Max Hort

536 citations
18 papers · 240 indexed · h-index 7

Impact in

Papers in

    • Adversarial Robustness in Machine Learning 5
    • Topic Modeling 3
    • Reinforcement Learning in Robotics 2
    • Privacy-Preserving Technologies in Data 2
    • Natural Language Processing Techniques 1
    • Ethics and Social Impacts of AI 7

Max Hort

14 papers receiving 230 citations

Peers

Max Hort
Comparison fields: 5 of 55
  • Safety Research 105
  • Health Informatics 15
  • Software 15
  • Artificial Intelligence 119
  • Information Systems 58
Replace Sergey Butakov with:
Sergey Butakov Canada
Yanghe Pan China
Bruno Castro da Silva United States
Daye Nam United States
Ching Nam Hang Taiwan
Fatemehsadat Mireshghallah United States
Hanjie Chen United States
Shraddha Barke United States
Stephen Macke United States
Max Hort relative to Sergey Butakov Canada Sergey Butakov's profile →
Citations per field
00.5×5.2×
Sergey Butakov · 1×
Citations per year

Countries citing papers authored by Max Hort

Since Specialization
Citations

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

Fields of papers citing papers by Max Hort

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 6 scholars most cited alongside Max Hort, 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 Max Hort Line = papers co-authored together Max Hort links everyone, so they are left out of the graph.

All Works

18 of 18 papers shown
#Work
1 202380
2 202146
3 202140
4 202423
5 202114
6 202210
7 20247
8 20244
9 20214
10 20224
11 20223
12 20233
13 20231
14 20201
15 20250
16 20220
17 20250
18 20250

About Max Hort

Max Hort is a scholar working on Artificial Intelligence, Safety Research, Information Systems, Computer Networks and Communications and Sociology and Political Science, having authored 18 papers that have together received 240 indexed citations. Recurring topics across this work include Ethics and Social Impacts of AI (7 papers), Adversarial Robustness in Machine Learning (5 papers), Software Engineering Research (4 papers), Topic Modeling (3 papers), Reinforcement Learning in Robotics (2 papers), Privacy-Preserving Technologies in Data (2 papers), Natural Language Processing Techniques (1 paper) and Caching and Content Delivery (1 paper). The work is most often cited by research in Safety Research (105 citations), Health Informatics (15 citations), Software (15 citations), Artificial Intelligence (119 citations) and Information Systems (58 citations). Max Hort has collaborated with scholars based in United Kingdom, Norway and Switzerland. Frequent co-authors include Federica Sarro, Mark Harman, Jie M. Zhang, Zhenpeng Chen, Maria Kechagia and Leon Moonen. Their work appears in journals such as ACM Transactions on Software Engineering and Methodology, IEEE Transactions on Software Engineering, Applied Soft Computing, Empirical Software Engineering and Information and Software Technology.

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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