EVENT CANCELED BECAUSE OF EMERGENCY - [WEBINAR] Data Sharing for Data Science

TO ACCESS THE WEBINAR URL PLEASE REGISTER VIA ZOOM: https://us02web.zoom.us/webinar/register/WN_tf5EqT6ITrCXhr-AUvCQlQ

ABSTRACT: Combining data in surprising ways is a powerful technique in data science. For example, weather data combined with the right economic indicators can improve retail demand forecasting, but weather data combined with socio-economic indicators is also being used to prevent insect-borne disease. Whatever the data combination you’re looking for, data sharing is often a key step, and this can bring its own surprising challenges. Data sharing is not just a matter of getting permission. It also requires discovering who has the data you need, how to access it, how to deal with mismatches between different data sets and much more.

Please join us as our panelists share stories from their different experiences to dig into the technical and organizational challenges of data sharing and what you can do about them. We’ll also discuss new data initiatives including European-based Gaia-X and Linux Foundation’s Agstack among others.

Data scientists, data engineers, CDOs, CTOs, CIOs and data architects all should find this event useful.

SPEAKERS:
Ted Dunning – CTO for Data Fabric, HPE + frmr CTO, MapR, frmr board Apache Software Foundation
Ted Dunning is Chief Technology Officer for Data Fabric at Hewlett Packard Enterprise and after having served as CTO at MapR, now an HPE company. Until very recently he was also a board member for the Apache Software Foundation, as well as a PMC member, and committer on a number of projects. He’s contributed to clustering, classification, and matrix decomposition algorithms in Mahout and to the new Mahout Math library and designed the t-digest algorithm used in several open-source projects and by a variety of companies. Previously, Ted was Chief Architect behind the MusicMatch (now Yahoo Music) and Veoh recommendation systems and built fraud-detection systems for ID Analytics (LifeLock). Ted has coauthored a number of books on big data topics, including several published by O’Reilly related to machine learning, and has 24 issued patents to date plus a dozen pending.

Ellen Friedman – Principal Technologist, HPE Ezmeral + committer, Apache Software projects [MOD]
Ellen Friedman, Principal Technologist for HPE Ezmeral at Hewlett Packard Enterprise, is an international speaker, author and scientist, with a doctorate in biochemistry from Rice University. She currently focuses on large-scale data analytics and AI/machine learning. Ellen was a committer for the Apache Drill and Apache Mahout open source software projects and co-author of multiple books published by O’Reilly Media, including AI & Analytics at Scale, AI & Analytics in Production, Machine Learning Logistics, A New Look at Anomaly Detection and Innovations in Recommendation.

Joanna McKenzie – Principal Data Scientist, The Data Lab - Innovation Centre
Joanna has a PhD in particle physics and has spent the last ten years working in the energy industry with a strong focus on wind farms. She has experience managing a team of analysts, and a particular interest in the way people use technology to solve problems.

Suparna Bhattacharya – Distinguished Technologist, HPE AI Research
Suparna Bhattacharya is a Distinguished Technologist in the Hewlett-Packard Enterprise Storage CTO office. She holds a PhD in Computer Science and Automation from the Indian Institute of Science and a B.Tech in Electronics and Electrical Communication from IIT Kharagpur. Suparna worked at IBM from 1993-2014, delving into operating systems and file-system internals on various platforms. At IBM Research, Suparna initiated exploratory projects on software-defined memory and systems-software co-design for extreme-scale contextual and cognitive computing. These days at HPE she focuses on the implications of emerging non-volatile memory technologies in future systems.