Ted Xiao

1.8k citations
17 papers · 233 · h-index 9

Impact in

Papers in

Ted Xiao

12 papers receiving 224 citations

Peers

Ted Xiao
Comparison fields: 5 of 56
  • Computer Vision and Pattern Recognition 73
  • Control and Systems Engineering 61
  • Artificial Intelligence 61
  • Architecture 2
  • Radiology, Nuclear Medicine and Imaging 21
Replace Xiangbo Lin with:
Xiangbo Lin China
Suraj Nair United States
Andrew Kimmel United States
Michelle Guo United States
Baoru Huang United Kingdom
Kaushik Shivakumar India
Lasse Hansen Germany
Kyung Jun Choi South Korea
Wanni Xu China
Richard V. Stebbing United Kingdom
Ted Xiao relative to Xiangbo Lin China Xiangbo Lin's profile →
Citations per field
00.5×10×14×
Xiangbo Lin · 1×
Citations per year

Countries citing papers authored by Ted Xiao

Since Specialization
Citations

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

Fields of papers citing papers by Ted Xiao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Ted Xiao, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ted Xiao Line = papers co-authored together Ted Xiao links everyone, so they are left out of the graph.

All Works

17 of 17 papers shown
#Work
1 202446
2 202340
3 202431
4 202427
5 202224
6 201921
7 201914
8 202412
9 20189
10 20235
11 20212
12 20241
13 20201
14 20240
15 20240
16 20210
17 20250

About Ted Xiao

Ted Xiao is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Surgery, Epidemiology and Pulmonary and Respiratory Medicine, having authored 17 papers that have together received 233 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (3 papers), Human Pose and Action Recognition (2 papers), Computability, Logic, AI Algorithms (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Folate and B Vitamins Research (1 paper), Robotics and Sensor-Based Localization (1 paper), Optical Imaging and Spectroscopy Techniques (1 paper) and Business Process Modeling and Analysis (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (73 citations), Control and Systems Engineering (61 citations), Artificial Intelligence (61 citations), Architecture (2 citations) and Radiology, Nuclear Medicine and Imaging (21 citations). Ted Xiao has collaborated with scholars based in United States, United Kingdom and Singapore. Frequent co-authors include Michael S. Cartwright, Dorsa Sadigh, Fei Xia, Brian Ichter, Anirudha Majumdar, Jonathan Tompson, Jia-Jun Wu, Chelsea Finn, Sean Rudnick and Quan Vuong. Their work appears in journals such as The American Journal of Gastroenterology, IEEE Robotics & Automation Magazine, Applied Clinical Informatics, Journal of General Internal Medicine and Frontiers in Neurology.

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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