
Chief Analytics Officer
Enova International
Room: Emperors II
PAW Healthcare

The ability of software to reason, answer questions and intelligently converse about clinical notes, patient stories or biomedical papers has risen dramatically in the past few years. This talk is intended for practicing data scientists and software engineers, and covers state of the art natural language processing, deep learning, and machine learning libraries in this space. We'll share notebooks & benchmarks from real projects using open-source software packages spaCy, Spark ML, TensorFlow, QuickUMLS, SyntaxNet and Spark NLP. The covered use cases will include clinical data abstraction, patient risk prediction, negation & temporal scope detection, topic modeling, and research paper classification.

Session description
Speaker
David Talby
Chief Technology Officer
Pacific AI
Room: Pompeian IV
PAW Manufacturing
Case study: Sanmina Corporation

Are Industry4.0 and IIoT just hype, or do these technologies provide a measurable ROI or benefit? This session explores video case studies of what forward-thinking companies are doing today, and the ROI and value of each::

•  Real-time data analytics and control of high technology “lights out” production lines
•  Cloud technology automatically tracking and replenishing raw material levels at workstations 
•  Global factory and supply chain (remote) visibility. Real time visibility of KPI’s, and real-time alerts for WIP, yield, throughputs on hundreds of production lines worldwide
•  How equipment connectivity automates quality records and “forces” day to day regulatory compliance management

Session description
Speaker
Gelston Howell
Senior VP of Marketing
Sanmina Corporation
Room: Pisa
Deep Learning World
Finance
Case Study: John Hancock

AlphaGo, AlphaGo Zero, Alpha Zero Chess. One might wonder what’s next. AI and Deep Learning (DL) seem more like interesting research topics. Adoption into mainstream enterprise seems some ways out. John Hancock approaches DL as an opportunity to potentially disrupt its traditional model building process wherein the DL algorithms start without any prior business knowledge. In this session, you will learn about the journey of (NLP and) DL using two use cases – to detect fraudulent patterns and in predicting Underwriting Risk Class. We will also discuss a broader range of opportunities for DL application in financial services.  In all cases, our starting point is (text) unstructured data.

Session description
Speaker
Eugene WenJohn Hancock Insurance
Vice President for Group Advanced Analytics
Manulife Financial/John Hancock
12:05 pm
Lunch
Lunch
 
Lunch
Room: Pisa
Deep Learning World Sponsored Session

Recent rapid developments in Machine Learning (ML) and Neural Networks (NN) have outstripped the capability of most programming languages to cope with the enormous volume of new technology and concepts. This has made understanding and use of these new paradigms very difficult for all but the computing cognoscenti. The solution is the high level symbolic/numeric hybrid Wolfram language that provides functional tools for top level Classification and Prediction as well as lower level construction of the neural network layers that form the basis of ML algorithms today.

Session description
Sponsored by
Wolfram Research
Speaker
Michael Kelly B.Sc.(Hons), PhD (Maths, UNSW)Wolfram Research
Senior Technology (Finance ) Consultant
Wolfram Research
12:15 pm
 
 
Lunch
 
Lunch
1:10 pm
Room: Augustus II
PAW Business EXPERT PANEL

Machine learning (aka predictive analytics) only delivers value when acted upon – that is, when deployed. Only a carefully designed management process ensures that the analytics' output is pragmatically viable for operationalization, and that company operations – your internal consumers of analytics – know best how to employ the product they're consuming.

Lead by moderator James Taylor, an industry leader in the operationalization of predictive models, this expert panel explores and expands upon PAW Business' Track 1 topic, operationalization, to provide insights as to how best to execute on the functional deployment of machine learning.

Session description
Moderator
James TaylorDecision Management Solutions
CEO
Decision Management Solutions
Panelists
Vivek AgarwalPrADS
CEO, Dun and Bradstreet Technologies, Executive Director, PrADS
Predictive Analytics and Decision Services Inc. (PrADS)
Steven RamirezBeyond the Arc
CEO
Beyond the Arc
Morgan VawterCaterpillar
Chief Analytics Director
Caterpillar
Room: Augustus II
PAW Financial EXPERT PANEL

Machine learning (aka predictive analytics) only delivers value when acted upon – that is, when deployed. Only a carefully designed management process ensures that the analytics' output is pragmatically viable for operationalization, and that company operations – your internal consumers of analytics – know best how to employ the product they're consuming.

Lead by moderator James Taylor, an industry leader in the operationalization of predictive models, this expert panel explores and expands upon PAW Business' Track 1 topic, operationalization, to provide insights as to how best to execute on the functional deployment of machine learning.

Session description
Moderator
James TaylorDecision Management Solutions
CEO
Decision Management Solutions
Panelists
Vivek AgarwalPrADS
CEO, Dun and Bradstreet Technologies, Executive Director, PrADS
Predictive Analytics and Decision Services Inc. (PrADS)
Steven RamirezBeyond the Arc
CEO
Beyond the Arc
Morgan VawterCaterpillar
Chief Analytics Director
Caterpillar
 
Room: Augustus II
PAW Manufacturing EXPERT PANEL

Machine learning (aka predictive analytics) only delivers value when acted upon – that is, when deployed. Only a carefully designed management process ensures that the analytics' output is pragmatically viable for operationalization, and that company operations – your internal consumers of analytics – know best how to employ the product they're consuming.

Lead by moderator James Taylor, an industry leader in the operationalization of predictive models, this expert panel explores and expands upon PAW Business' Track 1 topic, operationalization, to provide insights as to how best to execute on the functional deployment of machine learning.

Session description
Moderator
James TaylorDecision Management Solutions
CEO
Decision Management Solutions
Panelists
Vivek AgarwalPrADS
CEO, Dun and Bradstreet Technologies, Executive Director, PrADS
Predictive Analytics and Decision Services Inc. (PrADS)
Steven RamirezBeyond the Arc
CEO
Beyond the Arc
Morgan VawterCaterpillar
Chief Analytics Director
Caterpillar
 
1:25 pm
 
 
Room: Emperors II
PAW Healthcare EXPERT PANEL

The health care industry is seeing great advances in data-driven decision making, but lags behind several other industries that have considerably higher levels of adoption. Causes include the siloed nature of health care, the many professions involved, privacy rules, and long-standing hardened practices. Yet, if an organization does not develop a culture of analytics that permeates every level, department, and profession it will struggle to thrive with this powerful technology. This expert panel – moderated by Jeff Deal, who has managed teams in both worlds -- will explore important principles for sustained analytics success in health care.

Session description
Panelists
Chris Franciskovich
Manager of Advanced Analytics
OSF Healthcare System
David Talby
Chief Technology Officer
Pacific AI
 
Room: Pisa
Deep Learning World
Operations
Case Study: Seagate Technology

With the rise of deep learning as a promising solution in the area of object detection and image recognition, industries are embracing Convolution Neural Networks (CNN) in many applications such as autonomous cars, security surveillance, medical imaging, etc. This presentation will showcase how Seagate is refining its Industrial Internet of Things (IIoT) strategy by developing and deploying CNN algorithms in its factories. From product quality check to security surveillance, deep learning is helping shape the digital transformation of Seagate Technology operations by boosting the performance of deployed machine learning models to unprecedented accuracies.

Session description
Speaker
Abbas ChokorSeagate Technology
Staff Data Scientist
Seagate Technology
1:55 pm
Room: Augustus II
PAW Business Gold Sponsored Session

An explosion in data availability from integrated data supply chains cutting across traditional boundaries, cheap cost of data storage and massive increase in computing power are expanding the universe of business problems that could be addressed by using predictive analytics in general and machine learning in particular. While the use of machine learning to understand complex patterns and identify non-linear relationships offers a great opportunity to enhance the accuracy of predictive models, risk management as domain has relatively seen lower adoption of machine learning algorithms.

In this presentation, Vivek will discuss the key drivers for using machine learning and present a case study where machine learning algorithms were successfully used to enhance the accuracy of risk models for a large global organization as it transformed its data supply chain and downstream & upstream technology infrastructure to digest large amount of data and operationalise the analytical outcomes in more agile manner.

Session description
Sponsored by
PrADS
Speaker
Vivek AgarwalPrADS
CEO, Dun and Bradstreet Technologies, Executive Director, PrADS
Predictive Analytics and Decision Services Inc. (PrADS)
Room: Augustus II
PAW Financial Sponsored Session

An explosion in data availability from integrated data supply chains cutting across traditional boundaries, cheap cost of data storage and massive increase in computing power are expanding the universe of business problems that could be addressed by using predictive analytics in general and machine learning in particular. While the use of machine learning to understand complex patterns and identify non-linear relationships offers a great opportunity to enhance the accuracy of predictive models, risk management as domain has relatively seen lower adoption of machine learning algorithms.

In this presentation, Vivek will discuss the key drivers for using machine learning and present a case study where machine learning algorithms were successfully used to enhance the accuracy of risk models for a large global organization as it transformed its data supply chain and downstream & upstream technology infrastructure to digest large amount of data and operationalise the analytical outcomes in more agile manner.

Session description
Sponsored by
PrADS
Speaker
Vivek AgarwalPrADS
CEO, Dun and Bradstreet Technologies, Executive Director, PrADS
Predictive Analytics and Decision Services Inc. (PrADS)
 
Room: Augustus II
PAW Manufacturing Sponsored Session

An explosion in data availability from integrated data supply chains cutting across traditional boundaries, cheap cost of data storage and massive increase in computing power are expanding the universe of business problems that could be addressed by using predictive analytics in general and machine learning in particular. While the use of machine learning to understand complex patterns and identify non-linear relationships offers a great opportunity to enhance the accuracy of predictive models, risk management as domain has relatively seen lower adoption of machine learning algorithms.

In this presentation, Vivek will discuss the key drivers for using machine learning and present a case study where machine learning algorithms were successfully used to enhance the accuracy of risk models for a large global organization as it transformed its data supply chain and downstream & upstream technology infrastructure to digest large amount of data and operationalise the analytical outcomes in more agile manner.

Session description
Sponsored by
PrADS
Speaker
Vivek AgarwalPrADS
CEO, Dun and Bradstreet Technologies, Executive Director, PrADS
Predictive Analytics and Decision Services Inc. (PrADS)
 
2:00 pm
Room: Augustus II
PAW Business Gold Sponsor Presentation

The use of Predictive Analytics and Machine Learning is imperative across all channels and departments in Financial Services. Angoss differentiates itself by its easy-to-use visual data science platform with intuitive workflows and best-in-class Decision and Strategy Trees. We will discuss how best to utilize the Angoss Software Suite as the ultimate complete solution.

Session description
Sponsored by
Angoss Software Corporation
Speaker
Mark Do CoutoAngoss Software Corporation
Head of Sales
Angoss
Room: Augustus II
PAW Financial Sponsored Session

The use of Predictive Analytics and Machine Learning is imperative across all channels and departments in Financial Services. Angoss differentiates itself by its easy-to-use visual data science platform with intuitive workflows and best-in-class Decision and Strategy Trees. We will discuss how best to utilize the Angoss Software Suite as the ultimate complete solution.

Session description
Sponsored by
Angoss Software Corporation
Speaker
Mark Do CoutoAngoss Software Corporation
Head of Sales
Angoss
 
Room: Augustus II
PAW Manufacturing

The use of Predictive Analytics and Machine Learning is imperative across all channels and departments in Financial Services. Angoss differentiates itself by its easy-to-use visual data science platform with intuitive workflows and best-in-class Decision and Strategy Trees. We will discuss how best to utilize the Angoss Software Suite as the ultimate complete solution.

Session description
Sponsored by
Angoss Software Corporation
Speaker
Mark Do CoutoAngoss Software Corporation
Head of Sales
Angoss
 
2:10 pm
5-minute transition between sessions
5-minute transition between sessions
5-minute transition between sessions
5-minute transition between sessions
 
2:15 pm
Room: Pompeian III
PAW Business Track 1: BUSINESS - Analytics operationalization & management
Analytics management
2:15 - 2:35 pm
Case Study: Comcast

Decision-support systems based on machine-learning algorithms can require a significant investment to build. Ensuring the data pipeline is reliable, building the right models, and hiring the right staff can be a major undertaking. This presentation will cover best practices for building a business case to deploy machine learning algorithms for large scale product development or other production-level systems.

Session description
Speaker
Bob BressComcast Cable
Vice President of Analytics & Business Intelligence
Freewheel, A Comcast Company
Predictive Analytics World for Business
Track 1: BUSINESS - Analytics operationalization & management
Value-driven analytics management
2:40 – 3:00 pm

In this session, Robert Lanning will deliver an energetic and engaging discussion on the responsibilities of analytics professionals to ensure that the projects worked on actually accomplish meaningful change in the organization. Through the use of thought-provoking examples, the attendee will leave this session with an appreciation of the responsibility analytics professionals should bring to their projects. 

Session description
Speaker
Robert LanningSilicon Valley Bank
Director, HRIS and People Analytics
Silicon Valley Bank
Room: Augustus II
PAW Business Track 2: TECH - Predictive modeling & machine learning methods
Best practices

Preeminent consultant, author and instructor Dean Abbott, along with Karl Rexer of Rexer Analytics, field questions from an audience of predictive analytics practitioners about their work, best practices, and other tips and pointers.

Session description
Speakers
Dean AbbottSmarterHQ
Co-Founder and Chief Data Scientist
SmarterHQ
Karl RexerRexer Analytics
President
Rexer Analytics
Room: Augustus I
PAW Business Track 3: MARKETING - Analytics for customer acquisition & retention
Sales and marketing; B2B
Case Study: Ingram Micro

We searched for matches between 10+ million end customers and 250,000+ resellers along the B2B technology supply chain. The line of sight of our high-volume business is broad, capturing around half a billion transactions of 5+ million technology products globally. We built matching models and product recommendation engines based on statistical and machine learning approaches, including random forests and neural networks. Our deployed product offerings include lead generation, sales forecasting and cross-selling. In this session, we cover this project and show that the insights gained that pertain to feature engineering, model building and validation exercises can be scaled to industries with similar lead generation and matchmaking challenges, including Education, Healthcare and Travel.

Session description
Speakers
Doug HermanIngram Micro
Head of Data Science
Ingram Micro
Haoan LiuIngram Micro
Data Scientist
Ingram Micro
Room: Sicily
PAW Financial
Wealth management

In order to understand customer behaviors and provide better services, wealth management firms are investing greatly in data analytics. During this session, Meina Zhou will cover several examples of financial institutions that gained value with in-house predictive analytics solutions to improve wealth management services. She will also address multiple use cases built for different stages of the customer journey, including customer acquisition, customer personalization, and customer retention. She will discuss both the core analytics components and the challenges involved throughout the implementation process.

Session description
Speaker
Meina ZhouCapco
Data Scientist and Senior Consultant
Capco
Room: Emperors II
PAW Healthcare

MultiCare Health Systems’ OR administrators spent an inordinate amount of time sifting through a mountain of data to find insights about room utilization, number of cases, first case on-time starts, turnover, and other key performance indicators metrics. Building custom dashboards was labor-intensive and often didn’t contain all the information necessary for decision making. MultiCare found that having one source of truth that informs perioperative leaders and can “push” actionable insights is necessary to keep up with an ever-changing healthcare landscape. This session will detail how predictive analytics, machine learning, and cloud and mobile technologies helped MultiCare improve OR Operations.

Session description
Speakers
Jo Quetsch
Multicare Health System
Ashley WalshLeanTaaS
Director of Client Services
LeanTaas
Room: Pompeian IV
PAW Manufacturing

At Hitachi’s Center for Social Innovation, we have been using Machine learning and Artificial Intelligence to develop cutting edge solutions and push the envelope in the area of Industrial IoT. These problems range from increasing operational efficiencies, reducing costs to creating new AI enabled products and services. Drawing on this rich experience, we will present a systematic taxonomy of industrial analytics problems.  We will walk through the different problem areas and give examples how AI/ML is being used as a tool to address these problems. We will conclude by pointing out new research directions, and exciting new developments in the area of industrial analytics. 

Session description
Speaker
Chetan GuptaHitachi
Chief Data Scientist and Architect, Lab Manager, Industrial AI Lab
Hitachi America, Ltd
Room: Pisa
Deep Learning World
Finance
Case Study: Northwestern Mutual

Like many companies, Northwestern Mutual switched from being paper-based to relying on "electronic forms" for underwriting health surveys.  While this vastly improved underwriting processes, extraction of historical "paper-based" data is still desired to unlock its business value.  Learn how Northwestern Mutual, on its journey to algorithmic underwriting, successfully extracted data from scanned health surveys by leveraging computer vision and deep learning.

Session description
Speakers
Marek PietrzykNorthwestern Mutual
Senior Data Engineer
Northwestern Mutual
David WalechkaNorthwestern Mutual
Decision Sciences Technology Lead
Northwestern Mutual
3:00 pm
Exhibits & Afternoon Break
Exhibits & Afternoon Break
Exhibits & Afternoon Break
Exhibits & Afternoon Break
Exhibits & Afternoon Break
3:30 pm
Room: Pompeian III
PAW Business Track 1: BUSINESS - Analytics operationalization & management
Industry trends

This talk will review recent research by the International Institute For Analytics that studied the analytics maturity level of large enterprises. A range of interesting findings and trends were discovered. The talk will cover how maturity varies by industry and some of the key steps organizations can take to move up the maturity scale. The talk will also cover some exciting research that correlates analytics maturity with a wide range of corporate success metrics including financial and reputational measures. The results reinforce that organizations with more success in analytics have more success overall. 

Session description
Speaker
Bill FranksInternational Institute For Analytics
Chief Analytics Officer
International Institute For Analytics
Room: Augustus II
PAW Business Track 2: TECH - Predictive modeling & machine learning methods
Geospatial data

Machine learning on spatial data is hard. In this session, Peter Lenz, one of the few geographers in the machine learning world, will cover some of the unique issues when working with such data, how to treat it correctly, and why Dstillery ended up building two parallel spatial featurizers to maximize the utility of geo data for its machine learning stack.

Session description
Speaker
Peter LenzDstillery
Senior Geospatial Analyst
Dstillery
Room: Augustus I
PAW Business Track 3: MARKETING - Analytics for customer acquisition & retention
Text analytics for marketing
Case Study: Barclaycard

Natural Language Processing (NLP) algorithms are a part of emerging field within Artificial Intelligence (AI) that focuses on the interactions between human language and computers. By utilizing NLP algorithms, we can organize and structure knowledge to perform tasks such as automatic summarization, translation, relationship extraction, sentiment analysis, speech recognition, and topic segmentation. We leveraged NLP algorithms to dive deeper into customer complaint narratives and to identify major complaint themes. Moreover, we quantified underlying consumer sentiment and ultimately develop actionable consumer insights. These insights were useful for developing more effective Marketing and Customer Experience strategies.

Session description
Speaker
Vishal MordeBarclaycard US
Vice President - Data Science
Barclaycard US
Room: Sicily
PAW Financial
Insurance

The sooner that incoming insurance claims can be identified as high risk of becoming  a long latency claim, the sooner they can be prioritized and targeted with specialized resources and services for better outcomes. And improving worker outcomes for our most challenging claims is where we can have the biggest impact on the overall success of the insurance system.  The Workplace Safety and Insurance Board (Canada) has used only data available at the time of claim registration to segment insurance claims by risk, as they come in. Our custom model combines random forest and conventional logistic regression techniques and is continually refined over time through machine learning algorithms.

Session description
Speaker
Christina HoyWorkplace Safety and Insurance Board (WSIB)
Vice President
Workplace Safety and Insurance Board (WSIB)
Room: Emperors II
PAW Healthcare

The Low Carb Program is redefining chronic disease and wellness with over 275,000 global members and clinically-reviewed outcomes demonstrating 40% of people eliminate a diabetes medication and 1 in 4 completers reverse their type 2 diabetes at 1-year. The platform is global and engagement is completely AI-led, personalised to each patient and driven through real-time data feedback and insights. 

Session description
Speaker
Arjun Panesar
Founder and CEO
Diabetes Digital Media
Room: Pompeian IV
PAW Manufacturing

Prescriptive Analytics is the area of Machine Learning dedicated to finding the best course of action for a given situation. Prescriptive Analytics Inform And Evolve Decision Logic Whether To Act (not not act) And What Action To Take. In this session, we will understand Prescriptive Analytics, its components and methods. You will also learn how to go beyond just knowing in Predictive Maintenance into Prescriptive Service to deliver immediate ROI.

Session description
Speaker
Karpagam Narayanan
Founder & President
eKryp
Room: Pisa
Deep Learning World
IT
Case Study: Lyft

As Deep Learning tools and frameworks become more prevalent and lower the complexity of the development process, we see a shift in data-driven software development towards using Deep Learning instead of classical ML or more standard software. This change entails a different software development process that affects the way we frame the problem, iteratively develop the solution, allocate and manage compute and memory resources, and debug our system. The talk will cover the shift in some areas from classical software development to Deep Learning, success stories of employing Deep Learning, the organizational processes and required developer skills needed, and insights on how to best manage the entire model lifecycle - from ideating to deployment and maintenance.

Session description
Speaker
Gil ArditiLyft
Product Lead, Machine Learning
Lyft
4:15 pm
Room: Pompeian III
PAW Business Track 1: BUSINESS - Analytics operationalization & management
Analytics talent
Case Study: Cisco

Data professionals expect a lot from their leaders and organizations, and they are in higher demand than ever. How do you build an organization that Data Scientists are proud to be a part of, one that leverages the network effects of the analytics community as a whole? How do you keep your team focused on the projects that drive organizational goals forward, while satisfying the insatiable hunger for technical knowledge and “interesting work?” And a leader’s work doesn’t stop there: How do you get the most out of the cross-functional skills that your team members possess (both technical and otherwise), and how do you ensure that the critical analytics roles in your organization are properly recognized and rewarded for their contributions? Join Cisco for an insider look at what’s working today…and what’s planned for tomorrow.

Session description
Speaker
Kristen BurtonCisco
Director, Enterprise Data Science Office and Digital Process Transformation CoE
Cisco
Room: Augustus II
PAW Business Track 2: TECH - Predictive modeling & machine learning methods
Advanced methods

We've all heard the hype. Being Bayesian is cool, right? But does it really offer any clear benefits over the frequentist approach, or is it just a different way of thinking about the solutions we already have on hand? The Bayesian approach does enable a very powerful class of models: multi-level/hierarchical models. With a new generation of statistical modeling platforms like the free tool STAN, these models can be built with rich structures that enable more granular insights. We will learn about the power of multi-level models through three concrete examples: digital UX optimization, market response modeling, and programmatic media optimization.

Session description
Speaker
Curt BergmannElicit
Senior Data Scientist
Elicit
Room: Augustus I
PAW Business Track 3: MARKETING - Analytics for customer acquisition & retention
Time series analysis

Time series analysis has not benefited much from the data science halo effect compared to, say, statistical learning techniques. Yet time series model are often used to inform some of the most critical and fundamental business decisions.

Case in point: when evaluating the overall health of a business, we typically look at high-level metrics such as CAC, top-line growth and margin. And when it comes to forecasting these metrics and planning the underlying investments in marketing, inventory and promotions, you need time series analysis 

In this talk we’ll take a random walk through topics related to time series analysis as it pertains to planning:

  • Why having robust planning models is important, even though you’ll always be wrong
  • The skill set and attitude needed to be good at building the underlying models
  • Embracing uncertainty as opposed to trying to make it go away
  • How to think about promo/price versus top-of-funnel spending
  • When ML and time series come together
Session description
Speaker
Kim Larsen
VP of Data
ThirdLove
Room: Sicily
PAW Financial
Insurance: detecting incorrect payments
Case Study: John Hancock

We developed a Payment Defect Model for the John Hancock Long Term Care Insurance – Claims department. Long term care is different from most other insurance products, as it has much longer relationship with the clients, and the payout is recurring for several years. Since the reimbursement amount varies from payment to payment, detecting when over- or under-payments occur is especially challenging. Prior to this project, the Claims Audit department reached high detection levels only by monitoring nearly 50% of all payments. In comparison to a rule-based decision model, our machine learning model increased the detection rate, yet decreased manual effort by more than 60%.

In this session, we describe this project's full journey, from the problem statement to the data gathering, from the initial model and testing to a complete shut down and restart of the entire project. We break decades-old perceptions and solidified the necessity of involving subject matter experts in the model-building process.

Session description
Speaker
Richard LeeJohn Hancock Insurance
Manager of Operations Reporting Consistency
John Hancock Financial
Room: Emperors II
PAW Healthcare

Accurate data is necessary for purposes ranging from clinical decisions to service billing to scientific studies. However, with large volumes of data, error detection becomes difficult. To direct the efforts of coders poring through medical records looking for discrepancies in comorbidity reporting, the Data Science team at Fresenius Medical Care developed methods to detect patients likely to have unrecorded comorbidities. By using lab test results, natural language processing of rounding notes, and patient demographic information, we were able to improve the results of coders ten-fold. This session will discuss the development of these methods and the challenges along the way.

Session description
Speaker
Tommy Blanchard
Data Science Lead
Fresenius Medical Care North America
Room: Pompeian IV
PAW Manufacturing

Manufacturers have been actively exploring the use of predictive maintenance to improve productivity and optimize overall throughput. With advancements in the ability to easily access machine data and apply sophisticated analytics, predictive maintenance projects can be an effective tool for reducing the overall cost of unplanned downtime. However, we’ve learned that the most successful manufacturers are those that build capabilities for data-driven process reengineering before embarking on predictive maintenance efforts. By looking at predictive maintenance efforts within the context of a broader data-driven process reengineering foundation, manufacturers can build a foundation to ensure these projects deliver on their promise.

Session description
Speaker
Beth CraneSight Machine
Vice President of Data
Sight Machine
Room: Pisa
Deep Learning World
Healthcare
4:15 pm - 5:00 pm

One in six children suffer from developmental delay. In many cases,early screening is possible using behavioral phenotypes. We showcase the use of interactive storytelling sessions on tablets to collect video feeds for automated screeners powered by deep learning.

Session description
Speaker
Ford GarbersonCognoa
Senior Data Scientist
Cognoa
5:00 pm
 
 
 
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