Hybrid Cloud & AI
Technological innovations in hybrid cloud and artificial intelligence aim to expand the potential of edge computing and cloud security capabilities across public and private clouds. As the high-performance computing needs of global society ramp up, the ability to access curated data and processing power from multiple distributed data centers and workloads will be paramount.
With an emphasis on data protection and isolation, IBM and Illinois teams will collaborate to explore how open-source innovation and artificial intelligence can drive the next era of cloud computing, and define the essential workforce skills necessary for running increasingly powerful and critical workloads.
Current Research Projects
| Illinois Faculty | IBM Technical Leads | Topics |
|---|---|---|
|
Yongjoo Park, Jian Huang
|
Swaminathan Sundararaman, Hubertus Franke, Daniel Waddington | A Context-Aware Storage Infrastructure with Retrieval-Augmented Generation and System Co-Design |
| Nam Sung Kim, Saksham Agrawal | Hubertus Franke | Network and Compute Co-design of an Efficient LLM Training System Exploiting CXL |
| Yuxiong Wang, Vikram Adve, Lav Varshney, Reyhan Jabbarvand | Advancing Agentic AI Frameworks: Theory, Technology, and Applications in Software Engineering | |
| Tianyin Xu, Narendra Ahuja, Indranil Gupta, Lav Varshney, Deming Chen | Rohan Arora, Daby Sow, Ruchi Mahindru, Yu Deng, Amit Paradkar, Ruchir Puri, Krishna Ratakonda. | Agent-based Autonomous IT Reliability: Agent Design, Benchmarking, and Evaluation |
| Jian Huang, Zbigniew Kalbarczyk, Bin Hu, Ravishankar Iyer, Tamer Basar | Chen Wang, Srinivasan Parthasarathy | A Scalable Agentic Platform for Multi-Agent AI Cloud Management with Learning Techniques and Reasoning |
| Laxmikant Kale, Vlad Kindratenko, Radhika Mittal | Sara Kokkila-Schumacher, Chen Wang, Carlos Costas, Marquita Ellis, Apo Kayi, Apoorve Mohan, Pavlos Marinos | Distributed Adaptive Runtime, Collective Communication, and Programming Models for Hybrid Cloud |
| Charith Mendis, Vikram Adve, Deming Chen | Mudhakar Srivatsa | Algorithmic, systems and compiler efficiency improvements for optimizing deep neural networks |
|
Klara Nahrstedt, Josep Torrelas, Deming Chen, Tianyin Xu
|
Hubertus Franke, Tamar Eilam, Asser Tantawi, Alaa Youssef, Eun K. Lee | Energy-SLO-Optimized Agentic Systems with Cross-Layer KV Cache Management |
| Minjia Zhang | Raghu Kiran Ganti, Mudhakar Srivatsa, Davis wertheimer, Naigang Wang | Advancing the Inference and Training of Hybrid-Linear Time Sequence Modeling to Power Next-Generation AI at Scale |
| Heng Ji, Ying Diao, Nick Jackson | Radu Florian | Foundational AI Agents for Material Discovery |