We conduct research in computer architecture and computer systems, with the goal of making computing systems at all scales faster, more energy-efficient, cost-effective, and secure. Our recent work focuses on cloud computing infrastructure, systems for machine learning, and the use of machine learning to optimize systems.

Active Research Themes

AI Efficiency

AI workloads are growing in compute and memory demands far faster than hardware capabilities can keep up. We are developing systems techniques and AI-system interfaces that bridge this efficiency gap.

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Scale-out AI systems

Today’s AI infrastructure, rooted in HPC paradigms, is inflexible and expensive. We are designing AI systems around scale-out principles to improve scalability, resilience, and cost effectiveness.

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AI for Systems

We are applying AI techniques to design and optimize both hardware and software systems. We place particular emphasis on testing and verification to ensure AI-designed systems are trustworthy and robust.

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HW/SW co-design

We are co-designing hardware, system software, and applications to improve the efficiency of cloud infrastructure, frequently using AI techniques to automate optimization.

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News

2016-07-14

Christos featured in an IEEE interview on how do we fix data-movement bottlenecks in AI systems.

2026-05-01

Congratulations to Christos on receiving the 2026 IEEE Computer Society Harry H. Goode Memorial Award.

2026-02-24

Congratulations to Dr Athinagoras Skiadopoulos on submitting his PhD thesis titled “Flexible systems for dynamic artificial intelligence”. Athinagoras is joining NVIDIA Research.

2025-05-28

Congratulations to Dr Mark Zhao on submitting his PhD thesis titled “Scalable, efficient, and secure machine learning data systems”. Mark is joining the faculty at Boulder CS in the fall.

2025-05-27

Congratulations to Dr Timothy Chong on submitting his PhD thesis titled “Addressing endpoint-induced congestion with duplicate acknowledgment”.

2025-05-27

Congratulations to Zhiqiang Xie on being awarded an NVIDIA Graduate Fellowship.

People

Alumni

Design Adapted from MIT Visualization Group