Carl Burch
Impact in
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- Advanced Bandit Algorithms Research
- Auction Theory and Applications
- Media Technology top 10%
- Experimental Learning in Engineering
Papers in
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- Optimization and Search Problems 5
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- Experimental Learning in Engineering 6
- Co-authors
- Avrim Blum (5 shared papers)Andrew Tomkins (1 shared paper)Yair Bartal (1 shared paper)Adam Tauman Kalai (1 shared paper)Chenyi Hu (1 shared paper)
- Journals
- Machine Learning (1 paper)Journal of computing sciences in colleges (3 papers)ACM SIGCSE Bulletin (1 paper)Defense Technical Information Center (DTIC) (1 paper)
- Partner nations
- United States
In The Last Decade
Carl Burch
11 papers receiving 220 citations
Peers
Comparison fields: 5 of 54
- Management Science and Operations Research 81
- Media Technology 53
- Computer Science Applications 31
- Computer Networks and Communications 124
- Hardware and Architecture 32
Countries citing papers authored by Carl Burch
This map shows the geographic impact of Carl Burch'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 Carl Burch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Carl Burch more than expected).
Fields of papers citing papers by Carl Burch
This network shows the impact of papers produced by Carl Burch. 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 Carl Burch. The network helps show where Carl Burch may publish in the future.
Co-authors
The 5 scholars most cited alongside Carl Burch, 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 | 2002 | 67 | |
| 2 | 1997 | 55 | |
| 3 | 2000 | 35 | |
| 4 | 2003 | 21 | |
| 5 | 1997 | 19 | |
| 6 | Django, a web framework using Python: tutorial presentation | 2010 | 18 |
| 7 | 2004 | 10 | |
| 8 | 2004 | 4 | |
| 9 | Machine learning in metrical task systems and other on-line problems | 2000 | 3 |
| 10 | Jigsaw, a programming environment for Java in CS1 | 2009 | 2 |
| 11 | CS0: why, what, and how?: panel discussion | 2010 | 1 |
About Carl Burch
Carl Burch is a scholar working on Computer Networks and Communications, Media Technology, Management Science and Operations Research, Artificial Intelligence and Information Systems, having authored 11 papers that have together received 235 indexed citations. Recurring topics across this work include Experimental Learning in Engineering (6 papers), Optimization and Search Problems (5 papers), Advanced Bandit Algorithms Research (5 papers), Machine Learning and Algorithms (3 papers), Teaching and Learning Programming (3 papers), Complexity and Algorithms in Graphs (2 papers), Information Systems Education and Curriculum Development (2 papers) and Embedded Systems Design Techniques (1 paper). The work is most often cited by research in Management Science and Operations Research (81 citations), Media Technology (53 citations), Computer Science Applications (31 citations), Computer Networks and Communications (124 citations) and Hardware and Architecture (32 citations). Carl Burch has collaborated with scholars based in United States. Frequent co-authors include Avrim Blum, Andrew Tomkins, Yair Bartal, Adam Tauman Kalai and Chenyi Hu. Their work appears in journals such as Machine Learning, Journal of computing sciences in colleges, ACM SIGCSE Bulletin and Defense Technical Information Center (DTIC).
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.