• 08:00

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

  • APPLYING AI IN ENTERPRISE

  • 08:10
    Suju Rajan

    Empowering the Global Workforce: Using AI to Connect Talent with Opportunity

    Suju Rajan - Sr. Director, Enterprise AI - LinkedIn

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    Empowering the Global Workforce: Using AI to Connect Talent with Opportunity

    Creating economic opportunity on a global scale entails a complex interplay of tasks for different groups of users: the millions of companies searching for candidates and billions of workers looking for ways to further their careers create a complex marketplace between job seekers and hirers that needs to be optimized. The search, recommendation, and standardization problems in hiring also come with a unique, domain-specific set of considerations to maintain high levels of trust in the robustness of this ecosystem. In this talk, Suju Rajan, Sr. Director of Enterprise AI at LinkedIn, will discuss recent changes in how LinkedIn utilizes supervised deep learning, natural language processing, and other techniques to connect job seekers and hirers, helping both groups find the perfect match.

    Matching seekers to job opportunities involves deep understanding of members career journeys and available job postings. Pre-trained NLP models that are then fine-tuned on specific optimization tasks is a good recipe Investing in ML monitoring and automation technologies continue to have significant ROI

    Suju Rajan is a Senior Director at LinkedIn, where she leads the Standardization and Enterprise AI team at LinkedIn, reporting directly to Chief Data Officer Igor Perisic. Her team joins together all of the work done in Enterprise and Standardization AI at LinkedIn. Her team's work powers the hiring, marketing, and insights products that the company's customers rely on every single day. The speed and accuracy with which LinkedIn provides enterprise solutions (for learning, hiring, sales, etc.) relies heavily on well-constructed taxonomies of all of its Economic Graph data. By combining the Standardization team into Enterprise AI, her team tightens the feedback loop between the company's members/customers and their solutions, to improve our data and recommendations.

    Suju is also passionate about research. She and her team are exploring many innovative projects at LinkedIn in areas like applied deep learning, interesting problems in marketplace optimization, and large scale AI productivity.

    Finally, she is personally passionate about the ability of AI to make society more equitable and improve people's lives. As a woman in the male-dominated tech industry (and the even more male-dominated field of AI), she is an advocate for women in the field of AI and is interested in showing ways that AI can be used to mitigate social biases, rather than amplifying them.

    Previously, she worked as the head of AI at Criteo and began her career as a researcher at Yahoo! Labs.

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  • 08:35
    Mark Jancola

    Conversational AI: Time Saver and Money Maker

    Mark Jancola - CTO & VP of Engineering - Conversica

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    Conversational AI: Time Saver and Money Maker

    Conversational AI is transforming the way customer-facing teams including Marketing, Sales, and Customer Success to accelerate revenue by automating conversational interactions that attract, acquire, and grow customers at scale. Built on Conversational AI technologies, Conversica Intelligent Virtual Assistants serve as a virtual team member to engage contacts like prospects and customers in a human-like, two-way dialogue with a high-level relevance and accuracy to drive towards the next best action.

    Join us as Mark Jancola, CTO & Vice President of Engineering at Conversica, explains:

    How to build Conversational AI solutions that are relevant, accurate, and human-like,

    How Conversica leverages NLU, NLG, process automation, and deep learning capability to engage contacts over multiple communication channels via our Intelligent Virtual Assistants,

    And how Conversational AI saves time and resources while accelerating revenue across the customer lifecycle.

    Conversational AI is here and making a difference today. Join us to see how to build Conversational AI solutions that drive real results.

    Dedicated to the success of customers and partners, Mark Jancola is applying almost three decades of engineering, product, and business leadership in extending Conversica’s offerings, architecture, innovation, and operations. Most recently, Mark was head of global R&D at Apptio Inc, the leader in technology business management solutions. Over a ten year period, Mark built the team, the products, and the operations necessary for Apptio to go from startup to one of the fastest growing, successful enterprise software companies in history. Previously, Mark was VP of Engineering at HP Software, overseeing worldwide R&D activities in the Business Service Automation division (700+ engineers). Prior to HP, Mark held engineering leadership roles at Opsware, Midstream Technologies, Cisco Systems, and Active Voice Corp. Mark holds an MBA and BSEE from the University of Washington. He is a regular guest lecturer at the UW, a member of the Industry Advisory Board for the UW College of Electrical and Computer Engineering (ECE), and a speaker at industry / technical conferences.

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  • 09:00
    Peter Grabowski

    How Google Uses AI & Machine Learning in Enterprise

    Peter Grabowski - Austin Site Lead, Enterprise Machine Learning - Google

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    How Google Uses AI & Machine Learning in Enterprise

    This session will outline how Google’s Corporate Engineering team is using AI and machine learning to spur innovation within Google. Additionally, Peter will identify the work that his team does (the structure, example use cases, and mappings of business problems to ML solutions), 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).

    Key Takeaways: Pipeline for effective, open-source comment clustering Investigation using open source data set Discussion of how it might flit into your workflow

    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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  • 09:25

    COFFEE & NETWORKING BREAK

  • SUPPORTING ENTERPRISE AI

  • 09:35
    Hebo Yang

    Building Computational Graphs Across Multiple Open Source Frameworks

    Hebo Yang - Software Engineer - DoorDash

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    Building Computational Graphs Across Multiple Open Source Frameworks

    Modern machine learning (ML) teams have found great success with combining multiple ML models as ensembles for better predictive performance. However, algorithms implemented in different ML frameworks can’t be serialized into a single combined model for deployment. In this talk, we share how DoorDash uses a computational graph approach via a domain specific language (DSL) to allow teams to use multiple frameworks at once in a single combined model. We demonstrate how we generate and interpret instances of this DSL to allow our teams to quickly define and deploy customized models and get high runtime performance during inference.

    3 key takeaways:

    Interoperability: Support for multiple frameworks lets teams develop models in areas of their expertise, rather than having to conform to a standard.

    Accuracy: Unlocking a broad range of ensemble methods should enable greater predictive performance.

    Performance: The DSL approach makes it possible to unify model implementations in C++ which will give better performance.

    Hebo is a Machine Learning Platform Engineer at DoorDash, primarily working on model training and management tooling and pipelines. Previously he has worked on data ETL, high-available distributed systems, and machine learning-based cyber security detection cloud service. Hebo graduated from Columbia University with an MS in Computer Science and Applied Mathematics.

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  • Arbaz Khan

    Building Computational Graphs Across Multiple Open Source Frameworks

    Arbaz Khan - Machine Learning Platform Engineer - DoorDash

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    Building Computational Graphs Across Multiple Open Source Frameworks

    Modern machine learning (ML) teams have found great success with combining multiple ML models as ensembles for better predictive performance. However, algorithms implemented in different ML frameworks can’t be serialized into a single combined model for deployment. In this talk, we share how DoorDash uses a computational graph approach via a domain specific language (DSL) to allow teams to use multiple frameworks at once in a single combined model. We demonstrate how we generate and interpret instances of this DSL to allow our teams to quickly define and deploy customized models and get high runtime performance during inference.

    3 key takeaways:

    Interoperability: Support for multiple frameworks lets teams develop models in areas of their expertise, rather than having to conform to a standard.

    Accuracy: Unlocking a broad range of ensemble methods should enable greater predictive performance.

    Performance: The DSL approach makes it possible to unify model implementations in C++ which will give better performance.

    Arbaz is a Machine Learning Platform Engineer at DoorDash where he focuses on challenges around usability and scalability of online model serving. He has been directly involved in growing the scale of online model serving at DoorDash by more than 100x and helping multiple teams to productionize their ML business use cases. Previously, he had helped build machine learning platforms from the ground up for successful startups. Arbaz graduated from Indian Institute of Technology Kanpur (IIT-K) where he was awarded General Proficiency Medal for overall best academic performance in discipline of BTech-MTech dual degree in Computer Science.

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  • 10:00
    Kristopher Overholt

    Algorithmia in Action: Take Your ML Models from Training to Production

    Kristopher Overholt - Sales & Solutions Engineer - Algorithmia

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    Algorithmia in Action: Take Your ML Models from Training to Production

    Algorithmia is machine learning operations (MLOps) software that manages all stages of the ML lifecycle within existing operational processes. In this session, we’ll demonstrate Algorithmia in action and show you how it solves common challenges to deploy models, connect to data sources, automatically scale model inference, and manage the ML lifecycle in a centralized model catalog. Using an example use case, we'll demonstrate how to deploy a GPU-based deep learning model in Algorithmia, build a model serving pipeline, and monitor model performance metrics. We’ll also discuss how Algorithmia handles the underlying MLOps infrastructure and operations related to security, scalability, and governance.

    Key Takeaways 1. Easily build and publish CPU- and GPU-based deep learning models 2. Chain different languages and algorithm types together in a single model serving pipeline 3. Instrument model performance metrics to monitor data drift and concept drift

    Kristopher Overholt is a Sales and Solution Engineer at Algorithmia who works with machine learning operations, enterprise architecture, and data science workflows. He studied civil engineering at The University of Texas at Austin, where he completed his PhD in 2013. He has been working with enterprise customers for the last 6 years to help them move their data science and machine learning code into production.

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  • 10:25
    Roundtable Discussions & Demos with Speakers

    BREAKOUT SESSIONS

    Roundtable Discussions & Demos with Speakers - - AI EXPERTS

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    Join a roundtable discussion hosted by AI experts to get your questions answered on a variety of topics.

    You are free to come in and out of all sessions to ask your questions, share your thoughts, and learn more from the speakers and other attendees.

    Roundtable Discussions 28th January:

    • ‘Multiple Clouds. One Cluster’ hosted by Nathan Reid, Staff Solutions Architect, CAST.AI

    Roundtable Discussions 29th January:

    • ‘Curriculum Generation for Reinforcement Learning’ hosted by Natasha Jaques, Research Scientist, Google Brain

    • ‘The AI Economist’ hosted by Stephan Zhang, Lead Research Scientist, Salesforce Research

    • ‘A Win-Win in Precision Ag’ hosted by Jennifer Hobbs, Director of Machine Learning, Intelin Air

  • 10:45

    COFFEE & NETWORKING BREAK

  • 10:55

    PANEL: Best Practices for Realising ROI on AI Projects

  • Pratima Aiyagari

    MODERATOR

    Pratima Aiyagari - Venture Partner - Nauta Partners

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    Pratima is a Venture Partner at Nauta Capital and is based in London. At Nauta, Pratima focuses on enterprise software and deep tech domains.

    Pratima has 18+ years of professional experience. She is a software developer by background and spent several years in Silicon Valley working on developing intelligent network services in the network management domain. Pratima joins Nauta from US-based cybersecurity-focused investment firm Paladin Capital. Previously, Pratima led investments and acquisitions at Cisco Corporate Development in Europe with a specific focus on ML/AI, Enterprise Collaboration and Silicon. During her investment career, Pratima led an investment or held board seats in Aimotive, Adbrain, Evrythng, Secure Code Warrior, decentriq, Dashbird and several fund investments. She also managed investments in several companies including Behaviosec, Italtel, ip.access and Corvil.

    Pratima received her MBA from INSEAD in France and an MS in computer science from the Virginia Polytechnic Institute and State University in the US. Pratima is a co-author of a US patent in the area of network device clusters and high availability.

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  • Evgeny Blaichman

    PANELIST

    Evgeny Blaichman - Machine Learning Group Manager - Samsung Electronics

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    Evgeny Blaichman is the Machine Learning Group Manager at Samsung’s SSD Controller Development Center in Israel, holds MSEE, BSEE degrees from Tel Aviv University, Cum Laude. He served in the Communications Division of the Israel Defense Corps, ranked Captain, specializing in secured mobile networks. As part of his service he worked in the Israel's leading military equipment company Rafael, focusing on development of secured telecommunications algorithms. Later Evgeny worked in Texas Instruments Company as a Wi-Fi modem algorithms engineer. He joined Samsung in 2012, as an algorithms researcher. In 2017 he founded the Machine Learning Group that focuses on end-to-end learning based memory systems.

    Linkedin
  • Claire Lebarz

    PANELIST

    Claire Lebarz - Head of Data Science, Guest - AirBnb

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    Claire leads a team of 12-20 data scientists and managers improving the core guest experience, from the homepage, search & discovery of the relevant offering (homes, experiences - both online & real life), checkout, and post-booking / on-trip experience.

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  • Mario Lois

    PANELIST

    Mario Lois - Global Head & Senior Director of Artificial Intelligence - GE Healthcare

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    In 2018 Mario Lois was appointed as Global Head & Senior Director of Artificial Intelligence for GE Healthcare Women’s Health business. In this role Mario leads GE Healthcare’s global vision, strategy and efforts to develop and apply Artificial Intelligence (AI) into world-class solutions that improve screening, diagnosis and treatment of Breast Cancer (the most common cancer among women). Mario combines a solid background across science, product management and business leadership. During his 17 years at GE Healthcare, he has held a broad range of international leadership positions. He also held global positions at Philips Ultrasound prior to joining GE. Mario holds an advanced degree in Theoretical Physics from Universidad Autonoma in Madrid (Spain), a certificate in Strategic Management from Harvard University in Cambridge, MA (US), and a Specialization in AI/Deep Learning from Dr. Andrew Ng’s Deeplearning.ai.

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  • Adri Purkayastha

    PANELIST

    Adri Purkayastha - Global Head of AI and Digital Risk Analytics - BNP Paribas Group

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    Adri Purkayastha is Head of Technology and Cyber Risk Analytics at BNP Paribas Group. He is responsible for strategy and development of analytic solutions to support group’s ICT Risk Management Platform in areas such as Cyber, Digital, Technology and Fraud risk. In Deloitte Risk Advisory, Adri led strategy and R&D of AI and analytics solutions in areas such as Credit Risk, Market Abuse, Cyber Risk. As product owner of the firm’s Anomaly Detection platform, he led engineering, data science, strategic R&D, sales and partnership with sector leads globally. In Digital transformation, he led efforts in AI & analytics, data engineering for cloud native, multi-million-pound digital banking and lending proposition entirely developed in Google Cloud. Earlier, he worked as a Forensic Data Analytics consultant for FIDS, EY. Before EY, he worked in Digital Analytics Consulting for Pitney Bowes and in IoT analytics for Display Data. In the past, he founded companies in Consumer Internet, EdTech, renewable energy and Nanotechnology space.

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  • 11:45

    MAKE CONNECTIONS: Meet with Attendees Virtually for 1:1 Conversations and Group Discussions over Similar Topics and Interests

  • 12:00

    END OF SUMMIT

  • THIS SCHEDULE TAKES PLACE ON DAY 1

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