Thrilled to share that I’m awarded the AiNed Fellowship Grant (worth $1M) to lead the 5-year DataLibra research project at CWI in Amsterdam starting fall 2024. DataLibra is focused on democratizing insight retrieval from structured data through representation and generative learning over tables.
Altogether this project is funded by $1.5M by NWO and CWI and will host 4 PhD students and 1 Postdoc researcher besides myself. This means that I’ll be hiring PhDs and Postdocs for pioneering research on the intersection of AI, and tables and relational DBs! Are you interested? Share your info through and I’ll reach out soon! Please also share with friends and colleagues who might be interested.
I am very grateful for the initial nomination by CWI and for being awarded the grant by NWO and and AiNed. I look forward to making the DataLibra vision a reality @cwi_da, and embarking on various interdisciplinary and cross-institute collaborations.
The project abstract is below:
By 2023, approximately 120 zettabytes of data has been collected worldwide but less than 1% is actually used. We observe that structured data, e.g. tables, spreadsheets, and relational databases, is prevalent in the data landscape, typically driving important decision-making processes in healthcare, governments and finance. While large corporations are effectively developing their data science capabilities to put their data to use, smaller companies, non-profits and public institutions, are falling behind, inducing an inequality in data literacy. In the meantime, AI has demonstrated high impact for applications on unstructured data (e.g. text) and images, but proportional progress on structured data is lacking.
With the DataLibra project, we alleviate these gaps by democratizing insight retrieval from (semi-)structured data through Table Representation Learning. This ambitious goal translates into trustworthy, secure, and responsible data analytics, enabling everyone to become independent data-driven decision makers. The projects in DataLibra address challenges across the end-to-end data analytics pipeline; from efficient data storage and query execution to automated responsible data quality documentation and improvement, and from multimodal data integration and querying, to insight retrieval systems. To accomplish this ambitious objective spanning multiple expertise areas, the project will involve collaborations across various knowledge institutes and innovation labs.