Best of WiDS at IBM 2023: Talks on Language Models and Foundation Models

Topic repeated at 2 different times, please join the session that is best suited for your time zone.
This session is open to everyone interested in Data Science! Each year IBMers apply to be WiDS Ambassadors, and host a number of WiDS Regional events, often in collaboration with universities, partners, etc. The Ambassadors bring the Women in Data Science (WiDS) Worldwide conference, a program from Stanford University, to colleagues and communities at large.

Join this session to hear from two outstanding speakers who participated in WiDS events:

Talk Title: Open-Source Knowledge for Language Models
· Speaker: Dr. Rosario Uceda-Sosa, Senior Technical Staff Member, IBM
· Duration: 30 minutes including Q&A

Talk Title: Security of Foundation Models
· Speaker: Dr Nathalie Baracaldo, Research Staff Member, IBM
· Duration: 30 minutes including Q&A

WiDS @ IBM is an independent event that is organized by the Technical Meetup Group hosted by IBM as part of the annual WiDS Worldwide conference, the WiDS Datathon, and an estimated 200 WiDS Regional Events worldwide. Everyone is invited to attend all WiDS conference and WiDS Datathon Workshop events which feature outstanding women doing outstanding work.

Presenters: Dr. Rosario Uceda-Sosa, Dr Nathalie Baracaldo

Dr. Rosario Uceda-Sosa is a leading researcher in semantic information models in IBM T.J. Watson Research Lab and has worked in ontologies for various domains such as smart cities, financial services, healthcare, conversational systems, and tourism, among others. Her work focuses on the usability of ontologies as the next generation of data source schemas. Her industrial experience includes working as a consultant in software design and usability, modeling and web technologies for companies like Ford, Chrysler, GM and Kellogg. In 2008, Dr. Uceda-Sosa led the Global Technology Outlook, a worldwide IBM strategy process that helps drive IBM's 6-billion-dollar investment in R&D. She has delivered keynote speeches at AAAI-2014 Semantic Cities and was the Industry Day Chair for IJCAI-16 and has participated in program committees for conferences like AAAI, IJCAI and ACM Multimedia. She has authored 28 US and global patents and was named senior member of the ACM. She holds a PhD in Computer Science and Engineering, a MS in Mathematics, and a MS in Computer Engineering from the University of Michigan.

Dr Nathalie Baracaldo leads the AI Security and Privacy Solutions team and is a Research Staff Member at IBM’s Almaden Research Center in San Jose, CA. Nathalie is passionate about delivering machine learning solutions that are highly accurate, withstand adversarial attacks and protect data privacy. Her team focuses on two main areas: federated learning, where models are trained without directly accessing training data, and adversarial machine learning, where defenses are designed to withstand potential attacks to the machine learning pipeline. Nathalie is the primary investigator for the DARPA program “Guaranteeing AI Robustness Against Deception” (GARD). Nathalie co-edited the book “Federated Learning: A Comprehensive Overview of Methods and Applications” Springer, 2022. In 2020, Nathalie received the IBM Master Inventor distinction for her contributions to the IBM intellectual property and innovation. Nathalie also received the 2021 Corporate Technical Recognition, one of the highest recognitions provided to IBMers for breakthrough technical achievements to the Trusted AI Initiative, which has led to notable market and industry success for IBM. Nathalie received her Ph.D. degree from the University of Pittsburgh, USA in 2016.

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Please join us at the session that is best suited to your time zone. Note that this topic is:
1. Repeated at two different times to accommodate various time zones, because it is
2. Posted simultaneously in multiple meetup groups world-wide
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It is recommended that you register at this Webex link ahead of time to receive a calendar invite and reminder. https://ibm.webex.com/weblink/register/r2aadbcb51daafb7fdb7ae889e3b019d1