Ji Gao
- Artificial Intelligence top 5%
- Signal Processing top 5%
- Molecular Biology
- Computer Networks and Communications
- Information Systems top 10%
- Co-authors
- Yanjun QiMary Lou SoffaJack LanchantinGuanghui ShenZhen HuangJundong LiXiao HuangBeilun Wang
- Topics
- Adversarial Robustness in Machine Learning (7 papers)Anomaly Detection Techniques and Applications (4 papers)Speech Recognition and Synthesis (3 papers)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Ji Gao
26 papers receiving 554 citations
Hit Papers
Peers
Comparison fields: 5 of 81
- Artificial Intelligence 410
- Signal Processing 154
- Molecular Biology 97
- Computer Networks and Communications 51
- Information Systems 49
Countries citing papers authored by Ji Gao
This map shows the geographic impact of Ji Gao'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 Ji Gao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ji Gao more than expected).
Fields of papers citing papers by Ji Gao
This network shows the impact of papers produced by Ji Gao. 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 Ji Gao. The network helps show where Ji Gao may publish in the future.
Co-authorship network of co-authors of Ji Gao
This figure shows the co-authorship network connecting the top 25 collaborators of Ji Gao. A scholar is included among the top collaborators of Ji Gao 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 Ji Gao. Ji Gao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 1 | |
| 6 | 2 | |
| 7 | 2 | |
| 8 | 16 | |
| 9 | 51 | |
| 10 | 1 | |
| 11 | 27 | |
| 12 | STLnet: Signal Temporal Logic Enforced Multivariate Recurrent Neural Networks | 11 |
| 13 | 18 | |
| 14 | A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Samples | 8 |
| 15 | DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples | 4 |
| 16 | A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models | 2 |
| 17 | DeepMask: Masking DNN Models for robustness against adversarial samples. | 6 |
| 18 | A Theoretical Framework for Robustness of (Deep) Classifiers Under Adversarial Noise. | 7 |
| 19 | 10 | |
| 20 | Effect of music therapy on pain behaviors in rats with bone cancer pain. | 12 |
About Ji Gao
Ji Gao is a scholar working on Artificial Intelligence, Anesthesiology and Pain Medicine and Oncology, having authored 27 papers that have together received 573 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (7 papers), Anomaly Detection Techniques and Applications (4 papers) and Speech Recognition and Synthesis (3 papers). The work is most often cited by research in Signal Processing (154 citations), Artificial Intelligence (410 citations) and Software (10 citations). Ji Gao has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Yanjun Qi, Mary Lou Soffa, Jack Lanchantin, Guanghui Shen, Zhen Huang, Jundong Li, Xiao Huang, Beilun Wang, Mohammad Mahmoody and Prashant Nalini Vasudevan. Their work appears in journals such as SAE technical papers on CD-ROM/SAE technical paper series, Cytokine and Supportive Care in Cancer.
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.