Mike Wu

1.9k total citations
26 papers, 898 citations indexed

About

Mike Wu is a scholar working on Artificial Intelligence, Human-Computer Interaction and Computer Vision and Pattern Recognition. According to data from OpenAlex, Mike Wu has authored 26 papers receiving a total of 898 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 7 papers in Human-Computer Interaction and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Mike Wu's work include Machine Learning in Healthcare (5 papers), Innovative Human-Technology Interaction (5 papers) and Explainable Artificial Intelligence (XAI) (3 papers). Mike Wu is often cited by papers focused on Machine Learning in Healthcare (5 papers), Innovative Human-Technology Interaction (5 papers) and Explainable Artificial Intelligence (XAI) (3 papers). Mike Wu collaborates with scholars based in United States, Canada and Switzerland. Mike Wu's co-authors include Ravin Balakrishnan, Brian Richards, Finale Doshi‐Velez, Noah D. Goodman, Ron Baecker, Volker Röth, Julien Epps, Sonali Parbhoo, Maurizio Zazzi and Michael Hughes and has published in prestigious journals such as Investigative Ophthalmology & Visual Science, Journal of the American Medical Informatics Association and Frontiers in Oncology.

In The Last Decade

Mike Wu

25 papers receiving 849 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Mike Wu United States	14	433	292	244	226	40	26	898
Samuel White United States	8	280 0.6×	466 1.6×	255 1.0×	312 1.4×	16 0.4×	16	1.1k
Chandrika Jayant United States	8	500 1.2×	771 2.6×	298 1.2×	414 1.8×	52 1.3×	9	1.5k
Yu Zhong United States	12	185 0.4×	313 1.1×	234 1.0×	185 0.8×	29 0.7×	24	804
Roseli de Deus Lopes Brazil	13	253 0.6×	130 0.4×	43 0.2×	222 1.0×	11 0.3×	131	737
Nikola Banović United States	17	306 0.7×	217 0.7×	154 0.6×	139 0.6×	10 0.3×	43	797
Arthur I. Karshmer United States	13	181 0.4×	157 0.5×	81 0.3×	144 0.6×	65 1.6×	47	506
Katrin Wolf Germany	19	687 1.6×	423 1.4×	70 0.3×	242 1.1×	17 0.4×	94	1.2k
João M. F. Rodrigues Portugal	14	150 0.3×	226 0.8×	61 0.3×	279 1.2×	14 0.3×	94	663
John J. Dudley United Kingdom	15	461 1.1×	205 0.7×	151 0.6×	319 1.4×	12 0.3×	49	817

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-authorship network of co-authors of Mike Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Mike Wu. A scholar is included among the top collaborators of Mike Wu 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 Mike Wu. Mike Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Wu, Mike, et al.. (2023). Worldwide Incidence of Ocular Melanoma and Correlation With Pigmentation-Related Risk Factors. Investigative Ophthalmology & Visual Science. 64(13). 45–45. 26 indexed citations

Wu, Mike, et al.. (2021). Conditional Negative Sampling for Contrastive Learning of Visual Representations. arXiv (Cornell University). 3 indexed citations

Wu, Mike, Sonali Parbhoo, Michael C. Hughes, Volker Röth, & Finale Doshi‐Velez. (2021). Optimizing for Interpretability in Deep Neural Networks with Tree Regularization. Journal of Artificial Intelligence Research. 72. 1–37. 11 indexed citations

Wu, Mike, Richard L. Davis, Benjamin W. Domingue, Chris Piech, & Noah D. Goodman. (2020). Variational Item Response Theory: Fast, Accurate, and Expressive. arXiv (Cornell University). 1 indexed citations

Wu, Mike, Sonali Parbhoo, Michael Hughes, et al.. (2020). Regional Tree Regularization for Interpretability in Deep Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence. 34(4). 6413–6421. 18 indexed citations

Wu, Mike, Kristy Choi, Noah D. Goodman, & Stefano Ermon. (2020). Meta-Amortized Variational Inference and Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 34(4). 6404–6412. 6 indexed citations

Dréau, Didier, Laura J. Moore, Mike Wu, et al.. (2019). Combining the Specific Anti-MUC1 Antibody TAB004 and Lip-MSA-IL-2 Limits Pancreatic Cancer Progression in Immune Competent Murine Models of Pancreatic Ductal Adenocarcinoma. Frontiers in Oncology. 9. 330–330. 14 indexed citations

Fan, Judith E., Robert D. Hawkins, Mike Wu, & Noah D. Goodman. (2019). Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication. Computational Brain & Behavior. 3(1). 86–101. 20 indexed citations

Wu, Mike & Noah D. Goodman. (2018). Multimodal Generative Models for Scalable Weakly-Supervised Learning. Neural Information Processing Systems. 31. 5575–5585. 44 indexed citations

10.

Cisewski-Kehe, Jessi, Mike Wu, Brittany Terese Fasy, et al.. (2018). Investigating the Cosmic Web with Topological Data Analysis. AAS. 231. 2 indexed citations

11.

Wu, Mike, Marzyeh Ghassemi, Mengling Feng, et al.. (2016). Understanding vasopressor intervention and weaning: risk prediction in a public heterogeneous clinical time series database. Journal of the American Medical Informatics Association. 24(3). 488–495. 34 indexed citations

12.

Wu, Mike, et al.. (2013). Modeling ball impact on the wet mill liners and its application in predicting mill magnetic liner performance. Minerals Engineering. 61. 126–132. 11 indexed citations

13.

Suominen, Hanna, David Martínez, Mike Wu, Michelle Ananda‐Rajah, & Lawrence Cavedon. (2012). NICTA eHealth Initiatives on Clinical Speech and Text Processing [Abstract].

14.

Wu, Mike, Ronald M. Baecker, & Brian Richards. (2010). Field evaluation of a collaborative memory aid for persons with amnesia and their family members. 51–58. 10 indexed citations

15.

Wu, Mike, Abhishek Ranjan, & Khai N. Truong. (2009). An exploration of social requirements for exercise group formation. 79–82. 6 indexed citations

16.

Shen, Chia, Kathy Ryall, Clifton Forlines, et al.. (2006). Interfaces, Interaction Techniques and User Experience on Direct-Touch Horizontal Surfaces.. IEEE Computer Graphics and Applications. 1 indexed citations

17.

Epps, Julien, et al.. (2006). A study of hand shape use in tabletop gesture interaction. 748–753. 79 indexed citations

18.

Wu, Mike, Ron Baecker, & Brian Richards. (2005). Participatory design of an orientation aid for amnesics. 511–520. 50 indexed citations

19.

Chen, Fang, Eric H. C. Choi, Julien Epps, et al.. (2005). A study of manual gesture-based selection for the PEMMI multimodal transport management interface. 274–281. 9 indexed citations

20.

Wu, Mike & Ravin Balakrishnan. (2003). Multi-finger and whole hand gestural interaction techniques for multi-user tabletop displays. 193–202. 287 indexed citations

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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