Full Machine Learning Week 7-Track Agenda 2021 – Detailed Session Descriptions

Predictive Analytics World

May 24-28, 2021


See the full 7-track agenda for the six co-located conferences at Machine Learning Week. A Machine Learning Week Ticket is required for full access. To view the agenda for one individual conference, click here: PAW Business, PAW Financial, PAW Industry 4.0, PAW Climate, PAW Healthcare, or Deep Learning World.

Session Levels:

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

Predictive Analytics World - Virtual - Day 1 - Monday, May 24th, 2021

8:45 am
PAW Business
David Stephenson Ph.D.DSI Analytics
Author and Founder
DSI Analytics
PAW Financial
David Stephenson Ph.D.DSI Analytics
Author and Founder
DSI Analytics
PAW Healthcare
Jeff DealElder Research
Chief Operating Officer
Elder Research
PAW Climate
Eugene KirpichovWork On Climate
Co-founder
Work On Climate
Sasha LuccioniUniversité de Montréal
Postdoctoral Researcher
Université de Montréal
David RolnickMcGill University
Assistant Professor, School of Computer Science
McGill University
Deep Learning World
Luba Gloukhova
Consultant & Speaker
9:00 am
PAW Business MACHINE LEARNING WEEK KEYNOTE
Lessons from: Lyft
The Keynote Description will be available shortly.
Session description
Speaker
Vladimir Iglovikov Ph.D.Lyft
Senior Computer Vision Engineer, Level5, Self-Driving Division
Lyft, Inc.
PAW Financial MACHINE LEARNING WEEK KEYNOTE
Lessons from: Lyft
The Session Description will be available shortly.
Session description
Speaker
Vladimir Iglovikov Ph.D.Lyft
Senior Computer Vision Engineer, Level5, Self-Driving Division
Lyft, Inc.
PAW Healthcare
Lessons from: Lyft
The Session Description will be available shortly.
Session description
Speaker
Vladimir Iglovikov Ph.D.Lyft
Senior Computer Vision Engineer, Level5, Self-Driving Division
Lyft, Inc.
PAW Industry 4.0
Lessons from: Lyft
The Session Description will be available shortly.
Session description
Speaker
Vladimir Iglovikov Ph.D.Lyft
Senior Computer Vision Engineer, Level5, Self-Driving Division
Lyft, Inc.
PAW Climate MACHINE LEARNING WEEK KEYNOTE
Lessons from: Lyft
The Keynote Description will be available shortly.
Session description
Speaker
Vladimir Iglovikov Ph.D.Lyft
Senior Computer Vision Engineer, Level5, Self-Driving Division
Lyft, Inc.
Deep Learning World MACHINE LEARNING WEEK KEYNOTE
Lessons from: Lyft

Vladimir reveals how Lyft are exploring using the latest advances in computer vision.

Session description
Speaker
Vladimir Iglovikov Ph.D.Lyft
Senior Computer Vision Engineer, Level5, Self-Driving Division
Lyft, Inc.
9:50 am
PAW Business
The Session Description will be available shortly.
Session description
Sponsored by
SAS
Speaker
Robert BlanchardSAS
SAS Senior Data Scientist.
SAS
PAW Financial
The Session Description will be available shortly.
Session description
Sponsored by
SAS
Speaker
Robert BlanchardSAS
SAS Senior Data Scientist.
SAS
PAW Healthcare
The Session Description will be available shortly.
Session description
Sponsored by
SAS
Speaker
Robert BlanchardSAS
SAS Senior Data Scientist.
SAS
PAW Industry 4.0
The Session Description will be available shortly.
Session description
Sponsored by
SAS
Speaker
Robert BlanchardSAS
SAS Senior Data Scientist.
SAS
PAW Climate
The Session Description will be available shortly.
Session description
Sponsored by
SAS
Speaker
Robert BlanchardSAS
SAS Senior Data Scientist.
SAS
10:10 am
Room change
Room change
Room change
Room change
Room change
Room change
10:20 am
PAW Business OPERATIONAL

As companies continue to increase their investment in Machine Learning (ML) and Artificial Intelligence (AI), the focus is increasingly on how to make machine learning pervasive, creating business value by embedding it throughout a company's operations. But what does it take to get out of Proof of Concept purgatory and go from pilots to operational deployments? Do we need more technology, new roles, a different approach or what?

In this session, James will kick off the operational track with a discussion of what it takes to get value at scale from ML. He'll talk about critical issues and how to address them. He'll outline a proven approach that will let non-digital natives and even the most risk-averse companies operationalize and get value from their machine learning investments. Whatever your motivation for using machine learning and no matter how mature (or not) your machine learning team is, you'll learn how to succeed.

Session description
Speaker
James TaylorDecision Management Solutions
CEO
Decision Management Solutions
PAW Business TECHNICAL
Case Study: National Consumer Panel

Traditionally, successful predictive analytics initiatives have relied on the application of complex algorithms to a lot of numbers and interpreting the output into meaningful language that clients can easily understand. This presentation aims to turn that premise on its head. Starting with a lot of words provided by consumer panelists, NCP analyzed the text provided in their feedback to us and translated those words into numbers. These numbers added up to value for National Consumer Panel. This presentation will share lessons learned from combining text analytics insights with more traditional approaches leveraging quantitative analytics. Attendees can expect to learn how NCP's experience may be extrapolated to provide insights on other business questions.

Session description
Speaker
Thomas SchleicherNational Consumer Panel
Sr. Director, Measurement Science
National Consumer Panel
PAW Financial

Healthcare risk adjustment is the most impactful financial mechanism to account for the differences in risk in members that enroll with different carriers. In the ACA program alone, billions of dollars are moved each year between health plan carriers. The calculations rely on risk assessment programs which are typically based on linear regression. Simplicity, understandability, and transparency are paramount considerations in the modeling due to the zero-sum nature of risk adjustment. This brings us to an important question: if healthcare expenditures by member are non-linear (which in fact they are), is the additional complexity of machine learning worth it to model these more accurately?


This session provides an overview of this mathematics that is critical to healthcare financials in each line of business (commercial, medicare, medicaid) and also compares and contrasts traditional methods such as linear regression against machine learning variants that involve use of neural networks. We will also discuss the considerations involved in picking the right tools for a couple of applications related to risk assessment.

Session description
Speaker
Syed MehmudWakely Consulting Group
Principal & Senior Consulting Actuary, ASA
Wakely Consulting Group, Society of Actuaries
PAW Healthcare
Case Study: Precision Medicine

A single doctor, overworked and overwhelmed, will make a less-informed decision than a team of doctors. Now, imagine the potential knowledge we could gain if we combined insight from doctors all over the world. In this case study, we will explain how Innovative Precision Health (IPH) uses large amounts of aggregated anonymous population data to learn about the trajectory of a disease and predict the most effective treatment for a patient.  By exponentially increasing the amount of information available, IPH is able to provide better predictions for doctors, better care for patients, and profitable outcomes for insurance and pharmaceutical companies alike.

Session description
Speaker
Mark GudesblattInnovative Precision Health
Chief Medical Officer
Innovative Precision Health
PAW Industry 4.0
Keynote Industry 4.0

I4.0 has created a plethora of applications and vendors competing for attention in the manufacturing space. Many vendors are pushing platforms and enablement yet what manufacturers really need are hard core scalable use cases that deliver measurable impact and value. This cannot be solved by an individual vendor or platform. I4.0 success is dependent on cooperation and partnership between different players in the I4.0 landscape and a new approach to continuous improvement in order to massively reduce the time it takes to derive action, impact and value from data. 

Session description
Speaker
Paul TurnerStanley Black & Decker
Vice President I4.0 Applications & Analytics
Stanley Black and Decker
PAW Climate
Lessons from: Climate TRACE

Accurate, timely estimation of carbon emissions is critical for businesses and governments to take action on climate change. The Climate TRACE coalition aims to furnish these estimates for all major sources of emissions globally, on a near real-time basis. In this session we’ll give an overview of our work on estimating emissions from one of the most important sources of greenhouse gases, power generation from coal. The approach focuses upon detecting plumes using a variety of approaches, including multi instance deep learning.

Session description
Speaker
Joseph O’ConnorEnergy and Clean Air Analytics
Senior Data Scientist
Energy and Clean Air Analytics
Deep Learning World
Case Study: Nauto

Each real-world application of machine learning owns a set of defining characteristics that distinguish it from other problems. In Nauto's case, we discovered that our driving data is remarkably uniform. By leveraging this trait, we were able to achieve massive optimizations in model accuracy and inference speed, directly improving Nauto's core safety offerings like Forward Collision Warning.

This talk will explore the importance of identifying domain-specific traits when deploying AI solutions to the world. It will show how leveraging the quirks of your problem can lead to optimizations that occasionally even contradict established conventions and common intuition.

Session description
Speaker
Alexander WuNauto
Senior Deep Learning Engineer
Nauto
11:05 am
Break & Expo Hall
Break & Expo Hall
Break & Expo Hall
Break & Expo Hall
Break & Expo Hall
Break & Expo Hall
11:30 am
PAW Business OPERATIONAL
Case Study: Career Path

In this talk Dr. Anzelc will provide an overview of her own career path from particle physics PhD to insurance analytics executive to people analytics leader, providing a view of how she approached her job search and developed her career path from individual contributor to team leader. Practical advice on what to do, resources available, and other non-academic career paths of physicists will also be shared.

Session description
Speaker
Meghan AnzelcSpencer Stuart
Head of Data & Analytics
Spencer Stuart
PAW Business TECHNICAL
Case Study: Marriott

With COVID-19 shifting customer behavior the demand for Marriott’s Home and Villas offering has exploded. In this session we will share how deep neural network based Natural Language Generation techniques were used to support Marriott Homes and Villas generating property titles automatically. We will explore different techniques for title generation, provide performance comparisons and lessons learned to activate the model.

Session description
Speakers
Matt EckertMarriott International
Sr. Data Scientist
Marriott International
Eui-Hong (Sam) Han Ph.D.Marriott International
Vice President, Advanced Data Science
Marriott International
PAW Financial
Case Study: ABN AMRO

Hardly a week goes by without news of money laundering affecting this or that financial institution. As gatekeepers of the financial system, banks play a crucial role in finding and reporting instances of financial crime. In this talk, we describe the challenges of detecting money laundering and outline why employing machine learning techniques is critically important. We will discuss some of the challenges that we faced at ABN AMRO as well as the solutions we came up with, including a method to deal with changes in feature space while running models in production and an in-house-developed model explainability technique.

Session description
Speaker
Edo van UiterABN Amro
Senior Data Scientist/Team Lead
ABN Amro
PAW Healthcare
Improving Health with Analytics

This session details for big data collected on over 1.4 million people has been used to develop AI-led, personalised interventions that are clinically-proven to reverse type 2 diabetes, which has redefined the use of digital therapeutics in the USA, Canada, United Kingdom, Germany and India. 

Session description
Speaker
Arjun PanesarDiabetes Digital Media (DDM)
Founder and CEO
Diabetes Digital Media
PAW Industry 4.0
Case Study: NuFlare Technology

Scanning Electron Microscope (SEM) images are the primary means used by expert engineers to identify and diagnose defects in semiconductor variable shape beam (VSB) mask writers. Deep learning (DL) offers an attractive alternative to this tedious process. However, extremely robust mask writers preclude collecting a large variety of SEM images to train DL models. Using digital twins that can mimic SEM images provides an exceptional way to synthesize ample DL training data. This talk will take a deep dive into synthesizing SEM images and leveraging them to build DL models for VSB mask writer defects analysis.

Session description
Speaker
Suhas PillaiNuFlare Technology, Inc
Deep Learning Engineer
NuFlare Technology, Inc
PAW Climate
Lessons from: Resync

The energy landscape is going through a drastic transformation. We are moving away from centralized power plants to more distributed energy resources such as solar, electric vehicles. This transformation makes it very difficult for the grid to handle and manage. Through this session we would explore how using data science and artificial intelligence, industries can optimize their energy usage while aiming to reduce costs and meet their sustainability goals. We'd be delving deeper into some of the forecasting and predictive analytics techniques we have created and measuring their impact. Also, we would share insights from real-life use cases of our customers and how AI has helped them monitor, control, and optimize their energy assets.

Session description
Speaker
Jayantika SoniResync
Cofounder & CTO
Resync
Deep Learning World
Case Study: NuFlare Technology

Scanning Electron Microscope (SEM) images are the primary means used by expert engineers to identify and diagnose defects in semiconductor variable shape beam (VSB) mask writers. Deep learning (DL) offers an attractive alternative to this tedious process. However, extremely robust mask writers preclude collecting a large variety of SEM images to train DL models. Using digital twins that can mimic SEM images provides an exceptional way to synthesize ample DL training data. This talk will take a deep dive into synthesizing SEM images and leveraging them to build DL models for VSB mask writer defects analysis.

Session description
Speaker
Suhas PillaiNuFlare Technology, Inc
Deep Learning Engineer
NuFlare Technology, Inc
11:55 am
 
 
 
 
PAW Industry 4.0
Case Study: DataSwing

Engineering design simulation generates enormous amounts of structure data, the bulk of which is never studied or mined for insights. Several case studies will be presented for advancing the belief that simulation data presents an opportunity to allow innovation, hidden insights and guidance to emerge thru the use of simple ML techniques. 

Session description
Speaker
Sharan KalwaniData Swing
HPC practioner/Training Specialist
Data Swing
 
Deep Learning World

The increasing prevalence of high-fidelity industrial digital twins is providing a range of opportunities to apply Reinforcement Learning (RL) techniques outside of traditional academic examples and video games. While this trend is now well-established, most RL developments and deployments in the real world are done on an ad-hoc basis with little consideration given to how to repeat and scale similar initiatives in an efficient way. In this session we will address these shortcomings and illustrate them through our experience in optimising the design of a state-of-the-art sailing boat for a prominent competition with RL. Building an agent to control the boat is a very complex RL task for several reasons: imperfect information, loosely defined goals with delayed rewards, highly dynamic state and action spaces. In racing conditions, it takes a team of Olympic-level athletes to sail the boat and make it “fly” thanks to its underwater wings (read hydro-foiling). In order to control convergence variability and sampling efficiency, the working solution required a custom deep learning implementation of the Soft Actor Critic RL algorithm, with state of the art improvements such as experience replay buffer pooling, domain randomisation and curriculum learning. Beyond describing solutions to traditional RL considerations, we will also focus on the underlying workflows and technology stack required to carry out a deep learning project of this technical complexity in a scalable way. We will use facets of Software 2.0, such as higher-level APIs and the automation of end-to-end model development tasks, to highlight our iterative choices and the optimisation opportunities along the machine learning pipeline and ultimately the production system.

Session description
Speakers
Jacomo CorboQuantumBlack
Cofounder and Chief Scientist
QuantumBlack
Nicolas HohnMcKinsey & Company
Chief Data Scientist, Australia
McKinsey & Company
12:15 pm
PAW Business

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

PAW Financial

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

PAW Healthcare

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

PAW Industry 4.0

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

PAW Climate

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

Deep Learning World

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

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Session description
12:45 pm
End of Day 1
End of Day 1
End of Day 1
End of Day 1
End of Day 1
End of Day 1

Predictive Analytics World - Virtual - Day 2 - Tuesday, May 25th, 2021

8:00 am
PAW Business

Grab your real coffee and share experiences virtually with your peers to explore the new challenges of operating in a largely virtual world. Just like pre-show breakfast in a regular conference you’ll join a “round table” with seven fellow attendees and see where the conversation takes you.

PAW Financial

Grab your real coffee and share experiences virtually with your peers to explore the new challenges of operating in a largely virtual world. Just like pre-show breakfast in a regular conference you’ll join a “round table” with seven fellow attendees and see where the conversation takes you.

PAW Healthcare

Grab your real coffee and share experiences virtually with your peers to explore the new challenges of operating in a largely virtual world. Just like pre-show breakfast in a regular conference you’ll join a “round table” with seven fellow attendees and see where the conversation takes you.

PAW Industry 4.0

Grab your real coffee and share experiences virtually with your peers to explore the new challenges of operating in a largely virtual world. Just like pre-show breakfast in a regular conference you’ll join a “round table” with seven fellow attendees and see where the conversation takes you.

PAW Climate

Grab your real coffee and share experiences virtually with your peers to explore the new challenges of operating in a largely virtual world. Just like pre-show breakfast in a regular conference you’ll join a “round table” with seven fellow attendees and see where the conversation takes you.

Deep Learning World

Grab your real coffee and share experiences virtually with your peers to explore the new challenges of operating in a largely virtual world. Just like pre-show breakfast in a regular conference you’ll join a “round table” with seven fellow attendees and see where the conversation takes you.

8:55 am
PAW Healthcare
Jeff DealElder Research
Chief Operating Officer
Elder Research
Deep Learning World
Luba Gloukhova
Consultant & Speaker
9:00 am
PAW Business KEYNOTE
Lessons from: Amazon
The Keynote Description will be available shortly.
Session description
Speaker
Nathan SusanjAmazon.com
Applied Science Manager
Amazon
PAW Financial
The Keynote Description will be available shortly.
Session description
Speaker
Tarun SoodThe Vanguard Group
Head of Data and Analytics
Vanguard
PAW Healthcare
TUESDAY KEYNOTE

Medicine is an ever-unfolding quest to ensure patients receive life improving therapies to return to full life. Advances in intelligent data, AI, data automation, machine learning, and computational capability allow an efficient pursuit of better outcomes for patients while reducing health care costs. To help understand new approaches to healthcare and new forms of innovation, Maneesh Shrivastav PhD, Director of Market Development, Science and Analytics at Medtronic, will run through innovation at the medical technology company and provide examples of how the company is leveraging data science to improve patients’ lives.

Session description
Speaker
Maneesh ShrivastavMedtronic
Director of Market Development
Medtronic
PAW Industry 4.0
Lessons from: Microsoft

With the need to cater to a global audience, there is a growing demand for applications to support speech identification/translation/transliteration from one language to another. This talk starts off with a brief introduction to the topic of Machine Translation (MT), the evolution/application of Machine Translation. The focus will be on Neural Machine Translation (NMT). Then it moves on to introduce some of the typical customer cases and finally ends with how to embed such functionality in your application. The talk will showcase how maintenance and company records across various subsidiaries which might be in their local language can be standardized into a common language so that all the global data can be used for NLP models.

Session description
Speaker
Jaya MathewMicrosoft
Senior Data Scientist
Microsoft
PAW Climate
The Keynote Description will be available shortly.
Session description
Speaker
John PlattGoogle
Director of Applied Science
Google
Deep Learning World
KEYNOTE

In the field of Speech Recognition, the state of the art for generic conversations has reached superhuman levels. However, things are not nearly as good in specialized knowledge domains: conversation in which people with accents speak in a noisy environment often results in high error rates. Considering the low performance of Speech Recognizers on real data, it becomes imperative to customize the end-to-end probabilistic model. This session will focus on discussing the fundamentals of speech recognition and how Cisco has moved from multiple phoneme-based models to a single end-to-end grapheme-based Recurrent Neural Network architecture that can transcribe audios directly while also reducing latency by tweaking the model during inference time.

Session description
Speaker
Pranjal DagaCisco
Machine Learning Scientist
Cisco
9:50 am
Presentation from Leading Vendor
Presentation from Leading Vendor
Presentation from Leading Vendor
Presentation from Leading Vendor
Presentation from Leading Vendor
Presentation from Leading Vendor
10:10 am
Room Change
Room change
Room change
Room change
Room change
Room change
10:20 am
PAW Business OPERATIONAL
Case Study: Paychex

Due diligence prior to model deployment involves identifying bias risks, features associated with group differences, and potential missing information that could mitigate disparities. Stereotyping, or ascribing common traits to all individuals in a group, is a particular risk for machine learning models, which generate predictions based on feature similarities. Here, I explore stereotyping in regression or classification models. I demonstrate that common fairness metrics are unable to distinguish stereotyping from decisions based on arguably reasonable factors. Therefore, Paychex has developed a “due diligence” script to guide data scientists in assessing disparities. This process uses model outputs and metrics to identify areas of risk, then Shapley explainers with custom references identify features driving differences. Additional reasoning is suggested to investigate the underlying causes of inequalities, consider the effects of missing information that can mitigate risk, and the effects of sensitive features. This structured report format is suitable for stakeholders or oversight committees.

Session description
Speaker
Val CareyPaychex
Data Scientist
Paychex, Inc.
PAW Business TECHNICAL
The Session Description will be available shortly.
Session description
Speaker
Dean AbbottSmarterHQ
Co-Founder and Chief Data Scientist
SmarterHQ
PAW Financial
Case Study: Bank of America Merrill Lynch
The Session Description will be available shortly.
Session description
Speaker
George PapaioannouBank of America Merill Lynch
Director, Senior Trading Strategist
Bank of America Merrill Lynch
PAW Healthcare
Turning Data into Action

Anyone can make a pretty bar graph, but can you make sound decisions based on that graph? Is it actionable, or is it only fluff? How do you turn flashy concepts into actionable visualizations? Can you see the end result of those concepts; will they ever become reality? Do you have the vision to combine beauty with brains, thereby driving decisions with data? Or do you settle for destroying direction with disaster? American mathematician John Tukey once said, "The greatest value of a picture is when it forces us to notice what we never expected to see." What value do you see in your data? And what ideas do you have when you see it? Learn how you can capitalize on your ideas by blending internal with external, leveraging them into a cohesive strategy for both the short term AND the long term. See the five "Stages of the Spectrum" in action while discovering the difference between impact and influence, and how that difference plays into making data actionable. Catch the right blend of art and science, or beauty and brains, as you go from concept to reality. 

Session description
Speaker
Joe Perez Dr.NC Dept of Health & Human Services
Senior Systems Analyst / Team Lead
NC Dept of Health & Human Services
PAW Industry 4.0
Lessons from: Georgia Tech Research Institute

VIPR is a predictive reliability framework focusing on a boosted payload application. The project addresses and decreases high costs and flight vehicle maintenance downtimes due to reactive onboard sensing. By developing a proof of concept corresponding virtual sensor that matches an onboard sensor, predictive sensing and modeling can occur to address any number of fleet aging issues. This new capability will use enhanced machine learning and AI algorithmic framework, anchored with aero data, to develop a modular virtual sensing system which can be trained and applied to any vehicle with sufficient heritage onboard sensor data. 

Session description
Speaker
David PendletonGeorgia Tech Research Institute
Research Engineer
Georgia Tech Research Institute
PAW Climate
Lessons from: Tracks GmbH

"Can I drive more efficiently?" was the question to be answered at the beginning of all data science efforts at Tracks. How can we accurately measure efficient driving of the thousands of truck drivers on the roads? This is a typical real-life question that is difficult to solve with ML methods. The answer to this question has sparked a number of follow up business questions that we are tackling with Tracks' complex AI system. In this session you will learn how Tracks' solution looks like, which ML methods are employed and which business questions are being answered. In a nutshell, how to turn ML into saved CO2. 

Session description
Speaker
Daniel RohrTracks GmbH
Senior Data Scientist
Tracks GmbH
Deep Learning World
Case Study: Lyft

For machine learning problems, we don't lack great tools for certain problems, we lack a unified approach to use them for both prototyping and production. Fugue is a framework, aiming to bridge this gap. In this talk, we are going through a real deep learning example with preprocessing, training, and hyperparameter tuning. We will discuss the pain points and demonstrate how you can use Fugue to quickly iterate on small data and scale up on a Spark cluster without code change. We may also talk about some Lyft use cases, and how Fugue changed the game.

Session description
Speakers
Han WangLyft
Staff Engineer
Lyft
Jintao ZhangSquare
Software Engineer, Machine Learning
Square, Inc.
10:45 am
 
 
 
 
 
 
Deep Learning World
Case Study: Miele

Image classification has been solved successfully for many tasks e.g. using deep learning techniques. However, in many application scenarios, the set of classes from which input images are drawn is not completely known at time of modeling. It is then important to a) reduce false positives during inference and b) enable the description of "unknown" image content during annotation. This case study shows how we addressed these issues at Miele for recognition of food items together with a concept for dealing with unkown classes during data annotation.

Session description
Speaker
Felix ReinhartMiele
Data Scientist
Miele
11:05 am
Break & Expo Hall
Break & Expo Hall
Break & Expo Hall
Break & Expo Hall
Break & Expo Hall
Break & Expo Hall
11:30 am
PAW Business OPERATIONAL

A recurring theme among analytics and data science leaders is the concern of not being able to keep up with all of the rapid change taking place – both individually and as a team. In years past, it was possible to stand up an organization largely made up of analytics generalists who would handle initiatives end to end. In today’s world, it is necessary to utilize a range of specialists focused on either specific methods or specific points in the lifecycle of an analytics and data science initiative. This talk will discuss the trends driving the need to evolve your organization’s talent model, new roles you need to consider implementing, and how they all fit together.

Session description
Speaker
Bill FranksInternational Institute For Analytics
Chief Analytics Officer
International Institute For Analytics
PAW Business TECHNICAL
Lessons from: Microsoft

With the need to cater to a global audience, there is a growing demand for applications to support speech identification/translation/transliteration from one language to another. This talk starts off with a brief introduction to the topic of Machine Translation (MT), the evolution/application of Machine Translation. The focus will be on Neural Machine Translation (NMT). Then it moves on to introduce some of the typical customer cases and finally ends with how to embed such functionality in your application. The talk will end with a Python based solution demo and introduce the audience to some resources and tools that could help them as they continue to explore the domain.

Session description
Speaker
Jaya MathewMicrosoft
Senior Data Scientist
Microsoft
PAW Financial
Case Study: Thomson Reuters

Thomson Reuters produces timely financial news and alerts that is used by many of our corporate and institutional customers. This is extracted from multiple sources, including corporate disclosures, and presented as an ongoing series of alerts and bulletins. One of the major challenges in this kind of financial work is the lack of accurate or weak training data which required us to employ various innovative data cleaning techniques. We also discuss our NLP-based production system that makes strong use of a BERT pre-trained model in combination with an unsupervised strategy, as well as some of the engineering challenges around making the system operate adequately in real-time.

Session description
Speakers
Ian KnopkeThomson Reuters
Senior Data Scientist
Thomson Reuters
Viktoriia SamatovaThomson Reuters
Head of Technology Innovation
Thomson Reuters
PAW Healthcare
Case Study: Improving Emergency Room Care and Efficiency

Hospitals make lots of efforts to improve their capacity in emergency departments by adding boxes and beds, organizing shifts, processes and protocols. And, by also implementing systems to record, control and visualize better what's the situation in real time. But decisions, the ones that ultimately drive the output of the process, are entirely left to human capacity. What if an algorithm could help selecting the sequence for attending emergency patients so that life threatening situations are prioritized and overall queueing time is shortened, with no change in the physical resources? Benjamin Arias Gálvez shares the experience at one of the largest public hospitals in Chile, where an algorithm helps sequencing the waiting queue of patients at in the emergency room in real time, with no investment in the physical resources.    

Session description
Speaker
Benjamin Arias-GálvezForesta.io
General Manager
Foresta.io
PAW Industry 4.0
Case Study: Sira-Kvina kraftselkap

Elder Research, a data science consultancy, and Sira-Kvina kraftselkap, a large Norwegian power producer, have deployed deep learning models as part of a predictive maintenance solution to identify likely component failures within hydroelectric generator and turbine systems. In this session, we discuss the design concept, modeling approach, solution architecture, and how it is implemented as part of the maintenance team workflow. We will also explain what makes machine learning so challenging in the utlities industry where labeled cases are in short supply. 

Session description
Speaker
Ramon PerezElder Research
Director of UK Operations
Elder Research
PAW Climate
Lessons from: SilviaTerra

As demand for carbon credits accelerates, there is an immense challenge in scaling the supply of carbon offsets. It’s hard to create credits that are additional, non-leaky, and durable, and it’s impossible for all but the largest landowners to participate in carbon programs. Over the last 10 years, SilviaTerra has built technology that generates comprehensive forest inventories of unprecedented resolution and scale, enabling measurement and payment for a comprehensive set of beneficial outcomes across the landscape. This new market is making carbon and other types of natural capital work for all landowners - for every acre, every value, every year.

Session description
Speaker
Nan PondSilviaTerra
Chief Biometric Officer
SilviaTerra
Deep Learning World
Case Study: Datavisor

As online fraud increases in volume, malicious actors rely on automation to keep scaling. To spread false information, sell nonexistent products, or spend money from stolen credit cards, fraudsters use scripts and other automation tools to manage a large number of fake accounts. Such automation introduces a common thread in the profiles or communications of the accounts they control. We developed a deep learning model to detect suspicious patterns amongst different accounts. The model was successful in detecting coordinated attacks, even when deployed on a novel platform, increasing by up to 80% the detection of malicious actors.

Session description
Speaker
Nicola CorradiDatavisor
Research Scientist
Datavisor
11:55 am
 
 
 
 
 
 
Deep Learning World
Case Study: Copan Group

Deep learning solutions are nowadays a standard tool in many technological fields. Specifically, in the microbiological field, this is possible through Full Laboratory Automations.

The combination of those two game-changers, made available by COPAN, allow microbiologists to streamline their daily routine: the preparation, incubation and evaluation of thousands of samples (mainly from negative analysis) focusing their high skilled qualities directly on the most challenging and critical ones.

In this talk, Giovanni Turra, key member of the Imaging and Data Analysis team at COPAN, explains how deep learning supports and empowers the daily laboratory battle against diseases.

Session description
Speaker
Giovanni TurraCopan Group
Computer Vision, Machine Learning and Deep Learning Engineer
Copan Group S.p.a.
12:15 pm
PAW Business

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

PAW Financial

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

PAW Healthcare

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

PAW Industry 4.0

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

PAW Climate

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

Deep Learning World

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

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Session description
12:45 pm
End of Day 2
End of Day 2
End of Day 2
End of Day 2
End of Day 2
End of Day 2

Predictive Analytics World - Virtual - Day 3 - Wednesday, May 26th, 2021

8:00 am
PAW Business

Grab your real coffee and share experiences virtually with your peers to explore the new challenges of operating in a largely virtual world. Just like pre-show breakfast in a regular conference you’ll join a “round table” with seven fellow attendees and see where the conversation takes you.

PAW Financial

Grab your real coffee and share experiences virtually with your peers to explore the new challenges of operating in a largely virtual world. Just like pre-show breakfast in a regular conference you’ll join a “round table” with seven fellow attendees and see where the conversation takes you.

PAW Healthcare

Grab your real coffee and share experiences virtually with your peers to explore the new challenges of operating in a largely virtual world. Just like pre-show breakfast in a regular conference you’ll join a “round table” with seven fellow attendees and see where the conversation takes you.

PAW Industry 4.0

Grab your real coffee and share experiences virtually with your peers to explore the new challenges of operating in a largely virtual world. Just like pre-show breakfast in a regular conference you’ll join a “round table” with seven fellow attendees and see where the conversation takes you.

PAW Climate

Grab your real coffee and share experiences virtually with your peers to explore the new challenges of operating in a largely virtual world. Just like pre-show breakfast in a regular conference you’ll join a “round table” with seven fellow attendees and see where the conversation takes you.

Deep Learning World

Grab your real coffee and share experiences virtually with your peers to explore the new challenges of operating in a largely virtual world. Just like pre-show breakfast in a regular conference you’ll join a “round table” with seven fellow attendees and see where the conversation takes you.

8:55 am
PAW Healthcare
Jeff DealElder Research
Chief Operating Officer
Elder Research
Deep Learning World
Luba Gloukhova
Consultant & Speaker
9:00 am
PAW Business
The Session Description will be available shortly.
Session description
Speaker
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
PAW Financial Special Plenary
The Session Description will be available shortly.
Session description
Speaker
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
PAW Healthcare
The Session Description will be available shortly.
Session description
Speaker
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
PAW Industry 4.0
The Session Description will be available shortly.
Session description
Speaker
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
PAW Climate
The Session Description will be available shortly.
Session description
Speaker
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
Deep Learning World SPECIAL PLENARY
The Session Description will be available shortly.
Session description
Panelist
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
9:50 am
Presentation from Leading Vendor
Presentation from Leading Vendor
Presentation from Leading Vendor
Presentation from Leading Vendor
Presentation from Leading Vendor
Presentation from Leading Vendor
10:10 am
Room change
Room change
Room change
Room change
Room change
Room change
10:20 am
PAW Business OPERATIONAL

Ethics in AI is no longer optional, and profit and human values are not mutually exclusive. As the scale, scope and speed of AI adoption increase, we are seeing more examples of algorithmic discrimination, automated racism, ageism, deepfakes, large-scale manipulation ("nudging"), and other harms. Much of this is unintentional, but were you to deploy such an app, it could severely damage your organization's reputation and top or bottomline. Because no matter what business you are in, we are all in the business of trust. Join this session to lean how to adopt and scale AI ethically and responsibly.

Session description
Speaker
Natalia ModjeskaInfo-Tech Research Group
Director, Research, DnA (Data & Analytics)
Info-Tech Research Group
PAW Business TECHNICAL
Lessons from: IBM

IBM Sales created a machine learning based tool, which provides leaders insights into deals while increasing revenue. Sales Action Quadrants is a novel idea based on machine learning augmented classification technique, which takes into account seller input about opportunity progression in sales cycle then, based on the degree of progression, maps it against an ensemble of machine learning algorithms. The concept that differentiates SAQ from any modeling approach is the sophisticated implementation of ‘ensemble’ approach, based on the ‘No free lunch theorem in machine learning’ which states - ‘there is no one model that works best for every problem’.

Session description
Speaker
Stephen PiperIBM
Tools and Technology leader for Enterprise Operations
IBM
PAW Financial
Case Study: Paychex

The shock of a pandemic can be mitigated by developing a plan for being proactive with modeling that can inform decision making. At Paychex we developed a comprehensive COVID-19 insights package released in March 13 that was then transformed into a real time indicator site that included predictive modeling for client losses.

Session description
Speakers
Shadi SifainPaychex
Finance & Analytics Leader
Paychex, Inc.
Jing Zhu
Risk Modeling Analyst
Paychex Inc.
PAW Healthcare
Case Study: Capital District Physicians' Health Plan

The health care industry is changing rapidly, therefore, it’s necessary to improve efficiency to production with appropriate and targeted automation. CDPHP, a mid-size payer in New York’s Capital Region, is implementing an Analytics Factory to achieve this end. It does this using a CI/CD/CT framework. In this session, you will learn about our strategy, as well as practical lessons learned on scaling and operationalizing data products. Three use cases will be discussed to illustrate the streamlined process to production: (1) NLP-driven quality measure detection, (2) prioritizing member voice responses for action, and (3) deploying and hosting a readmission model. 

Session description
Speaker
Matthew PietrzykowskiCapital District Physicians’ Health Plan
Director, Data Science & Transformational Analytics
Capital District Physicians' Health Plan
PAW Industry 4.0

Unplanned downtime is one of the largest sources of lost production in the manufacturing world, leading directly to a loss of possible revenue. The rise of the Internet of Things has led to the development of advanced predictive maintenance solutions aiming to help manufacturers achieve the ultimate goal: zero unplanned downtime. While advances in technology have led to broader adoption of these IoT solutions, many of them are still not accurate enough to rely on. Markus Larsson from Xerox’s Palo Alto Research Center (PARC) talks about what innovators can do to bring IoT predictive maintenance to the next level. 

Session description
Speaker
Markus LarssonPARC, a Xerox Company
Head of Predictive Maintenance
Palo Alto Research Center (PARC)
PAW Climate
Case Study: One Concern

Climate change is increasing the frequency and severity of natural disasters. Natural catastrophes impact all critical infrastructures, and their resilience is essential for businesses and cities to operate effectively and safely. At One Concern, we combine machine learning and hazard modeling along with ML operational tools to better model the impacts of natural disasters on these critical infrastructures. By taking advantage of modeling, we can understand these potential impacts sooner to plan for and mitigate them. This helps to make our communities more resilient. This session will cover how One Concern applies Machine Learning algorithms to Natural Disaster Modeling. 

Session description
Speaker
Shabaz PatelOne Concern
Director of Data Science
One Concern
Deep Learning World

Graph embedding on very large graphs is a challenging task with many real-world applications such as fraud detection and cross-sell recommendation. The huge computational workload can only be efficiently handled by scalable distributed systems such as Spark or GPU-based deep learning frameworks such as PyTorch. Fugue is an open source Python framework which can manage various backend computing engines with constant interfaces. We will demonstrate how we scale up the transductive embedding algorithm “node2vec” using Spark to handle giant graphs in the Fugue framework. We also applied Fugue and Spark to the inductive embedding algorithm “GraphSAGE” for faster edge sampling.

Session description
Speakers
Han WangLyft
Staff Engineer
Lyft
Jintao ZhangSquare
Software Engineer, Machine Learning
Square, Inc.
10:45 am
 
 
 
 
 
 
Deep Learning World
Case Study: Laing O'Rourke

Every year in Australia ~200 people lose their lives and 100,000+ are seriously injured in potentially avoidable work place incidents. The challenges presented by the extreme conditions and complex work environments typical in heavy industries need to be overcome. This talk presents an advanced computer vision edge AI/ML for these harsh environments. Details are presented on the journey from the R&D group of one of the world’s larger construction companies to a venture capital funded startup; along with the requisite non-trivial performance, market adoptability and technology scalability accomplishments, and the underpinning approaches for Computer Vision & Edge AI/ML.

Session description
Speaker
Nathan KirchnerPresien
Founder, CTO
Presien
11:05 am
PAW Business

Take a break or join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

PAW Financial

Take a break or join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

PAW Healthcare

Take a break or join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

PAW Industry 4.0

Take a break or join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

PAW Climate

Take a break or join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

Deep Learning World

Take a break or join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

11:30 am
PAW Business OPERATIONAL
Case Study: Barron’s Group

Learn how Barron’s Group is leveraging automation and Machine Learning to produce direct-to-reader content and power dynamic editorial tools. This talk will focus on both the technical infrastructure necessary to automate content, and the innumerable product and editorial decisions made throughout the design, development, and deployment phases.

Over the past year, Barron’s Group has used Wordsmith to manage the text generation component of its automated story systems. Auto-generated stories are templated to summarize relevant stock-market events, and may be triggered within our own system to publish under any conditions specified. Because templates can be designed for any relevant use-case, this system is easily extended to different story topics, languages, and business segments. Barron’s currently produces stories on the state of the market at company close, and plans to expand to publish japanese-language translations, automated flash headlines, and morning stock summaries.

Barron’s Group is also in the process of building a Machine Learning-powered system for editorial assists. This tool will rely on anomaly detection performed with Amazon Sagemaker, and will alert editors when noteworthy events occur. In the short-term, this tool is intended as a time-saver for editors who would otherwise have to manually sift through price and volume data for dozens of stocks. In the long term, this kind of technology could be combined with Wordsmith’s templating functionality to produce entire headlines or articles to send off to editors, for review or publication. Here, we will focus primarily on our current process for automated article publication with Wordsmith, along with our work to this point on the Sagemaker editorial assist tool. We will review the results of our work, and discuss future steps that can be taken to expand upon these systems.

Session description
Speakers
Sarah SchmollerDow Jones & Company, Inc.
Software Engineer - ML Automation
Dow Jones & Company, Inc.
Alex SiegmanBarron’s
Director, Automation and Machine Learning
Barron's Group
PAW Business TECHNICAL
Lessons from: Google

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

Session description
Speaker
Peter GrabowskiGoogle
Software Engineering Manager
Google
PAW Financial
Case Study: Goldman Sachs

Algorithmic trading revolves around optimal scheduling, when and how to trade each asset of a portfolio. The schedules produced, however, should not be static; being able to optimally adapt to changing intraday market conditions and analysing effectively other exogenous information is of paramount importance. In this talk, we focus on how machine learning methods like RNNs, LSTMs and decision trees, used in conjunction with classical dynamical system techniques, such as kalman filters, can enrich the toolbox available for real-time algorithmic trading.

Session description
Speakers
Andreas PetridesGoldman Sachs
Quantitative Researcher
Goldman Sachs
Michael SteliarosGoldman Sachs
Managing Director
Goldman Sachs
PAW Healthcare
Case Study: Real World Application of Natural Language Processing

The speaker will review case studies from real-world projects that built AI systems using Natural Language Processing (NLP) in healthcare. These case studies cover projects that deployed automated patient risk prediction, automated diagnosis, clinical guidelines, and revenue cycle optimization. He will also cover why and how NLP was used, what deep learning models and libraries were used, and what was achieved. Key takeaways for attendees will include important considerations for NLP projects including how to build domain-specific healthcare models and using NLP as part of larger and scalable machine learning and deep learning pipelines in distributed environment. 

Session description
Speaker
Veysel KocamanJohn Snow Labs
Lead Data Scientist
John Snow Labs
PAW Industry 4.0
Case Study: BonsAI

Machine teaching is a complementary approach to machine learning. It helps those without AI expertise break a complex problem into simpler tasks and give the AI model important clues about how to find a solution quickly. In this session, we talk about the current limitations with control systems and how AI is bridging that gap by training intelligent controllers .

Session description
Speaker
Rishabh GaurMicrosoft
Technical Architect
Microsoft
PAW Climate
Lessons from: Terrafuse AI

We give an overview of recent developments in physics-informed AI and big data that are transforming the prediction of climate and weather in applications ranging from climate risk modeling for insurance to real-time forecasting for energy. Traditional climate and weather models require computationally expensive simulation of physical laws on supercomputers with hours to days of processing time and have limited capacity to incorporate ground-truth data sources. The development of cloud-based AI workflows based on deep neural networks provides an alternative approach to develop physical emulators of climate and weather processes that are highly scalable and natively tuned to utilize the petabytes of remote-sensing, ground-based and numerical simulation data from Earth observation that are generated daily. We present work that we are doing at Terrafuse AI, a startup out of Berkeley National Lab, to develop an AI-native climate risk and forecasting platform for problems ranging from high-resolution mapping of wildfire risk in California to real-time wind forecasting for aviation and renewable energy. 

Session description
Speaker
Brian WhiteTerrafuse AI
CTO and Chief Scientist
Terrafuse
Deep Learning World
Case Study: Datavisor

Online fraud is flourishing as online services extend to more industries, including financial service providers, insurance companies, online retailers, and social networks. Network traffic data is invaluable for identifying such malicious actors. In this talk, we will show how to train a self-supervised deep learning model using only unlabeled network logs. Deriving the representation from the "structure" of the bits in an IP address, the model can process novel entities not encountered in the training data. Results show that our proposed framework can identify anomalous network accesses up to 16 times better than Amazon SageMaker's state-of-the-art model.

Session description
Speaker
Nicola CorradiDatavisor
Research Scientist
Datavisor
11:55 am
 
 
 
 
 
 
Deep Learning World
Case Study: WeBank

Since 2018, Transformer models revolutionized the planet NLP by bridging the gap between universal and task-specific representations. Transformers accelerated the shift from a world in which each NLP application requires training deep neural networks from scratch to the emergence of large pretrained models with great generalization capabilities, which are finetuned for downstream tasks with limited amount of labelled data. Yet, using Transformers for industrial applications poses various challenges. In this accessible presentation, Hadrien Van Lierde from Tencent-backed WeBank will describe how China’s leading digital bank uses Transformers to improve its customer service chatbot while maintaining low latency and high throughput.

Session description
Speaker
Hadrien Van LierdeWeBank
Machine Learning Engineer
WeBank
12:15 pm
End of Day 3
End of Day 3
End of Day 3
End of Day 3
End of Day 3
End of Day 3

Predictive Analytics World - Virtual - Day 4 - Thursday, May 27th, 2021

8:00 am
PAW Business

Grab your real coffee and share experiences virtually with your peers to explore the new challenges of operating in a largely virtual world. Just like pre-show breakfast in a regular conference you’ll join a “round table” with seven fellow attendees and see where the conversation takes you.

PAW Financial

Grab your real coffee and share experiences virtually with your peers to explore the new challenges of operating in a largely virtual world. Just like pre-show breakfast in a regular conference you’ll join a “round table” with seven fellow attendees and see where the conversation takes you.

PAW Healthcare

Grab your real coffee and share experiences virtually with your peers to explore the new challenges of operating in a largely virtual world. Just like pre-show breakfast in a regular conference you’ll join a “round table” with seven fellow attendees and see where the conversation takes you.

PAW Industry 4.0

Grab your real coffee and share experiences virtually with your peers to explore the new challenges of operating in a largely virtual world. Just like pre-show breakfast in a regular conference you’ll join a “round table” with seven fellow attendees and see where the conversation takes you.

PAW Climate

Grab your real coffee and share experiences virtually with your peers to explore the new challenges of operating in a largely virtual world. Just like pre-show breakfast in a regular conference you’ll join a “round table” with seven fellow attendees and see where the conversation takes you.

Deep Learning World

Grab your real coffee and share experiences virtually with your peers to explore the new challenges of operating in a largely virtual world. Just like pre-show breakfast in a regular conference you’ll join a “round table” with seven fellow attendees and see where the conversation takes you.

8:55 am
PAW Business
The Session Description will be available shortly.
Session description
Speaker
David Stephenson Ph.D.DSI Analytics
Author and Founder
DSI Analytics
PAW Healthcare
Jeff DealElder Research
Chief Operating Officer
Elder Research
Deep Learning World
Luba Gloukhova
Consultant & Speaker
9:00 am
PAW Business PANEL
The Session Description will be available shortly.
Session description
Moderator
James TaylorDecision Management Solutions
CEO
Decision Management Solutions
PAW Financial
The Session Description will be available shortly.
Session description
Moderator
James TaylorDecision Management Solutions
CEO
Decision Management Solutions
PAW Healthcare
EXPERT PANEL

Analytics adoption in healthcare has come a long way over the last two decades. In the early years, advanced machine learning was limited to a handful of experts working on narrowly-defined subjects such as hospital readmissions. Among most healthcare professionals, there was limited understanding of what machine learning is, and even less eagerness to integrate analytic models into medical practice or organizational operations in any meaningful way. Today, any healthcare organization that is not using advanced analytics is an anomaly. Analytic methods are being employed almost everywhere in healthcare from insurance to precision medicine with tailored treatment plans guided by artificial intelligence. And, analytics are embedded in the systems used by hospitals, such as the HIS or the laboratory technology. Slowly, advanced analytics is being integrated throughout the healthcare system even though many professionals are not aware of its influence. Join our expert panel as we discuss the current state of analytics/machine learning, the work that remains, and opportunities to better use this incredible technology. Attendees are invited to join in the conversation and contribute questions and comments to the discussion.

Session description
Moderator
Jeff DealElder Research
Chief Operating Officer
Elder Research
PAW Industry 4.0
The Session Description will be available shortly.
Session description
Speaker
Steven RamirezBeyond the Arc
CEO
Beyond the Arc
PAW Climate EXPERT PANEL

Discover how early-stage climate tech companies are using machine learning to help meet their challenges.

Session description
Panelists
Michel GelobterReflective Earth
Managing Director
REFLECTIVE EARTH
Elizabeth NyekoModularity Grid
CEO & Founder
Modularity Grid
Sierra Peterson
Climate Tech Investor
Diego Saez-GilPachama
Co-founder & CEO
Pachama
Deep Learning World

We propose a fast single-stage 3D scene understanding method for LIDAR data. Previous methods often make domain-specific design decisions, for example projecting points into a bird-eye view image in autonomous driving scenarios. In contrast, we propose a general-purpose method that works on both indoor and outdoor scenes. The core novelty of our method is a fast, single-pass architecture that both detects objects in 3D and estimates their shapes. 3D bounding box parameters are estimated in one pass for every point, aggregated through graph convolutions, and fed into a branch of the network that predicts latent codes representing the shape of each detected object. The latent shape space and shape decoder are learned on a synthetic dataset and then used as supervision for the end-to-end training of the 3D object detection pipeline. Thus our model is able to extract shapes without access to ground-truth shape information in the target dataset. During experiments, we find that our proposed method achieves state-of-the-art results by ~5% on object detection in ScanNet scenes, and it gets top results by 3.4% in the Waymo Open Dataset, while reproducing the shapes of detected cars.

Session description
Speaker
Alireza FathiGoogle
Senior Research Scientist
Google
9:50 am
PAW Business

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

PAW Financial

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

PAW Healthcare

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

PAW Industry 4.0

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

PAW Climate

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

Deep Learning World

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

10:10 am
Room change
Room change
Room change
Room change
Room change
Room change
10:20 am
PAW Business OPERATIONAL

About 80% of machine learning projects fail – not because the data scientists failed, but because of the many known and unknown unknowns especially of data, but also of business and users. Consequently, the key to success is to “fail early to succeed sooner” i.e., to identify the critical break points and to focus on the use case with high chances of success. The identification of the right use cases is the purpose of a data strategy and data thinking is the process of thinking through the right solution. This interactive session introduces a method for data thinking, called Data Strategy Design. The free Data Strategy Designkit consists of 15 visual tools to collaboratively and interdisciplinary develop data strategies and data products. It is already used by hundreds of companies worldwide and by participating in this deep dive session you will also learn how to apply the method and tools to your projects. For the joint exercise we will use Miro, an online whiteboard platform, and we will work together on an exemplary machine learning project.

Session description
Speaker
Martin SzugatDatentreiber GmbH
Founder & Managing Director
Datentreiber GmbH
PAW Business TECHNICAL
Case Study: Booking.com

While hotels' star ratings are widely recognized and used across the globe, there are no similar rating system exists for the large majority of vacation rentals, making it difficult for guests to search and compare options and hard for vacation rental suppliers to market their product effectively.

In this talk, we will present a Quality Rating System for Vacation Rentals based on Machine Learning, focusing on automated explainable vacation rental quality ratings. This system was successfully deployed and validated through Randomized Controlled Trials at Booking.com, impacting millions of accommodations and guests.

Session description
Speaker
Anastasiia KornilovaBooking.com
Machine Learning Scientist
Booking.com
PAW Financial
Case Study: Safety National Casualty Corporation

The Safety National Casualty Corporation is the leader in Excess Workers' Compensation. Predictive Analytics is used internally to identify those claims that have potential of exceeding high-cost levels in years. Structured claim data, such as claimant age, nature of injury and body parts, provide fundamental features for predicting claim severity. However, in many cases, they are not informative and distinguishing enough to identify extremely sever claims which are rare in the claim data pool. To address this issue, we explore the unstructured claim data – claim notes, and investigate how different text mining techniques can be used to extract predictive insights from claim notes.

Session description
Speakers
Bala Venkatram BalantrapuSafety National Casualty Corporation
Data Scientist
Safety National Casualty Corp
Carrie Lu Ph.D.Safety National Casualty Corporation
Senior Data Scientist
Safety National Casualty Corporation
PAW Healthcare
The Session Description will be available shortly.
Session description
Speaker
Chris FranciskovichOSF Healthcare
Director, Advanced Analytics
OSF Healthcare System
PAW Industry 4.0
Lessons from: Seagate Technology

Seagate is a multinational data storage solution company with over 40,000 employees from around the world. We have embarked on the advanced analytics journey 6 years ago. In the last 6 years, teams worked on many initiatives to help the company and its staff to transform digitally and get ready for industry 4.0. In this talk, we will share our business and technical journey and talk about some of the challenges we encountered in the last 6 years. We will explain the solutions we came up with and the initiatives we established to help with the digital transformation journey at Seagate. We will also share practices and approaches that work well for us and share examples of our data science solutions. We hope our experience will serve as a good industrial reference for companies that are interested in starting their own digital transformation and analytics journey. 

Session description
Speaker
Allan LukSeagate Technology
Director of Data Science and Analytics Business Solutions
Seagate
PAW Climate
Lessons from: Kairos Aerospace

Methane, the primary component of natural gas, is responsible for 15% of global warming. Our mission of finding and stopping greenhouse gas emissions at huge scale is a critical step towards controlling climate change, but it also presents unique challenges. And, as a small startup, navigating the trade-offs between speed, accuracy, and cost in our data pipeline can often be the difference between survival and failure. In this talk, we will examine the difficulties ML pipeline design in cases where information, time, and money are constrained, and how to do so while hiding the sausage-making from our customers, who just want to know where their equipment is leaking, and want to know fast. By using a lean, iterative approach that involves input from every department, including engineering, operations, and business development, we stay focused on creating analytics that maximize value while reducing risk to the company. 

Session description
Speaker
Matthew GordonKairos Aerospace
Principal Software Engineer
Kairos Aerospace
Deep Learning World
Case Study: Stripe

Terms of service (ToS) violators at Stripe are merchants on our ecosystem who are selling items and services that are prohibited by our terms of service. This talk will present how we build a multimodal ToS violation deep learning detection system that combines text, images, and tabular data. We will also discuss how we enable interpretability of model predictions.

Session description
Speaker
Carter LinStripe
Data Science Manager
Stripe
11:05 am
PAW Business

Take a break or join your fellow practitioners in an interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

Session description
PAW Financial

Take a break or join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

Session description
PAW Healthcare

Take a break or join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

PAW Industry 4.0

Take a break or join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

PAW Climate

Take a break or join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

Deep Learning World

Take a break or join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

11:30 am
PAW Business OPERATIONAL
Case Study: Dow Jones & Company

Modeling the risk of customer churn in large B2B organizations can be tricky. In this session, we will outline our approach to handling that complexity by developing an interpretable model and an interactive front-end tool to highlight risk factors to a team of non-technical stakeholders. We highlight the importance of transparent modeling (as opposed to black-box modeling) in B2B retention, where understanding the risk factors is as important to our customer experience stakeholders as the model itself.

Session description
Speakers
Steve BishopDow Jones & Company, Inc.
Data Scientist
Dow Jones & Company, Inc.
Matt KlubeckDow Jones & Company, Inc.
Data Scientist
Dow Jones & Company, Inc.
PAW Business TECHNICAL
Case Study: Cape Fear Collective

Systemic racism is a complex and multi-dimensional problem that manifests in several different ways. No single narrative or data point can fully capture the pervasive and damaging nature of this crisis. Through our work at Cape Fear Collective, we attempt to decompose the far-reaching impacts of systemic racism into tangible components. The culmination of this work is a Racial Equity Index, which we believe will aid in the development of strategies and solutions to combat systemic racism and forge a more equitable future.

Session description
Speaker
Nicholas PylypiwCape Fear Collective
Director of Data Science
Cape Fear Collective
PAW Financial
Case Study: Axis Capital

Predictive Analytics is transforming commercial insurance underwriting. It delivers more accurate and real-time risk assessment which helps underwriters efficiently acquire information, prepare more accurate quotes, and speed up and even automate the underwriting process. This talk presents one practical application of predictive analytics on building the triage process to help underwriters prioritize their attention on the most valuable submissions.

Session description
Speaker
Min Yu
Lead Data Scientist
Axis Capital
PAW Healthcare
Protecting the Pharmaceutical Supply Chain

Counterfeited, adulterated, and stolen pharmaceuticals are threats to US citizens. The Drug supply Chain Security Act (DSCSA) was established to secure the supply chain through various means including the establishment of an interoperable traceability system. The current industry stakeholder design is a distributed structure with exchanges between established trading partners. While this is flexible, it creates challenges to detecting nefarious activity and performing supply-demand management. We have implemented an interoperable, traceable prototype system which solves some of the problems and fulfills DSCSA requirements that must be met by 2023 .

Session description
Speaker
Jaya TripathiMITRE
Principal, Data Analytics
MITRE Corporation
PAW Industry 4.0

Enhanced accessibility and availability to data has resulted in the increasing demand for data scientists within the logistical and supply end of industry. Yet, with this great reservoir of information, data scientists are faced with key challenges in the development of their solutions. In this session, we explore both the traditional as well as new challenges of the data scientist in this environment. The alignment of the right data infrastructure in solving the right business problem is just one example. New data sources and new tools exacerbate this issue but the disciplined process approach towards the data science process is the constant. By looking at a variety of industries such as insurance, travel , and health, we examine how to develop the best practices within this process. A number of examples and case studies will be presented to explore the increased shift and emphasis towards data science skills that focus less on the technical and more on identifying the business problem. 

Session description
Speaker
Richard BoireBoire Analytics
President
Boire Analytics
PAW Climate
Lessons from: AMP Robotics

The global recycling industry has been in economic crisis over the last few years as China and other international importers of recyclables enacted stricter requirements on the purity of recycled materials. COVID-19 exacerbated the recycling crisis, forcing many businesses to suspend operations due to concerns for worker safety. At the same time, the pandemic increased demand for high-quality recycled feedstock to overcome supply chain interruptions and shifts in raw material availability. AMP’s technology applies computer vision and deep learning to identify and differentiate recyclables found in the waste stream by color, size, shape, opacity, consumer brand, and more, storing data about each item it perceives. AMP’s AI can recognize and recover material as small as a bottlecap and as unique as a Keurig coffee pod from complex, mixed material streams of plastics, cardboard, paper, cans, cartons, and many other container and packaging types reclaimed for raw material processing for the global supply chain. Learn how AMP is helping the industry overcome the crisis by modernizing recycling—keeping recycling businesses open, ensuring worker safety, increasing productivity, improving bale purity, overcoming labor shortages, lowering the costs to recycle, diverting materials from landfill, and increasing overall rates of recycling. 

Session description
Speaker
Matanya HorowitzAMP Robotics
Founder
AMP Robotics
Deep Learning World
Case Study: Google

Many organizations deal with incoming customer problems either internally or externally. Frequently, these problems are handled one at a time by humans. We’ll discuss how at Google we are doing early detection of broad IT incidents before they become too large. We'll go through unsupervised learning techniques that when combined with deep learning-based language modeling creates a powerful, robust system that has saved Googlers hundreds of thousands of hours in productivity.

Session description
Speaker
Patrick MillerGoogle
Software Engineering Manager
Google
11:55 am
 
 
 
 
 
 
Deep Learning World
Lessons from: Thomson Reuters

Thomson Reuters produces timely financial news and alerts that is used by many of our corporate and institutional customers. This is extracted from multiple sources, including corporate disclosures, and presented as an ongoing series of alerts and bulletins. One of the major challenges in this kind of financial work is the lack of accurate or weak training data which required us to employ various innovative data cleaning techniques. We also discuss our NLP-based production system that makes strong use of a BERT pre-trained model in combination with an unsupervised strategy, as well as some of the engineering challenges around making the system operate adequately in real-time.

Session description
Speaker
Ian KnopkeThomson Reuters
Senior Data Scientist
Thomson Reuters
12:15 pm
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Predictive Analytics World - Virtual - Day 5 - Friday, May 28th, 2021

8:55 am
PAW Healthcare
Jeff DealElder Research
Chief Operating Officer
Elder Research
Deep Learning World
Luba Gloukhova
Consultant & Speaker
9:00 am
PAW Business KEYNOTE
Humanitarian Applications of ML

A global NGO identifies and rehabilitates tens of thousands of survivors of human trafficking and slavery situations. Different situations necessitate different rehabilitation “journeys.” We analyzed case worker notes from check-ins with survivors to help understand how specific programs, and especially “trauma-informed” programs, might be affecting survivors’ emotional wellbeing and likelihood to finish their journeys. We found generally negative associations between survivor sentiment and interactions with the justice system, and generally positive associations with training programs, especially for rebuilding social support networks and economic empowerment. These findings have implications for journey planning as the NGO tries to scale up its worldwide operations.

Session description
Speaker
Muneeb AlamQuantumBlack
Specialist, Data Science
Quantum Black
PAW Financial KEYNOTE
Humanitarian Applications of ML

A global NGO identifies and rehabilitates tens of thousands of survivors of human trafficking and slavery situations. Different situations necessitate different rehabilitation “journeys.” We analyzed case worker notes from check-ins with survivors to help understand how specific programs, and especially “trauma-informed” programs, might be affecting survivors’ emotional wellbeing and likelihood to finish their journeys. We found generally negative associations between survivor sentiment and interactions with the justice system, and generally positive associations with training programs, especially for rebuilding social support networks and economic empowerment. These findings have implications for journey planning as the NGO tries to scale up its worldwide operations.

Session description
Speaker
Muneeb AlamQuantumBlack
Specialist, Data Science
Quantum Black
PAW Healthcare
The Session Description will be available shortly.
Session description
Speaker
Dean AbbottAbbott Analytics
President
Abbott Analytics
PAW Industry 4.0
Case Study: Department of the Navy

Reducing unplanned downtime requires the proactive maintenance of assets to keep operation running smoothly. Unfortunately, many organizations rely on time-based or usage-based maintenance rather than actual need to determine when to best schedule a maintenance activity. Predictive maintenance instead uses data from operations to determine the health of the asset and can be used to determine when it is best to perform maintenance. We developed intelligent digital twin technology that utilizes sensor data to assess the health and prognostics of a system based on predictive models built with physics-based simulation data. A digital twin is a digital representation of a physical asset that acts as a “single source of truth” for the asset. By incorporating predictive analytics into a digital twin, we can calculate maintenance-related parameters that would allow for appropriate intervention. In this session we will discuss some of the technology available to implement digital twins. We will also discuss how to augment the basic capabilities of digital twins with predictive algorithms for condition-based maintenance. In particular we will discuss developing a digital twin of a naval vessel that assesses the efficiency of various propulsion system components to determine the state of decay of the components.

Session description
Speakers
Rob MontalvoDataCrunch Lab
President
DataCrunch Lab
Zeydy OrtizDataCrunch Lab
CEO
DataCrunch Lab
PAW Climate EXPERT PANEL

Coming soon!

Session description
Deep Learning World KEYNOTE
Case Study: Shopify

Deep Learning image classifiers represent a breakthrough in image recognition and classification tasks. However they come with their own quirks and kinks: A "clean" product image can be easily classified, but what about a stock image of "a woman running in the rain"? Is it a raincoat? Her shoes? The phone cover? At Shopify we were tasked with mapping a large, complex, and "dirty" catalogue of products into duplicates, similar products and categories. In this case study I will walk you through our journey and how we were able to harness the strengths of CNNs while avoiding the major pitfalls.

Session description
Speaker
Yizhar TorenShopify
Data Science Lead
Shopify
9:25 am
PAW Business KEYNOTE
Humanitarian Applications of ML

There are many non-profit organizations trying to help develop and build economic opportunities for families in developing countries around the world by aiding individuals in starting and maintaining small businesses. Vision Fund International is one such organization that uses microfinance loans to jump start business opportunities in these countries. However, these developing countries do not have the credit institutions that countries in the developed world have, which leaves banks restricted on their ability to make loan decisions. Analytical modeling approaches are widely used in major banks in developed countries to efficiently make these decisions. This talk details a project worked on in partnership with Vision Fund International that develops scorecard models for use in loan decisions and the impact it is having on the growth of economies around the world.

Session description
Speaker
Aric LaBarrInstitute for Advanced Analytics at NC State University
Associate Professor of Analytics
Institute for Advanced Analytics at NC State University
PAW Financial KEYNOTE
Humanitarian Applications of ML

There are many non-profit organizations trying to help develop and build economic opportunities for families in developing countries around the world by aiding individuals in starting and maintaining small businesses. Vision Fund International is one such organization that uses micro-finance loans to jump start business opportunities in these countries. However, these developing countries do not have the credit institutions that countries in the developed world have, which leaves banks restricted on their ability to make loan decisions. Analytical modeling approaches are widely used in major banks in developed countries to efficiently make these decisions. This talk details a project worked on in partnership with Vision Fund International that develops scorecard models for use in loan decisions and the impact it is having on the growth of economies around the world.

Session description
Speaker
Aric LaBarrInstitute for Advanced Analytics at NC State University
Associate Professor of Analytics
Institute for Advanced Analytics at NC State University
 
 
 
 
9:50 am
PAW Business

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

PAW Financial

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

Session description
PAW Healthcare

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

PAW Industry 4.0

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

PAW Climate

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

Deep Learning World

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

10:10 am
Room change
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10:20 am
PAW Business OPERATIONAL
Case Study: Mercer

Most business leaders claim to be "results driven," an orientation reflected in their embrace of a "pay for performance" philosophy. Many performance measures commonly used are “noisy” or contaminated by situational factors far removed from employees’ control. Hence, they fail to capture the actual value that employees deliver, with unintended, negative consequences for employer and employees.

Using case examples, the presenter will show how advanced analytics can address this problem by adjusting performance measures to remove the effects of random or situational factors. By distinguishing employee “value” from “performance.” organizations can deliver more effective incentives at less cost to shareholders.

Session description
Speaker
Haig NalbantianMercer
Senior Partner, Co-leader Mercer Workforce Sciences Institute
Mercer
PAW Business TECHNICAL
Lessons from: Cisco

Webex Contact Center is a multi-tenanted Cloud Contact Center solution from Cisco, that scales to thousands of concurrent interactions. Improving Agent Performance directly impacts the efficiency and effectiveness of Contact Centers. We are building the next generation AI enabled interaction experiences to create super agents. With the help of AI, agents can now focus on the customer, understand their sentiment and answer questions quickly. AI capabilities that were built include Speech-To-Text transcriptions, sentiment analysis, Question-Answering and Topic Modeling. AI Models by themselves don't become a scalable solution. Serving these models at scale, specifically for a multi-tenanted cloud service, takes additional planning, architectures, capabilities and operations. This session discusses the unique challenges, solutions and takeaways in building and deploying these models and solutions. Key topics addressed are:

- Multi-tenanted Cloud Services: unique considerations
- Model Scaling Challenges for real time
- Architecture options for scaling Models
- Adapting Models to serve at scale
- Tuning Models for Resource & Cost scaling

Session description
Speaker
Kumaran PonnambalamCisco
Senior AI Architect
Cisco Systems, Inc
PAW Financial
Case Study: HSBC

Our Next Best Action journey commenced with the simple aspiration of delivering smarter personalised experiences at the moment of need for our customers. While the aspiration was simple, orchestrating NBA is a long-distance race often fraught with challenges, twists in the road, and steep hills to climb. There are many moving parts to orchestrate the right experiences specially when customers adapt their own non-linear paths, as they move through the different aspects of life events. But how does one set in motion a truly effective execution? In this session, Harphajan would share the art & science of his journey in executing Next Best Action strategy within retail financial services especially where customer relationships evolve over long periods of time

Session description
Speaker
Harphajan SinghHSBC
Head Of Analytics
HSBC
PAW Healthcare
Evaluating COVID Policy

While the counts of covid cases and deaths and policies are imperfect, the efforts to make these data available have been immense. So, despite the inaccuracy of the measurements, we can see some of the truths of what the genuine correlations are. Now is a good time to look at them through robust modeling techniques.

Session description
Speaker
Mike ThurberElder Research
Principal Scientist
Elder Research
PAW Industry 4.0
Lessons from: Cisco Systems Inc

Webex Contact Center is a multi-tenanted Cloud Contact Center solution from Cisco, that scales to thousands of concurrent interactions. Improving Agent Performance directly impacts the efficiency and effectiveness of Contact Centers. We are building the next generation AI enabled interaction experiences to create super agents. With the help of AI, agents can now focus on the customer, understand their sentiment and answer questions quickly. AI capabilities that were built include Speech-To-Text transcriptions, sentiment analysis, Question-Answering and Topic Modeling. AI Models by themselves don't become a scalable solution. Serving these models at scale, specifically for a multi-tenanted cloud service, takes additional planning, architectures, capabilities and operations. This session discusses the unique challenges, solutions and takeaways in building and deploying these models and solutions. Key topics addressed are- Multi-tenanted Cloud Services: unique considerations- Model Scaling Challenges for real time- Architecture options for scaling Models- Adapting Models to serve at scale- Tuning Models for Resource & Cost scaling

Session description
Speaker
Kumaran PonnambalamCisco
Senior AI Architect
Cisco Systems, Inc
PAW Climate
Case Study: Sust Global

Financial institutions are playing an increasing role in the low-carbon transition by taking steps to accurately estimate, price, and disclose future climate risk. By quantifying their exposure to climate risks, financial institutions can more effectively allocate investments, avoid ‘stranded’ assets, and track adherence to Paris Agreement goals and shareholder commitments. However, it remains difficult for these institutions to assess climate related risks across a portfolio of assets and across different benchmark warming scenarios.I will cover large scale data transformation approaches as part of an end-to-end framework for quantifying annual, asset-level climate risk over multiple climate hazards including wildfires, inland flooding, and heat waves using simulations from global climate models participating in the Coupled Model Inter-comparison Project Phase 6 (CMIP6).We will be discussing techniques to quantify forward looking climate risk from 2020 to 2050 under multiple climate scenarios such as high-emissions (SSP5-8.5) and medium-emissions (SSP2-4.5) warming scenarios. I will also showcase intermediate steps to make the climate simulations and spatiotemporal data interpretable and actionable. We will cover ways to harmonize near real time observations from ground measurements and satellite derived data with forward looking climate risk projections for acute physical hazards for high accuracy predictive modeling.

Session description
Speaker
Gopal ErinjippurathSust Global
CTO, Head of Product
Sust Global
Deep Learning World

In today's world of elastic cloud/on-prem resources to support deployments, it is essential to be right-sized. We present a Hybrid Deep Learning and Statistical approach to model future demand. An accurate measure of incoming demand enables us to be right-sized while keeping guarantees on reliability, resiliency, and availability. We will share our experience dealing with demand volatility and how we temper them to enable actionability with tradeoffs.

Session description
Speaker
Aashish SheshadriPayPal
Staff Machine Learning Engineer
PayPal Inc.
11:05 am
PAW Business

Take a break or join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

PAW Financial

Take a break or join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

Session description
PAW Healthcare

Take a break or join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

PAW Industry 4.0

Take a break or join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

PAW Climate

Take a break or join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

Deep Learning World

Take a break or join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

11:30 am
PAW Business OPERATIONAL

Many analytics teams wait too long to start working on deployment. The most common reason they give is that they want to make sure that the model is “good enough” before they start to plan deployment. The truth is that doing so virtually guarantees project failure, or at a minimum, massive delays. Projects don’t fail because of a failure to find patterns in the data. Projects fail because models that fail to align perfectly with organizational priorities will never be embraced by the end-users of the deployed solution.

Session description
Speaker
Keith McCormick
Data Science Consultant, Trainer, Author, and Speaker
PAW Business TECHNICAL
Lessons from: Google

This session will outline how Google’s Corporate Engineering team is using AI and machine learning to spur innovation within Google. Additionally, Rich will identify the work that his team does (the structure, example use cases etc.), and the research that’s driving the work his team does and the democratization of AI (work in ML Fairness, Privacy, Interpretability and AutoML technologies).

Session description
Speaker
Richard DuttonGoogle
Head of Machine Learning for Corporate Engineering
Google
PAW Financial
Case Study: Visa

One of the critical trends that is impacting customer experience is driving personalization and relevancy in various customer touch points. Customer expectation is being reset everyday thanks to ultra-personalized experiences driven by Google, Facebook and now even some FinTechs. There is a constant hunt for 'better data' that can enhance our understanding of the customer preferences. Payments, in addition to being a means of closing commerce transactions, is in fact, a very rich source of data. Especially for credit and debit cards (which enjoy extremely high usage in NA), every single transactions comes with rich metadata (point of sale device, use of mobile wallets, merchant categories, location, etc.). This spend data provides unique insights into customer preferences and trends and can be transformed into actionable personas. Merchants and Financial Institutions can leverage this data to build personalized digital customer experiences that can delight the customer translating into high customer value. This talk goes over the structure on how we should look at the digital customer experience stack and how payments data & analytics layer can enhance the customer experience.

Session description
Speaker
Abhishek Joshi ‘AJ’Visa
Senior Director
Visa Consulting & Analytics
PAW Healthcare
Case Study: Reducing Ambulance Transports

Germany is a “super-aged” society with increasing demands placed on the healthcare system due to the rise in chronic diseases. The country increasingly leverages e-health solutions to accommodate a healthier older population. In conjunction with Germany’s largest health insurer Techniker Krankenkasse, we are conducting a pilot study in which we deploy predictive modeling to identify elderly at risk of emergency ambulance transport based on e-health data. A case manager reaches out to predicted high-risk patients and recommends interventions. Initial results demonstrate a significant reduction in ambulance dispatch rate. This presentation will cover predictive model development, deployment and preliminary findings. 

Session description
Speaker
Jorn op den BuijsPhilips Research
Senior Scientist
Philips Research
PAW Industry 4.0
Lessons from: Google

This session will outline how Google’s Corporate Engineering team is using AI and machine learning to spur innovation within Google. Additionally, Rich will identify the work that his team does (the structure, example use cases etc.), and the research that’s driving the work his team does and the democratization of AI (work in ML Fairness, Privacy, Interpretability and AutoML technologies).

Session description
Speaker
Richard DuttonGoogle
Head of Machine Learning for Corporate Engineering
Google
PAW Climate
Lessons from: Afresh

About 30-40% of food produced worldwide is wasted. This represents a $165B loss to the US economy and poses major environmental problems: it is estimated that food waste contributes to up to 25% of all greenhouse gas emissions. This session explores how artificial intelligence can be used to automate decisions across the food supply chain in order to reduce waste and increase the quality and affordability of food. We focus our attention on supermarkets — combined with downstream consumer waste, these contribute to 40% of total US food losses — and we describe an intelligent decision support system for supermarket operators that optimizes purchasing decisions and minimizes losses. The core of our system is a model-based reinforcement learning engine for perishable inventory management. Our system is currently deployed across 220 supermarkets in the US (handling ~2% of US produce volume) and has led to waste reductions of up to 50%. We hope that this talk will bring the food waste problem to the attention of the machine learning community. 

Session description
Speaker
Volodymyr KuleshovAfresh
Co-Founder & Chief Technologist
Afresh Technologies
Deep Learning World
Case Study: Alterra.ai

Combining automated speech recognition with natural language processing allows one to build semi-automatic note taking apps. With a minimal input from the call participants, it would create transcribe speech on the fly and extract and highlight intents and entities, thus creating a semi-structured meeting minutes, ready to be exported to structured databases. For practical applications, it is important for the system to recognize custom lexicon, without expensive acoustic model retraining, solely by automatically building custom language models.

Session description
Speaker
Sergei BurkovAlterra AI
CEO and Founder
Alterra.ai
11:55 am
 
 
 
 
 
 
Deep Learning World
Case Study: Orbital Insight

The number of Earth observation satellites has increased significantly and these satellites are producing huge volumes of images at different spatial resolutions and spectral bands. Manually understanding all the pixels is nearly impossible, as it would take years simply to look at them. This talk will cover the challenges and approaches involved in understanding satellite imagery, which are different than those involved in working with other real-world images. At Orbital Insight, we transform multiple sources of geospatial data - including satellite images - into actionable insights to understand economic, societal, and environmental trends at global, regional, and hyper-local scales.

Session description
Speaker
Gowdhaman SadhasivamOrbital Insight, Inc.
Senior Computer Vision Scientist
Orbital Insight, Inc.
12:15 pm
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12:30 pm
 
 
 
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