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Siyu Lv

dblp:180/5290 · DBLP profile ↗
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4ranked-venue papers
1as first author
4since 2021 · last 2025
—ORCID · conflict

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

Databases, data management, data science and information retrieval · 2 · 2 since 2021Applied, interdisciplinary, general and emerging computing · 2 · 1 first-author · 2 since 2021

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.

Theoretical computer science
2 papers
Algorithmic game theory and mechanism design · 58% Mathematical optimization · 42%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Mathematical optimization › control theory › optimal control
stochastic control
0.912025
Sufficient stochastic maximum principle for mean-field control problems with regime switching in an infinite horizon · Sci. China Inf. Sci. 2025
Algorithmic game theory and mechanism design › stochastic games
stochastic differential game
0.812024
A zero-sum hybrid stochastic differential game with impulse controls · Sci. China Inf. Sci. 2024
Algorithmic game theory and mechanism design
zero-sum game
0.812024
A zero-sum hybrid stochastic differential game with impulse controls · Sci. China Inf. Sci. 2024

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

stochastic maximum principle · 0.9stochastic differential game theory · 0.8
YearPublicationVenuePosition
2025 Sufficient stochastic maximum principle for mean-field control problems with regime switching in an infinite horizon
Siyu Lv
Sci. China Inf. Sci.2
2024 A zero-sum hybrid stochastic differential game with impulse controls
Siyu Lv, Zhen Wu 0003, Jie Xiong 0004
Sci. China Inf. Sci.1
2024 Enhanced NSGA-II-based feature selection method for high-dimensional classification
Min Li 0020, Huan Ma 0007, Siyu Lv, Lei Wang 0191, Shaobo Deng
Inf. Sci.3
2023 What should we teach? A human-centered data science graduate curriculum model design for iField schools
abstract
Abstract The information schools, also referred to as iField schools, are leaders in data science education. This study aims to develop a data science graduate curriculum model from an information science perspective to support iField schools in developing data science graduate education. In June 2020, information about 96 data science graduate programs from iField schools worldwide was collected and analyzed using a mixed research method based on inductive content analysis. A wide range of data science competencies and skills development and 12 knowledge topics covered by the curriculum were obtained. The humanistic model is further taken as the theoretical and methodological basis for course model construction, and 12 course knowledge topics are reconstructed into 4 course modules, including (a) data‐driven methods and techniques; (b) domain knowledge; (c) legal, moral, and ethical aspects of data; and (d) shaping and developing personal traits, and human‐centered data science graduate curriculum model is formed. At the end of the study, the wide application prospect of this model is discussed.
Dan Wu 0003, Hao Xu 0031, Yaqi Sun, Siyu Lv
J. Assoc. Inf. Sci. Technol.4