Todd Kulesza

2.7k total citations · 2 hit papers
18 papers, 1.6k citations indexed

About

Todd Kulesza is a scholar working on Artificial Intelligence, Software and Computer Science Applications. According to data from OpenAlex, Todd Kulesza has authored 18 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 7 papers in Software and 6 papers in Computer Science Applications. Recurrent topics in Todd Kulesza's work include Spreadsheets and End-User Computing (7 papers), Machine Learning and Data Classification (6 papers) and Mobile Crowdsensing and Crowdsourcing (5 papers). Todd Kulesza is often cited by papers focused on Spreadsheets and End-User Computing (7 papers), Machine Learning and Data Classification (6 papers) and Mobile Crowdsensing and Crowdsourcing (5 papers). Todd Kulesza collaborates with scholars based in United States, United Kingdom and France. Todd Kulesza's co-authors include Margaret Burnett, Simone Stumpf, Saleema Amershi, W. Bradley Knox, Maya Çakmak, Weng‐Keen Wong, Irwin Kwan, Sherry Yang, Amy J. Ko and Rich Caruana and has published in prestigious journals such as IEEE Transactions on Software Engineering, AI Magazine and ACM Transactions on Interactive Intelligent Systems.

In The Last Decade

Todd Kulesza

18 papers receiving 1.5k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Power to the People: The Role of Humans in Interactive Machine Learning

2014 583 citations Saleema Amershi, Maya Çakmak et al. AI Magazine profile →
Principles of Explanatory Debugging to Personalize Interactive Machine Learning

2015 308 citations Todd Kulesza, Margaret Burnett et al. City Research Online (City University London) profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Todd Kulesza United States	11	1.0k	314	304	198	194	18	1.6k
Adam Fourney United States	18	636 0.6×	252 0.8×	231 0.8×	183 0.9×	264 1.4×	54	1.5k
Dan Weld United States	6	450 0.4×	223 0.7×	155 0.5×	97 0.5×	109 0.6×	6	973
Rachel Bellamy United States	24	734 0.7×	329 1.0×	149 0.5×	402 2.0×	756 3.9×	82	2.1k
Justin D. Weisz United States	13	397 0.4×	143 0.5×	102 0.3×	83 0.4×	137 0.7×	37	856
Jim Waldo United States	16	496 0.5×	144 0.5×	243 0.8×	307 1.6×	541 2.8×	54	1.8k
Ujwal Gadiraju Netherlands	23	734 0.7×	188 0.6×	104 0.3×	738 3.7×	268 1.4×	112	1.6k
Tongshuang Wu United States	17	608 0.6×	93 0.3×	208 0.7×	73 0.4×	133 0.7×	48	971
Nava Tintarev United Kingdom	17	688 0.7×	111 0.4×	242 0.8×	93 0.5×	697 3.6×	49	1.3k
Mark Riedl United States	30	2.5k 2.4×	186 0.6×	807 2.7×	119 0.6×	149 0.8×	157	3.2k
Ig Ibert Bittencourt Brazil	24	721 0.7×	107 0.3×	99 0.3×	809 4.1×	650 3.4×	193	2.3k

Countries citing papers authored by Todd Kulesza

Since Specialization

Citations

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

Fields of papers citing papers by Todd Kulesza

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Todd Kulesza

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

All Works

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18 of 18 papers shown

Kulesza, Todd, Margaret Burnett, Weng‐Keen Wong, & Simone Stumpf. (2015). Principles of Explanatory Debugging to Personalize Interactive Machine Learning. City Research Online (City University London). 126–137. 308 indexed citations breakdown →

Amershi, Saleema, Maya Çakmak, W. Bradley Knox, & Todd Kulesza. (2014). Power to the People: The Role of Humans in Interactive Machine Learning. AI Magazine. 35(4). 105–120. 583 indexed citations breakdown →

Groce, Alex, Todd Kulesza, Chaoqiang Zhang, et al.. (2014). You Are the Only Possible Oracle: Effective Test Selection for End Users of Interactive Machine Learning Systems. IEEE Transactions on Software Engineering. 40(3). 307–323. 46 indexed citations

Kulesza, Todd, Saleema Amershi, Rich Caruana, Danyel Fisher, & Denis Charles. (2014). Structured labeling for facilitating concept evolution in machine learning. 3075–3084. 66 indexed citations

Amershi, Saleema, Maya Çakmak, W. Bradley Knox, Todd Kulesza, & Tessa Lau. (2013). IUI workshop on interactive machine learning. 121–124. 2 indexed citations

Kulesza, Todd, Simone Stumpf, Margaret Burnett, et al.. (2013). Too much, too little, or just right? Ways explanations impact end users' mental models. City Research Online (City University London). 3–10. 221 indexed citations

Kulesza, Todd, Simone Stumpf, Margaret Burnett, & Irwin Kwan. (2012). Tell me more?. City Research Online (City University London). 1–10. 151 indexed citations

Curran, William J., Todd Kulesza, Weng‐Keen Wong, et al.. (2012). Towards recognizing "cool". City Research Online (City University London). 285–288. 7 indexed citations

Kulesza, Todd. (2012). An explanation-centric approach for personalizing intelligent agents. 375–378. 1 indexed citations

10.

Kulesza, Todd, et al.. (2011). Why-oriented end-user debugging of naive Bayes text classification. ACM Transactions on Interactive Intelligent Systems. 1(1). 1–31. 55 indexed citations

11.

Kulesza, Todd, Margaret Burnett, William J. Curran, et al.. (2011). Mini-crowdsourcing end-user assessment of intelligent assistants: A cost-benefit study. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam). 321. 47–54. 10 indexed citations

12.

Kulesza, Todd, et al.. (2010). Explanatory Debugging: Supporting End-User Debugging of Machine-Learned Programs. City Research Online (City University London). 41–48. 64 indexed citations

13.

Kulesza, Todd. (2010). Toward End-User Debugging of Machine-Learned Classifiers. 253–254. 2 indexed citations

14.

Wong, Weng‐Keen, et al.. (2009). End-user feature engineering in the presence of class imbalance. 1 indexed citations

15.

Kulesza, Todd, et al.. (2009). End-user debugging of machine-learned programs : toward principles for baring the logic. 2 indexed citations

16.

Burnett, Margaret, Christopher Bogart, Jill Cao, et al.. (2009). End-user software engineering and distributed cognition. 1–7. 3 indexed citations

17.

Kulesza, Todd, Weng‐Keen Wong, Simone Stumpf, et al.. (2009). Fixing the program my computer learned. City Research Online (City University London). 187–196. 58 indexed citations

18.

Grigoreanu, Valentina, Jill Cao, Todd Kulesza, et al.. (2008). Can feature design reduce the gender gap in end-user software development environments?. 149–156. 39 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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