CRA-I Blog

The CRA-I Blog frequently shares news, timely information about the computing research industry community, and items of interest to the general community. Subscribe to blog emails here to stay connected.

NSF Privacy-Preserving Data Sharing in Practice (PDaSP)

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In our hyperconnected world, increasing computational power and the rapid growth of data offer vast opportunities for data-driven decision-making and scientific advancement. To harness these benefits responsibly, especially for training AI models, we need scalable technologies for privacy-preserving data sharing. Despite significant research progress, these technologies are still at varying stages of practical deployment.

The National Science Foundation (NSF) just released a new solicitation for a new program called Privacy-Preserving Data Sharing in Practice (PDaSP). 

From the press release: 

The goals of the PDaSP program are aligned with the Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence (AI EO), which emphasizes the role for privacy-enhancing technologies (PETs) in a responsible and safe AI future. The EO directs NSF to, “where feasible and appropriate, prioritize research — including efforts to translate research discoveries into practical applications — that encourage the adoption of leading-edge PETs solutions for agencies’ use.” It also tasks NSF with “developing and helping to ensure the availability of testing environments, such as testbeds, to support the development of safe, secure, and trustworthy AI technologies, as well as to support the design, development, and deployment of associated PETs.” In addition to meeting these directives in the AI EO, the PDaSP program strives to address key recommendations made in the National Strategy to Advance Privacy Preserving Data Sharing and Analytics (PPDSA). In particular, the program strives to advance the strategy’s priority to “Accelerate Transition to Practice,” which includes efforts to “promote applied and translational research and systems development,” develop “tool repositories, measurement methods, benchmarking, and testbeds,” and “improve usability and inclusiveness of PPDSA solutions.”

The full proposal deadline is September 27, 2024 and it welcomes proposals from qualified researchers and multidisciplinary teams in the following tracks with expected funding ranges for proposals as shown below.

Track 1: Advancing key technologies to enable practical PPDSA solutions:

  • Track 1 projects are expected to be budgeted in the $500K – $1M range for up to 2 years

Track 2: Integrated and comprehensive solutions for trustworthy data sharing in application settings: 

  • Track 2 projects are expected to be budgeted in the  $1M – $1.5M range for up to 3 years

Track 3: Usable tools, and testbeds for trustworthy sharing of private or otherwise confidential data.

  • Track 3 projects are expected to be budgeted in the $500K – $1.5M range for up to 3 years

The PDaSP program represents the collaborative efforts of the NSF Technology, Innovation and Partnerships (TIP) and Computer and Information Science and Engineering (CISE) directorates, Intel Corporation and VMware LLC as industry partners, and the U.S. Department of Transportation Federal Highway Administration (FHWA) and the U.S. Department of Commerce National Institute of Standards and Technology (NIST) as federal agency partners.

There is a virtual question and answer session in which program directors will discuss eligibility and how the program is structured. After an initial presentation, ample time will be allotted for questions from attendees.  They will be held on Friday, July 12, 1:30–3:00 p.m. EDT  and Tuesday, July 23, 1–2:30 p.m. EDT. Please click those dates to register for a session. 

CRA-I Welcomes New Leadership!

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It is July 1st, which means it is CRA-Industry (CRA-I)’s first official change in leadership! We are delighted to welcome Divesh Srivastava and Fatma Özcan as our new Co-chairs! 

Fatma Özcan (Google)- Fatma Özcan works as a Principal Software Engineer at Google. She earned her PhD in computer science from University of Maryland, College Park. Fatma has been on the CRA-I Steering Committee since 2021 and has [helped] organized many CRA-I roundtables and workshops including  “Computing Research in Industry” and “Best Practices on using the Cloud for Computing Research” (roundtable and workshop). 

Divesh Srivastava (AT&T) – Divesh Srivastava is the Head of Database Research at AT&T. He earned his PhD in computer science from the University of Wisconsin, Madison. Divesh has been on the CRA-I Steering Committee since 2020 and the CRA-I Council Chair since 2023, helping to grow the group to 11 individuals from 11 different companies and institutions. He has also been the organizer for many CRA-I roundtables and workshops including  “Accessible Technology for All,” “Sharing Healthcare Data,” and “Corporate Responsibility and Computing Research.”

Fatma and Divesh will take over for Vivek Sarkar (Georgia Tech) and Ben Zorn (Microsoft) who did a tremendous job serving as CRA-I’s initial Co-chairs starting in 2021. They were chartered by the CRA Board to stand up the new Industry-focused committee and have grown it from just an idea to its current state – a full Steering Committee and (almost full) Council made up of 17 different companies and institutions of different sizes. We thank them for their service, and for turning CRA-I into an asset for the entire computing research community.

CRA-I GenAI for Research and Science Roundtable

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Recently, the Computing Research Association – Industry (CRA-I) held a dynamic roundtable event on “Generative AI (GenAI) for Research and Science,” bringing together industry leaders, researchers, and experts to delve into the transformative potential of GenAI, a subset of AI that generates new content by learning data patterns, across various scientific disciplines.

CRA-I Council member Elizabeth Bruce from Microsoft moderated the roundtable, and she emphasized its significance in automating creative processes and its broad applicability across sectors. The panelists were Travis Johnson (Director of Bioinformatics at the Indiana BioSciences Research Institute), Jing Liu (Executive Director of the Michigan Institute for Data Science), Vijay Murugesan (Staff Scientist at Pacific Northwest National Lab), and Neil Thompson (Director of the MIT FutureTech Lab). 

Johnson shared insights into how GenAI is revolutionizing drug discovery by generating new molecules based on existing data, expediting the identification of novel drug targets. Liu highlighted the potential of GenAI in facilitating interdisciplinary research and enhancing the scale of scientific endeavors. She also emphasized its role in distinguishing between valuable research and spurious results. Murugesan discussed a collaborative project with Microsoft leveraging GenAI to accelerate materials discovery for battery technology. He outlined how AI-driven screening of millions of materials drastically reduced the time and resources required for research. Thompson provided an economic perspective on the productivity gains enabled by GenAI. He underscored its role in empowering scientists to leverage computation more effectively and its potential to revolutionize scientific understanding through data-driven exploration.

At the end of the roundtable, Bruce asked the panelists what advice they would give young researchers today. Thompson initiated the final segment with sage advice from economist Hal Varian, emphasizing the importance of identifying areas complementary to emerging technologies like GenAI. He encouraged young researchers to focus on domains that can leverage AI advancements effectively, such as material science and biology, and to continuously adapt and evolve their research focus in response to technological advancements. Echoing Thompson’s sentiments, Murugesan underscored the significance of adaptability in the rapidly evolving research landscape. He highlighted the transformative impact of GenAI on traditional research paradigms and emphasized the need for researchers to stay agile and adaptable in their thinking and research focus to remain relevant and impactful. Liu shared a poignant reflection from her postdoctoral mentor, emphasizing the importance of mindful contribution to science amidst technological advancements. She encouraged young researchers to reflect on their research goals and aspirations, prioritizing rigor and meaningful contributions to scientific knowledge while leveraging evolving tools and methodologies. Finally, Johnson concluded the discussion by emphasizing the importance of responsible AI utilization in research. While advocating for the use of GenAI as a powerful tool for hypothesis generation and data analysis, he cautioned against over-reliance on AI-generated insights. Johnson urged young researchers to maintain expertise in their respective fields while leveraging AI as a supplementary tool to enhance research outcomes.

Overall, the CRA-I roundtable served as a forum for thought-provoking dialogue and collaboration, shedding light on the transformative impact of Generative AI in driving scientific innovation. See the full recording here.