Michelle S. Lam
- Artificial Intelligence top 10%
- Computer Science Applications top 5%
- Safety Research top 10%
- Sociology and Political Science
- Communication
- Co-authors
- Michael S. BernsteinJeffrey T. HancockMitchell GordonTatsunori HashimotoKayur PatelJoon Sung ParkRyo SuzukiNiloufar Salehi
- Topics
- Ethics and Social Impacts of AI (6 papers)Explainable Artificial Intelligence (XAI) (3 papers)Hate Speech and Cyberbullying Detection (3 papers)
- Journals
- ACM Transactions on Computer-Human InteractionProceedings of the ACM on Human-Computer InteractionCHI Conference on Human Factors in Computing Systems
- Partner nations
- United StatesSouth KoreaMexico
In The Last Decade
Michelle S. Lam
12 papers receiving 208 citations
Peers
Comparison fields: 5 of 55
- Artificial Intelligence 113
- Computer Science Applications 56
- Safety Research 54
- Sociology and Political Science 52
- Communication 32
Countries citing papers authored by Michelle S. Lam
This map shows the geographic impact of Michelle S. Lam'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 Michelle S. Lam with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michelle S. Lam more than expected).
Fields of papers citing papers by Michelle S. Lam
This network shows the impact of papers produced by Michelle S. Lam. 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 Michelle S. Lam. The network helps show where Michelle S. Lam may publish in the future.
Co-authorship network of co-authors of Michelle S. Lam
This figure shows the co-authorship network connecting the top 25 collaborators of Michelle S. Lam. A scholar is included among the top collaborators of Michelle S. Lam 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 Michelle S. Lam. Michelle S. Lam is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 11 | |
| 5 | 12 | |
| 6 | 6 | |
| 7 | 11 | |
| 8 | 16 | |
| 9 | 25 | |
| 10 | 81 | |
| 11 | 1 | |
| 12 | 48 |
About Michelle S. Lam
Michelle S. Lam is a scholar working on Health Informatics, Computer Science Applications and Safety Research, having authored 12 papers that have together received 216 indexed citations. Recurring topics across this work include Ethics and Social Impacts of AI (6 papers), Explainable Artificial Intelligence (XAI) (3 papers) and Hate Speech and Cyberbullying Detection (3 papers). The work is most often cited by research in Computer Science Applications (56 citations), Health Informatics (10 citations) and Safety Research (54 citations). Michelle S. Lam has collaborated with scholars based in United States, South Korea and Mexico. Frequent co-authors include Michael S. Bernstein, Jeffrey T. Hancock, Mitchell Gordon, Tatsunori Hashimoto, Kayur Patel, Joon Sung Park, Ryo Suzuki, Niloufar Salehi, Danaë Metaxa and James A. Landay. Their work appears in journals such as ACM Transactions on Computer-Human Interaction, Proceedings of the ACM on Human-Computer Interaction and CHI Conference on Human Factors in Computing Systems.
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.