Todd Kulesza
Impact in
- Health Informatics top 0.5%
- Artificial Intelligence in Healthcare and Education
- Safety Research top 1%
- Ethics and Social Impacts of AI
Papers in
-
- Machine Learning and Data Classification 6
- Data Stream Mining Techniques 4
- Machine Learning and Algorithms 3
- Explainable Artificial Intelligence (XAI) 3
- Imbalanced Data Classification Techniques 1
- Software 7
- Spreadsheets and End-User Computing 7
- Co-authors
- Margaret Burnett (13 shared papers)Simone Stumpf (11 shared papers)Saleema Amershi (3 shared papers)W. Bradley Knox (2 shared papers)Maya Çakmak (2 shared papers)Weng‐Keen Wong (10 shared papers)Irwin Kwan (2 shared papers)Sherry Yang (1 shared paper)
- Journals
- IEEE Transactions on Software Engineering (1 paper)ACM Transactions on Interactive Intelligent Systems (1 paper)AI Magazine (1 paper)City Research Online (City University London) (6 papers)ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (1 paper)
- Partner nations
- United StatesUnited KingdomFrance
In The Last Decade
Todd Kulesza
18 papers receiving 1.6k citations
Todd Kulesza's Hit Papers
Peers
Comparison fields: 5 of 100
- Health Informatics 141
- Safety Research 322
- Computer Science Applications 196
- Artificial Intelligence 1.1k
- Information Systems and Management 173
Countries citing papers authored by Todd Kulesza
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
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-authors
The 25 scholars most cited alongside Todd Kulesza, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Power to the People: The Role of Humans in Interactive Machine Learning Hit paper breakdown → | 2014 | 617 |
| 2 | Principles of Explanatory Debugging to Personalize Interactive Machine Learning Hit paper breakdown → | 2015 | 324 |
| 3 | 2013 | 228 | |
| 4 | 2012 | 157 | |
| 5 | 2010 | 67 | |
| 6 | 2014 | 67 | |
| 7 | 2009 | 58 | |
| 8 | 2011 | 56 | |
| 9 | 2014 | 46 | |
| 10 | 2008 | 39 | |
| 11 | 2011 | 10 | |
| 12 | 2012 | 7 | |
| 13 | 2009 | 3 | |
| 14 | End-user debugging of machine-learned programs : toward principles for baring the logic | 2009 | 2 |
| 15 | 2010 | 2 | |
| 16 | 2013 | 2 | |
| 17 | End-user feature engineering in the presence of class imbalance | 2009 | 1 |
| 18 | 2012 | 1 |
About Todd Kulesza
Todd Kulesza is a scholar working on Artificial Intelligence, Software, Computer Science Applications, Information Systems and Management Science and Operations Research, having authored 18 papers that have together received 1.7k indexed citations. Recurring topics across this work include Spreadsheets and End-User Computing (7 papers), Machine Learning and Data Classification (6 papers), Mobile Crowdsensing and Crowdsourcing (5 papers), Data Stream Mining Techniques (4 papers), Machine Learning and Algorithms (3 papers), Explainable Artificial Intelligence (XAI) (3 papers), Educational Games and Gamification (2 papers) and Imbalanced Data Classification Techniques (1 paper). The work is most often cited by research in Health Informatics (141 citations), Safety Research (322 citations), Computer Science Applications (196 citations), Artificial Intelligence (1.1k citations) and Information Systems and Management (173 citations). Todd Kulesza has collaborated with scholars based in United States, United Kingdom and France. Frequent 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. Their work appears in journals such as IEEE Transactions on Software Engineering, ACM Transactions on Interactive Intelligent Systems, AI Magazine, City Research Online (City University London) and ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam).
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.