Mike Wu

1.9k citations
26 papers · 898 · h-index 14

Impact in

Papers in

    • Machine Learning in Healthcare 5
    • Explainable Artificial Intelligence (XAI) 3
    • Adversarial Robustness in Machine Learning 3
    • Domain Adaptation and Few-Shot Learning 3
    • Machine Learning and Algorithms 2
    • Innovative Human-Technology Interaction 5
    • Interactive and Immersive Displays 3

Mike Wu

25 papers receiving 849 citations

Peers

Mike Wu
Comparison fields: 5 of 116
  • Human-Computer Interaction 433
  • Health Informatics 23
  • Cognitive Neuroscience 292
  • Computer Vision and Pattern Recognition 226
  • Occupational Therapy 39
Replace John J. Dudley with:
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Citations per field
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Citations per year

Countries citing papers authored by Mike Wu

Since Specialization
Citations

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

Fields of papers citing papers by Mike Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Mike Wu, 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 Mike Wu Line = papers co-authored together Mike Wu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 26 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2003287
2 2018136
3 200679
4 200550
5
Multimodal Generative Models for Scalable Weakly-Supervised Learning
201844
6 200443
7 201634
8
Predicting intervention onset in the ICU with switching state space models.
201729
9 201928
10 202326
11 200826
12 201920
13 202018
14 201914
15 201311
16 202111
17 201010
18 20059
19 20096
20 20206

About Mike Wu

Mike Wu is a scholar working on Artificial Intelligence, Human-Computer Interaction, Computer Vision and Pattern Recognition, Occupational Therapy and Cognitive Neuroscience, having authored 26 papers that have together received 898 indexed citations. Recurring topics across this work include Innovative Human-Technology Interaction (5 papers), Machine Learning in Healthcare (5 papers), Interactive and Immersive Displays (3 papers), Explainable Artificial Intelligence (XAI) (3 papers), Adversarial Robustness in Machine Learning (3 papers), Domain Adaptation and Few-Shot Learning (3 papers), Multimodal Machine Learning Applications (2 papers) and Machine Learning and Algorithms (2 papers). The work is most often cited by research in Human-Computer Interaction (433 citations), Health Informatics (23 citations), Cognitive Neuroscience (292 citations), Computer Vision and Pattern Recognition (226 citations) and Occupational Therapy (39 citations). Mike Wu has collaborated with scholars based in United States, Canada and Switzerland. Frequent co-authors include Ravin Balakrishnan, Brian Richards, Finale Doshi‐Velez, Noah D. Goodman, Ron Baecker, Sonali Parbhoo, Volker Röth, Julien Epps, Michael Hughes and Maurizio Zazzi. Their work appears in journals such as Minerals Engineering, Frontiers in Oncology, Computational Brain & Behavior, Journal of Artificial Intelligence Research and Investigative Ophthalmology & Visual Science.

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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