Presented By
O’Reilly + Intel AI
Put AI to Work
April 15-18, 2019
New York, NY
Discover opportunities for applied AI
Organizations that successfully apply AI innovate and compete more effectively. How is AI transforming your business?
Be a part of the program—apply to speak by October 16.

Schedule: Financial Services sessions

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9:00am - 5:00pm Monday, April 15 & Tuesday, April 16
Implementing AI, Models and Methods
Location: Green Room
Francesca Lazzeri (Microsoft)
Francesca Lazzeri will walk you through the core steps for using Azure Machine Learning services to train your machine learning models both locally and on remote compute resources. Read more.
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9:00am12:30pm Tuesday, April 16, 2019
Location: Sutton South
Garrett Hoffman (StockTwits)
Garrett Hoffman walks you through deep learning methods for natural language processing and natural language understanding tasks, using a live example in Python and TensorFlow with StockTwits data. Methods include word2vec, recurrent neural networks and variants (LSTM, GRU), and convolutional neural networks. Read more.
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1:45pm5:15pm Tuesday, April 16, 2019
AI Business Summit
Location: Mercury Ballroom
Alex Siegman (Dow Jones), Kabir Seth (Wall Street Journal)
This tutorial walks attendees through the steps necessary to appropriately leverage AI in a large organization: This includes ways to identify business opportunities that lend themselves to AI, as well as best practices on everything from data intake and manipulation to model selection, output analysis, development and deployment, all while navigating a complex organizational structure. Read more.
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11:05am11:45am Wednesday, April 17, 2019
Case Studies, Machine Learning
Location: Sutton South
Pamela Vagata (Stripe)
Explore how Stripe applies deep-learning to user-behavior for fraud detection. This deep-dive will include data-preparation, modeling methods and performance comparisons. Read more.
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1:50pm2:30pm Wednesday, April 17, 2019
Anand Rao (PwC)
Broader AI adoption and gaining trust from customers requires AI systems to be fair, interpretable, robust, and safe. This talk synthesizes the current research in FAT (Fairness, Accountability, Transparency) into a step-by-step methodology to address these issues. Case studies from financial services and healthcare are used to illustrate the approach. Read more.
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2:40pm3:20pm Wednesday, April 17, 2019
AI Business Summit, Case Studies
Location: Sutton North/Center
Andrew Chin (AllianceBernstein), Celia Chen
Overview of data science applications within the asset management industry Specific use cases using ML to derive better investment insights and improve client engagement Read more.
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4:05pm4:45pm Wednesday, April 17, 2019
AI Business Summit, Case Studies
Location: Sutton North/Center
Kyle Hoback (WorkFusion), Mikhail Abramchik (WorkFusion)
Using AI to combat financial crime is more than strong fraud detection models monitoring transactions. Banks follow significant Anti-Money Laundering (AML) and Know-Your-Customer (KYC) laws and procedures, wrought with growth chained to cost and requiring auditable automation. This session will walk-through a series of case studies that utilize AI-powered RPA that address AML and KYC. Read more.
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4:55pm5:35pm Wednesday, April 17, 2019
Angelo Calvello (Rosetta Analytics)
While there is no doubt that AI could be used to create better investment outcomes, successfully adopting AI requires asset managers to radically transform their existing business models and investment processes. Faced with such possible disruption, leadership will instead choose to maintain the status quo and introduce diluted forms of AI. Read more.
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4:55pm5:35pm Wednesday, April 17, 2019
AI Business Summit, Case Studies
Location: Sutton North/Center
Scott Clark (SigOpt)
Increasingly, companies building modeling platforms to empower their researchers to efficiently scale the development and productionalization of their models. During this talk, we use a case study from a leading algorithmic trading firm to draw general best practices for building these types of platforms in any industry. Read more.
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11:05am11:45am Thursday, April 18, 2019
Case Studies, Machine Learning
Location: Sutton South
vishal hawa (Vanguard)
While Deep Learning has shown significant promise towards model performance, it can quickly become untenable particularly when data size is short. RNNs can quickly memorize and over-fit . The presentation explains how a combination of RNNs and Bayesian Network (PGM) can improvise sequence-modeling behavior of RNNs. Read more.
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1:50pm2:30pm Thursday, April 18, 2019
Machine Learning, Models and Methods
Location: Grand Ballroom West
Arun Kejariwal (Independent), Ira Cohen (Anodot)
In this talk we shall shares a novel two-step approach for building more reliable prediction models by integrating anomalies in them. Further, we shall walk the audience through how to marry correlation analysis with anomaly detection, discusses how the topics are intertwined, and details the challenges you may encounter based on production data. Read more.
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4:05pm4:45pm Thursday, April 18, 2019
Implementing AI
Location: Regent Parlor
Aric Whitewood (WilmotML)
Our firm focuses on the application of AI to investment management. Topics covered in this presentation include the application of AI to the problem of asset selection, dealing with low signal-to-noise ratios in financial time series data, the development of real-time macroeconomic indicators from social media data, and the use of heterogeneous compute architectures, specifically GPUs and FPGAs. Read more.
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4:05pm4:45pm Thursday, April 18, 2019
Case Studies, Machine Learning
Location: Sutton South
Chakri Cherukuri (Bloomberg LP)
In this talk we will see how machine learning and deep learning techniques can be applied in the field of quantitative finance. We will look at a few use-cases in detail and see how machine learning techniques can supplement and sometimes even improve upon already existing statistical models. We will also look at novel visualizations to help us better understand and interpret these models. Read more.