Keynotes

Keynote 1: Manish Parashar

TITLE: Catalyzing the Transformative Impact of Open Science Data

ABSTRACT: Open data is integral to the scientific process and a key enabler of innovations and discoveries across all disciplines, and essential to addressing important scientific and societal grand challenges, especially given the increasing use of AI/ML. As a result, open and equitable access to this data is more critical than ever. This talk will discuss the importance and urgency of open data access and explore some of the barriers and challenges to such access. It then introduces the vision and architecture of the National Data Platform project, as part of a broader national cyberinfrastructure, aimed at catalyzing an open and extensible data ecosystem, and highlights key use cases.

BIO: Manish Parashar is the Chief AI Officer at the University of Utah. He is also the Director of the Scientific Computing and Imaging (SCI) Institute, Chair in Computational Science and Engineering, and Presidential Professor at the Kalhert School of Computing. Manish’s expertise is in high-performance parallel and distributed computing, large-scale data management, and cyberinfrastructure. Manish is the founding chair of the IEEE Technical Community on High Performance Computing (TCHPC) and is a Fellow of AAAS, ACM, and IEEE.

Keynote 2: Rob Ross

TITLE: TBD

ABSTRACT: TBD

Keynote 3: Laura Biven

TITLE: Trends and research challenges for data and computing ecosystems

ABSTRACT: TBD

BIO: Laura Biven is the Chief Data Officer for Jefferson Laboratory where she leads data strategy for both mission and operations and contributes to the High Performance Data Facility project. Prior to joining JLab, Laura led the Integrated Infrastructure and Emerging Technologies branch at the NIH Office of Data Science Strategy. In this role, she led the development of federated, interoperable data infrastructure and multi-disciplinary, NIH-wide research programs in artificial intelligence. Her projects included the NIH Cloud Platform Interoperability program, the Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) program, and the Advancing Health Research through Ethical, Multimodal AI Initiative. Before joining NIH, Laura spent 12 years at the Department of Energy, where she led strategic efforts in data management and data science including the development and implementation of the department-wide data management plan requirements; the PuRE Data initiative; and a portfolio of research in data management, analysis, and visualization. While in government, Laura served on multiple interagency working groups including as co-chair of the NITRD Interagency Working Group on Big Data and developing guidance for research data repositories and sharing. She is currently a member of the Academic Data Science Alliance board and the FAIR Digital Objects Forum Steering Committee. Laura began in public service as a AAAS Science and Technology Policy Fellow, serving first in the U.S. Department of Agriculture and then in the Department of State. She holds a first class MSci in math and physics from the University of Bristol and a Ph.D. in applied mathematics from the University of Warwick, both in the UK.

Keynote 4: Xian-He Sun

TITLE: TBD

ABSTRACT: TBD