Yingjie Yang | Data Science | Best Researcher Award

Best Researcher Award

Yingjie YangDe Montfort University

Yingjie Yang
Affiliation De Montfort University
Country United Kingdom
Scopus ID 7409384730
Documents 236
Citations 5720
h-index 38
Subject Area Data Science
Event International Research Data Analysis Excellence & Awards
ORCID 0000-0003-4525-5624

Yingjie Yang is a researcher affiliated with De Montfort University whose scholarly profile is associated with Data Science and interdisciplinary research involving data-driven methods. The available Scopus indicators record 236 documents, 5720 citations and an h-index of 38, providing a quantitative basis for assessing publication activity and scholarly influence in relation to the Best Researcher Award. [1]

Abstract

This article presents an academic recognition profile of Yingjie Yang, affiliated with De Montfort University, for consideration under the Best Researcher Award. Available bibliometric indicators, including 236 documents, 5720 citations and an h-index of 38, are considered alongside research activity in Data Science and related scholarly contributions. [1]

Keywords

Best Researcher Award; Yingjie Yang; De Montfort University; Data Science; Research Data Analysis; Bibliometric Assessment; Scholarly Publications; Citation Impact; Research Excellence; Academic Recognition. [2]

Introduction

Academic recognition commonly considers research productivity, citation performance, publication continuity and contribution to a specialist field. Yingjie Yang’s available scholarly indicators provide a measurable profile for examining research achievement within Data Science. Such evaluation supports transparent comparison of documented academic output and influence using established bibliometric information. [3]

Research Profile

Yingjie Yang is affiliated with De Montfort University in the United Kingdom and is associated with research in Data Science. The available Scopus profile records 236 documents, 5720 citations and an h-index of 38. These indicators collectively describe sustained scholarly activity and a documented record of academic visibility. [1]

Research Contributions

The research profile demonstrates contributions represented through a substantial body of indexed scholarly documents. Within the Data Science context, such work contributes to the continuing development, application and evaluation of data-driven knowledge. The accumulated publication record also indicates engagement with research communication and dissemination across relevant academic channels. [1]

Publications

The available Scopus record lists 236 documents associated with the researcher profile, indicating sustained publication activity. Indexed publications provide an important basis for evaluating scholarly productivity because they document research dissemination and enable subsequent citation analysis. Specific publication details should be interpreted through the linked author profile and corresponding publisher records.[2]

Research Impact

The profile records 5720 citations and an h-index of 38, indicating that the published work has received measurable scholarly attention. Citation indicators are not complete measures of research quality, but they provide useful evidence of academic visibility and uptake. Their interpretation is strengthened when considered with disciplinary context and documented research outputs. [3]

Award Suitability

Based on the available publication and citation indicators, Yingjie Yang presents a documented academic profile relevant to consideration for the Best Researcher Award. The combination of publication volume, citation performance and an h-index of 38 provides objective evidence for assessment. Final recognition should remain subject to the event’s eligibility criteria and review process. [1]

Conclusion

The available research profile of Yingjie Yang reflects sustained scholarly publication activity and measurable citation impact in association with Data Science. With 236 documents, 5720 citations and an h-index of 38, the profile provides evidence suitable for structured academic evaluation. These documented indicators support informed consideration for research recognition. [1]

 References

  1. Predicting the number of care beds for older people by a novel grey Verhulst cosine self-memory model: two case studies of Jiangsu and Shanghai, China.
    https://link.springer.com/article/10.1186/s12877-026-07337-6
  2. Interpretable Temporal Graph Attention Network and Cross-Modal Fusion for Early Rumor Detection.
    https://www.researchgate.net/publication/405011816_Interpretable_Temporal_Graph_Attention_Network_and_Cross-Modal_Fusion_for_Early_Rumor_Detection
  3. A novel time-varying Wiener process for adaptive RUL prediction under multiple uncertainties
    https://www.researchgate.net/publication/401712852_A_novel_time-varying_Wiener_process_for_adaptive_RUL_prediction_under_multiple_uncertainties

Snezhana Abarzhi | Data Science | Best Researcher Award

Prof Dr. Snezhana Abarzhi | Data Science | Best Researcher Award

Prof Dr. Snezhana Abarzhi at University of Western Australia, United States

👨‍🎓 Profile

Summary 🌟

Prof. Dr. Snezhana Abarzhi is a prominent researcher in Theoretical and Applied Physics, with a focus on the dynamics of complex systems. Her pioneering work addresses fluid instabilities and mixing, making significant contributions to scientific computing and applied mathematics.

Education 🎓

She earned her PhD in Mathematics & Physics in 1994 from the Landau Institute for Theoretical Physics and the Kapitza Institute for Physical Problems in Russia. Prior to that, she completed her MS in Applied Mathematics & Physics at the Moscow Institute for Physics & Technology in 1990, and obtained her BS in Applied Mathematics & Physics as well as Molecular Biology in 1987 from the same institution.

Professional Experience 💼

Since 2016, Prof. Abarzhi has served as Professor and Chair of Applied Mathematics at the University of Western Australia. She has held prestigious positions, including Guest Professor at Caltech and Visiting Professor at Stanford University. Her previous roles include professorships at Carnegie Mellon University and the University of Chicago, among other leading institutions worldwide.

Research Interests 🔍

Her research interests span the dynamics of plasmas, fluids, and materials, with expertise in theoretical analysis of complex systems, including far-from-equilibrium and non-linear dynamics. She has made notable contributions to understanding fluid instabilities, interface dynamics, and mixing processes.

📖  Top Noted Publications

On kinematic viscosity, scaling laws and spectral shapes in Rayleigh-Taylor mixing plasma experiments

Author: Snezhana I. Abarzhi; Kurt C. Williams
Journal: Physics Letters A
Year: 2024

Data-Based Kinematic Viscosity and Rayleigh–Taylor Mixing Attributes in High-Energy Density Plasmas

Author: Snezhana I. Abarzhi; Kurt C. Williams
Journal: Atoms
Year: 2024

Interlinking Rayleigh–Taylor/Richtmyer–Meshkov interfacial mixing with variable acceleration and canonical Kolmogorov turbulence

Author: Snezhana I. Abarzhi
Journal: Physics of Fluids
Year: 2024

Perspective: group theory analysis and special self-similarity classes in Rayleigh–Taylor and Richtmyer–Meshkov interfacial mixing with variable accelerations

Author: Snezhana I. Abarzhi
Journal: Reviews of Modern Plasma Physics
Year: 2024

An analysis of the buoyancy and drag parameters in Rayleigh-Taylor dynamics

Author: Des Hill; Snezhana Abarzhi
Journal: Mathematical Modelling of Natural Phenomena
Year: 2023