• TIMES IN PDT

  • 08:00

    WELCOME & OPENING REMARKS - 8am PDT | 11am EDT | 4pm BST

  • ADVANCEMENTS IN AI

  • 08:10
    Olivier Nguyen Quoc

    AI for Advertising Production Strategy

    Olivier Nguyen Quoc - Tech Lead, Senior Data Scientist - L’Oréal

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    AI for Advertising Production Strategy

    3rd advertiser in the world, L'Oréal Group produces and sells beauty products from mass market to luxury cosmetics. Its marketing teams have developed a long last expertise in innovating in video advertising. Their success follows a folds strategy:

    1. a global brand strategy: Ensuring brand universe coherence around products.

    2. a local adaptation strategy: Adapting advertising content to local market specificities.

    However, due to their scale and the digital transformation of advertising landscape, brands could meet some difficulties identifying and analyzing content that works.

    Olivier Nguyen Quoc will outline their process and solutions to help marketing teams to have a global picture on the content usage, check their creatives and make sure consumers capture the identity essence of the brand.

    Tech lead of the digital data team at L'Oréal, Olivier Nguyen Quoc is working since 4 years on building the next generation of tools for marketing teams. From creatives strategy to digital ad bidding optimization, he provides various digital solutions, leveraging various AI technics as recommendation systems, computer vision and ad creative innovations. Animating internal data science community, he is in charge of organizing worldwide internal data science conferences to spread the use of data into the whole group.

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  • 08:35
    Muhammed Ahmed

    Zero-To-Hero: Solving the NLP Cold Start Problem

    Muhammed Ahmed - Senior Data Scientist - Mailchimp

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    Zero-To-Hero: Solving the NLP Cold Start Problem

    Mailchimp is the world's largest marketing automation platform. Over a billion emails are sent everyday by users through the platform. This mass of marketing text data creates lots of opportunities to leverage natural language processing to improve and create content for users. Like many natural language processing (NLP) practitioners, data scientists at Mailchimp have found annotating text data to be costly, time consuming, and in some cases legally prohibited. So how do they work around it? We'll do a deep dive into how Mailchimp uses state-of-the-art NLP models and unlabeled data to cold start NLP products. We'll cover its data-centric (over model-centric) approach and how it positions its products to facilitate a data flywheel.

    Muhammed Ahmed is a Senior Data Scientist at Mailchimp who specializes in natural language processing and computer vision. At Mailchimp, he has majorly contributed to the implementation and deployment of several AI-assisted products including multimodal classification, preview text generation, stock photo recommendation, campaign engagement scoring, and semi-supervised topic clustering using large transformer models (T5, BART, RoBERTa, UNITER, and similar). Most recently, his focus has been on developing a systematic approach to use zero-shot learning to extract arbitrary information from text.

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  • 09:00
    James Fort

    Power up Your Visual AI with Synthetic Data

    James Fort - Senior Product Manager, Computer Vision - Unity

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    PRESENTATION: Power Up Your Visual AI with Synthetic Data

    Computer Vision is rapidly changing the retail landscape with respect to both the customer experience and in-store day to day logistics like inventory monitoring, brand logo detection, shopper behavior analysis , autonomous checkout. Traditional methods of training models with real world data is becoming a big bottleneck to faster deployment of these vision models. Learn how machine learning and computer vision engineers are using Unity to get faster, cheaper and more unbiased access to high quality synthetic training data and accelerating model deployment.

    Key Takeaways: 1) Computer vision is becoming essential in retail with applications ranging from planogram verification to inventory monitoring to cashier-less checkout. 2) Labeled data is critical to computer vision but the traditional approach of using real-world training data is expensive, time-consuming, and often insufficient for training a production-level system. In contrast, synthetic datasets are less expensive, faster to produce, perfectly labeled, and tailored with the end application in mind. 3) Unity has technology to produce synthetic datasets with structured environments and randomizations that lead to robust model performance. This presentation shows samples from a Unity retail-oriented dataset that you can download.

    ROUNDTABLE: Solving Retail Challenges with Computer Vision

    Join Unity Computer Vision Experts and peers to discuss the rapidly growing field of computer vision and how it is impacting the retail world and some of the challenges associated with deploying computer vision in Retail. This is a freeform session where you can come to the table with your questions and we will have an engaging and interactive conversation around those topics. You can also use this time to talk with the Unity team about using synthetic data for training computer vision models and dig deeper into customer stories and proof points around synthetic data

    James has 14 years of experience building and applying simulation and artificial intelligence technologies. He started his career in the simulation brand at Dassault Systèmes, where he worked on mechanical simulation solutions for automotive and aerospace customers. He spent several years managing the delivery of natural language systems for Alexa at Amazon. He has worked as a product manager in the AI organization at Unity since 2019 focusing on Unity Simulation and Unity’s solutions for computer vision and is excited about the next frontiers in AI.

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  • 09:25

    COFFEE & NETWORKING BREAK: MEET WITH ATTENDEES VIRTUALLY FOR 1:1 CONVERSATIONS

  • 09:35
    Nour Karessli

    AI in Fashion Size & Fit

    Nour Karessli - Senior Applied Scientist - Zalando

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    AI in Fashion Size & Fit

    Online fashion shopping has been increasingly attracting customers at an unprecedented rate, yet choosing the right size and fit remains a major challenge. The absence of the sensory feedback leads to uncertainties in the buying decision and the hurdle of returning items that don't fit well. This causes frustration on the customer side and a large ecological and economical footprint on the business side. Recent research work on determining the right size and fit for customers is still in its infancy and remains very challenging. In this talk, we will navigate through the complex size & fit problem space and focus on how intelligent size and fit recommendation systems and machine learning solutions leverage different data sources to address this challenging problem.

    Key takeaways 1) Fashion size and fit is a complex multifaceted problem that entails items and customers challenges. 2) Tackling this problem has a huge potential on both customers' experience and the environment. 3) How to employ different data sources and machine learning solutions to provide customers with size information and advice in various forms.

    Nour is a senior applied scientist at Zalando focused on computer vision and machine learning-driven solutions to tackle the complex challenge of delivering size and fit advice to millions of customers. In parallel, she's on the organizing committee of Zalando Data Science Community (DSC) and Zalando Women in Tech Employee Resource Group (ZWT ERG). Outside of Zalando, Nour is co-organizing Women in Computer Vision (WiCV) workshop at the CVPR conference aiming to raise the visibility of female researchers and sharing experiences between students and professionals. She completed a Master degree in Computer Science from Saarland University - Max Planck Institute for Informatics. In her free time, she enjoys walking in nature, video games, and cooking.

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  • 10:00
    Mamta Saxena

    Reimagine Customer Experience: Powered by AI & CDP

    Mamta Saxena - Managing Director - Accenture

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    Reimagine Customer Experience : Powered by AI & CDP

    Customer Experience has become mainstream, leading to a sea of sameness. To offer great experiences we most change how we deliver them. Unified data is at the heart of creating engaging customer experiences, and as a result a new breed of tools classified as Customer Data Platforms (CDPs) are gaining prominence. CDPs have a layer of intelligence that uses machine learning for predictive models and recommendations to produce more powerful and actionable insights. More and more companies are now choosing AI to improve and hyper personalize the customer experience through accurate and personalized recommendations, efficient and friendly Service, real-time data-supported decision-making, improve product recommendations and round the clock customer support.

    Key takeaways: 1) How AI helps marketing realize ‘Business of Experience’ and what are the tech building blocks to achieve it 2) How are CDPs today addressing customer experience problem  3) What should Brands do to reimagine the customer experience

    Mamta is working as Managing Director for Accenture Interactive Business in Accenture ATCi (Advanced Technology Centre India). She has 27+ of experience in IT Industry and specializes in Digital transformation programs across Customer Experience, Commerce, Marketing and Content for Global Clients in Retail, Airlines, Travel and Transportation, Quick Service Restaurants, Health and Beauty products, Financial Services, Energy etc. At present, she is the Business Lead for Digital Marketing Studio for Accenture and responsible for Go to Market Strategy, Business Growth, Capability Development, Consulting and Customized Solution Development, Innovation and Delivery. Beside this she is Industry Lead, Inclusion and Diversity lead and Salesforce Customer Experience lead for Interactive Group at ATCi.

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  • Kani Manoharan

    Reimagine Customer Experience: Powered by AI & CDP

    Kani Manoharan - Associate Director - Accenture

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    Reimagine Customer Experience : Powered by AI & CDP

    Customer Experience has become mainstream, leading to a sea of sameness. To offer great experiences we most change how we deliver them. Unified data is at the heart of creating engaging customer experiences, and as a result a new breed of tools classified as Customer Data Platforms (CDPs) are gaining prominence. CDPs have a layer of intelligence that uses machine learning for predictive models and recommendations to produce more powerful and actionable insights. More and more companies are now choosing AI to improve and hyper personalize the customer experience through accurate and personalized recommendations, efficient and friendly Service, real-time data-supported decision-making, improve product recommendations and round the clock customer support.

    Key takeaways: 1) How AI helps marketing realize ‘Business of Experience’ and what are the tech building blocks to achieve it 2) How are CDPs today addressing customer experience problem  3) What should Brands do to reimagine the customer experience

    Working as Associate Director for Accenture Interactive, she is an experienced Digital Marketing professional providing tech-consulting services defining and delivering omni-channel customer experience. She has deep expertise in analytics, campaign management and leads management and works across the marketing technology stack to develop industry specific solutions.

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  • 10:25

    BREAKOUT SESSIONS: ROUNDTABLE DISCUSSIONS WITH SPEAKERS

  • Vladimir Teodosiev

    ROUNDTABLE: AI: Speech Recognition in Financial Services - Making a Significant Contribution to the UK Economy

    Vladimir Teodosiev - Sales Manager - Nuance Communications

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    PRESENTATION: Speech Recognition: Comprehensive Insights in Real-World AI for Business Leaders

    Learn how to streamline your workload, boost efficiency and automate processes with Dragon Speech Recognition. This live short tutorial will show you how you can overcome challenges such as documentation, collaboration across teams in real time and how to get key business insights that will help you perform better as a business and improve your services to customers.

    ROUNDTABLE: AI & Speech Recognition in Financial Services

    Economies are responding to many challenges (e.g. Brexit and the Covid-19 pandemic). Businesses need to find ways to remain both competitive and sustainable. As markets and profitability wax and wane under these influences, the financial services sector faces real and present pressures. How can we advance speech recognition in this time, and consequently build better products & provide better services?

    Vladimir Teodosiev is a Sales Manager at Nuance delivering value to high-profile private and public organisations. Commercially savvy and with a strong interest in technology, he has extensive experience in creating and delivering value to high-profile private and public organisations. Highly customer-focused and results-driven, he supports businesses in developing best practices that facilitate efficient document creation.

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  • Nataliya Le Vine

    ROUNDTABLE: Introduction to Swiss Re’s Risk Resilience Platform

    Nataliya Le Vine - Lead Data Scientist - Swiss Re

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    Developing Early Warning System to Identify Relevant Events in Unstructured Data

    Swiss Re is a leading player in the global reinsurance sector. Its role is to anticipate, understand and price risk in order to help insurers manage their risks and absorb some of their biggest losses. As one way to stay ahead of the curve and provide thought leadership to its clients, Swiss Re is developing an early warning expert community platform based around big data and natural language processing. The platform is intended to work on the front lines, to detect events that have the potential to change our view on risk drivers and to help us make business decisions in shorter timescales.

    Key Takeaways: 1) The traditional methods to identify relevant events become unreliable when information volume rapidly increases; 2) Uncoordinated views pose a challenge in taking proactive and strategic actions to manage risks; 3) Early warning expert community platform leverages new data techniques to identify relevant signals and helps integrating experts in a more joined up process.

    Nataliya Le Vine is a data scientist at Advanced Analytics Center of Excellence at Swiss Re, bringing machine learning and AI to drive the technology transformation in insurance. Over the last decade, she worked in academia, tech and insurance industries both in EMEA and Americas with a core expertise in predictive modeling and machine learning.

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  • Annie Xue

    Host

    Annie Xue - Head Global L&H Product Risk Data Service - Swiss Re

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    Annie leads the global product team of Swiss Re’s Risk Data Service platform along with various client risk management services for L&H insurers.

    A recent example of Annie’s work is with the Swiss Re Risk Resilience Center Center. The ambition is to join the forces of academia, insurers, and data partners to solve Covid-related problems that are most relevant to the insurance industry and beyond.

    In a past life, she has over a decade experience in Reserving, Inforce Management and Experience Studies. Annie is an eternal optimist and brings unparalleled energy and passion into her work.

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  • Brian Alexander

    ROUNDTABLE: Defining a Governance Framework Covering the Appropriate Level of Automation/Human Control

    Brian Alexander - CEO North America - Omina Technologies

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    PRESENTATION: Reduce Compliance Risks with Trustworthy AI

    Increasing complexity in the regulatory environment, rates of regulatory change and need for accountability are driving new compliance risks for financial services companies. Trustworthy AI reduces compliance risks while balancing human control and oversight with accountability. AI can automatically identify relevant regulatory changes and predict the impact to the organization (e.g., business units, policies, controls, products/services, contracts). Regulatory changes can be routed to impacted business units with compliance risk indicators/ratings and impact predictions. The AI solution can predict and automatically take actions necessary to maintain compliance, which can be accepted or overridden by the business unit.

    ROUNDTABLE: Defining a Governance Framework Covering the Appropriate Level of Automation/Human Control

    Points of Discussion: Defining compliance and regulatory risks Identifying and rating regulatory/organizational changes Defining compliance risk treatment strategies Including user feedback to improve the AI-enabled compliance management

    Brian is responsible for the strategy and management of Omina Technologies US. He has a B.S. Mech. Eng. and a J.D. Brian has spent the last 25 years working with technology companies and new technologies in capacities ranging from legal advisor to executive to investor. While working in private law practice, Brian represented technology organizations in intellectual property, regulatory and litigation matters. Brian also has advised technology, financial services, energy/utilities and healthcare companies regarding corporate risk and compliance. Prior to joining Omina, Brian worked for C2C with responsibilities including internal legal advisory, corporate strategy and software development, as well as client management on numerous projects covering diverse risk/compliance matters such as information/cybersecurity and data privacy.

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  • James Fort

    ROUNDTABLE: Solving Retail Challenges with Computer Vision

    James Fort - Senior Product Manager, Computer Vision - Unity

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    PRESENTATION: Power Up Your Visual AI with Synthetic Data

    Computer Vision is rapidly changing the retail landscape with respect to both the customer experience and in-store day to day logistics like inventory monitoring, brand logo detection, shopper behavior analysis , autonomous checkout. Traditional methods of training models with real world data is becoming a big bottleneck to faster deployment of these vision models. Learn how machine learning and computer vision engineers are using Unity to get faster, cheaper and more unbiased access to high quality synthetic training data and accelerating model deployment.

    Key Takeaways: 1) Computer vision is becoming essential in retail with applications ranging from planogram verification to inventory monitoring to cashier-less checkout. 2) Labeled data is critical to computer vision but the traditional approach of using real-world training data is expensive, time-consuming, and often insufficient for training a production-level system. In contrast, synthetic datasets are less expensive, faster to produce, perfectly labeled, and tailored with the end application in mind. 3) Unity has technology to produce synthetic datasets with structured environments and randomizations that lead to robust model performance. This presentation shows samples from a Unity retail-oriented dataset that you can download.

    ROUNDTABLE: Solving Retail Challenges with Computer Vision

    Join Unity Computer Vision Experts and peers to discuss the rapidly growing field of computer vision and how it is impacting the retail world and some of the challenges associated with deploying computer vision in Retail. This is a freeform session where you can come to the table with your questions and we will have an engaging and interactive conversation around those topics. You can also use this time to talk with the Unity team about using synthetic data for training computer vision models and dig deeper into customer stories and proof points around synthetic data

    James has 14 years of experience building and applying simulation and artificial intelligence technologies. He started his career in the simulation brand at Dassault Systèmes, where he worked on mechanical simulation solutions for automotive and aerospace customers. He spent several years managing the delivery of natural language systems for Alexa at Amazon. He has worked as a product manager in the AI organization at Unity since 2019 focusing on Unity Simulation and Unity’s solutions for computer vision and is excited about the next frontiers in AI.

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  • Kevin Kim

    ROUNDTABLE: Brainstorming AI Design Principles – Implementation & Theory

    Kevin Kim - Data Scientist - Nasdaq

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    AI Beyond Pattern Recognition: Decision Making Systems

    While machine learning and artificial intelligence technologies are now advanced enough to outperform humans in variety of tasks, how we make decisions with models varies by practitioner. Reinforcement learning is promising but it is limited to adversarial settings; or, in vernacular, situations where decisions directly impact the environment. Without figuring out how AI systems can make good decisions in environments they cannot influence, we may forever be stuck in a limbo of pattern recognition, prediction, and analytics. What if we can develop a “theory” of AI decision making? Can we view different decision making situations as a set of engineering systems? Can we define key components of an AI decision maker? Answering such questions would enable us to design AI systems in a modular fashion much like how we design many industrial goods like cars. We may even be able to develop industry standards and manuals on how to design AI decision makers. Using actual use cases and other potential real-world applications in both financial and non-financial settings as examples, a systems view on decision making AI systems is proposed. Furthermore, ways to design and build such systems are explored.

    Key Takeaways: 1) AI systems are far more valuable making decisions than making simple predictions and pattern recognitions

    2) We need a “theory of design” for AI decision making systems: For AI to become a trusted part of decision making both in and out of industry, we need to understand and generalize components of AI systems and define what it means to be “robust”.

    3) This starts with looking at actual use cases, identifying similarities, and quantifying key parameters – so we may use standard design techniques to design AI systems.

    Kevin is a data scientist with strong interest in an interdisciplinary approach that combines artificial intelligence, operations research, systems engineering, economics, and quantitative finance. He is a member of Nasdaq’s Machine Intelligence Lab, a team dedicated to using AI to improve capital markets. At Nasdaq, he has worked on projects that cover topics such as alternative data, capital market operations, financial surveillance, and portfolio management. His most recent interest is in developing a procedure for designing robust and fail-proof decision-making AI systems. Kevin holds a Bachelor’s degree in Computer Science from Washington University in St. Louis.

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  • 10:45

    COFFEE BREAK

  • 10:55

    PANEL: Lessons in Building AI for Retail & Marketing: From Development to Deployment

  • Hélio Pais

    PANELIST

    Hélio Pais - Data Science Manager - Trivago

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    I have been a Data Scientist at trivago for five years and I currently lead the Data Scientists at the marketplace department. In this function I support the development of data products that enable our advertisers to optimise their marketing campaigns at trivago. I studied Computer Science in the University of Lisbon and got a PhD in Computational Biology from the University of East Anglia. Before joining trivago I worked at the University of Oxford, where I applied computational methods in cancer research.

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  • Víctor Morón Tejero

    PANELIST

    Víctor Morón Tejero - Lead Data Scientist - Nectar Loyalty

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    Victor Moron is a Data Scientist at Nectar Loyalty. With a PhD in theoretical and computational chemistry, Victor has always been interested in modelling and simulating processes that could not be understood otherwise. At Nectar, he leads a recently created Data Science team developing projects for personalisation, targeting and recommendation systems to improve the way customers are approached. The goal is to increase loyalty and cut the gap between proximity and new ways of shopping. Victor has previous experience in companies involved in understanding customer´s behaviour like Dunham, owner of Tesco club card or Abaka, a start-up nudging its users to take financially healthy decisions.

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  • Wariya Erez

    PANELIST

    Wariya Erez - Senior Data Scientist - The Home Depot

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    Wariya Erez is a Principal Data Scientist with Home Depot Online where she is building large-scale personalized recommendation systems to help customers discover the best products and content at the right time on any digital touchpoint. Prior to Home Depot, Wariya was the Director of Data Services at Moxie - a digital-first advertising and CRM agency - where she led the analytics and data platform to help Fortune 500 clients such as Best Buy and OfficeMax to develop data-driven loyalty programs. Wariya earned her MS at Stanford University and BS at the University of Tokyo. She loves traveling and had visited over 40 countries.

  • Shahmeer Mirza

    PANELIST

    Shahmeer Mirza - Director of R&D - 7-Eleven

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    Shahmeer Mirza is the Director of R&D at 7-Eleven, where he leads the development of technologies that enable the next-generation of convenience. During his time at 7-Eleven, he has launched multiple projects, including 7-Eleven’s Checkout-Free technology. He is passionate about applied AI and inventing solutions for real-world problems, and has over 20 patents. He was previously at PepsiCo, where he developed advanced automation, computer vision, and machine learning solutions for Industry 4.0 applications. He holds an M.S. in Computer Science and a B.S. in Chemical and Biomolecular Engineering, both from Georgia Tech.

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  • Mari Joller

    PANELIST

    Mari Joller - Founder and CEO - Snackable AI

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    Mari Joller is the Founder and CEO of Snackable AI, content discovery engine for the audio-first world. For the past six years, she has been in the trenches driving AI-based startups, observing first hand the psychology of how people interact with machines and its impact on designing AI-powered products.
 Before Snackable, Mari successfully founded and sold her company Scarlet, a cloud platform for delivering personal and branded content to the emerging audio ecosystem. Prior to Scarlet she co-founded Snazz, software for powering live events for businesses and brands. She previously built and scaled products at Virgin Mobile and Nokia.

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  • 11:45

    MAKE CONNECTIONS: MEET WITH ATTENDEES VIRTUALLY FOR 1:1 CONVERSATIONS & GROUP DISCUSSIONS

  • 12:15

    END OF DAY 1

  • TIMES IN PDT

  • 08:00

    WELCOME & OPENING REMARKS - 8am PST | 11am EST | 4pm GMT

  • UTIIZING AI TECHNOLOGIES

  • 08:10
    Vaibhav Verdhan

    How Can Machine Learning Help in Getting the Maximum out of the Marketing Budget or Minimize Marketing Waste

    Vaibhav Verdhan - Principal Data Scientist - Johnson & Johnson

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    How Can Machine Learning Help in Getting the Maximum out of the Marketing Budget or Minimize Marketing Waste

    Organizations spend a fortune on marketing. Every dollar spent on marketing demands ROI. With a number of marketing channels in place, be it offline or online - organizations always strive to improve the targeting. Marketing aims to target customers at the right time, with the right product, at optimized price and best time. All of this marketing effort makes it really difficult for the marketing teams to plan and allocate resources and budget. Marketing budgets (particularly after a strong social media presence) have to optimize the budgets and improve the targeting methodology. Data analysis, machine learning and AI allows us to improve customer targeting, product placements. At the same time, these capabilities allow marketing teams to really get the maximum from their budgets. In other words, the teams can minimize the “marketing waste”. This talk focuses on sharing insights on - how can the organizations improve their customer targeting and optimize their marketing budgets.

    Key Takeaways 1) What is required to have a successful ML/AI setup for retail 2) What are the areas in the retail industry which can be benefitted by ML/AI 3) What are the common pitfalls to avoid

    Vaibhav Verdhan is a seasoned data science professional with rich experience spanning across geographies and domains. He is a published author with books on machine learning and deep learning. He is a hands-on technical expert and has led multiple engagements in Machine Learning and Artificial Intelligence. He is a leading industry expert, is a regular speaker at conferences and meet-ups and mentors students and professionals. Currently he resides in Ireland and is working as a Principal Data Scientist at Johnson and Johnson.

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  • 08:35
    Brian Ames

    Fireside Chat: DeepBrew – AI at Starbucks

    Brian Ames - Senior Technical Program Manager - DeepBrew - Starbucks

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    DeepBrew – AI at Starbucks

    DeepBrew is in the news and on the front lines for Starbucks now. But – several years ago, Artificial Intelligence at Starbucks was considered an impossible task. In this talk, we will cover what happened in the journey to stand up the first artificial intelligence application at Starbucks – and discuss the implications for any firm that wants to enter this new space. Lessons learned, common misconceptions, taking risks, and stepping out of swimlanes – all the in name of supporting a project – will be discussed.

    I lead the team that built DeepBrew - the AI and Machine Learning Platform at Starbucks. My team built this from the ground up and is responsible for all aspects of performance - from revenue impact to uptime. This platform (and the models) is at the heart of significant change at Starbucks.

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  • 09:00
    Emily Bailey

    Machine Learning at Rent the Runway: More Than Just A Deep Closet

    Emily Bailey - Director of Data Products & Machine Learning - Rent The Runway

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    Machine Learning at Rent the Runway: More Than Just A Deep Closet

    Unlike traditional retail companies, Rent the Runway intentionally holds inventory for very long periods of time and rents that inventory out over and over again to a long-term subscriber base. The cyclical nature of the business generates a wealth of data and unique opportunities to leverage that data in high-impact algorithms that improve the customer experience and ultimately the entire operation.

    Emily Bailey is the Director of Data Products & Machine Learning at Rent the Runway. Her team of scientists and engineers leverages expertise from a broad set of domains including natural language processing, computer vision, operations research, personalization & more. Prior to joining Rent the Runway in 2020 she spent 5 years leading various global data teams at Uber and solved forecasting problems at cleantech startup Opower. Emily's educational background is in Economics (Duke University, BS) and Computer Science (Columbia University, MS).

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  • 09:25

    COFFEE BREAK

  • FUTURE OF APPLIED AI

  • 09:35
    Katie Winterbottom

    The Future of Fashion: Unlocking Discovery

    Katie Winterbottom - Senior Manager Data Science and Analytics - Nordstrom

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    The Future of Fashion: Unlocking Discovery

    The experience of discovery in digital retail asks a lot the customer. This is the consequence of a two-decade-old paradigm that forces customers to learn to navigate within a set of rigid product taxonomies and hierarchies as defined by the retailer. Recent breakthroughs in AI have unlocked new capabilities that empower us to challenge the historical discovery paradigm, lead a step-change in the space and create a more powerful, interconnected discovery experience. In this talk, we’ll discuss how we’re leveraging deep learning re-imagine how customers discover products and style.

    Katie is a Senior Manager of Data Science at Nordstrom working on Personalization, recommender systems, search, AI-driven styling and fraud identification.

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  • 10:00
    David Lucas

    How to Create Better Retail Experiences with ML

    David Lucas - Senior Director, Product Management - Tealium

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    How to Create Better Retail Experiences with ML

    Retailers have a huge opportunity to turn prospects into repeat buyers and deepen existing connections based on the recent massive surge in e-commerce. Join this lively chat to see how leveraging the power of AI and ML can help retail brands predict the moments that matter most while providing powerful experiences their customers will rave about.

    Dave Lucas has over 15 years of experience in building programs for data, analytics, and machine learning. He focuses on ensuring that data creates actual end value for humans, a challenge he refers to as “the last mile.” In 2019, Dave joined Tealium as Sr. Director of Product, where he creates innovative products related to AI/ML, Business Intelligence, and data storage.  Prior to Tealium, Dave was Head of Data for Fracture and managed multi-billion-dollar process improvements at large enterprises.  He is a licensed Six Sigma Black Belt with graduate degrees in Business Administration and IT.

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  • 10:25

    BREAKOUT SESSIONS: ROUNDTABLE DISCUSSIONS WITH SPEAKERS

  • Clara Bove-Ziemann

    ROUNDTABLE: Challenges of ML Interpretability and Business Opportunities

    Clara Bove-Ziemann - Design+ ML Researcher - AXA

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    PRESENTATION: How to Explain an ML Prediction to Non-Expert Users?

    Machine Learning has provided new business opportunities in the insurance industry, but its adoption is for now limited by the difficulty to explain the rationale behind the prediction provided. In our latest research, we explore how we can enhance one type of explanations we can extracted from interpretability method called local feature importance explanations for non-expert users. We propose design principles to present these explanations to non-expert users and are applying them to a car insurance smart pricing interface. We present preliminary observations collected during a pilot study using an online A/B test to measure objective understanding, perceived understanding and perceived usefulness of our designed explanations.

    ROUNDTABLE: Challenges of ML Interpretability and Business Opportunities

    The rise of Machine Learning (ML) has provided new business opportunities in the insurance industry. ML can for instance help improve pricing strategies, fraud detection, claim management or the overall customer experience. Yet, its adoption can be for now limited by the difficulty for ML to explain the rationale behind predictions. What can be explained from ML models? What do people need to be explained of? How to present explanations? These are some challenges we want to address in ML Interpretability

    I am currently working as a Researcher at AXA and am a PhD Candidate at Laboratoire Informatique de Paris 6 (LIP6). I conduct research on eXplainable AI (XAI) and User Experience (UX) in Machine Learning. I graduated from a Design Master’s Degree in 2015 and works for several years as a User Experience Designer in various fields before starting research on Human+AI interactions.

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  • Xavier Renard

    Host

    Xavier Renard - Research Data Scientist - AXA

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    Xavier Renard is a researcher in data science and machine learning at AXA. He completed a Ph.D. in computer science at Sorbonne University in Paris. His research focuses on the topic of Responsible and Trustworthy AI, in particular on machine learning interpretability.

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  • Pamela Negosanti

    ROUNDTABLE: The Value of Language in Finance: Demo & Discussion

    Pamela Negosanti - Head of Sales and Sector Strategy, FSI - expert.ai

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    PRESENTATION: Finance Is Not All About Numbers...What About Words?

    Every company, regardless of industry, owns some level of business expertise. Every company, at one point or another, experiences the same challenge: scaling that expertise. As a bank, how do you deliver quality service to your customers when inquiries triple on your digital channel in a matter of months?   How do you scale risk management or your onboarding operations when those teams are already at capacity? The capability to integrate technology that can read and understand unstructured documents is no longer a “nice-to-have” within the financial services industry. It’s a necessity. Learn how Natural Language Understanding can power your business.

    Key Takeaways: * Why language must be leveraged as data

    • How language can be transformed into actionable knowledge

    *How to easily use our Natural Language Understanding technology

    ROUNDTABLE: The Value of Language in Finance: Demo & Discussion

    Learn how your organization can use Natural Language Understanding based solutions to scale your enterprise. The capability to integrate technology that can read and understand unstructured documents is no longer a “nice-to-have” within the financial services industry. It’s a necessity. Join us to listen to real world examples and see, firsthand, how Natural Language can power your business.

    Key discussion topics: Live demonstration which goes beyond data mining. Discover how our AI automatically reads, learns and understands the financial industry knowledge at speed and scale. Learn more about how to transform language into value thanks to concrete KPIs and benefits.Learn how your organization can use Natural Language Understanding based solutions to scale your enterprise. The capability to integrate technology that can read and understand unstructured documents is no longer a “nice-to-have” within the financial services industry. It’s a necessity. Join us to listen to real world examples and see, firsthand, how Natural Language can power your business.

    Key discussion topics: *Live demonstration which goes beyond data mining.

    *Discover how our AI automatically reads, learns and understands the financial industry knowledge at speed and scale.

    *Learn more about how to transform language into value thanks to concrete KPIs and benefits.

    Pamela Negosanti is the head of sales and sector strategy for financial services and insurance at expert.ai. She specializes in helping companies execute business transformation through artificial intelligence. Pamela has spent the majority of her career in the technology sector where she has established expertise in artificial intelligence, cognitive computing, intelligent automation, natural language understanding and semantics, to name a few. She owns a degree in translating and interpreting with a specialization in computational linguistics, and speaks fluent Italian, English, French and German. Fueled by change and curiosity, she is firm believer in the power of knowledge share and communities.

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  • Katie Ferriter

    ROUNDTABLE: Discussing Barriers to Adopting Emerging Technologies

    Katie Ferriter - Head, Technology Research & Innovation - BMO Financial Group

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    PRESENTATION: Emerging Technology Trends in North American Financial Services

    Join this presentation to delve into the latest technology trends impacting financial service institutions. Katie will spend 20 mins discussing the status, potential impact and the journey to understanding and piloting these trends.

    ROUNDTABLE: Discussing Barriers to Adopting Emerging Technologies

    Join Katie to discuss issues and barriers that prevent emerging technology adoption including but not limited to; maturity, change management, risk management, user acceptance, upskilling. What do you see as pressing issues to solve when it comes to implementing technology solutions?

    Katie is the Head, Technology Research & Innovation at BMO Financial Group. This mandate sees her seeking out knowledge of emerging technologies and determining their maturity and applicability for Financial Services. She runs a team that undertakes research in house as well as partnering with institutions & organizations that are shaping the future of financial technology.

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  • Nitesh Soni

    ROUNDTABLE: Cross Industry Extended Q&A - Designing, Strategizing & Deploying AI

    Nitesh Soni - Director - Advanced Analytics & AI for Technology - Scotiabank

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    Transformation of Core Business Function Areas Using Advanced Analytics and AI

    Financial institutions have already started to embrace AI as a part of their core business strategy. The AI investments are not only focused on enhancing the customer and risk controls analytics but also to transform and modernize their core business function areas. The AI technologies (Automation, ML, NLP, Deep Learning etc) combined with the unprecedented amount of data are helping to increase the operational efficiencies and reduction in cost while mitigating Risk and Compliance issues by creating timely awareness and preventative measures. This talk will touch upon the challenges to establish the AI framework i.e. how to start a journey from foundational analytics to advanced analytics; processes and methodologies to identify and prioritize the business problems those can be solved and scale quickly across business function areas. It will include the POCs of various types of analytics (Self-serving analytics, ML & NLP) with real examples from function areas such as Technology, Finance, Audit & HR.

    Key Takeaways: 1. Modernization of Business Core Functions is must and should be a part of overall corporate strategy 2. Variety of AI technologies are been leveraged to achieve this 3. AI solutions are bringing Gain in operational efficiencies and mitigation in operation and reputational risks.

    Nitesh Soni is a Director at Scotiabank where he is leading end-to-end advanced analytics and AI projects for Corporate Functions. His career spans over 15 years across top tier banks, consulting firm and world leading research institutes with a core expertise in Predictive modeling, Machine Learning, AI and Big Data. He is a regular speaker at the various national and international conferences. While as a researcher, he has been a part of a couple of major discovery experiments and has won few prestigious awards. He holds a PhD degree in Experimental Particle Physics.

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  • Manan Sagar

    Guest

    Manan Sagar - CTO - Fujitsu

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    Trust Me, I’m a Robot – The Future of AI and Automation in Insurance

    Technology is changing the way we live and consume service. Insurance has perhaps been the slowest to react to these changes but it is apparent that data is starting to drive personalisation and precision in insurance. “Real-time-risk-management” enabled by the “subscription model” is, in the very near future, going to become main stream in personal insurance. Advancements in data sciences coupled with a fourfold increase in the number of sensors is going to lead to a seismic shift in insurance models from the traditional insurance model of “Protection” to “Prevention”.

    Key Takeaways: 1. Data is enabling a shift in the insurance model from protection to prevention.

    1. Whilst Automation is going to help reduce processing costs, AI will provide deep insights and the ability to predict.

    2. AI will enable insurance costs to be viewed as a “service charge” rather than an “annual tax”

    Manan is a highly experienced insurance professional and a pragmatic business leader. He has previously lead Lockton’s Singapore business where he delivered organisation-wide changes and then went on to manage one of the largest acquisitions in the insurance industry. A Chartered Accountant by profession and now a technologist by trait, Manan is well regarded for his thought leadership. His career has spanned across the Americas, EMEA and Australasia. As Fujitsu’s Insurance CTO, Manan is responsible for defining the innovation strategy for the insurance sector. In his role he is a strategic advisor to the insurance sector on digital transformation and connected technology solutions.

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  • Vaibhav Verdhan

    Guest

    Vaibhav Verdhan - Principal Data Scientist - Johnson & Johnson

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    How Can Machine Learning Help in Getting the Maximum out of the Marketing Budget or Minimize Marketing Waste

    Organizations spend a fortune on marketing. Every dollar spent on marketing demands ROI. With a number of marketing channels in place, be it offline or online - organizations always strive to improve the targeting. Marketing aims to target customers at the right time, with the right product, at optimized price and best time. All of this marketing effort makes it really difficult for the marketing teams to plan and allocate resources and budget. Marketing budgets (particularly after a strong social media presence) have to optimize the budgets and improve the targeting methodology. Data analysis, machine learning and AI allows us to improve customer targeting, product placements. At the same time, these capabilities allow marketing teams to really get the maximum from their budgets. In other words, the teams can minimize the “marketing waste”. This talk focuses on sharing insights on - how can the organizations improve their customer targeting and optimize their marketing budgets.

    Key Takeaways 1) What is required to have a successful ML/AI setup for retail 2) What are the areas in the retail industry which can be benefitted by ML/AI 3) What are the common pitfalls to avoid

    Vaibhav Verdhan is a seasoned data science professional with rich experience spanning across geographies and domains. He is a published author with books on machine learning and deep learning. He is a hands-on technical expert and has led multiple engagements in Machine Learning and Artificial Intelligence. He is a leading industry expert, is a regular speaker at conferences and meet-ups and mentors students and professionals. Currently he resides in Ireland and is working as a Principal Data Scientist at Johnson and Johnson.

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  • 10:45

    COFFEE & NETWORKING BREAK: MEET WITH ATTENDEES VIRTUALLY FOR 1:1 CONVERSATIONS

  • 10:55

    PANEL: Moving Forward: Considering the Impact of COVID-19 on the Development & Application of AI in Retail & Marketing

  • Mehdi Hosseini

    MODERATOR

    Mehdi Hosseini - Head of Data Science - Marks & Spencer

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    Mehdi’s background is in computer science with a PhD in machine learning from University College London and a post-doc at the University of Cambridge. Mehdi also has more than 15 years of experiences in the field of data science and advanced analytics working with various sectors such as software technology, telecommunication, marketing technology and retail. He currently works as the head of data science for Marks and Spencer that is one of the major retailers in the UK. He joined the corporate about one and half years ago, set up the central data science function with more than 20 data scientists and started working very closely with the rest of business to drive value by data science at scale. Mehdi’s work spans several domains such as e-commerce, clothing & home retail, Food retail, supply chain, marketing and loyalty.

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  • Ella Hilal

    PANELIST

    Ella Hilal - Director of Data Science - Shopify

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    As the Director of Data Science for Growth & Revenue at Shopify, Ella Hilal, Ph.D., is responsible for overseeing data operations that power the company’s marketing and international strategy. Dr. Hilal is experienced in managing data intelligence teams, leading data and insights mining, and an expert in machine learning concepts that empower Shopify’s more than one million merchants. Beyond Shopify, Dr. Hilal is an adjunct assistant Professor at the University of Waterloo at the Center of Pattern analysis and Machine Intelligence. She is also the Industrial Advisor for the Statistics Society of Canada on Data Science and Analytics.

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  • Christie Rice

    PANELIST

    Christie Rice - Director Retail Business Development - Intel Internet of Things Group

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    Christie Rice is the director of business development for Intel’s internet of things retail segment. In her role she works to ensure that Intel technology meets retailers’ ever-changing needs. She also works closely with the broad and expanding partner ecosystem to connect customers to technology providers and promote solutions that provide a frictionless and experiential shopping experience.

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  • Sunanda Parthasarathy

    PANELIST

    Sunanda Parthasarathy - Senior Director Data Science - CVS Health

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    Sunanda Parthasarathy, is a Senior Director of Enterprise Analytics at CVS Health where she leads teams of data scientists, analysts & solution strategists that aim to improve health outcomes for all using machine learning & causal inference techniques. Prior to this, she led data science teams at Wayfair and DataXu. As part of this journey she has hired and scaled multiple high performing ML teams and takes an active part in the career pathing discussions of this emerging field. She is an active member in the local start-up, tech and data science community and is the co-organizer of one of the oldest meetup groups in Boston- The Data Scientist and the co-lead of the Women in Big Data Boston Chapter. She enjoys bringing together leading minds in the field of data science and AI, to engage in thought leadership in this nascent field. Her contributions to the field have been recognized by multiple awards and mentions including The Emerging Exectuive Award from MassTLC 2018 and REWORK’s 30 influential women advancing AI in Boston. Before entering the data science world, she was a Princeton Postdoctoral fellow, working on the forefront of the next generation quantum materials that will replace silicon in a computer chip. Prior to that she received her PhD in physics from Purdue University working on solving open problems in the field of transport physics. Her research accomplishments have been recognized in the form of many awards including the H.Y.Fan Award for excellence in physics research.

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  • 11:45

    MAKE CONNECTIONS: MEET WITH ATTENDEES VIRTUALLY FOR 1:1 CONVERSATIONS & GROUP DISCUSSIONS

  • 12:15

    END OF SUMMIT

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