Tessa Lau
- Information Systems top 1%
- Artificial Intelligence top 2%
- Information Systems and Management top 1%
- Software top 1%
- Computer Science Applications top 1%
- Co-authors
- Daniel S. WeldJeffrey NicholsEben M. HaberPedro DomingosAllen CypherTara MatthewsGilly LeshedNicholas Kushmerick
- Topics
- Software Engineering Research (18 papers)Personal Information Management and User Behavior (13 papers)Web Data Mining and Analysis (12 papers)
- Partner nations
- United StatesIrelandArgentina
In The Last Decade
Tessa Lau
54 papers receiving 1.6k citations
Peers
Comparison fields: 5 of 75
- Information Systems 750
- Artificial Intelligence 543
- Information Systems and Management 327
- Software 319
- Computer Science Applications 309
Countries citing papers authored by Tessa Lau
This map shows the geographic impact of Tessa Lau'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 Tessa Lau with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tessa Lau more than expected).
Fields of papers citing papers by Tessa Lau
This network shows the impact of papers produced by Tessa Lau. 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 Tessa Lau. The network helps show where Tessa Lau may publish in the future.
Co-authorship network of co-authors of Tessa Lau
This figure shows the co-authorship network connecting the top 25 collaborators of Tessa Lau. A scholar is included among the top collaborators of Tessa Lau 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 Tessa Lau. Tessa Lau is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 14 | |
| 3 | 25 | |
| 4 | 20 | |
| 5 | 4 | |
| 6 | 3 | |
| 7 | 14 | |
| 8 | Interpreting written how-to instructions | 25 |
| 9 | 35 | |
| 10 | 4 | |
| 11 | 1 | |
| 12 | 35 | |
| 13 | CoScripter: Sharing 'How-to' Knowledge in the Enterprise | 3 |
| 14 | 55 | |
| 15 | Activity-centric email: a machine learning approach | 10 |
| 16 | 121 | |
| 17 | 19 | |
| 18 | Programming by demonstration using version space algebra DRAFT to appear in Machine Learning | 2 |
| 19 | 20 | |
| 20 | Version Space Algebra and its Application to Programming by Demonstration | 63 |
About Tessa Lau
Tessa Lau is a scholar working on Information Systems and Management, Human-Computer Interaction and Information Systems, having authored 56 papers that have together received 1.7k indexed citations. Recurring topics across this work include Software Engineering Research (18 papers), Personal Information Management and User Behavior (13 papers) and Web Data Mining and Analysis (12 papers). The work is most often cited by research in Software (319 citations), Computer Science Applications (309 citations) and Human-Computer Interaction (287 citations). Tessa Lau has collaborated with scholars based in United States, Ireland and Argentina. Frequent co-authors include Daniel S. Weld, Jeffrey Nichols, Eben M. Haber, Pedro Domingos, Allen Cypher, Tara Matthews, Gilly Leshed, Nicholas Kushmerick, James Lin and Lawrence D. Bergman. Their work appears in journals such as Communications of the ACM, Computer and Machine Learning.
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.