Kate Keahey is one of the pioneers of infrastructure cloud computing. She created the Nimbus project, recognized as the first open-source Infrastructure-as-a-Service implementation, and continues to work on research aligning cloud computing concepts with the needs of scientific data centers and applications. She leads the Chameleon project, which provides a deeply reconfigurable, large-scale, and open experimental platform for computer science research. She also co-founded and serves as co-Editor-in-Chief of the SoftwareX journal. Kate is a Senior Scientist at Argonne National Laboratory and a Senior Scientist in the University of Chicago Consortium for Advanced Science and Engineering (UChicago CASE).

Technology is advancing at an ever accelerating pace creating new opportunities for science: improved sensor technologies now enable precise measurement of a growing range of physical phenomena; single-board computers (SBCs) allow computing to be deployed in hitherto unviable settings cost-effectively; and networking technologies, improving in both quality and coverage, allow us to connect them all into a powerful observatory instrument, adaptable to investigating a wide range of questions. Like a telescope array that uses computation to stitch together input from multiple single telescopes to obtain the desired picture of the sky, a generalized instrument of this kind can rely on a combination of sensing and computation deployed in the field, to "stitch together" a picture of qualities relating to fields ranging from hydrology to medicine. Powered by Al capabilities it can then allow us to investigate problems from floods and wildfires to social inequalities. However, building new experimental systems is both complex and risky: What problems should they solve? How do we put together new technologies to create scientific instruments? If we build them, will they come? At the same time, it is both rewarding and necessary to invest in experimental platforms as they are an essential catalyst for science. In this talk, I will present lessons learned from the development of two NSF-funded, influential, and very distinct experimental systems: the Chameleon project and the FLOTO instrument for distributed broadband research. Chameleon represents over 10 years of experience in constructing, operating, and evolving an instrument for computer science research and education that has to date been used by over 14,000 users, who collectively produced over 1,300 scholarly publications in computer science. It has supported innovative educational ventures, student competitions, and artifact evaluation initiatives, changing community practices on how to share research and education experiences in a digital form. FLOTO has built an edge-to-cloud instrument that deployed an unprecedented 600 SBCs nationwide in the United States to measure broadband, highlight areas of need, and help ensure equitable access to the Internet for all Americans. Both represent experimental systems with distinct objectives and challenges - and both provide lessons and illustrations on how to leverage the exponentially growing opportunities in experimental system design.

Cullen Bash is a Vice President of R&D and Deputy Director of HPE Labs. He also serves as Director of the Systems Architecture Lab, a multidisciplinary team advancing next-generation systems architecture from research to revenue. His work spans system and fabric architecture, system software, simulation and modeling, software-hardware co-design, optimization, and sustainability. Previously, he directed the Platform Architecture Lab and served as interim Director of the Sustainable Ecosystems Research Group, where he led research into the sustainability of IT equipment and the use of IT to improve the sustainability of customer ecosystems. He was also Principal Investigator of the Sustainable Data Center project. He is a Fellow of both IEEE and the American Society of Mechanical Engineers.

AI is reshaping the world, but its energy footprint is surging with data centers spreading into new communities, particularly in the United States. As such, energy use needs to be considered holistically, from generation, to powering IT infrastructure, and to reuse. This talk with explore how intelligent systems adapt across the data center and energy lifecycle – balancing demand, integrating distributed power, and enhancing grid stability. We’ll discuss ways in which AI infrastructure can shift from energy consumer to active grid partner by powering scalable, resilient intelligence for the future.

Ravi Pasula is a technology and AI leader with over 20 years of experience driving digital transformation through data science, machine learning, and advanced analytics. As Senior Director of Machine Learning at Equinix, he leads global teams developing AI-powered solutions that optimize operations, accelerate business decisions, and enable intelligent digital infrastructure at worldwide scale.

Shaolei Ren is a Professor of Electrical and Computer Engineering at the University of California, Riverside. His research focuses on modeling and algorithms for efficient AI and computing systems. He is a recipient of the U.S. National Science Foundation CAREER Award (2015). He received his Ph.D. degree from the University of California, Los Angeles.

Zoran Dimitrijevic is an independent technologist and startup advisor focusing on AI-driven large-scale distributed systems. He previously held principal engineering leadership roles at Airbnb, SAP, and Altiscale, and was an early software engineer at Google. He has led foundational work across storage, data platforms, search products, and core infrastructure. He holds M.Sc. and Ph.D. degrees in Computer Science from UC Santa Barbara and is an IEEE Senior Member.