“Navigating the Data Deluge: AI, Infrastructure, and Decision-Making in the Era of Big Data”

Abstract:

Science has traditionally harnessed data to inform decisions. Historically, data was sufficiently low-dimensional and manageable for human processing. However, the rapid expansion of sensing technologies across disciplines has overwhelmed traditional human-centric methods with vast, high-velocity data streams from diverse and often unreliable sources. Despite the remarkable advances in computers and large language models like ChatGPT, their capabilities remain limited. Current AI algorithms predominantly excel in interpolation, not extrapolation, leading to unrealistic and nonsensical outputs when stretched beyond their training data.

This talk explores the intersection of massive data influx and AI, focusing on their limitations and potential in enhancing decision-making, particularly in data-driven infrastructure. We propose a “humanistic carrot” – not the “stick” approach to address pressing challenges in scientific data management, spotlighting DataFed – a comprehensive data management system. This platform facilitates autonomous pipelines for the curation, sharing, searching, and fine-grain access control of data and metadata. We demonstrate how DataFed can streamline data management for experimentalists, enhancing data stewardship while reducing their workload.

We also delve into the intricacies of handling high-velocity data streams, where gigabits per second of data necessitate immediate processing for critical decision-making or autonomous control. This section covers deploying high-availability inference servers for on-demand data analysis and reduction. Additionally, we explore the concept of AI co-design, where algorithms are optimized to fit on programmable logic, enabling rapid, intelligent analysis, decision-making, and control on ultra-low cost, low-power devices at unprecedented speeds. Finally, we discuss the broad applicability of these methodologies across various fields, from particle physics to astronomy, highlighting their potential to revolutionize our approach to data and AI integration.

Biography: Dr. Joshua C. Agar is an Assistant Professor in the Department of Mechanical Engineering and Mechanics at Drexel University. With a foundational background in experimental materials science, Dr. Agar is predominantly renowned for his pioneering contributions to AI algorithms, computing infrastructure, and the development of cyber-physical systems in the fields of materials synthesis and microscopy. His expertise has been applied across a wide array of disciplines, including particle and plasma physics, materials science, and fluid dynamics. An active member of various AI communities, particularly the FastML community, which emphasizes ultra-low latency ML co-design, Dr. Agar has earned recognition as a leader in AI innovation. His work has garnered attention from prestigious institutions such as the National Academy of Engineering and the National Science Foundation.

The UW Data Science Seminar is an annual lecture series at the University of Washington that hosts scholars working across applied areas of data science, such as the sciences, engineering, humanities and arts along with methodological areas in data science, such as computer science, applied math and statistics. Our presenters come from all domain fields and include occasional external speakers from regional partners, governmental agencies and industry.