Yada Zhu

808 total citations
51 papers, 486 citations indexed

About

Yada Zhu is a scholar working on Artificial Intelligence, Management Science and Operations Research and Safety, Risk, Reliability and Quality. According to data from OpenAlex, Yada Zhu has authored 51 papers receiving a total of 486 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Artificial Intelligence, 9 papers in Management Science and Operations Research and 8 papers in Safety, Risk, Reliability and Quality. Recurrent topics in Yada Zhu's work include Stock Market Forecasting Methods (8 papers), Advanced Graph Neural Networks (7 papers) and Reliability and Maintenance Optimization (7 papers). Yada Zhu is often cited by papers focused on Stock Market Forecasting Methods (8 papers), Advanced Graph Neural Networks (7 papers) and Reliability and Maintenance Optimization (7 papers). Yada Zhu collaborates with scholars based in United States, China and Netherlands. Yada Zhu's co-authors include Jingrui He, Elsayed A. Elsayed, Hanghang Tong, Бо Ли, Boxin Wang, Hengzhi Pei, Pin‐Yu Chen, Xiao Ju, Yunan Ye and Dzung T. Phan and has published in prestigious journals such as Technometrics, European Journal of Operational Research and IEEE Transactions on Knowledge and Data Engineering.

In The Last Decade

Yada Zhu

48 papers receiving 468 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Yada Zhu United States	13	191	133	77	73	61	51	486
Yeou-Ren Shiue Taiwan	17	139 0.7×	66 0.5×	12 0.2×	146 2.0×	55 0.9×	28	748
Arnab Nilim United States	8	204 1.1×	177 1.3×	22 0.3×	40 0.5×	54 0.9×	12	530
Wai Ki Ching Hong Kong	11	187 1.0×	62 0.5×	29 0.4×	12 0.2×	38 0.6×	35	788
Likang Yin China	8	129 0.7×	238 1.8×	46 0.6×	106 1.5×	36 0.6×	16	477
Abdel Lisser France	16	69 0.4×	321 2.4×	15 0.2×	44 0.6×	76 1.2×	85	742
Lixiang Shen China	10	316 1.7×	144 1.1×	19 0.2×	16 0.2×	7 0.1×	20	693
Belén Martín-Barragán United Kingdom	14	187 1.0×	58 0.4×	5 0.1×	16 0.2×	50 0.8×	25	497
Scott Karlin United States	12	100 0.5×	165 1.2×	8 0.1×	24 0.3×	31 0.5×	19	935
Rico Zenklusen Switzerland	15	142 0.7×	153 1.2×	29 0.4×	32 0.4×	9 0.1×	53	779
Y. S. Chow Singapore	4	95 0.5×	244 1.8×	13 0.2×	81 1.1×	162 2.7×	7	624

Countries citing papers authored by Yada Zhu

Since Specialization

Citations

This map shows the geographic impact of Yada Zhu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Yada Zhu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yada Zhu more than expected).

Fields of papers citing papers by Yada Zhu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yada Zhu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yada Zhu. The network helps show where Yada Zhu may publish in the future.

Co-authorship network of co-authors of Yada Zhu

This figure shows the co-authorship network connecting the top 25 collaborators of Yada Zhu. A scholar is included among the top collaborators of Yada Zhu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yada Zhu. Yada Zhu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Zhu, Yada, Kinh Tieu, Onkar Bhardwaj, et al.. (2025). ClimateBench-M: A Multi-Modal Climate Data Benchmark with a Simple Generative Method. 6367–6371.

Tong, Hanghang, et al.. (2024). Fairgen: Towards Fair Graph Generation. 2285–2297. 1 indexed citations

Jing, Baoyu, Kaize Ding, Yada Zhu, et al.. (2024). Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization. VTechWorks (Virginia Tech). 3045–3056. 4 indexed citations

Zhu, Yada, et al.. (2024). AIM: Attributing, Interpreting, Mitigating Data Unfairness. arXiv (Cornell University). 2014–2025. 3 indexed citations

Zhu, Yada, et al.. (2024). Paraphrase and Solve: Exploring and Exploiting the Impact of Surface Form on Mathematical Reasoning in Large Language Models. 2793–2804. 1 indexed citations

Jing, Baoyu, Kaize Ding, Chanyoung Park, et al.. (2024). Sterling: Synergistic Representation Learning on Bipartite Graphs. Proceedings of the AAAI Conference on Artificial Intelligence. 38(12). 12976–12984. 6 indexed citations

Li, Yunyao, et al.. (2022). Stock Price Volatility Prediction: A Case Study with AutoML. 48–57. 2 indexed citations

Chen, Di, Yada Zhu, Miao Liu, & Jianbo Li. (2022). Cost-Efficient Reinforcement Learning for Optimal Trade Execution on Dynamic Market Environment. 386–393. 1 indexed citations

Zhu, Yada, et al.. (2021). Hierarchical Modeling with Tensor Inputs. Proceedings of the AAAI Conference on Artificial Intelligence. 26(1). 1233–1239.

10.

Li, Jianbo, et al.. (2021). Outlier Impact Characterization for Time Series Data. Proceedings of the AAAI Conference on Artificial Intelligence. 35(13). 11595–11603. 4 indexed citations

11.

Zhang, Wei, Ziming Huang, Yada Zhu, et al.. (2021). On Sample Based Explanation Methods for NLP: Faithfulness, Efficiency and Semantic Evaluation. 5399–5411. 7 indexed citations

12.

Zhu, Yada, Giovanni Mariani, & Jianbo Li. (2020). Pagan: Portfolio Analysis with Generative Adversarial Networks. SSRN Electronic Journal. 12 indexed citations

13.

Zhou, Dawei, et al.. (2020). Domain Adaptive Multi-Modality Neural Attention Network for Financial Forecasting. 2230–2240. 28 indexed citations

14.

Li, Jianbo, Jingrui He, & Yada Zhu. (2018). E-tail Product Return Prediction via Hypergraph-based Local Graph Cut. 519–527. 25 indexed citations

15.

He, Jingrui, et al.. (2017). HiMuV: Hierarchical Framework for Modeling Multi-modality Multi-resolution Data. 26. 267–276. 1 indexed citations

16.

Yang, Pei, Hasan Davulcu, Yada Zhu, & Jingrui He. (2016). A Generalized Hierarchical Multi-Latent Space Model for Heterogeneous Learning. IEEE Transactions on Knowledge and Data Engineering. 28(12). 3154–3168. 2 indexed citations

17.

Zhu, Yada & Jinjun Xiong. (2015). Modern Big Data Analytics for "Old-fashioned" Semiconductor Industry Applications. International Conference on Computer Aided Design. 776–780. 3 indexed citations

18.

Zhu, Yada & Elsayed A. Elsayed. (2012). Optimal design of accelerated life testing plans under progressive censoring. IIE Transactions. 45(11). 1176–1187. 11 indexed citations

19.

Zhu, Yada & Elsayed A. Elsayed. (2011). Design of Equivalent Accelerated Life Testing Plans under Different Stress Applications. Quality Technology & Quantitative Management. 8(4). 463–478. 22 indexed citations

20.

Zhu, Yada. (2010). Optimal design and equivalency of accelerated life testing plans. Rutgers University Community Repository (Rutgers University). 11 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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