14–17 Oct 2019

Schedule: Temporal data and time-series sessions

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10:1510:30 Wednesday, 16 October 2019
Location: King's Suite
Jeff Jonas (Senzing)
Average rating: ***..
(3.50, 2 ratings)
Entity resolution—determining “who is who” and “who is related to whom”—is essential to almost every industry, including banking, insurance, healthcare, marketing, telecommunications, social services, and more. Jeff Jonas details how you can use a purpose-built real-time AI, created for general-purpose entity resolution, to gain new insights and make better decisions faster. Read more.
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11:0511:45 Wednesday, 16 October 2019
Location: Buckingham Room - Palace Suite
Qun Ying (Microsoft)
Anomaly detection may sound old fashioned, yet it's super important in many industry applications. Tony Xing, Bixiong Xu, Congrui Huang, and Qun Ying detail a novel anomaly-detection algorithm based on spectral residual (SR) and convolutional neural network (CNN) and explain how this method was applied in the monitoring system supporting Microsoft AIOps and business incident prevention. Read more.
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11:5512:35 Wednesday, 16 October 2019
Location: King's Suite - Sandringham
Arun Kejariwal (Independent), Ira Cohen (Anodot)
Average rating: *****
(5.00, 2 ratings)
Sequence to sequence (S2S) modeling using neural networks has become increasingly mainstream in recent years. In particular, it's been used for applications such as speech recognition, language translation, and question answering. Arun Kejariwal and Ira Cohen walk you through how S2S modeling can be leveraged for these use cases, visualization, real-time anomaly detection, and forecasting. Read more.
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11:0511:45 Thursday, 17 October 2019
Location: Buckingham Room - Palace Suite
Zaid Tashman (Accenture Labs)
Today traditional approaches to predictive maintenance fall short. Zaid Tashman dives into a novel approach to predict rare events using a probabilistic model, the mixed membership hidden Markov model, highlighting the model's interpretability, its ability to incorporate expert knowledge, and how the model was used to predict the failure of water pumps in developing countries. Read more.
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11:5512:35 Thursday, 17 October 2019
Location: King's Suite - Balmoral
Ganes Kesari (Gramener), Soumya Ranjan (Gramener)
In many countries, policy decisions are disconnected from data, and very few avenues exist to understand deeper demographic and socioeconomic insights. Ganes Kesari and Soumya Ranjan explain how satellite imagery can be a powerful aid when viewed through the lens of deep learning. When combined with conventional data, it can help answer important questions and show inconsistencies in survey data. Read more.
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13:4514:25 Thursday, 17 October 2019
Location: Blenheim Room - Palace Suite
Sridhar Alla (BlueWhale)
Average rating: ***..
(3.00, 1 rating)
Any business, big or small, depends on analytics, whether the goal is revenue generation, churn reduction, or sales or marketing purposes. No matter the algorithm and the techniques used, the result depends on the accuracy and consistency of the data being processed. Sridhar Alla examines some techniques used to evaluate the quality of data and the means to detect the anomalies in the data. Read more.
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16:0016:40 Thursday, 17 October 2019
Location: King's Suite - Balmoral
Weifeng Zhong (Mercatus Center at George Mason University)
Average rating: ****.
(4.00, 1 rating)
Weifeng Zhong explores a novel method to learn structural changes embedded in unstructured texts based on the Policy Change Index (PCI) framework developed by economists Julian Chan and Weifeng Zhong. He explains how an off-the-shelf application of deep learning—with an important twist—can help you detect structural break points in time series text data. Read more.
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16:5017:30 Thursday, 17 October 2019
Location: Blenheim Room - Palace Suite
Vignesh Gopakumar (United Kingdom Atomic Energy Authority)
Vignesh Gopakumar explores image mapping of the temporal evolution of physics parameters as plasma interacts with the reactor wall using a data-inferred approach. The model captures how parameters such as temperature and density evolve across space and time. By analyzing the patterns found in simulation data, the model learns the existing physics relations implicitly defined within the data. Read more.

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