Put AI to Work
April 15-18, 2019
New York, NY
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Schedule: Case Studies sessions

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11:05am–11:45am Wednesday, April 17, 2019
Media meets AI: How we give superpowers to BuzzFeed's social curators
Location: Sutton North/Center
Secondary topics:  AI case studies, Interfaces and UX, Media, Marketing, Advertising
Lucy Wang (BuzzFeed), Swara Kantaria (BuzzFeed)
Average rating: *****
(5.00, 2 ratings)
As BuzzFeed’s content production and social networks grow, curation becomes increasingly difficult. The company first built publishing tools that let people work more efficiently, then built artificial intelligence tools that let people work more intelligently. Join Lucy Wang and Swara Kantaria to learn more about this evolution. Read more.
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11:05am–11:45am Wednesday, April 17, 2019
Fraud detection without feature engineering
Location: Sutton South
Secondary topics:  AI case studies, Financial Services
Pamela Vagata (Stripe)
Average rating: ****.
(4.00, 1 rating)
Pamela Vagata explains how Stripe has applied deep learning techniques to predict fraud from raw behavioral data. Join in to learn how the deep learning model outperforms a feature-engineered model both on predictive performance and in the effort spent on data engineering, model construction, tuning, and maintenance. Read more.
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1:00pm–1:40pm Wednesday, April 17, 2019
ML at Twitter: A deep dive into Twitter's timeline
Location: Sutton South
Secondary topics:  AI case studies, Media, Marketing, Advertising, Platforms and infrastructure, Text, Language, and Speech
Cibele Halasz (Apple), Satanjeev Banerjee (Twitter)
Average rating: *****
(5.00, 1 rating)
Twitter is a company with massive amounts of data, so it's no wonder that the company applies machine learning in myriad of ways. Cibele Montez Halasz and Satanjeev Banerjee describe one of those use cases: timeline ranking. They share some of the optimizations that the team has made—from modeling to infrastructure—in order to have models that are both expressive and efficient. Read more.
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1:50pm–2:30pm Wednesday, April 17, 2019
Beyond Word2Vec: Using embeddings to chart out the ebb and flow of tech skills
Location: Sutton South
Secondary topics:  AI in the Enterprise, Text, Language, and Speech
Maryam Jahanshahi (TapRecruit)
Average rating: *****
(5.00, 6 ratings)
Word embeddings such as word2vec have revolutionized language modeling. Maryam Jahanshahi discusses exponential family embeddings, which apply probabilistic embedding models to other data types. Join in to learn how TapRecruit implemented a dynamic embedding model to understand how tech skill sets have changed over three years. Read more.
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2:40pm–3:20pm Wednesday, April 17, 2019
Leveraging data science in asset management
Location: Sutton North/Center
Secondary topics:  AI case studies, Financial Services
Andrew Chin (AllianceBernstein), Celia Chen (AllianceBernstein)
Average rating: *****
(5.00, 2 ratings)
Andrew Chin and Celia Chen offer an overview of data science applications within the asset management industry, covering use cases on using ML to derive better investment insights and improve client engagement. Read more.
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2:40pm–3:20pm Wednesday, April 17, 2019
Industrialized capsule networks for text analytics
Location: Sutton South
Secondary topics:  Models and Methods, Text, Language, and Speech
Vijay Agneeswaran (Walmart Labs), Abhishek Kumar (Publicis Sapient)
Average rating: ***..
(3.00, 1 rating)
Vijay Agneeswaran and Abhishek Kumar offer an overview of capsule networks and explain how they help in handling spatial relationships between objects in an image. They also show how to apply them to text analytics. Vijay and Abhishek then explore an implementation of a recurrent capsule network and benchmark the RCN with capsule networks with dynamic routing on text analytics tasks. Read more.
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4:05pm–4:45pm Wednesday, April 17, 2019
Fighting financial crime with AI: Beyond fraud detection with AI-powered RPA
Location: Sutton North/Center
Secondary topics:  AI case studies, AI in the Enterprise, Financial Services, Text, Language, and Speech
Kyle Hoback (WorkFusion), James Lawson (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. Kyle Hoback walks you through a series of case studies that utilize AI-powered RPA that address AML and KYC. Read more.
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4:55pm–5:35pm Wednesday, April 17, 2019
Best practices for scaling modeling platforms
Location: Sutton North/Center
Secondary topics:  AI case studies, Automation in machine learning and AI, Financial Services, Platforms and infrastructure
Scott Clark (SigOpt), Matt Greenwood (Two Sigma Investments)
Average rating: **...
(2.00, 1 rating)
Companies are increasingly building modeling platforms to empower their researchers to efficiently scale the development and productionalization of their models. Scott Clark and Matt Greenwood share a case study from a leading algorithmic trading firm to illustrate best practices for building these types of platforms in any industry. Read more.
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11:05am–11:45am Thursday, April 18, 2019
Regularization of RNNs through Bayesian networks
Location: Sutton South
Secondary topics:  AI case studies, Financial Services, Models and Methods
vishal hawa (Vanguard)
While deep learning has shown significant promise for model performance, it can quickly become untenable particularly when data size is short. RNNs can quickly memorize and overfit. Vishal Hawa explains how a combination of RNNs and Bayesian networks (PGM) can improve the sequence modeling behavior of RNNs. Read more.
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1:00pm–1:40pm Thursday, April 18, 2019
Deep learning for third-party risk identification and evaluation at Dow Jones
Location: Sutton South
Yulia Zvyagelskaya (Dow Jones), Victor Llorente (Dow Jones)
Average rating: **...
(2.00, 1 rating)
Companies have a strong need for complying with anti-money laundering, antibribery, corruption, and economic sanctions regulation in mitigating third-party risk. Yulia Zvyagelskaya and Victor Llorente highlight how Dow Jones Risk & Compliance uses deep learning and NLP for efficient compliance solutions. Read more.
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1:50pm–2:30pm Thursday, April 18, 2019
How deep learning can improve medical outcomes now
Location: Sutton North/Center
Secondary topics:  AI case studies, Computer Vision, Health and Medicine
Eric Oermann (Mount Sinai Health System), Katie Link (Allen Institute for Brain Science)
Average rating: *****
(5.00, 1 rating)
There's significant interest in applying deep learning-based solutions to problems in medicine and healthcare. Eric Oermann and Katie Link identify actionable medical problems, recast them as tractable deep learning problems, and discuss techniques to solve them. Read more.
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1:50pm–2:30pm Thursday, April 18, 2019
How to train your model (and catch label leakage)
Location: Sutton South
Till Bergmann (Salesforce), Leah McGuire (Salesforce)
Label leakage is a pervasive problem in predictive modeling data, and it takes on monstrous proportions at enterprise companies, where the data is populated by diverse business processes, making it hard to distinguish cause from effect. Till Bergmann and Leah McGuire explain how Salesforce—which needs to churn out thousands of customer-specific models for any given use case—tackled this problem. Read more.
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2:40pm–3:20pm Thursday, April 18, 2019
How to use AI to improve efficiency, safety, and patient satisfaction in radiology
Location: Sutton North/Center
Secondary topics:  AI case studies, Computer Vision, Health and Medicine
Enhao Gong (Subtle Medical), Greg Zaharchuk (Stanford University)
Clinical radiology currently faces several clinical issues: improving imaging efficiency, reducing risks, and developing higher imaging quality. Enhao Gong and Greg Zaharchuk explain how Subtle Medical's deep learning/AI solution addresses these problems by enabling faster MRI and faster PET and low-dose scans, providing real clinical and financial benefit to hospitals. Read more.
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2:40pm–3:20pm Thursday, April 18, 2019
Adversarial machine learning in digital forensics
Location: Sutton South
Secondary topics:  AI case studies, Computer Vision, Ethics, Privacy, and Security, Models and Methods, Reinforcement Learning
Alina Matyukhina (Canadian Institute for Cybersecurity)
Average rating: ****.
(4.00, 2 ratings)
Machine learning models are often susceptible to adversarial deception of their input at test time, which leads to poorer performance. Alina Matyukhina investigates the feasibility of deception in source code attribution techniques in real-world environments and explores attack scenarios on users' identities in open source projects—along with possible protection methods. Read more.
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4:05pm–4:45pm Thursday, April 18, 2019
Using AI to transform high-volume, confidential, disparate data for the United States Patent Office
Location: Sutton North/Center
Secondary topics:  AI case studies, Data and Data Networks, Text, Language, and Speech
Tammy Bilitzky (DCL)
Tammy Bilitzky shares a case study that details lights-out automation and explains how DCL uses AI to transform massive volumes of confidential disparate data into searchable and structured information. Along the way, she outlines considerations for architecting a solution that processes a continuous flow of 5M+ “pages” of complex work units. Read more.
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4:05pm–4:45pm Thursday, April 18, 2019
Applied machine learning in finance
Location: Sutton South
Secondary topics:  AI case studies, Financial Services, Text, Language, and Speech
Chakri Cherukuri (Bloomberg LP)
Chakri Cherukuri demonstrates how to apply machine learning techniques in quantitative finance, covering use cases involving both structured and alternative datasets. The focus of the talk will be on promoting reproducible research (through Jupyter notebooks and interactive plots) and interpretable models. Read more.