Travis Mandel

595 citations
27 papers · 338 · h-index 11

Impact in

Papers in

    • Reinforcement Learning in Robotics 6
    • Data Stream Mining Techniques 3
    • Machine Learning and Data Classification 3
    • Machine Learning and Algorithms 2
    • Coding theory and cryptography 2
    • Intelligent Tutoring Systems and Adaptive Learning 2
    • Advanced Bandit Algorithms Research 7

Travis Mandel

24 papers receiving 319 citations

Peers

Travis Mandel
Comparison fields: 5 of 75
  • Human-Computer Interaction 72
  • Computer Science Applications 64
  • Artificial Intelligence 138
  • Management Science and Operations Research 53
  • Cognitive Neuroscience 57
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Citations per field
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Citations per year

Countries citing papers authored by Travis Mandel

Since Specialization
Citations

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

Fields of papers citing papers by Travis Mandel

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 21 scholars most cited alongside Travis Mandel, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Travis Mandel Line = papers co-authored together Travis Mandel links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201377
2 201455
3 201723
4
Trading Off Scientific Knowledge and User Learning with Multi-Armed Bandits
201422
5 201520
6 202220
7 202016
8
Predicting player moves in an educational game: A hybrid approach
201313
9 202112
10 201412
11
Efficient Bayesian clustering for reinforcement learning
201610
12 20209
13 20139
14 20167
15 20216
16 20195
17
Investigating CRC Polynomials that Correct Burst Errors.
20094
18 20214
19 20163
20 20123

About Travis Mandel

Travis Mandel is a scholar working on Artificial Intelligence, Management Science and Operations Research, Geometry and Topology, Computer Science Applications and Information Systems, having authored 27 papers that have together received 338 indexed citations. Recurring topics across this work include Advanced Bandit Algorithms Research (7 papers), Reinforcement Learning in Robotics (6 papers), Algebraic structures and combinatorial models (3 papers), Data Stream Mining Techniques (3 papers), Machine Learning and Data Classification (3 papers), Machine Learning and Algorithms (2 papers), Coding theory and cryptography (2 papers) and Intelligent Tutoring Systems and Adaptive Learning (2 papers). The work is most often cited by research in Human-Computer Interaction (72 citations), Computer Science Applications (64 citations), Artificial Intelligence (138 citations), Management Science and Operations Research (53 citations) and Cognitive Neuroscience (57 citations). Travis Mandel has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include Zoran Popović, Emma Brunskill, Yun-En Liu, Shwetak Patel, Jacob O. Wobbrock, Mayank Goel, Sergey Levine, Jens Mache, Ben Davison and Mark Jimenez. Their work appears in journals such as Inventiones mathematicae, Frontiers in Marine Science, Proceedings of the ACM on Human-Computer Interaction, Remote Sensing and Pattern Recognition.

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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