21 - 22 May 2020

Applied AI Summit Applied AI Summit schedule

RE•WORK Austin AI Summit

  • 08:00

    REGISTRATION & LIGHT BREAKFAST

  • 09:00

    WELCOME

  • CURRENT LANDSCAPE OF APPLIED AI

  • 09:15

    Driving Innovation with AI in Enterprise

  • 09:40
    Seshu Yalamanchili

    The Roadmap to Applied AI

    Seshu Yalamanchili - Director for AI Application Strategy - Visa

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    Seshu is the Director for Artificial Intelligence Application Strategy at Visa. He is responsible for developing long term vision, roadmap and execution of AI to transform the business. Prior to Visa, Seshu was responsible for AI application strategy at General Motors’ Autonomous Vehicle Development organization. Seshu has over twenty years experience in developing and executing large scale, Global transformational initiatives in Aerospace, Automotive and Financial Industries, leveraging the emerging technologies such as Mobile, IoT, Cloud, AI, etc. Seshu has MBA from Northwestern University’s Kellogg School Management and Master’s in Engineering from Virgnia Tech.

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  • 10:05

    The Art of Successful AI Implementation

  • 10:30

    COFFEE

  • APPLYING AI METHODS TO SOLVE CHALLENGES IN INDUSTRY

  • USE CASES: MACHINE LEARNING

  • 11:15
    Jun Jia

    Machine Learning Across Linkedin

    Jun Jia - Senior Staff Software Engineer - Linkedin

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    Jun Jia is a Senior Staff Software Engineer in the Artificial Intelligence team at LinkedIn where he works on developing state-of-the-art algorithms to improve LinkedIn's Search and Recommendation systems. He has extensive experience in R&D and software engineering and numerous publications in top-tier journals and conferences. Prior to LinkedIn, he obtained his MS and PhD in CS and Math from the University of North Carolina at Chapel Hill and worked as a technical staff member at ORNL.

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  • Sandeep Jha

    Machine Learning Across Linkedin

    Sandeep Jha - Staff Technical Program Manager - Linkedin

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    Sandeep Jha is a Staff Technical Program Manager in the Artificial Intelligence group at LinkedIn, where he leads programs that empowers LinkedIn's Search and Recommendation systems. Before LinkedIn, he was a Sr. Technical Program Manager in Search Science and AI team at Amazon, where he worked on improving the search result in the first page of Amazon worldwide. Before that, he worked at Facebook, where he led initiatives to enhance ad quality and launch of multiple commerce products such as Facebook Marketplace and Instagram Shopping.

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  • 11:35

    Applying AI to Cyber Defence for Enterprises

  • USE CASES: REINFORCEMENT LEARNING

  • 11:55

    Reinforcement Learning for Stock Optimization

  • 12:15

    Using DRL to Out-Perform Humans in Gaming

  • 12:35

    LUNCH

  • USE CASES: DEEP LEARNING

  • 13:35

    Increasing Safety in Manufacturing

  • 13:55
    Simon Hughes

    Deep Learning for Recommender Systems

    Simon Hughes - Senior Data Scientist - The Home Depot

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    Deep Learning for Recommendations

    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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  • APPLIED AI: COMPUTER VISION & IMAGE RECOGNITION

  • 14:15
    Robinson Piramuthu

    Adversarial Learning for Fine-Grained Image Search

    Robinson Piramuthu - Chief Scientist of Computer Vision - eBay

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    Adversarial Learning for Fine-Grained Image Search

    While computer vision has been extensively studied, it still remains a challenging problem. In particular, fine-grained image search is a challenging problem due to the difficulty in capturing subtle differences regardless of pose variations of objects from fine-grained categories. Numerous algorithms using deep neural networks have achieved state-of-the-art

    performance on fine-grained categorization, but they are not directly applicable to fine-grained image search. In this presentation, eBay’s Chief Scientist for Computer Vision, Robinson Piramuthu will present on eBay research that proposes a solution called FGGAN, which learns discriminative representations by implicitly studying geometric transformation from multi-view images for fine-grained image search.

    As Chief Scientist for Computer Vision, Robinson drives eBay’s computer vision science strategy. With over 20 years of experience in computer vision, his expertise includes large scale visual search, coarse and fine-grained visual recognition, object detection, computer vision for fashion, 3D cues from 2D images, figure-ground segmentation and deep learning for vision, among other topics. Before joining eBay in 2011, he received his PhD in Electrical Engineering and Computer Science from the University of Michigan in 2000 specializing in information theory and statistical image processing. He also has a MS in control theory from the University of Florida, specializing in robust and nonlinear control systems.

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  • 14:35
    Priya Sundararaman

    Safety First: AI To Detect Distracted Driving

    Priya Sundararaman - Principal Data Scientist - State Farm

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    Safety First: AI To Detect Distracted Driving

    Distracted driving is one of the leading causes of auto accidents, according to the National Highway Traffic Safety Administration (NHTSA). This talk will demonstrate the use of Artificial Intelligence to analyze driver images and identify distracted driving behavior autonomously. Two deep learning models were created using videos of drivers from a 3D image sensor and a 2D web camera. An ensemble of the models was used to classify the action of the driver. I will discuss the methodology, results and suggested areas of future work to improve driver safety.

    Priya Sundararaman is a Principal Data Scientist at State Farm. Priya has an undergraduate engineering degree in Computer Science and masters in Predictive Analytics with 16 years of industry experience. She is a pragmatic data scientist who believes that we are already in the midst of the fourth industrial revolution, with AI being a key enabler, permeating all aspects of business. At State Farm, she intends to make her contribution by using machine learning to solve hard business problems for demonstrable, measurable, and sustainable ROI.

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  • 14:55

    COFFEE

  • APPLIED AI: SPEECH, TEXT & NLP

  • 15:40

    Natural Language Processing & Sentiment Analysis in Industry

  • 16:00

    Machine Translation & Chatbots

  • OPPORTUNITIES & CHALLENGES OF APPLIED AI

  • 16:20

    PANEL: Strategies for Effectively Building, Deploying & Monitoring AI

  • 17:00

    CONVERSATION & DRINKS

  • 08:00

    DOORS OPEN

  • 09:00

    WELCOME

  • APPLYING AI METHODS TO SOLVE CHALLENGES IN SOCIETY

  • 09:15

    Adaptive & Accessible Education with Virtual Personalized Assistants

  • 09:35

    AI for Sustainable Cities & Water Management

  • 09:55

    Design and AI for Mental Health

  • 10:15
    Kay Firth-Butterfield

    WEF’s Guidelines for the 4th Industrial Revolution

    Kay Firth-Butterfield - Head of AI & ML - World Economic Forum

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    I am the Head, Artificial Intelligence and Machine Learning, at the Center for the Fourth Industrial Revolution at the World Economic Forum. Prior to taking this position I was Executive Director of AI-Global and led the Ethics Advisory Panel of Lucid.ai.I have worked for the past three decades as a barrister, mediator, arbitrator, business owner, professor and judge in the United Kingdom. In the United States, I have taught at the undergraduate and law school levels and worked as a professional lecturer. I am a humanitarian with a strong sense of social justice and have advanced degrees in Law and International Relations. In my role as Chief officer of the EAP I have advised governments, think tanks and non-profits about artificial intelligence. I am co-founder of the Consortium for Law and Policy of Artificial Intelligence and Robotics at the Robert E. Strauss Center, University of Texas and teach a course at the UT Law School for the Consortium: "Artificial Intelligence and emerging technologies: Law and Policy". Additionally, I am a Distinguished Scholar of the Robert E Strauss Center at the University of Texas and Vice Chair of the IEEE Industry Connections "Global Initiative for Ethical Considerations in the Design of Autonomous Systems.

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  • 10:35

    COFFEE

  • CASE STUDIES & BUSINESS INSIGHTS

  • 11:20
    Kishore Karra

    Applied AI in Banking

    Kishore Karra - Vice President, Model Risk Governance & Review - J.P. Morgan

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    The role of ML in AML (Anti-Money Laundering)

    As financial institutions face increasingly complex money laundering activites, traditional rules-based AML systems are becoming less efficient for real-time monitoring and detection. This talk will highlight the current challenges faced by banks in this space and discuss how Machine Learning algorithms can be used to 'learn' from past behaviors to increase efficiency and identify previously undetected transactional patterns and relationships.

    Kishore Karra is the lead reviewer of models used in Anti-Money Laundering at JP Morgan Chase. In this role, he assesses and mitigates risk posed by models used for the purposes of Sanctions Screening and Transaction Monitoring. Kishore holds a Master's degree in Mathematics from Rutgers University and an MBA from the Indian School of Business.

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  • 11:40
    Donal McMahon

    Deep in the Heart of Texas - How Indeed Embraced Deep Learning to Help People Get Jobs!

    Donal McMahon - Senior Director of Data Science - Indeed.com

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    Deep in the Heart of Texas - How Indeed Embraced Deep Learning to Help People Get Jobs!

    Over the past decade, Deep Learning has demonstrated significant performance gains in numerous important application domains. This has resulted in a marked shift in the machine learning landscape. Although we dabbled prior, in 2017 Indeed began concerted efforts to explore how these techniques could be leveraged to deliver transformational business value. Ultimately, we decided to hire a technical Deep Learning lead to accelerate these initiatives. In this talk, we will outline our deep learning journey. We’ll describe the progress we’ve made so far, the challenges we’ve overcome, and the lessons we’ve learned along the way. We’re hoping you can learn from our experience, both the successes and the bruises we’ve amassed. Along the way, we’ll discuss the Deep Learning solutions we’ve deployed - including state of the art natural language processing techniques, deep learning based recommender systems, and methods to improve existing systems using representation learning.

    Donal McMahon is the Group Manager for Data Platforms at Indeed. He proudly leads a team of over 300 Data Scientists, Data Engineers, Business Intelligence Analysts, Product Managers, Data Governance Analysts, and Software Engineers. He’s been “helping people get jobs” for the past four years. He estimates that if Indeed keeps growing at its current rate, we’ll employ over 50% of the world’s workforce by 2038, just before civilization collapses because of the Year 2038 Problem. Until then, though, Donal is going to use math, science and software to help millions of people make smarter, faster and geekier employment decisions.

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  • Thomas Lake

    Deep in the Heart of Texas - How Indeed Embraced Deep Learning to Help People Get Jobs!

    Thomas Lake - Deep Learning Lead - Indeed.com

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    Deep in the Heart of Texas - How Indeed Embraced Deep Learning to Help People Get Jobs!

    Over the past decade, Deep Learning has demonstrated significant performance gains in numerous important application domains. This has resulted in a marked shift in the machine learning landscape. Although we dabbled prior, in 2017 Indeed began concerted efforts to explore how these techniques could be leveraged to deliver transformational business value. Ultimately, we decided to hire a technical Deep Learning lead to accelerate these initiatives. In this talk, we will outline our deep learning journey. We’ll describe the progress we’ve made so far, the challenges we’ve overcome, and the lessons we’ve learned along the way. We’re hoping you can learn from our experience, both the successes and the bruises we’ve amassed. Along the way, we’ll discuss the Deep Learning solutions we’ve deployed - including state of the art natural language processing techniques, deep learning based recommender systems, and methods to improve existing systems using representation learning.

    Thom Lake is the Deep Learning Technical Lead at Indeed. There, he finds ways to use deep learning to "help people get jobs," and guides the application of modern machine learning techniques across the company. Throughout his career, he's had the opportunity to design and develop machine learning solutions for a variety of domains, from motion recognition in resource-constrained embedded environments, to product recommendation on one of the largest e-commerce websites in the world. His interests include neural networks and deep learning, inductive bias, natural language processing, and the interaction between humans and adaptive systems.

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  • 12:00
    Cheng Zhan

    AI for Optimization & Long Term Forecasting

    Cheng Zhan - Senior Data Scientist - Anadarko Petroleum

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    Hybrid Deep Learning Approach To Speed Up Reservoir Performance Forecast

    The deep learning method has achieved many phenomenal results in image segmentation, speech recognition and even some relatively creative tasks like writing a WIKI page. Intuitively speaking, deep learning can be interpreted as a complex geometric transformation in a high-dimensional space, and the underlying model usually consists of many layers’ representations. Because of the fundamental principle in the design of statistical learning, it normally requires a large amount of data sample to train the model. In many real-world problems, gaining too many data samples is not al-ways practical, (reservoir simulation in the oil and gas industry, for example). In order to overcome this dilemma, we propose to incorporate some physics equation to reduce the amount of data sample needed for training the neural network. In other words, we suggest using prior knowledge in the neural network to accelerate its learning process.

    Cheng Zhan is a Senior Data Scientist at Anadarko Petroleum, where he works on field development optimization and long-term production forecasting. He focuses on building machine learning algorithms to create strategic and financial impact for the company. Prior to his current role, he worked as a Geophysicist at TGS and CGG, utilizing seismic data and inversion methods to help operators make better decisions in exploration. He holds a PhD in mathematics from University of Houston, and a B.S. in Mathematics from Sun Yat-sen University.

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  • LESSONS LEARNT: CROSS INDUSTRY IMPLEMENTATION

  • 12:20

    PANEL: Tackling Challenges Sharing Cross-Industry & Cross-Level Expertise

  • 12:40

    LUNCH

  • PROGRESSING APPLIED AI

  • 13:40

    Integrating DevOps Ideas & Scaling AI Projects

  • 14:00
    Sandeep Golkonda

    Let the Models Speak: Model Interpreters

    Sandeep Golkonda - Professional Data Scientist - AT&T

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    Let the Models Speak: Model Interpreters

    Curiosity kills. We have advanced to building machine learning algorithms for real-time predictions that can help us predict almost accurately every time. It’s like we have built a magic box that will answer all our questions. The more curios question is why did the magic box predict what it did. During the session I would like to share why you should/shouldn’t trust your model but you should explain the reasoning of your predictions that help Business users to understand your Magic box. I would like to share about how at AT&T we are using Model Interpreters to help business users identify the elements of the predictions that can give intuitive reasoning behind each prediction.

    Sandeep David Golkonda is a Data Scientist at AT&T Chief Data Office he actively researches and applies Machine Learning and AI solutions for real-time analytics problems. His, current work is focused on building AI framework to optimize network events. He is an active member of School of AI and Teaching Assistant for machine learning and deep learning. He holds M.S in computer science and M.S in business analytics.

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  • 14:20

    PANEL: Is Ethics the Biggest Obstacle in Applying AI?

  • 15:00

    END OF SUMMIT

RE•WORK Austin AI Summit

RE•WORK Austin AI Summit

21 - 22 May 2020

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