D2K Industry Insights

The D2K Industry Insights Seminar Series brings together industry and academic computational researchers for seminars on applied data science research or data science case studies from a variety of industries and disciplines. Rice students and faculty learn how data science tools are applied in a real-world setting in these 60-minute Zoom seminars. It is a great way to hear about research in the industry, network with data science professionals and ask questions.

Recent Event:

Although the field of optical character recognition (OCR) has been around for half a century, document parsing and field extraction from images remain an open research topic. We utilize an end-to-end deep learning architecture to predict regions of interest within documents and automatically extract their text.

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Past Events

As organizations progress in their AI journey, an AI strategy that fosters inclusion becomes crucial. This talk will define the elements of an inclusive AI strategy and how different organizations articulate and implement it. Read more >>

In an ever-changing marketplace, the validity of solutions in all environments can be a roadblock. Instead, algorithms, lacking mathematical rigor, but incorporating appropriate safety checks, can be leveraged to capture the desired business value in a more quick and iterative manner. Being able to simply and easily communicate a solution is a valuable albeit difficult to quantify metric.

In this seminar, we explored the algorithms and techniques that make up the prediction system and cover the custom data science infrastructure that’s been created by the bp High-Performance Computing team to support running data science at extreme scale on bp’s supercomputer.

Senior data scientist Dennis Furlaneto (ExxonMobil) shares insights into how a data science team is structured to tackle large projects at ExxonMobil.

Shell data scientists shared how artificial intelligence was used to unlock insights from ESP sensor data to understand the operating conditions which lead to a trip and failure of these systems.

Machine learning engineer from bill.com shared an automatic payment due date extraction method that gets customers free from manual typing in due date for all his bills on bill.com platform.

Interested in speaking at future events? Please email us at d2k@rice.edu.

D2K Industry Insights