• 09:00

    WELCOME & OPENING REMARKS (ALL TIMES EDT)

  • AI IN RETAIL VIRTUAL SUMMIT

  • 09:10

    PLENARY SESSION

  • 09:10
    Javier Perez

    The Growth of AI Open-Source Software in Unexpected Platforms

    Javier Perez - Open Source Program Strategist - IBM

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    The Growth of AI Open-Source Software in Unexpected Platforms

    Today Open Source Software (OSS) is more prevalent than in any other era and continues to grow with the latest technologies from AI and Data Science to Blockchain and Autonomous Vehicles. In this session, we are going to review AI open-source in unexpected platforms. Specifically, we are going to cover OSS in the modern mainframe, the platform used by most financial services organizations, including now fintech startups every large financial institution.

    Tensorflow, Python, Spark, and many other widely used OSS have become the building blocks of all AI and ML applications. Open-source is addressing the major trends in the Financial industry: Modernization with AI and big data, regulatory compliance, and DevOps.
    Open Source Software for mainframes is neither widely known nor something new. This session is going to present information on how open source is done for mainframes and how to port existing software to a modern platform available in all Linux distributions.

    Key Takeaways: • Learn about available open source software in AI • Learn about the platform of choice for AI in Financial institutions • Learn how to continue the growth of the open-source ecosystem for AI

    Javier Perez leads the Open Source Program for the IBM Z and LinuxONE ecosystem at IBM. Javier has been in the Open Source, Cloud, SaaS, and Mobile industries for 20+ years. He has been working directly with Open Source Software (OSS) for over 10 years, more recently leading product strategy of the Software Composition Analysis product line at Veracode. Prior to Veracode, Javier was at Axway leading a successful open source project, Appcelerator, and at Red Hat where he was Director of Product Management driving the OpenShift-based Mobile Application Platform offering for developers and enterprises including containerized applications. Javier has had the opportunity to speak at webinars and conferences all over the world covering open source, security, cloud, and application development topics. Javier has held leadership positions in Product Management and Sales Engineering for different startups, leading successful product exits and product integrations post-acquisition. Javier holds an honors degree in Computer Systems and an MBA.

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  • AI TOOLS FOR RETAIL & MARKETING

  • 09:30
    Rico Meinl

    How Sainsbury's is Bridging the Gap From Model Creation to Production

    Rico Meinl - Machine Learning Engineer - Sainsbury's

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    How Sainsbury's is Bridging the Gap from Model Creation to Production

    Metaflow is a human-friendly Python library that helps scientists and engineers build and manage real-life data science projects. Metaflow was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning. Join Rico, a Machine Learning Engineer from Sainsburys, as he talks through how Metaflow provides a unified API to the infrastructure stack that is required to execute data science projects, from prototype to production.

    Key Takeaways: • Metaflow allows data science teams to take a workflow from creation to the cloud to production within hours instead of days or even weeks. • It uses an internal DAG (directed acyclic graph) structure to orchestrate workflows; these can be turned into AWS Step Functions with just one command. • It solves a big problem for companies who are looking to bridge the gap between data science and engineering.

    Enterprising, extroverted Data Scientist with a passion for Artificial Intelligence (AI)/Machine Learning (ML) and a unique ability to easily forge relationships with colleagues, customers, and other business partners. I help data science teams deliver value and ship models with measurable results for the business. At Sainsbury's Tech, I am responsible for building Data Science and Machine Learning models and engineering them to run in production using AWS.

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  • 09:50
    Alex Fly

    Applied Intelligence: A Scalable Path to Production for Your AI Initiatives

    Alex Fly - CEO & Co-Founder - Quickpath

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    Applied Intelligence: A Scalable Path to Production for Your AI Initiatives

    Across the industry, nearly 90% of models fail to be leveraged for production decisioning and the “successful” 10% take too long and cost too much. With the statistics not in your favor, understanding the root causes of failed AI projects is critical to ensure that your initiatives don’t share a similar fate. Learn best practices and how to establish a repeatable, outcome-driven approach that aligns data science and IT teams and enables the AI lifecycle from idea to production to realized business value. With companies finally beginning to find success in building and validating quality models, the session will focus on the “last mile” problem of using those models in production to automate and optimize business decisions and customer interactions. Discover how capabilities like feature stores, model deployment, data and ML pipelines, feedback loops, human-in-the-loop and automated model retraining, drift and anomaly detection, and model and decision performance monitoring are all essential to fully leverage Applied Intelligence throughout your organization.

    Key Takeaways: • Ensure success by selecting the right use cases and establishing early wins for your organization • Define a repeatable path to production decisioning for AI models • Learn how to increase analytic throughput and other KPIs used to measure productivity and ROI of your AI initiatives

    Alex Fly is the Co-founder and CEO of Quickpath, an Applied Intelligence company focused on enabling businesses to make automated, intelligent decisions throughout their organizations using machine learning and artificial intelligence. He is an experienced speaker, thought-leader, and industry expert in leveraging applied intelligence to automate and optimize business processes and customer interactions. Alex is a trusted partner to Quickpath’s customers, helping them deliver real-time data and analytic products that provide massive business value across their organizations.

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  • 10:10
    Juwel Rana

    Developing an End-to-End Personalization Engine in Varner for Varner

    Juwel Rana - Head Of Analytics - Varner

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    Developing an End-to-End Personalization Engine in Varner for Varner

    In this talk we will share our journey of building an end-to-end personalization engine to serve our large volume customer’s base in the most effective manner. We build all the necessary building block in a way that simplifies the develop process, at the time offers speed, and scale. As we all believe, the demand of AI in retail is high, however the materialization of AI in retail are not happening that much. With this example, we will share a successful story of adapting AI. Finally, the talk will also contain some of strategic pointers that can be generalized in developing AI products for retailers.

    Juwel Rana, PhD is a global analytics leader located at Oslo, Norway and leads the analytical development at Varner Group, holding the position as Head of Analytics. He is AI product focused, believer of value driven development. Juwel possess full stack development principles from architecting AI product to algorithmic design as well as strategic and business objective setting. When it comes to AI technology, Juwel thinks business first, and when it comes to business, Juwel puts customer first.

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  • CUSTOMER EXPERIENCE

  • 10:30
    Dipjyoti Das

    Discussion Regarding Gaussian Mixture Model to Acquire New Customers in Non Native Territories

    Dipjyoti Das - Data Scientist - Duke Energy

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    Discussion Regarding Gaussian Mixture Model to Acquire New Customers in Non Native Territories

    Duke Energy wants to acquire new non residential commercial customers outside of its native footprint who would be interested in buying energy efficiency programs like HVAC, Lighting, Refrigeration and other appliances. The current leads provided by Business energy advisors through their business relationship are very low in number. The challenge is to model the behavior of this population and find out others who may be interested in energy efficiency programs. Gaussian mixture model is a probabilistic clustering approach and may help us in finding patterns within data. This can help the Business energy advisors by providing them with more effective lead generation.

    Key Takeaways: • Business case study understanding • Lead Generation with unsupervised M in any industry • Technical knowledge of Gaussian Mixture model

    Dipjyoti Das is an experienced Data Scientist and end-to-end solution provider having worked in various industries – Energy & Utilities, Financial Services & Insurance, Logistics and Automotive manufacturing. Das has managed clients in cross functional business units – Marketing, Sales, Distribution, Product and Operations. His AI/ML models have contributed to millions of dollars in incremental revenue and cost savings across different industries. His current work in the natural gas business at Duke Energy acted as a foundation and led to development of multiple analytics projects in other areas. He has a MS in Materials Science and Engineering, University of Florida.

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  • COFFEE

  • 11:30
    Shahmeer Mirza

    7-Eleven’s Digital Transformation: Using Applied AI to Disrupt Convenience

    Shahmeer Mirza - Machine Learning Engineer & Team Lead - 7-Eleven

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    7-Eleven’s Digital Transformation: Using Applied AI to Disrupt Convenience

    7-Eleven was founded in 1927 as the world’s first convenience store, and for decades has operated as the marketplace leader in the convenience retail business. Through the years, 7-Eleven has continued its obsession with “giving the customers what they want, when and where they want it,” leading the way with a number of innovations in the industry. The first self-serve soda fountains, Slurpees, and to-go coffee were key milestones that kept the business ahead of competition. The last two decades have seen a rapidly changing technology landscape, and thus in 2016, 7-Eleven began its Digital Transformation to ensure its future as an innovation leader in the retail space. Today we’ll talk about the latest breakthrough in that transformation journey…

    Shahmeer Mirza is a Tech Lead and Machine Learning Engineer at 7Next, the R&D Division of 7-Eleven. Over the last several months he has led the team developing 7-Eleven’s Checkout-Free technology. In November of 2019, the team opened their first store at 7-Eleven’s headquarters, a culmination of their work in computer vision, machine learning, algorithms, distributed computing, and hardware engineering. He was previously at PepsiCo, where he developed next generation automation, computer vision, and machine learning solutions for Industry 4.0 applications. Shahmeer is also passionate about democratizing AI capabilities; while at PepsiCo, he created the first in a series of Data Analytics courses to upskill associates across the Snacks R&D organization. He holds a B.S. in Chemical and Biomolecular Engineering from Georgia Tech, and is currently pursuing his M.S. in Computer Science at Georgia Tech.

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  • APPLICATIONS IN RETAIL

  • 11:50
    Cécile Tezenas du Montcel

    World’s Largest Airline GPU Accelerated Workstation to Better Model Cargo Shipments, Improve Weight Distribution & Save Fuel

    Cécile Tezenas du Montcel - Virtual Reality & Data Science BD Manager - HP

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    World’s Largest Airline GPU Accelerated Workstation to Better Model Cargo Shipments, Improve Weight Distribution & Save Fuel

    American airline uses Data Science to better model cargo shipments, improve weight distribution and save fuel, using Workstation edge computing powered by NVIDIA Quadro.

    Key Takeaways: • AI usage in logistic • GPU accelerated computer do the job • Gain productivity and save cost in your infrastructure

    Cecile has a PhD In Material Science, she joined HP 18 years ago in the Concept R&D labs developing "concept PCs" and future technology. She then spent 8 years as product marketing manager on several portfolios. Passionate for innovation and technology, she is currently leading Virtual Reality Business development for EMEA region.

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  • 12:10
    Michael Sollami

    The Future of NLP in e-Commerce: Generative Multimodal Language Models

    Michael Sollami - Lead Data Scientist - Salesforce

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    The Future of NLP in e-Commerce: Generative Multimodal Language Models

    Recent advances in Deep Learning have resulted in a new generation of natural language technologies. In the e-commerce setting, new transformer-based models have enabled enhancements across a vast array of product features and services. For instance, in online shopping product descriptions, call-to-actions, and blogs are all primary ways to inform and attract customers, playing crucial roles in conversion rates and SEO. At Einstein, we have developed new multimodal conditional natural language models trained to automatically craft unique, interesting, contextualized copy. We will present these new methods for generating new and enhancing existing text e-commerce, e.g. product catalogs, merchant sites, and other marketing channels.

    Michael received a doctorate in mathematics from the University of Wyoming. Since 2012 he has led research and development teams at a number of successful Boston-based startups. Currently a lead data scientist on Salesforce's Einstein team, he enoys designing and building deep learning systems with applications to e-commerce and computer vision.

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  • 12:30
    Ioanna Karkatsouli

    Data Science Applications in Retail Pricing and Promotions

    Ioanna Karkatsouli - Senior Pricing Statistician - Staples

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    Data Science Applications in Retail Pricing and Promotions

    Many retailers have been slow in incorporating analytics as well as data science tools when making decisions on optimal prices and promotions for their products and instead tend to rely on traditionally used methods. In this presentation I will first share my experiences from working on a newly formed Pricing Data Science team at Staples over the last two years, the challenges faced and lessons learned. Then, I will talk about various applications in Pricing that can benefit from the use of machine learning techniques and deep dive into one of those – predicting the effectiveness of retail promotions, particularly price drops.

    Key Takeaways: • Which applications related to retail pricing and promotions can benefit from the use of analytics and machine learning tools? • How to organize a data science team dedicated to pricing? What are the skills required? • Project deep dive: predicting the effectiveness of retail promotions (product price drops)

    Ioanna is a Data Scientist based in Boston, Massachusetts. Over the last two years she worked as a Senior Pricing Statistician at Staples where she focused on optimizing pricing strategies and promotions for retail. The retail sector is undergoing a big transformation and Ioanna is passionate about applying data science to help overcome the challenges that this sector is facing as well as automating processes and creating tools that help others generate insights from data. She is also passionate about constantly learning new skills and sharing her knowledge. Before joining Staples Ioanna worked as an economic consultant at The Brattle Group and also as a Research Assistant at MIT where she did energy and climate modeling. She holds a Master in Technology and Policy from MIT and a Bachelor in Electrical and Computer Engineering from the National Technical University of Athens, Greece.

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  • 12:50

    VIDEO ROUNDTABLE NETWORKING MIXER

  • 09:00

    WELCOME & OPENING REMARKS (ALL TIMES EDT)

  • SEARCH, RECOMMENDATION & REVIEW

  • 09:10
    Simon Hughes

    Deep Learning for Recommendations

    Simon Hughes - Senior Data Scientist - The Home Depot

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    Deep Learning for Recommendations

    Complimentary recommendations are an important form of recommendations in retail and e-commerce. When a customer purchases a product, such as a patio table, they usually also need to purchase complimentary products, such as patio chairs, or an umbrella. We developed a hybrid content-based complimentary recommendation system that combines textual and visual features to produce recommendations that are complimentary in both style and function. I describe how this model is powered by a combination of Siamese Neural Network and a simple visual similar model. Finally, I will describe how we scale this model to produce recommendations for 4 million different products.

    Key Takeaways: • Effective use of DL models for recommendations • Efficacy of combining multiple models to produce more robust recommendations • How to scale a DL recommendation model on a large item set.

    Simon is a senior data scientist at The Home Depot where he works on personalization and ranking for the core recommendations team. In his time at Home Depot, he has built a number of different recommender systems including a personalized deals recommender, and a system for recommending how to guides for customers working on home improvement projects. Simon has a PhD in Machine Learning and Natural Language Processing from DePaul University, over 6 years’ experience working in data science, and 15 years’ experience in software development.

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  • 09:30
    Peter Grabowski

    Drawing Insights from Customer Feedback Using NLP

    Peter Grabowski - Enterprise Machine Learning - Google

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    Drawing Insights from Customer Feedback Using NLP

    Companies are frequently faced with large amounts of unstructured text data, like product reviews, customer feedback, or comments on social media. Understanding these data can help target marketing efforts by revealing what your customers care about most, 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.

    Key Takeaways: • Pipeline for effective, open source text clustering • Investigation using open source data set • Discussion of how it’s useful for your marketing teams"

    Peter Grabowski is a longtime Googler and former Nest employee. He's currently the manager of the Enterprise Machine Learning team in Austin. Previously, he managed a data engineering team at Nest and helped build the Assistant for Kids team at Google. Outside of Google, he teaches machine learning as part of UC Berkeley's Master's in Data Science, and is a managing partner of PXN Residential, LLC.

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  • 09:50
    Bettina Hermant

    Developing a Recommender System for a Public Service Broadcaster

    Bettina Hermant - Senior Data Scientist - BBC

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    Developing a Recommender System for a Public Service Organisation

    The BBC is on a journey to make our audience experience more relevant and personalised, and a key part of our success lies in our ability to provide recommendations. At the BBC, we believe that recommendations should reflect the breadth and diversity of our content, while meeting our editorial guidelines. In this talk, I will describe how we develop a recommender system for BBC Sounds, including the Machine Learning model used, the architecture behind the engine, and the unique challenges we face to uphold the editorial policy and the values of the organisation.

    Key Takeaways: • AI in a public service organisation brings unique challenges • ML is a very small part in building a recommender system for production • Interdisciplinary collaboration is key

    Bettina is a Senior Data Scientist at the BBC. Her team aims to use machine learning algorithms to provide a better experience to their audiences, mainly through personalisation. She works very closely with the Data Engineering, Editorial and Product teams. Bettina has mostly been involved in building a recommender system for production use for one of the main BBC products. The Machine Learning algorithm used is hybrid and the code is developed in Python. Google Cloud Platform tools are used to manage the resources and to store the data, Airflow for the automation, and Redis for serving. Bettina has been involved in all of the steps: from the algorithm development, to the engine productionisation, but also in making sure that the recommendations are compliant with the editorial policies and company values.

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  • 10:10
    Moshe Mishali

    An Integrated Training and Inference Solution for AI at the Edge

    Moshe Mishali - CTO - Deep AI Technologies

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    An Integrated Training and Inference Solution for AI at the Edge

    Training deep learning models require large compute resources and performed in the cloud or data centers. Yet, the data is generated at the edge and transporting the data to the cloud leads to unsustainable network bandwidth, high cost, slow responsiveness and compromises data privacy. Deep-AI Technologies is launching its accelerated deep learning solution for the edge, and will present its breakthrough technology for training at fixed-point Int8 coupled with high sparsity, to enable deep learning at a fraction of the cost /power of GPU systems for fast, secure, and scalable AI at the edge.

    Moshe has over 15 years of technical leadership in various research and development roles. Prior to founding Deep-AI, Moshe was algorithm architect at EZchip semiconductor (NASDAQ:EZCH). His professional experience also includes multiple consulting roles in hardware and software companies and also 6 years of service in Elite Technology IDF unit. He holds Ph.D. in Electrical Engineering from the Technion.

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  • 10:30

    COFFEE

  • PREDICTIONS & FORECASTS

  • 11:15
    Prabhakar Narasimhadevara

    AI/ML Driven Improvement of Demand Forecasts

    Prabhakar Narasimhadevara - Director of Data Science - Stanley Black and Decker

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    AI/ML Driven Improvement of Demand Forecasts

    Stanley Black and Decker, has 60,000+ products in its portfolio and in 90+ countries. A large portfolio of products drives a robust design needs to handle a large heterogenous set of SKUs with different lifecycles, demand profiles (continuous vs discrete), zero demand events, short and long history of SKUs. With over 60K+ SKUs and 100K+ demand forecasting units planning is a compute and people intensive task and in the absence of robust methods to understand and predict demand the overhead to maintain service level can be an unseen headwind hidden as the cost of doing business. This talk will cover technical challenges related to scaling compute architecture, engineering complexity, forecasting approaches and limitations, challenges of integration of AutoML engines, challenges of architecting for future algorithm inclusion to handle the complexities of product demand heterogeneity while building a maintainable system future proofed for business continuity. Additionally, the speaker will cover the full scope of what a transformational challenge this situation provides and the operating model to implement this challenge and similar challenges in more AI/ML driven approaches. The speaker will also cover how then to use such a demand forecasting system to help drive strategic actions in Product Portfolio Management, Promotions, Pricing, Sales and Marketing to showcase an ecosystem of business decision making.

    Prabhakar Narasimhadevara, Director of data science, Advanced Analytics and Data Engineering, Stanley Black & Decker (Atlanta, GA)

    Prabhakar is responsible for delivery of data engineering and analytics solutions in Stanley Black & Decker organization. He has significant experience delivering analytical solutions in the healthcare, automotive, industrial distribution, industrial manufacturing, digital marketing and services industries. Many of his projects involved Machine learning, artificial intelligence, advanced analytics, architecture, strategy development and implementation, process improvement, and data lake creation.

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  • 11:35
    Sofie De Cnudde

    Personalising Product Recommendations at ASOS

    Sofie De Cnudde - Machine Learning Scientist - ASOS

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    Personalising Product Recommendations at ASOS

    ASOS' recommendations are loved by its customers and are one of ASOS' core AI projects. One application of our recommender system is the You Might Also Like feature on product pages which shows similar products to customers. This talk will focus on the journey we embarked on where we aimed at personalising these product recommendations for our customers. We will talk about how we built hypotheses, how we iterated over multiple (simple and complex) models, how we performed offline and onvline tests, how we collaborated with engineers and most importantly about the successes and failures along this journey.

    Key Takeaways: • Build simple models first • Iterate quickly

    Sofie De Cnudde is a Machine Learning Scientist at ASOS.com. After obtaining Master’s degrees in Computer Science and Business Economics at Ghent University, she started a PhD at the University of Antwerp. Her PhD was focused on how to leverage fine-grained, human behavioural data to make predictions about people’s future actions or interests. Four publications resulted from the research where theoretical results were applied to benefit areas such as micro lending, cultural government programs and retail. She started working at ASOS in 2018 and has worked across different business areas such as Supply Chain Optimisation and Marketing, and is currently working in Recommendations.

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  • 11:55

    PANEL: How to Avoid Getting Left Behind in a Data-Driven Future

  •  Tyan Hynes

    Moderator:

    Tyan Hynes - Senior Product Manager, Data & Machine Learning - Zulily

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    Tyan is a Technology Product Manager at Zulily where she develops ML products for search and personalization. At Zulily Tyan implemented a testing framework that increased model testing from once per quarter to several a month, created a pipeline to extract product information from text, and developed infrastructure to A/B test changes to search relevance. Tyan has Master's degrees in Organic Chemistry and Library & Information Science from the University of Washington. She has over 13 years of experience in the data & ML space, seven years of experience in Technical Product Management and 5 years of experience in ML Product Management.

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  • Jeffrey Ng

    Panelist:

    Jeffrey Ng - Chief Scientific Officer/ Managing Director of AI Sector - Founders Factory

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    Jeffrey has co-founded two AI startups raising in aggregate more than £70M, one of which is a London unicorn. He spent seven years reverse-engineering the human visual system at Imperial College and ran technology, AI and product teams as CTO and subsequently advised many companies from medical imaging startups to social fashion platforms to big digital transformation companies. He has published more than 50 scientific publications and is an author on three granted patents. Jeffrey also holds a Ph.D. in Computer Vision and Machine Learning.

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  • Josh Baylin

    Panelist:

    Josh Baylin - Senior Director of Strategy - Brain Corp

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    Josh Baylin joined Brain Corp in 2019 and focuses on strategy and business development, which include understanding a wide variety of on robot and off robot data to improve decision making for the company. He is passionate about using data and automation to drive value and has worked on several sides of the table, covering technology as a reporter, industry analyst, and being an investor and operator. As a reporter with Bloomberg News, he covered technology and telecommunications policy at the start of the mobile communications revolution. Josh’s data-driven style and investor focus led to invites to the White House Correspondents Dinner and Congressional Internet Caucus and roles as an industry analyst and investor for Legg Mason Wood Walker and SAC Capital Advisors LP. After leaving Wall Street, he founded Velocity Growth Inc., a data and automation company focused on robotic process automation, data analytics, and visualization. He is a graduate of the University of North Carolina and the Harvard Business Analytics Program.

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  • Akash Jairath

    Panelist:

    Akash Jairath - Chief Data Officer for Media - Dentsu Aegis Network

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    Akash Jairath is the Chief Data Officer for Media at Dentsu Aegis Network, focused on building scalable solutions to drive value from data, both internal and through partnerships.

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  • 12:30

    VIDEO ROUNDTABLE NETWORKING MIXER

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