Sep 23–26, 2019

Schedule: Financial Services sessions

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9:00am5:00pm Tuesday, September 24, 2019
Location: 1A 06
Richard Evans (Statistics Canada), Rosaria Silipo (KNIME), Leah Xu (Spotify), Arup Nanda (Capital One), Victoriya Kalmanovich (Navy), Shreya Sharma (Expedia Inc.), Martin Mendez-Costabel (Bayer Crop Science), Gloria Macia (Roche AG), Gwen Campbell (Revibe Technologies, Inc), Moise Convolbo (Rakuten), Muhammed Idris (Capria VC | TeraCrunch)
From banking to biotech, retail to government, every business sector is changing in the face of abundant data. Get better at defining business problems and applying data solutions at Strata. Read more.
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9:00am5:00pm Tuesday, September 24, 2019
Location: 1A 08
Alistair Croll (Solve For Interesting), Jennifer Yang (Wells Fargo ECS), Nitzan Mekel-Bobrov (Capital One), Dan Barker (RSA Security), Rochelle March (Trucost), Catherine Gu (Stanford University), Moto Tohda (Tokyo Century (USA) Inc.), Mikheil Nadareishvili (TBC Bank), Jennifer Kloke (Ayasdi)
From analyzing risk and detecting fraud to predicting payments and improving customer experience, take a deep dive into the ways data technologies are transforming the financial industry. Read more.
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1:30pm5:00pm Tuesday, September 24, 2019
Location: 1A 12/14
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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11:20am12:00pm Wednesday, September 25, 2019
Location: 1A 06/07
Ying Yau (AllianceBernstein)
Time series forecasting techniques can be applied in a wide range of scientific disciplines, business scenarios, and policy settings. This session discusses the application of deep learning techniques to time series forecasting and compares them to time series statistical models when forecasting time series with trends, multiple seasonality, regime switch, and exogenous series. Read more.
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1:15pm1:55pm Wednesday, September 25, 2019
Location: 1A 06/07
Every NLP based document processing solution depends on converting scanned documents/ images to machine readable text using an OCR solution. However, accuracy of OCR solutions is limited by quality of scanned images. We show that generative adversarial networks can be used to bring significant efficiencies in any document processing solution by enhancing resolution and de-noising scanned images. Read more.
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1:15pm1:55pm Wednesday, September 25, 2019
Location: 1A 08/10
James Tang (WalmartLabs), Yiyi Zeng (WalmartLabs), Linhong Kang (WalmartLabs)
How No1 retailer provides secure and seamless shopping experience through machine learning and large scale data analysis on centralized platform. Read more.
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2:55pm3:35pm Wednesday, September 25, 2019
Location: 1A 12/14
Jari Koister (FICO )
Machine Learning and Constraint-based Optimization are both used to solve critical business problems. They come from distinct research communities and have traditionally been treated separately. This talk describes how they are similar, how they differ and how they can be used to solve complex problems with amazing results. Read more.
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2:55pm3:35pm Wednesday, September 25, 2019
Location: 1A 15/16
Weisheng Xie (China Telecom BestPay Co., Ltd), Sijie Guo (ASF)
As a Fintech company of China Telecom with half billion registered users and 41 million monthly active users, risk control decision deployment has been critical to the success of the business. In this talk we share how we leverage Apache Pulsar to boost the efficiency of our risk control decision development for combating financial frauds over 50 million transactions a day. Read more.
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4:35pm5:15pm Wednesday, September 25, 2019
Location: 1A 06/07
Siddha Ganju (NVIDIA), Meher Kasam (Square)
Optimizing deep neural nets to run efficiently on mobile devices. Read more.
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11:20am12:00pm Thursday, September 26, 2019
Location: 1A 08/10
Anjali Samani (CircleUp)
The application of smoothing and imputation strategies is common practice in predictive modelling and time series analysis. With a technique-agnostic approach, this session will provide qualitative and quantitative frameworks that address questions related to smoothing and imputation of missing values to improve data density. Read more.
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3:45pm4:25pm Thursday, September 26, 2019
Location: 1A 12/14
David Mack (Octavian)
Graphs are a powerful way to represent knowledge. Organizations (in fields such as bio-sciences and finance) are starting to amass large knowledge graphs, but lack the machine-learning tools to extract the insights they need from them. In this presentation, I’ll give an overview of what insights are possible and survey the most popular approaches. Read more.
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3:45pm4:25pm Thursday, September 26, 2019
Location: 1A 23/24
Vitaliy Baklikov (Development Bank of Singapore), Dipti Borkar (Alluxio )
In this presentation, Vitaliy Baklikov from DBS Bank and Dipti Borkar from Alluxio will share how DBS Bank has built a modern big data analytics stack leveraging an object store even for data-intensive workloads like ATM forecasting and how it uses Alluxio to orchestrate data locality and data access for Spark workloads. Read more.
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4:35pm5:15pm Thursday, September 26, 2019
Location: 1E 07/08
Evgeny Vinogradov (Yandex.Money)
With a microservice architecture, DWH is a first place where all the data gets together. It supplied by many different datasources. It is used for many purposes – from near-OLTP till models fitting and realtime classifying. Talk will cover our experience in management and scaling of data Engineering Team and infrastructure for support of 20+ Product Teams. Read more.

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