Accelerate AI 2019 Schedule

Schedule Guide for Pass Holders

The Accelerate AI schedule is for Tuesday October 29th and Wednesday October 30th. It is available to Accelerate AI 2 Day & 4 Day pass holders and ODSC Platinum Business, and VIP pass holders

The ODSC Talks/Workshops schedule includes Thursday October 31st and Friday November 1st. It is available to Accelerate AI 4 Day Pass holders plus ODSC Silver, Gold, Platinum, Platinum Business, and VIP Pass holders

Speaker and speaker schedule times are subject to change. More sessions added weekly.


–Accelerate AI West
09:30 - 10:05
Accelerate AI Keynote: Practice of Data Science and AI across Enterprise and Departments

Keynote

 

Coming soon!..more details

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Dr. Anand S. Rao
Global Artificial Intelligence Lead, Partner | PwC
09:30 - 10:05
Accelerate AI Keynote: Making Data Useful

Keynote

 

Despite the rise of data engineering and data science functions in today’s corporations, leaders report difficulty in extracting value from data. Many organizations aren’t aware that they have a blindspot with respect to their lack of data effectiveness and hiring experts doesn’t seem to help. Let’s talk about how you can change that!..more details

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Cassie Kozyrkov, PhD
Chief Decision Scientist | Google, Inc.
10:05 - 10:40
Accelerate AI Keynote: Accelerate AI: Moving AI Off Your Roadmap and Into Your Products

Keynote

 

Artificial intelligence (AI) can transform products, customer experiences, and entire business models. But data architecture, deep learning, natural language processing, and so on are only part of the AI journey. Putting AI to work requires a holistic view, from the way you incorporate data science into your organization, to the approach you take to product and experience design, to the makeup of the teams who execute your strategy.
Entrusted with the rich financial data of 50 million customers, Intuit is in a unique position to take advantage of AI to help solve some of the biggest financial pain points for consumers and small businesses. Drawing upon real-world experiences at Intuit, Ashok Srivastava explains how to make your organization AI ready, determine the right AI applications for your business and products, and accelerate your AI efforts with speed and scale.
..more details

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Ashok Srivastava, PhD
Chief Data Officer & Senior Vice President | Intuit
10:05 - 10:40
Accelerate AI Keynote

Keynote

 

Coming soon!..more details

Accelerate AI Keynote image
Michael I. Jordan, PhD
Distinguished Professor, ACM/AAAI Allen Newell Award Laureate | University of California, Berkeley
11:10 - 11:40
From R&D to ROI: Realize Value by Operationalizing Machine Learning

Business Talk | AI Management

 

An ML model on a laptop is just a science project. To generate business value at scale, models need to feed applications, model pipelines, and reporting tools, but getting there isn’t easy. The path to production operationalization—and ROI—involves the automation of very specific deployment and management processes for which standard development tools are not designed.

This talk will touch on the unique challenges machine learning introduces to development organizations and detail the strategic decisions businesses must consider to create efficient processes that unlock real insights and maximize productivity of your data science and DevOps teams…more details

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Diego Oppenheimer
Founder and CEO | Algorithmia
11:10 - 11:40
Scaling Computer Vision in the Cloud and AI Chips

Business Talk | AI Innovation

 

Computer Vision is becoming the ultimate sensor. We present several applications where sensors from other domains are replaced with Computer Vision, reducing costs and increasing generalizability of the sensor. These deployments run on Matroid, detailing customized visual search and stream monitoring to a large number of users. Along the way, we explain how Matroid creates, trains, and visualizes CV models without programming, accessible to typical computer users who are not developers, allowing them to monitor video streams and visually search large collections of media. We conclude with some inspiring applications of CV in the medical domain, pushing the boundaries of medicine with cutting-edge Glaucoma detection using Computer Vision…more details

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Reza Zadeh
Founder & CEO at Matroid & Adjunct Professor at Stanford University
11:10 - 11:40
Enterprise Adoption of Reinforcement Learning

Business Talk | AI Expertise

 

The third and final shift in reinforcement learning has been making waves in the artificial intelligence research community and business enterprises. The earlier successes with DeepMind AlphaGo have revolutionized several industries such as healthcare, retail, manufacturing, IoT, Robotics, finance, industrial, geospatial platforms, recommendation systems, and text mining in building the real-world applications. Programming stacks such as TensorFlow, Python, and PyTorch deployed on production landscapes of many top-tier companies such as Google, OpenAI, DeepMind, Spotify, Quora, and Reddit with machine learning and reinforcement learning algorithms. Reinforcement learning and function approximations are built on the mathematical foundations based on the Markov decision processes (memoryless) with optimal state and Q-value functions that operate on the state and action pairs…more details

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Dr. Ganapathi Pulipaka
Chief Data Scientist | Accenture
11:10 - 11:40
Establishing a Data and Analytics Organization

Business Talk | AI Management

 

Cisco is transforming to a customer lifecycle value-based business, and data is foundational to that transformation. Shanthi Iyer, Cisco’s Chief Data Officer, will talk about the five requirements for a data-driven business: Provide a single point of engagement for data needs for the business; deliver an integrated platform and foundational data capabilities, with a technology toolkit for analytics; deliver enterprise analytics for innovating and scaling critical business priorities; provide data governance, quality controls, standards and policies on data lifecycle management; and incubate analytics talent and an experienced community of practice…more details

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Shanthi Iyer
Chief Data Officer | Cisco Systems, Inc.
11:50 - 12:20
Designing a User-Centric Data Science Product

Business Talk | AI Innovation

 

This presentation will give a crash course in user-centric design, present arguments for why data scientists should care about design, and provide data scientists who build internal tools with design best practices. User-centric design, at its core, is a framework for understanding the user and his or her problems, and using those as a focal point for product design. Leading product companies spend huge resources to design their products well, but not all organizations with data scientists have designers to help them. At the same time, though, data scientists are increasingly building internal products for their organizations that embed data science into decision-making processes, so data scientists who want to see their tools get used should learn the best practices from user-centric design. This talk, co-presented by a data scientist and a designer, will start with design basics like building an understanding of the user, and then move on to more specific examples of user-centric data science products we’ve built…more details

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Katie Malone, Ph.D
Director of Data Science | Tempus
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Annie Darmofal
Senior Product Designer | Tempus
11:50 - 12:20
Robots Learning Dexterity

Business Talk | AI Innovation

 

Deep reinforcement learning provides a path towards solving many outstanding challenges in robotics. It lets machines learn more like humans do, by trial and error. The main obstacle has been getting enough data for training. Recent advances show that sim-to-real techniques, training entirely in simulation and transferring to a real robot, may bridge the gap and enable a new wave of applications. To showcase these techniques, we train a deep neural network to solve Rubik’s Cube in simulation, and then deploy it to a real world human-like robot hand. This shows that reinforcement learning isn’t just a tool for virtual tasks, but can solve physical-world problems requiring unprecedented dexterity....more details

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Peter Welinder, PhD
Research Scientist | OpenAI
11:50 - 12:20
Developing Machine Learning-driven Customer-Facing Product Features

Business Talk | AI Expertise

 

As Machine Learning becomes a core component of any forward-looking company, how can we weave ML-driven functionality into the products and services we offer? This talk will explain the methodology we follow at Square to develop ML-driven customer-facing product features, which is based on paying close attention to four key and interdependent aspects: Design, Modeling, Engineering, and Analytics. Design is concerned about the usefulness and remarkability of the feature, and thus cares about the overall functionality, ease of use, and aesthetics of the experience. Modeling is concerned about the accuracy of the ML model, and thus cares about the training data, the features and performance of the model, and —crucially for a customer-facing product— how the application behaves in the face of the mistakes the model will inevitably make (false positives, false negatives, lack of predictions above a certain confidence)…more details

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Marsal Gavalda, PhD
Head of Machine Learning | Square
11:50 - 12:20
Harnessing AI: Data Evangelism Must Be Data-driven

Business Talk | AI Expertise

 

As enterprises strive to harness data science and AI from inward (i.e. automation to reduce operating expenses) and outward (i.e. creating new lines of business and routes to market which were previously not technically feasible) perspectives, the range of outcomes that have been achieved (or not achieved) across and within industries continues to be incredibly wide. A common foundation within successful enterprises is that they all have effective data enablement practices. In the same way that high achieving sales organizations are bolstered by competent sales enablement team(s), data science and AI practitioners are significantly more impactful when provided the same type of support. This is the role that data evangelism should and can fill- and in doing so, enable greater achievements by organizations’ AI and data science team(s). Through an approach as data-driven as data science itself, data evangelism has the potential to not only inspire individuals to leverage data, but to enable them to do so through a unique set of tools. This talk dives into this opportunity, presenting specific models and actionable insights…more details

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Jennifer Redmon
Chief Data Evangelist | Cisco Systems, Inc.
13:30 - 14:00
Dominant Pattern Detection in Undirected Graphs

Business Talk | AI Management

 

Given a million+ legitimate Wal-Mart Stores returns daily, identifying fraudulent returns in real-time with minimal customer friction is a challenging problem. One reason is that there is a lack of customer identity associated with in-store transactions. Besides, there are no confirmed fraud labels in situations where the fraudulent return is suspected. Finally, the customer is present when a decision to accept or deny the return is conveyed. Thus, incorrectly accusing the customer of return fraud typically insults the customer and damages customer relations. Accordingly, it would be desirable to provide an improved store return fraud detection system. We propose a system that supports intelligent detection of anomalous sequences of activities, together with comprehensive evaluation of distinct characteristics of fraudulent activities, enables the generation of high-confidence fraud labels to some activity patterns…more details

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Henry Chen, PhD
Principal Data Scientist | Walmart Labs
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Vidhya Raman
Data Science Manager | Walmart Labs
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Jingru Zhou, PhD
Data Scientist | Walmart Labs
13:30 - 14:00
Challenges of Digital Transformation and AI

Business Talk | AI Expertise

 

In today’s digital world, customers expect businesses to understand their needs. While this may sometimes sound like an exercise in clairvoyance, the truth is that many customers are able to articulate these expectations.

By using AI and machine learning to gather and analyse behavioral, social and transactional data; it is possible for organizations to develop a far deeper, more personal understanding of their customer, thus addressing their unique needs in a personal and relevant way.

This informative session will cover the challenges companies across industries are facing in driving AI-driven Digital Transformation and what successful organizations are doing to address those challenges in the real world. The challenges include the development of business cases, using data, scaling AI and ML, organizational structures to reduce friction, and re-orienting cultures…more details

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Rashed Haq, PhD
Global Head of AI and Data | Sapient
13:30 - 14:00
Data-driven Approaches to Forecasting

Business Talk | AI Expertise

 

How do we know which forecasts to trust for our most critical business decisions? When stakes are high, big data and machine learning techniques can drive significant value across a wide variety of applications. However, finding the right approach is difficult. A tempting solution may perform well in one context but poorly in others, rely on unavailable information, or incur impractical costs. Whether it’s demand forecasting, supply chain management, or any other application, getting it right requires balancing the need for performance with the constraints of implementation and complexity.

We will discuss why organizations are turning to data-driven approaches to forecasting, applications and types of solutions, and challenges (both technical and practical) that arise during implementation. Attendees will leave oriented towards:
– Identifying types of forecasting applications and issues;
– Understanding the range of techniques available and related challenges;
– Evaluating potential data-driven approaches for your business;
– Measuring performance in the context of business objectives…more details

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Javed Ahmed, PhD
Senior Data Scientist | Metis
13:30 - 14:00
The Last Frontier of Machine Learning – Data Wrangling

Business Talk | AI Expertise

 

The bane of any organization deploying high quality machine learning technology is data wrangling. Data wrangling consists of data pre-processing, feature cleaning, and feature engineering. We estimate that data scientists spend upwards of 90% of their time wrestling with data making it the biggest bottleneck to widespread Machine Learning adoption. Automating aspects of data wrangling would dramatically increase the adoption of Machine Learning technology across enterprise organizations. In this talk, Alex Holub, PhD, draws upon his experience in both industry and academe to illustrate both why data wrangling is a challenge and some of the solutions being developed to automate the data wrangling…more details

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Alex Holub, PhD
CEO | Vidora
14:10 - 14:40
Scaling 200b+ Pins Using a Mix of Machine Learning and Human Curation

Business Talk | AI Expertise

 

Because people label and save Pins to specific boards, they all add context to Pins every time they Pin, which helps Pinterest identify taste and the overlapping interests between people. The future of visual discovery and personalization. The work that goes into predicting what someone will love next (from style to beauty to traveling to Hawaii to chicken recipes), and powering a recommendations engine that surfaces billions of ideas to hundreds of millions of people. A deep dive into recent advancements in computer vision and their applications in commerce including Lens camera search, automated Shop the Look, Complete the Look and the evolution of visual embeddings…more details

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Chuck Rosenberg, PhD
Head of Computer Vision | Pinterest
14:10 - 14:40
Why We Should Hire More Analysts for Data Science Teams

Business Talk | AI Management

 

When companies want to become great at most competencies – we want to be design driven! we want a great brand! – they often invest in building robust teams around those disciplines. When companies want to become more data driven, however, the instinct is different: the first focus is often on tooling and efficient scaling.

Nobody believes they can become a leading tech company and only hire a few engineers. Nobody believes they can be the next Apple by buying the design tools that Apple designers buy. Nobody believes they can have a brand like Nike by using their marketing automation tools. In these disciplines, companies understand that expertise comes from investing in the experts…more details

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Benn Stancil
Chief Analyst | Mode Analytics
14:10 - 14:40
An Introduction to AI’s Impact in the Life Sciences

Business Talk | AI Innovation

 

In this talk we provide an overview of how artificial intelligence/machine learning techniques are being used in life sciences research, biomedicine, and drug discovery. We highlight important specific applications of AI/ML techniques, across domains such as medical imaging, genomics from experimental data interpretation to understanding the genome, small molecule drug discovery. We also discuss recent advances using deep learning techniques to model protein sequences and structures, from basic scientific research to the design of novel proteins for chemical and therapeutic applications. We end with a brief overview of some open challenges in the field…more details

 

 

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Mark DePristo
Founder, CEO | BigHat Biosciences
14:10 - 14:40
RACE your FACTs: Making AI work for Enterprises

Business Talk | AI Expertise

 

There is renewed interest among companies these days to implement and deploy AI models in their business processes either to increase automation, or to improve human productivity. AI models are making their way as chat bots in customer support scenarios, as doctors’ assistants in hospitals, as legal research assistants in legal domain, as marketing manager assistants in marketing, and as face detection applications in security domain, just to name a few use cases. Making AI work for enterprises requires a whole new and different set of concerns to be addressed than those for traditional software applications or for consumer-facing AI models such as targeted advertising and product recommendations…more details

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Rama Akkiraju
IBM Fellow, Master Inventor, IBM Academy Member, and Director | IBM Watson
14:50 - 15:20
Data Literacy and Democratization of Data in the 4th Industrial Revolution

Business Talk

 

Data Literacy, or the lack thereof, is one of the biggest inhibitors to success in data and analytic projects. As we move into the 4th Industrial Revolution data is becoming more pervasive and accessible. It’s critical for people to obtain skills to think critically about data to make effective decisions. Too often there is analysis paralysis, the gap between the data being produced and our ability to consume it is growing significantly. It prompts the question: are organizations getting any better at all in making data driven decisions? We know that data literacy will be one of the defining themes of the 4th industrial revolution, migrating us from the information age into the analytics age. More recent research even shows that corporate data literacy has a direct correlation with enterprise value and performance. This presentation and discussion aims to talk about how the data literacy skills gap can be closed, from an individual and organizational perspective, and will help illuminate key characteristics to be developed for a data-drive culture…more details

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Jacob Dockendorf
Senior Customer Success Manager | Qlik
14:50 - 15:20
Sources of Bias: Strategies for Tackling Inherent Bias in Ai

Business Talk | AI Expertise

 

Artificial intelligence is making its way into budgets at enterprises and startups alike. Companies are ramping up on investments in AI and machine learning in the hopes of transforming their business with automated insights based on algorithms. But are they prepared to implement AI the right way? As AI implementations reach a broader set of companies, there are important lessons to be learned on how to avoid algorithms that are inherently biased, or that will make unethical or immoral conclusions based on skewed or misleading data…more details

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Harry Glasser
President, Data Business & GM | Sisense
14:50 - 15:20
Natural Language Processing: Deciphering the Message within the Message – Stock Selection Insights using Corporate Earnings Calls

Business Talk | AI Expertise

 

Astute investors have shifted their attention to explore the information content in unstructured data sets to differentiate their source of alpha. In this presentation, we will explore a number of sentiment- and behavioral-based signals using the content from earnings call transcripts via NLP that have historically demonstrated stock selection power in the U.S. market…more details

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Frank Zhao
Senior Director, Quantamental Research | S&P Global Market Intelligence
14:50 - 15:20
Accelerating AI-driven Innovation in Your Enterprise

Business Talk | AI Innovation

 

The presentation will focus on best practices to develop Machine Learning powered applications that can move the needle on business critical KPIs. We will walk through a rapid prototyping framework to develop effective personalization experiences that customers find engaging, the mindsets and skills required to develop and execute on an innovation roadmap, how to continuously evaluate and work with vendors that provide ‘AI-powered’ solutions, and how to design online experiments to quickly iterate towards a better experience for customers…more details

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Pallav Agrawal
Director, Data Science | Levi Strauss & Co.
15:50 - 16:20
AI in Medicine: Avoiding Hype and False Conclusions

Business Talk | AI Innovation

 

With the advance of AI techniques and data to bring modeling into clinical bio-medicine, we will discuss a framework for assessing the maturity and knowledge gaps associated with these technologies. The goal of this presentation is to provide key information concerning the scientific, regulatory, legal, and cultural factors essential for successful introduction of AI in healthcare. We will also arm the audience member with critical methods to avoid false conclusions and exaggerated expectations associated with AI, and we will discuss select real-world examples…more details

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Michael Zalis
Chief, Clinical Solutions and Strategy | One Brave Idea / BWH Cardiovascular Innovation
15:50 - 16:20
AI in Healthcare: the State of Adoption

Business Talk | AI Innovation

 

The latest AI advances have the potential to massively improve our health and well-being. However, most of the work is yet to be done. In this talk, we will explore the most important opportunities for AI in healthcare as well as specific challenges facing AI in healthcare. We will start by examining AI ability to diagnose major life-threatening conditions much earlier then other methods, sometimes years earlier. We will talk about AI ability to recommend dramatically more effective and less harmful treatment plans based on AI interpretation of patient’s medical history, treatment effectiveness and real time patient monitoring. Finally, we will talk about AI role in making our healthcare system effective and affordable for everyone. For each area, we will discuss both the latest progress made as well as the challenges yet to be solved…more details

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Alex Ermolaev
Director of AI | Change Healthcare
15:50 - 16:20
On ROI: The Questions You Need to Be Asking

Business Talk | AI Management

 

Leaders across industry have been increasing investment in advanced analytics, data science, and AI. Yet, many have struggled to recognize a return on their investment.Many of these technical teams are making
transformative contributions to their companies, yet they aren’t being acknowledged for it. This usually occurs simply because their success not being properly measured.
Other teams are becoming frustrated because their successful data science projects are not being translated into success business projects. This often occurs because leaders are unable to differentiate high impact data science projects from low impact ones. Without the ability to do so, leaders cannot effectively lead a team to choose impactful projects…more details

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Kerstin Frailey
Senior Data Scientist | Metis
15:50 - 16:20
The Anatomy of a Payment: Dissecting Data Science

Business Talk 

 

The world of tech moves at an unpredictable speed. Yet, the financial industry has struggled to keep pace. Payments is big business, but the existing infrastructure is outdated, slow, and expensive—it hasn’t been updated for 40 years. Blockchain technology has created boundless opportunities to improve the payment system by utilizing digital assets and data insights to make global transactions more efficient. This talk will dissect the elements of a transaction, and highlight the processes and applications used to yield results and improve cross-border payments. By analyzing payments technology using data science, we can challenge the existing system to highlight solutions that enable more efficient, reliable, and rapid transfers. We will also explore the emerging solutions addressing consumer needs for speed and transparency in the world of payments. This session will showcase the impact of digital assets on our global infrastructure as the financial industry continues to embrace these new technologies. Data scientists and researchers interested in learning about applications and use cases in the payments and blockchain field will benefit from this talk, as Jen will break down the anatomy of a payment (the size, prefunding, speed, and cost), and its journey across borders. Jen will share use cases highlighting how data science and machine learning help identify areas of improvement in the cross-border payments process.
By the end of the talk, attendees will have:
– An understanding of the various elements that make up a cross-border payment
– Unique ideas for generating solutions using data mapping
– Insight into the effectiveness of digital assets as a solution to the existing financial infrastructure…more details

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Jennifer Xia
Data Scientist | Ripple
16:30 - 17:00
Beyond Conventional AI – Proven in Space, Now Available on Earth

Business Talk

 

This talk will highlight cognitive AI and how a team of data scientists from Beyond Limits is bringing this advanced technology used in NASA space missions to the most demanding industries on earth. What is Cognitive AI? This session will explain how cognitive AI differs from conventional, numeric AI approaches by incorporating human-like reasoning into the system’s recommendations. Discover how cognitive AI is designed to reduce uncertainty, increase transparency and deliver trusted, explainable results. …more details

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Dr. Giovanni Gentile
Data Scientist | Beyond Limits
16:30 - 17:00
Building AI Products: Delivery Vs Discovery

Business Talk | AI Innovation

 

I will discuss the challenges in building real world AI products in today’s enterprise environment, and, in particular, the tradeoffs between “Discovery vs. Delivery.” It seems every company today wants AI; but plug-and-play AI offerings are far and few between. I will describe the balance between the R&D necessary to create bespoke products and get them working within existing IT deployment environment. Topics will include cultural differences between IT and AI, how to scope a successful R&D project, data mining vs product development, model governance, existing deployment solutions, and testing…more details

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Charles Martin, PhD
CEO at Calculation Consulting
16:30 - 17:00
Transaction Data Enrichment – an Opportunity for Business Growth and Risk Mitigation

Business Talk | AI Expertise

 

Transaction Data has immense potential to go beyond traditional data aggregation, by banks, to connecting the dots and providing valuable customer insights across industries. By acquiring financial data, and then cleansing and enriching it, organizations can derive insights that could solve business issues like supply chain gaps, identify financial lending opportunities, improve marketing efforts of a retail giant, identify growth opportunity for clients of investment research/PE/VC, mitigate losses of and much more. Enriched transaction data is the purest form of data providing real insights into what customers are doing and what they want to do with their funds. In this session, we will explore the importance of utilizing alternative data (such as transaction data) and applying machine learning algorithms to datasets to clarify and categorize the transactional data. Institutions can leverage this customer data to provide personal experience and advice…more details

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Pramod Singh, PhD
Chief analytics officer and vice president of data sciences and analytics | Yodlee Envestnet