ApertureData has been awarded a National Science Foundation (NSF) Small Business Innovation Research (SBIR) grant for $225,000 to conduct research and development (R&D) work on scaling ApertureData’s visual data management platform for enterprise scale applications.
The proposed research will take ApertureData closer to realizing the vision of a unified data backend for all stages of machine learning (ML) from edge to cloud, removing inefficiencies introduced by repurposing systems designed for other workloads. Capturing business value from data via ML comprises multiple steps (data collection, curation, training, etc) and is currently being addressed by multiple siloed solutions that, when integrated, result in an inefficient system. Given that each of the different steps interacts with data in one way or another, offering a unified and efficient way to interact with the data regardless of the stage reduces the complexity of ML pipelines as they scale.
“NSF is proud to support the technology of the future by thinking beyond incremental developments and funding the most creative, impactful ideas across all markets and areas of science and engineering,” said Andrea Belz, Division Director of the Division of Industrial Innovation and Partnerships at NSF. “With the support of our research funds, any deep technology startup or small business can guide basic science into meaningful solutions that address tremendous needs.”
“Improvements in ML have made it possible for businesses to extract rich insights from visual data (images, videos). Handling big-visual-data for ML requires storage and access methods that are designed with visual ML in mind. With the current off-the-shelf alternatives, ML engineers and data scientists are forced to glue data solutions not designed for visual data management. With our focus on the data side of ML deployments, ApertureData is well positioned to be the technical leader in addressing the next generation of challenges for ML based applications. This NSF grant will enable us to address the scalability challenges that are particularly magnified when dealing with image, videos, and its corresponding metadata like annotations and embeddings”, said Vishakha Gupta-Cledat, CEO and Co-founder of ApertureData, who will serve as the Principal Investigator for this grant.
Once a small business is awarded a Phase I SBIR/STTR grant (up to $256,000), it becomes eligible to apply for a Phase II grant (up to $1,000,000). Small businesses with Phase II grants are eligible to receive up to $500,000 in additional matching funds with qualifying third-party investment or sales.
Startups or entrepreneurs who submit a three-page Project Pitch will know within three weeks if they meet the program’s objectives to support innovative technologies that show promise of commercial and/or societal impact and involve a level of technical risk. Small businesses with innovative science and technology solutions, and commercial potential are encouraged to apply.All proposals submitted to the NSF SBIR/STTR program, also known as America’s Seed Fund powered by NSF, undergo a rigorous merit-based review process. To learn more about America’s Seed Fund powered by NSF, visit: https://seedfund.nsf.gov/
About the National Science Foundation’s Small Business Programs: America’s Seed Fund powered by NSF awards $200 million annually to startups and small businesses, transforming scientific discovery into products and services with commercial and societal impact. Startups working across almost all areas of science and technology can receive up to $1.75 million to support research and development (R&D), helping de-risk technology for commercial success. America’s Seed Fund is congressionally mandated through the Small Business Innovation Research (SBIR) program. The NSF is an independent federal agency with a budget of about $8.1 billion that supports fundamental research and education across all fields of science and engineering.
About the Author – Vishakha Gupta
Vishakha is Co-founder and CEO of ApertureData. Prior to that, she worked at Intel Labs for over 7 years where she led the design and development of VDMS (the Visual Data Management System) which forms the core of the ApertureData Platform. Vishakha holds a Ph.D in Computer Science from the Georgia Institute of Technology and a M.S. in Information Networking from Carnegie Mellon University. Her research interests encompass systems in general with particular inclination towards virtualization, embedded and real time systems as well as distributed systems. She has worked on graph based storage and applications on non volatile memory systems. She loves to work on systems which impose stringent requirements in terms of software design and coding and call for innovative solutions. She has served on the program and steering committees of several premier systems conferences.