Bo Zhao is a Lecturer (Assistant Professor) in Computer Science at Queen Mary University of London and an Honorary Research Fellow at Imperial College London. Bo’s research focuses on efficient data-intensive systems at the intersection of scalable reinforcement learning systems and distributed data management systems, as well as compilation-based optimization techniques. His long-term goal is to explore and understand the fundamental connections between data management and modern machine learning systems to make decision-making transparent, robust and efficient. Please find more details via http://www.eecs.qmul.ac.uk/~bozhao/.

AG 1, AG 2, AG 3, INET, AG 4, AG 5, D6, SWS, RG1, MMCI   Info

AG Audience

English

60 Minutes

Saarbrücken

Efficient data-intensive systems translate data into value for decision making. As data is collected at unprecedented rates for timely analysis, the model-centric paradigm of machine learning (ML) is shifting towards a data-centric and system-centric paradigm. Recent breakthroughs in large ML models (e.g., GPT 4 and ChatGPT) and the remarkable outcomes of reinforcement learning (e.g., AlphaFold and AlphaCode) have shown that scalable data management and its optimizations are critical to obtain state-of-the-art performance. This talk aims to answer the question “how to co-design multiple layers of the software/system stack to improve scalability, performance, and energy efficiency of ML and data-intensive systems”. It addresses the challenges to build fully automated data-intensive systems that integrate the ML layer, the data management layer, and the compilation-based optimization layer. Finally, this talk will sketch and explore the vision to leverage the computational advantage of quantum computing on hybrid classic/quantum systems in the post-Moore era.

Please contact office for zoom link information.

Isabel Thät

+49 681 9303 9106

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Kaiserslautern

G26

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