Distinguished Scientist Award
Ying Tan
Peking University, China
The Distinguished Scientist Award profile recognizes Ying Tan’s reported scholarly record in computer science and its relevance to computational intelligence, optimization, and machine learning. Associated publication information includes research on fireworks algorithms, neural network optimization, and time-series prediction. The profile presents bibliometric indicators alongside selected publications to support an informed assessment of research activity. Award recognition remains subject to the event’s applicable evaluation and verification procedures.
Abstract
This article summarizes the supplied research profile of Ying Tan, affiliated with Peking University, China. The reported Scopus metrics comprise 359 documents, 8,578 citations, and an h-index of 46. Selected publications address optimization algorithms, neural networks, and sequence-based prediction. These topics connect computational methods with challenges in efficient model training and data-driven forecasting. The profile provides a structured basis for reviewing publication activity and research visibility, while acknowledging that bibliometric indicators alone cannot establish the originality or practical value of individual contributions. [1]
Keywords
Computer science; computational intelligence; swarm intelligence; fireworks algorithm; neural network optimization; contrastive learning; time-series prediction; bibliometrics; research evaluation.
Introduction
Computer science research increasingly combines optimization strategies, machine learning architectures, and intelligent computational systems. Research evaluation in this environment benefits from considering both measurable scholarly activity and the substance of published work. Ying Tan’s supplied profile and publication records provide examples of research addressing algorithmic search and predictive modeling. The Distinguished Scientist Award context offers an opportunity to assess this work through transparent criteria rather than relying exclusively on citation totals. [2]
Research Profile
The supplied profile identifies Peking University as the institutional affiliation and computer science as the subject area. Reported bibliometric indicators are 359 documents, 8,578 citations, and an h-index of 46. These figures describe the provided record and should be checked against the live Scopus profile before formal publication, since indexing and citation counts can change over time. [1]
Research Contributions
The supplied publication titles indicate several related research directions:
- Developing fireworks-based optimization methods and investigating their enhancement through multimodal large language models.
- Exploring contrastive learning to improve selectivity in Mamba-based time-series prediction.
- Implementing multi-guiding spark fireworks algorithms on GPUs for black-box neural network optimization.
Together, these topics concern search efficiency, predictive modeling, and computational implementation. Their specific contributions should be assessed from the full papers and experimental results. [3]
Publications
- “Visual-Enhanced Fireworks Algorithm via multimodal large language models.” Swarm and Evolutionary Computation, listed publication date November 2026. [3]
- “Repetitive contrastive learning enhances Mamba’s selectivity in time series prediction.” Neural Networks, April 2026. [4]
- “A GPU Implementation of Multi-guiding Spark Fireworks Algorithm for Efficient Black-Box Neural Network Optimization.” Book chapter, 2026.. [5]
The first item’s supplied publication date is November 2026, which is later than the current date of 27 October 2026; it should therefore be treated as forthcoming or scheduled unless the publisher confirms otherwise.
Research Impact
The reported citation count and h-index suggest an established indexed publication record. However, citation indicators vary by discipline, publication age, co-authorship, and database coverage. A balanced assessment should also examine methodological soundness, reproducibility, independent uptake, and the significance of individual findings. DOI records provide stable routes for verifying publication metadata and locating the associated works. [6]
Award Suitability
The profile presents evidence relevant to consideration for a Distinguished Scientist Award, including sustained publication activity, citation visibility, and research spanning optimization and machine learning. A complete award assessment should independently verify affiliation, authorship, publication status, and bibliometric data, and evaluate originality and research quality against the award’s published criteria. This profile does not itself establish an award decision.
Conclusion
Ying Tan’s supplied research profile combines substantial reported bibliometric indicators with publications addressing computational optimization and predictive learning. These details provide a useful starting point for scholarly recognition, provided that publication records and metrics are verified and qualitative research assessment remains central.
External Links
References
- Elsevier. (n.d.). Scopus author details: Ying Tan, Author ID 56098810800. Scopus. Profile metrics are reported from the supplied input and require current verification.
https://www.scopus.com/authid/detail.uri?authorId=56098810800
- ORCID. (n.d.). Ying Tan researcher identifier. ORCID record.
https://orcid.org/0000-0001-8243-4731
- Elsevier. (2026). Visual-Enhanced Fireworks Algorithm via multimodal large language models. Swarm and Evolutionary Computation. DOI: 10.1016/j.swevo.2026.102577.
https://doi.org/10.1016/j.swevo.2026.102577
- Elsevier. (2026). Repetitive contrastive learning enhances Mamba’s selectivity in time series prediction. Neural Networks. DOI: 10.1016/j.neunet.2025.108290.
https://doi.org/10.1016/j.neunet.2025.108290
- Springer Nature. (2026). A GPU Implementation of Multi-guiding Spark Fireworks Algorithm for Efficient Black-Box Neural Network Optimization. Book chapter. DOI: 10.1007/978-981-95-0982-9_5.
https://doi.org/10.1007/978-981-95-0982-9_5
- Global Innovation Technologist Awards. (n.d.). Official awards website. Award information and applicable recognition criteria should be consulted directly.
innovationtechnologist.com