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Invited Speakers - Data Scientists from Sparkcognition
Speakers: 1. Kevin Gullikson - Senior Data Scientist at SparkCognition 2. Sari Andoni - Senior Data Scientist at SparkCognition Topic: 1. Anomaly detection and Unsupervised Clustering in real world data. 2. Automating Machine Learning to Solve Real World Problems Kevin's bio: Kevin Gullikson is a Senior Data Scientist at SparkCognition, where his focus has been on predicting or detecting failures in a variety of industrial assets ranging from airplane engines to energy-producing combustion turbines. He has developed several clustering and anomaly detection models for these assets to aid in the prediction. Kevin has a PhD from the University of Texas in Astronomy. Kevin's topic: "So you've clustered your data. Now what?" Unsupervised learning in real data is easy to do with modern tools, but very hard to interpret or validate. This talk will start with a brief introduction to standard clustering and anomaly detection methods, and discuss ways to estimate important features for clusters and anomalies. I will then describe ways to measure how well a clustering and anomaly detection algorithm is working in various contexts. Sari's Bio: Sari Andoni is a Senior Data Scientist at SparkCognition, Inc. He has extensive experience in machine learning, neural networks and deep learning combined with a research background in neurobiology. With published research in leading journals, Sari currently focuses on biology-inspired algorithms for automated model building and multivariate time-series data analysis. He received his Bachelors degree in Computer Sciences and Mathematics from Brigham Young University, and a PhD from the Institute for Neuroscience at The University of Texas at Austin. For his dissertation, Sari studied the auditory midbrain and how the auditory system classifies natural vocalizations into behaviorally relevant perceptions. In his postdoctoral research, he studied the visual system focusing on the interaction of spontaneous activity with stimulus-evoked responses in the thalamocortical circuit. Sari's Topic: "Automating Machine Learning to Solve Real World Problems" With the proliferation of large amounts of data in the world, I will discuss how automated machine learning (AutoML) can help up scale the data science process. I will start with the current state of AutoML using both traditional ML models and deep neural networks, how it is evolving, and show how automating model building can help solve real world applications and use cases.

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701 Brazos St 16th Floor · Austin, TX

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Austin Group to discuss and present Technical topics on Big Data, AI , Machine Learning,Deep Learning, AI related technologies.

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