Tzu-Kuo Huang is a scholar working on Artificial Intelligence, Control and Systems Engineering and Automotive Engineering.
According to data from OpenAlex, Tzu-Kuo Huang has authored 10 papers receiving a total of 893 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 3 papers in Control and Systems Engineering and 2 papers in Automotive Engineering. Recurrent topics in Tzu-Kuo Huang's work include Machine Learning and Algorithms (4 papers), Control Systems and Identification (3 papers) and Gaussian Processes and Bayesian Inference (3 papers). Tzu-Kuo Huang is often cited by papers focused on Machine Learning and Algorithms (4 papers), Control Systems and Identification (3 papers) and Gaussian Processes and Bayesian Inference (3 papers). Tzu-Kuo Huang collaborates with scholars based in United States and India. Tzu-Kuo Huang's co-authors include Jeff Schneider, Liang Xiong, Jaime G. Carbonell, Xi Chen, Nemanja Djuric, Vladan Radosavljević, Fang‐Chieh Chou, Tsung-Han Lin, Thi Nguyen and Henggang Cui and has published in prestigious journals such as Figshare, Neural Information Processing Systems and International Conference on Machine Learning.
In The Last Decade
Tzu-Kuo Huang
10 papers
receiving
863 citations
Hit Papers
What are hit papers?
Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks
2019414 citationsHenggang Cui, Vladan Radosavljević et al.profile →
Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization
2010410 citationsLiang Xiong, Xi Chen et al.Figshareprofile →
Citations per year, relative to Tzu-Kuo Huang Tzu-Kuo Huang (= 1×)
peers
Laisen Nie
Countries citing papers authored by Tzu-Kuo Huang
Since
Specialization
Citations
This map shows the geographic impact of Tzu-Kuo Huang'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 Tzu-Kuo Huang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tzu-Kuo Huang more than expected).
This network shows the impact of papers produced by Tzu-Kuo Huang. 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 Tzu-Kuo Huang. The network helps show where Tzu-Kuo Huang may publish in the future.
Co-authorship network of co-authors of Tzu-Kuo Huang
This figure shows the co-authorship network connecting the top 25 collaborators of Tzu-Kuo Huang.
A scholar is included among the top collaborators of Tzu-Kuo Huang 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 Tzu-Kuo Huang. Tzu-Kuo Huang is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Cui, Henggang, Vladan Radosavljević, Fang‐Chieh Chou, et al.. (2019). Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks. 2090–2096.414 indexed citations breakdown →
3.
Ma, Yifei, Tzu-Kuo Huang, & Jeff Schneider. (2015). Active search and bandits on graphs using sigma-optimality. Uncertainty in Artificial Intelligence. 542–551.14 indexed citations
4.
Wang, Xuezhi, Tzu-Kuo Huang, & Jeff Schneider. (2014). Active Transfer Learning under Model Shift. International Conference on Machine Learning. 1305–1313.30 indexed citations
5.
Huang, Tzu-Kuo & Jeff Schneider. (2013). Spectral Learning of Hidden Markov Models from Dynamic and Static Data. International Conference on Machine Learning. 630–638.1 indexed citations
6.
Huang, Tzu-Kuo & Jeff Schneider. (2013). Learning Hidden Markov Models from Non-sequence Data via Tensor Decomposition. Neural Information Processing Systems. 26. 333–341.1 indexed citations
7.
Huang, Tzu-Kuo & Jeff Schneider. (2011). Learning Auto-regressive Models from Sequence and Non-sequence Data. Neural Information Processing Systems. 24. 1548–1556.8 indexed citations
8.
Huang, Tzu-Kuo, Le Song, & Jeff Schneider. (2010). Learning Nonlinear Dynamic Models from Non-sequenced Data. International Conference on Artificial Intelligence and Statistics. 350–357.3 indexed citations
9.
Xiong, Liang, Xi Chen, Tzu-Kuo Huang, Jeff Schneider, & Jaime G. Carbonell. (2010). Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization. Figshare. 211–222.410 indexed citations breakdown →
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.