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Interview with Michael Schmidt

Interview with Micha...

Bio: Michael Schmidt’s research focuses on “Machine Science” – a direction in artificial intelligence research to accelerate data-driven discovery. Over the past 6 years, he has worked on algorithms and techniques to automate knowledge discovery from data. In particular, he has published extensively on identifying mathematical relationships (such as laws of physics) in experimental data, and […]

Interview with Kaz Sato

Interview with Kaz S...

Bio: Kaz Sato is Staff Developer Advocate at Cloud Platform team, Google Inc. He leads the developer advocacy team for Machine Learning and Data Analytics products, such as TensorFlow, Vision API and BigQuery. Kaz also has been leading and supporting developer communities for Google Cloud for over 7 years. He is interested in hardwares and IoT, […]

Interview with Michael Segala

Interview with Micha...

Bio: After completing my PhD in Particle Physics from Brown University, I worked as a data scientist in several well known tech companies, such as Compete Inc. and Akamai Technologies. More recently, I co-founded SFL Scientific, a data science consulting firm that specializes in big data solutions. We use advanced machine learning and analytics techniques […]

Breaking Down Paywalls for Online Health

Breaking Down Paywal...

Abstract: 25% of people searching for health information online hit a paywall. By analyzing Reddit Scholar, we find the number of social media users requesting scientific papers is growing 37% per month, and the public’s primary interest is in biomedical topics. Medical knowledge is locked up in non-open access scientific research papers which have copyright […]

Interview with Lukas Biewald

Interview with Lukas...

In this interview with ODSC, Lukas Biewald, CEO of Crowdflower, tells us how being the first data scientist hired at a startup inspired him to start his own company.

Machine Learning for Suits

Machine Learning for...

Abstract: You will learn the basic concepts of machine learning – such as Modeling, Model Selection, Loss or Profit, overfitting, and validation – in a non-mathematical way, so that you can ask for data analysis and interpret the results of a model in the context of making business decisions. The concepts behind machine learning are […]

The Information Commons

The Information Comm...

“The Information Commons: Ecosystems, Public Discourse, and Open Source GIS” Abstract: The recent open data revolution and open source technology have become one of the most important public assets of our time. This session will explore the importance of this new technological paradigm and foster an understanding about the importance of data accessibility, and tools […]

Key Performance Indicators Design and Vis.

Key Performance Indi...

“Key Performance Indicators (KPIs) Design and Visualization in the Era of Data Science” Abstract: This talk presents the capabilities and functionalities of Cortana Intelligence Suite (CIS) to visualize different types of KPIs in the era of data science. CIS delivers big data and advanced analytics capabilities to help enterprises transform their data into intelligent action. […]

Big Data: The Future Today

Big Data: The Future...

Abstract: The availability of new data, be it from businesses, from scientific experiments, from social networks, or from the Internet of Things (IoT) are ushering in a new era of scientific enlightenment, social revolution and business Darwinism. These new eras will fundamentally reshape the way we work, live, and think, and will retrain us to […]