Brian Quanz

1.4k total citations
20 papers, 272 citations indexed

About

Brian Quanz is a scholar working on Artificial Intelligence, Molecular Biology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Brian Quanz has authored 20 papers receiving a total of 272 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 4 papers in Molecular Biology and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Brian Quanz's work include Machine Learning and Data Classification (5 papers), Domain Adaptation and Few-Shot Learning (4 papers) and Face and Expression Recognition (3 papers). Brian Quanz is often cited by papers focused on Machine Learning and Data Classification (5 papers), Domain Adaptation and Few-Shot Learning (4 papers) and Face and Expression Recognition (3 papers). Brian Quanz collaborates with scholars based in United States, India and Ireland. Brian Quanz's co-authors include Jun Huan, Jun Huan, Jun Huan, Costas Tsatsoulis, Hongliang Fei, Jingrui He, Ajay Deshpande, Yada Zhu, Jianbo Li and Pavithra Harsha and has published in prestigious journals such as Scientific Reports, IEEE Transactions on Knowledge and Data Engineering and Manufacturing & Service Operations Management.

In The Last Decade

Brian Quanz

20 papers receiving 264 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Brian Quanz United States	8	168	78	42	24	23	20	272
Uiwon Hwang South Korea	8	146 0.9×	141 1.8×	47 1.1×	21 0.9×	6 0.3×	13	327
Sjoerd de Jong Netherlands	3	120 0.7×	61 0.8×	17 0.4×	15 0.6×	20 0.9×	5	253
Dianhui Wang Australia	11	207 1.2×	63 0.8×	16 0.4×	68 2.8×	19 0.8×	28	342
Yuxuan Luo China	4	140 0.8×	82 1.1×	18 0.4×	32 1.3×	13 0.6×	12	260
Song Li China	10	109 0.6×	27 0.3×	91 2.2×	11 0.5×	6 0.3×	46	332
Xiaohu Cheng China	6	187 1.1×	104 1.3×	16 0.4×	15 0.6×	4 0.2×	9	273
Litao Yu Australia	8	174 1.0×	262 3.4×	18 0.4×	8 0.3×	8 0.3×	27	399

Countries citing papers authored by Brian Quanz

Since Specialization

Citations

This map shows the geographic impact of Brian Quanz'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 Brian Quanz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brian Quanz more than expected).

Fields of papers citing papers by Brian Quanz

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Brian Quanz. 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 Brian Quanz. The network helps show where Brian Quanz may publish in the future.

Co-authorship network of co-authors of Brian Quanz

This figure shows the co-authorship network connecting the top 25 collaborators of Brian Quanz. A scholar is included among the top collaborators of Brian Quanz 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 Brian Quanz. Brian Quanz is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Harsha, Pavithra, Ashish Jagmohan, Jayant Kalagnanam, Brian Quanz, & Divya Singhvi. (2025). Deep Policy Iteration with Integer Programming for Inventory Management. Manufacturing & Service Operations Management. 27(2). 369–388. 1 indexed citations

Quanz, Brian, et al.. (2025). Post-variational classical quantum transfer learning for binary classification. Scientific Reports. 15(1). 23682–23682. 1 indexed citations

Chen, I‐Chi, et al.. (2024). A Survey of Classical and Quantum Sequence Models. 27. 1006–1011. 1 indexed citations

Harsha, Pavithra, et al.. (2023). End-to-End Learning for Optimization via Constraint-Enforcing Approximators. Proceedings of the AAAI Conference on Artificial Intelligence. 37(6). 7253–7260. 5 indexed citations

Jati, Arindam, et al.. (2023). Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting. 891–900. 3 indexed citations

Quanz, Brian, et al.. (2022). Distributed Incremental Machine Learning for Big Time Series Data. 2022 IEEE International Conference on Big Data (Big Data). 15. 2356–2363. 2 indexed citations

Cintas, Célia, Payel Das, Brian Quanz, et al.. (2022). Towards Creativity Characterization of Generative Models via Group-Based Subset Scanning. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. 4929–4935. 3 indexed citations

Harsha, Pavithra, Ashish Jagmohan, Jayant Kalagnanam, Brian Quanz, & Divya Singhvi. (2021). Math Programming based Reinforcement Learningfor Multi-Echelon Inventory Management. SSRN Electronic Journal. 3 indexed citations

Quanz, Brian, et al.. (2021). Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence. 35(10). 9117–9125. 39 indexed citations

10.

Zhu, Yada, Jianbo Li, Jingrui He, Brian Quanz, & Ajay Deshpande. (2018). A Local Algorithm for Product Return Prediction in E-Commerce. 3718–3724. 8 indexed citations

11.

Quanz, Brian & Jun Huan. (2012). CoNet. 1273–1282. 5 indexed citations

12.

Quanz, Brian, et al.. (2012). Knowledge Transfer with Low-Quality Data: A Feature Extraction Issue. IEEE Transactions on Knowledge and Data Engineering. 24(10). 1789–1802. 48 indexed citations

13.

Quanz, Brian, et al.. (2011). Knowledge transfer with low-quality data: A feature extraction issue. 769–779. 17 indexed citations

14.

Fei, Hongliang, Brian Quanz, & Jun Huan. (2010). Regularization and feature selection for networked features. 1893–1896. 6 indexed citations

15.

Fei, Hongliang, Brian Quanz, & Jun Huan. (2009). GLSVM: Integrating Structured Feature Selection and Large Margin Classification. 362–367. 1 indexed citations

16.

Quanz, Brian, Hongliang Fei, Jun Huan, et al.. (2009). Anomaly Detection with Sensor Data for Distributed Security. 7. 1–6. 5 indexed citations

17.

Quanz, Brian & Jun Huan. (2009). Aligned Graph Classification with Regularized Logistic Regression. 353–364. 8 indexed citations

18.

Quanz, Brian & Jun Huan. (2009). Large margin transductive transfer learning. 1327–1336. 102 indexed citations

19.

Quanz, Brian, et al.. (2008). Biological pathways as features for microarray data classification. 5–12. 7 indexed citations

20.

Quanz, Brian & Costas Tsatsoulis. (2008). Determining Object Safety Using a Multiagent, Collaborative System. 25–30. 7 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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