Alexander Ku
Impact in
- Health Informatics top 5%
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- Multimodal Machine Learning Applications
- Advanced Image and Video Retrieval Techniques
Papers in
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- Multimodal Machine Learning Applications 6
- Advanced Image and Video Retrieval Techniques 3
- Human Pose and Action Recognition 2
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- Topic Modeling 3
- Speech and dialogue systems 3
- Domain Adaptation and Few-Shot Learning 3
- Co-authors
- Mark A. DePristo (1 shared paper)Sam Gross (1 shared paper)Scott Schwartz (1 shared paper)Ryan Poplin (1 shared paper)Thomas Colthurst (1 shared paper)David H. Alexander (1 shared paper)Pegah Tootoonchi Afshar (1 shared paper)Cory Y. McLean (1 shared paper)
- Journals
- Cognitive Science (1 paper)Nature Biotechnology (1 paper)arXiv (Cornell University) (2 papers)
- Partner nations
- United States
In The Last Decade
Alexander Ku
11 papers receiving 1.1k citations
Alexander Ku's Hit Papers
Peers
Comparison fields: 5 of 105
- Health Informatics 23
- Computer Vision and Pattern Recognition 243
- Genetics 295
- Cancer Research 136
- Artificial Intelligence 253
Countries citing papers authored by Alexander Ku
This map shows the geographic impact of Alexander Ku'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 Alexander Ku with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alexander Ku more than expected).
Fields of papers citing papers by Alexander Ku
This network shows the impact of papers produced by Alexander Ku. 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 Alexander Ku. The network helps show where Alexander Ku may publish in the future.
Co-authors
The 25 scholars most cited alongside Alexander Ku, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | A universal SNP and small-indel variant caller using deep neural networks Hit paper breakdown → | 2018 | 824 |
| 2 | 2020 | 132 | |
| 3 | 2019 | 49 | |
| 4 | 2021 | 25 | |
| 5 | 2023 | 21 | |
| 6 | 2019 | 19 | |
| 7 | Effective and General Evaluation for Instruction Conditioned Navigation using Dynamic Time Warping | 2019 | 12 |
| 8 | 2024 | 3 | |
| 9 | 2024 | 2 | |
| 10 | 2021 | 2 | |
| 11 | Capturing Human Category Representations by Sampling in Deep Feature Spaces | 2018 | 1 |
About Alexander Ku
Alexander Ku is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Molecular Biology, Ocean Engineering and Signal Processing, having authored 11 papers that have together received 1.1k indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (6 papers), Topic Modeling (3 papers), Speech and dialogue systems (3 papers), Advanced Image and Video Retrieval Techniques (3 papers), Domain Adaptation and Few-Shot Learning (3 papers), Human Pose and Action Recognition (2 papers), RNA and protein synthesis mechanisms (1 paper) and Genomics and Rare Diseases (1 paper). The work is most often cited by research in Health Informatics (23 citations), Computer Vision and Pattern Recognition (243 citations), Genetics (295 citations), Cancer Research (136 citations) and Artificial Intelligence (253 citations). Alexander Ku has collaborated with scholars based in United States. Frequent co-authors include Mark A. DePristo, Sam Gross, Scott Schwartz, Ryan Poplin, Thomas Colthurst, David H. Alexander, Pegah Tootoonchi Afshar, Cory Y. McLean, Pi-Chuan Chang and Nam V. Nguyen. Their work appears in journals such as Cognitive Science, Nature Biotechnology and arXiv (Cornell University).
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.