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Showing results 261-270 of 814.
DING Debbie
Assistant Professor of Practice
FU Courtney
Lecturer
ESD Research Seminar by Themis Gouleakis – Beyond worst-case: The rise of learning-augmented online algorithms
ESD Research Seminar by Themis Gouleakis – Online algorithms are traditionally analysed in adversarial worst-case settings, leading to guarantees that often fail to capture practical performance. The emerging framework of learning-augmented algorithms integrates machine-learned predictions into online decision making while preserving rigorous guarantees.
Congratulations to Assistant Professor Zehui Xiong for winning the IEEE TCSVC 2023 Rising Star Award
Congratulations to Assistant Professor Zehui Xiong for winning the IEEE TCSVC 2023 Rising Star Award
ISTD COIL Seminar by James Ong – AI for humanity: why it matters?
ISTD COIL Seminar by James Ong – As AI is progressing at exponential rate, are we steering toward a more sustainable development of AI that meets humanity’s needs? James will share his advocacy of why and how to embrace AI for Humanity.
Festival of Ideas in the Humanities and Social Sciences: “How the humanities and social sciences can help us hack the STEM fields”
Festival of Ideas in the Humanities and Social Sciences: “How the humanities and social sciences can help us hack the STEM fields”
ISTD Seminar by Maonan Wang – A multi-modal LLM-augmented deep reinforcement learning framework for adaptive traffic signal control
ISTD Seminar by Maonan Wang – Urban traffic congestion continues to pose severe challenges for sustainable mobility, and adaptive traffic signal control is widely recognized as a key solution.
Congratulations to Associate Professor Wu Lin’s graduate student in winning the best student paper award
Congratulations to Associate Professor Wu Lin’s graduate student in winning the best student paper award
QIN Yanxia
Lecturer
ESD Research Seminar by Lun Yu – Near-optimal data-driven control for assemble-to-order systems
ESD Research Seminar by Lun Yu – The literature on periodic-review assemble-to-order (ATO) systems has long assumed that the demand distribution is exactly known. In practice, this may not be true and only a limited sample of historical demand data is available. In this paper, we study data-driven ATO systems under the criteria of long-run average cost