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Yikui Zhang

dblp:43/2491 · DBLP profile ↗
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2ranked-venue papers
1as first author
1since 2021 · last 2025
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Security and privacy · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Environmental and earth informatics · 70% Medical and health informatics · 30%
Artificial intelligence
1 paper
Deep learning architectures and training · 100%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Deep learning architectures and training › state space model
mamba
0.912025
RiverMamba: A State Space Model for Global River Discharge and Flood Forecasting · NeurIPS 2025
Machine learning › Deep learning architectures and training
state space model
0.912025
RiverMamba: A State Space Model for Global River Discharge and Flood Forecasting · NeurIPS 2025
Medical and health informatics › clinical prediction
early warning
0.912025
RiverMamba: A State Space Model for Global River Discharge and Flood Forecasting · NeurIPS 2025
Environmental and earth informatics › hydrology
flood forecasting
0.912025
RiverMamba: A State Space Model for Global River Discharge and Flood Forecasting · NeurIPS 2025
Environmental and earth informatics
hydrology
0.912025
RiverMamba: A State Space Model for Global River Discharge and Flood Forecasting · NeurIPS 2025

Methods — techniques the papers use, named apart from their topics

pre-training · 1.7mamba blocks · 0.9mamba block · 0.9
YearPublicationVenuePosition
2025 RiverMamba: A State Space Model for Global River Discharge and Flood Forecasting
abstract
Recent deep learning approaches for river discharge forecasting have improved the accuracy and efficiency in flood forecasting, enabling more reliable early warning systems for risk management. Nevertheless, existing deep learning approaches in hydrology remain largely confined to local-scale applications and do not leverage the inherent spatial connections of bodies of water. Thus, there is a strong need for new deep learning methodologies that are capable of modeling spatio-temporal relations to improve river discharge and flood forecasting for scientific and operational applications. To address this, we present RiverMamba, a novel deep learning model that is pretrained with long-term reanalysis data and that can forecast global river discharge and floods on a $0.05^\circ$ grid up to 7 days lead time, which is of high relevance in early warning. To achieve this, RiverMamba leverages efficient Mamba blocks that enable the model to capture spatio-temporal relations in very large river networks and enhance its forecast capability for longer lead times. The forecast blocks integrate ECMWF HRES meteorological forecasts, while accounting for their inaccuracies through spatio-temporal modeling. Our analysis demonstrates that RiverMamba provides reliable predictions of river discharge across various flood return periods, including extreme floods, and lead times, surpassing both AI- and physics-based models. The source code and datasets are publicly available at the project page https://hakamshams.github.io/RiverMamba.
Mohamad Hakam Shams Eddin, Yikui Zhang, Stefan Kollet, Juergen Gall
NeurIPS2
2011 Teaching Method Practicing on Embedded Software Engineering Curriculum
abstract
Serving as a part of the engineering education, curriculum reform plays an important role in higher education reforms. This paper presents the reform practice on curriculum of Intel Atom Platform Embedded Application and Practice and Test Techniques. It deeply discussed specific measures in the Embedded Software Engineering Curriculum by referencing a variety of teaching reform methods. On summarizing the feedback information from undergraduates of grade 2008 and 2009, the outcome is well meet the expectation, therefore, the approach can be considered as a new attempt for the teaching reform on practical curriculum of software engineering.
Yikui Zhang, Yunfen Tian, Liwei Zhu, Haoxiang Zuo
TrustCom1