ͬÐÔÁµÉ«Çé

Dr. Xian-He Sun is a University Distinguished Professor, the Ron Hochsprung Endowed Chair of Computer Science, and the director of the Gnosis Research Center for AI-driven data management at the Illinois Institute of Technology (Illinois Tech). Before joining Illinois Tech, he worked at DoE Ames National Laboratory, at ICASE, NASA Langley Research Center, at Louisiana State University, Baton Rouge, and was an ASEE fellow at Navy Research Laboratories. Dr. Sun is an IEEE fellow and is known for his memory-bounded speedup model, also called Sun-Ni’s Law, for scalable computing. His research interests include high-performance data management, memory and I/O systems, and performance evaluation and optimization. He has over 350 publications and 7 patents in these areas and is currently leading multiple large software development projects in high performance data management systems. Dr. Sun is the Editor-in-Chief of the IEEE Transactions on Parallel and Distributed Systems, and a former department chair of the Computer Science Department at Illinois Tech. He received the Golden Core award from IEEE CS society in 2017, the ACM Karsten Schwan Best Paper Award from ACM HPDC in 2019, and the first prize best paper award from ACM/IEEE CCGrid in 2021. More information about Dr. Sun can be found on his web site www.cs.iit.edu/~sun/.

The recent success of large language models is closely tied to the scaling law—that is, increasing the amount of data and computing power consistently yields more accurate solutions. The repeated validation of this scaling behavior motivates a renewed examination of scalable computing, the long-standing memory-wall problem, and the intertwined evolution of technology and applications. In this talk, we first review several turning points in scalable computing, highlighting their key discoveries and impacts. Drawing on the lessons learned and the emerging AI challenges, we then present our scalable high‑performance data management solution under the von Neumann architecture. Two NSF‑supported cyberinfrastructure systems—Hermes and ChronoLog—are used to illustrate this solution and demonstrate its effectiveness. Hermes focuses on technology‑driven memory‑wall issues, whereas ChronoLog addresses the application‑driven data‑log challenge, where AI technology is applied to timing for the first time. Finally, we will discuss our ongoing IOWrap data management system for AI applications, which is shaped by both technological advances and application needs.

Prashant Shenoy is currently a Distinguished Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst. He received the B.Tech degree in Computer Science and Engineering from the Indian Institute of Technology, Bombay and the M.S and PhD degrees in Computer Science from the University of Texas, Austin. His research interests lie in distributed systems and networking, with a recent emphasis on cloud and sustainable computing. He has been the recipient of several best paper awards at leading conferences, including two ACM Test of Time Awards. He is a fellow of the ACM, IEEE, AAAS, and AAIA.

The exponential growth of cloud computing has been a defining trend of our time, fueled by rapidly growing demands from online and data-intensive workloads. Despite the end of Denard scaling, the cloud's energy demand grew more slowly than expected over the past decade due to the aggressive implementation of energy-efficiency optimizations. However, the rise of AI workloads, which are often more resource-intensive than traditional cloud workloads, has led to rapid growth in data centers with power-hungry accelerators such as GPUs and TPUs, leading to a resurgence in the cloud's energy consumption and a strain on our electric grids.

In this talk, I will provide a systems perspective on the challenges and opportunities in enhancing the efficiency and sustainability of cloud platforms in the face of rising AI demand. I will discuss how systems resource management techniques, such as workload shifting, can enhance the efficiency of cloud platforms by exploiting the spatio-temporal variability in grid demand, energy availability, and electricity prices.  I will then discuss how these systems techniques introduce new tradeoffs in performance, efficiency, and cost during their operations and present ideas for navigating these tradeoffs. I will conclude with several open research challenges that the systems community must address to ensure the continued growth of AI-driven cloud platforms.

 

Learning the Action Grammar

Yiannis Aloimonos
Professor of Computational Vision and Intelligence at the Department of Computer Science, University of Maryland

---

Planning Agents for Collaborative Reasoning and Multimodal Generation

Mohit Bansal
John R. & Louise S. Parker Distinguished Professor of Computer Science, UNC Chapel Hill

---

The Eye In AI: Human Visual System (HVS) Approaches for Robust Artificial Intelligence

Karen Panetta
Distinguished Professor, Tufts University School of Engineering

---

A new way of looking at the 3D World for Better Representation and Understanding

Chandra Kambhamettu
Professor of Computer Science at the University of Delaware

---

Unlocking the Future: 30 Years of Innovation at the Crossroads of Visualization, Immersion, and Digital Twins

Carolina Cruz-Neira
Agere Chair Professor of Computer Science, University of Central Florida

---

Traversing the AI + X Continuum for Impact

Nitesh Chawla
Frank M. Freimann Professor of Computer Science and Engineering, University of Notre Dame

---