World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
42
Citations
13285
World Ranking
8172
National Ranking
37

Overview

Rasmus Pagh is affiliated with the University of Copenhagen in Denmark and has a research profile rooted primarily in computer science with an emphasis on artificial intelligence and related subfields. Their work spans various areas including privacy-preserving technologies, cryptography, data security, and advanced methods in image and video retrieval.

The scientist has contributed extensively to the field of computer science, particularly with publications focusing on privacy and security, algorithms, and data compression. Their publication record includes numerous papers addressing these topics followed by research in internet traffic analysis, secure electronic voting, and machine learning algorithms.

Key topics covered in Rasmus Pagh's work include:

  • Privacy-Preserving Technologies in Data
  • Cryptography and Data Security
  • Advanced Image and Video Retrieval Techniques
  • Algorithms and Data Compression
  • Internet Traffic Analysis and Secure E-voting
  • Machine Learning and Algorithms
  • Complexity and Algorithms in Graphs

Rasmus Pagh has coauthored papers frequently with several researchers, including Martin Aumüller, Badih Ghazi, Pasin Manurangsi, Ravi Kumar, and Ameya Velingker.

Their recent papers demonstrate a wide scope, with titles such as:

  • "Advances and Open Problems in Federated Learning" (2020, Foundations and Trends® in Machine Learning)
  • "HyperLogLogLog" (2022, Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining)
  • "InfiniFilter: Expanding Filters to Infinity and Beyond" (2023, Proceedings of the ACM on Management of Data)
  • "Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead" (2021, arXiv (Cornell University))
  • "Fair near neighbor search via sampling" (2021, ACM SIGMOD Record)

The scientist regularly publishes in several respected venues, including arXiv (Cornell University), Leibniz-Zentrum für Informatik (Schloss Dagstuhl), Proceedings of the ACM on Management of Data, ACM SIGMOD Record, and Lecture Notes in Computer Science.

Rasmus Pagh has also contributed to academic literature beyond articles by authoring a book titled Similarity Search and Applications, published by Springer Science+Business Media in 2020.

Best Publications

  • Advances and Open Problems in Federated Learning

    Peter Kairouz;H. Brendan McMahan;Brendan Avent;Aurélien Bellet

  • Cuckoo Hashing

    Rasmus Pagh;Flemming Friche Rodler

  • Advances and open problems in federated learning

    Peter Kairouz;H. Brendan McMahan;Brendan Avent;Aurélien Bellet

  • Fast and scalable polynomial kernels via explicit feature maps

    Ninh Pham;Rasmus Pagh

  • Space Efficient Hash Tables With Worst Case Constant Access Time

    Dimitris Fotakis;Rasmus Pagh;Peter Sanders;Paul G. Spirakis

  • Low Redundancy in Static Dictionaries with Constant Query Time

    Rasmus Pagh

  • An optimal Bloom filter replacement

    Anna Pagh;Rasmus Pagh;S. Srinivasa Rao

  • Colorful triangle counting and a MapReduce implementation

    Rasmus Pagh;Charalampos E. Tsourakakis

  • Compressed matrix multiplication

    Rasmus Pagh

  • Tight thresholds for cuckoo hashing via XORSAT

    Martin Dietzfelbinger;Andreas Goerdt;Michael Mitzenmacher;Andrea Montanari

  • A near-linear time approximation algorithm for angle-based outlier detection in high-dimensional data

    Ninh Pham;Rasmus Pagh

  • Simple and space-efficient minimal perfect hash functions

    Fabiano C. Botelho;Rasmus Pagh;Nivio Ziviani

  • Deterministic Dictionaries

    Torben Hagerup;Peter Bro Miltersen;Rasmus Pagh

  • Uniform Hashing in Constant Time and Optimal Space

    Anna Pagh;Rasmus Pagh

  • Monotone minimal perfect hashing: searching a sorted table with O(1) accesses

    Djamal Belazzougui;Paolo Boldi;Rasmus Pagh;Sebastiano Vigna

  • Space Efficient Hash Tables with Worst Case Constant Access Time

    Dimitris Fotakis;Rasmus Pagh;Peter Sanders;Paul G. Spirakis

  • Faster join-projects and sparse matrix multiplications

    Rasmus Resen Amossen;Rasmus Pagh

  • Uniform hashing in constant time and linear space

    Anna Ostlin;Rasmus Pagh

  • Succinct Data Structures for Retrieval and Approximate Membership (Extended Abstract)

    Martin Dietzfelbinger;Rasmus Pagh

  • MapReduce Triangle Enumeration With Guarantees

    Ha-Myung Park;Francesco Silvestri;U. Kang;Rasmus Pagh

  • Scalable and Differentially Private Distributed Aggregation in the Shuffled Model.

    Badih Ghazi;Rasmus Pagh;Ameya Velingker

Frequent Co-Authors

Mikkel Thorup
Mikkel Thorup University of Copenhagen
Michael Mitzenmacher
Michael Mitzenmacher Harvard University
David P. Woodruff
David P. Woodruff Carnegie Mellon University
Sebastiano Vigna
Sebastiano Vigna University of Milan
Paolo Boldi
Paolo Boldi University of Milan
Ravi Kumar
Ravi Kumar Google (United States)
Nivio Ziviani
Nivio Ziviani Universidade Federal de Minas Gerais
Yoshihiro Yamanishi
Yoshihiro Yamanishi Nagoya University
Peter Sanders
Peter Sanders Karlsruhe Institute of Technology
Marco Gruteser
Marco Gruteser Rutgers, The State University of New Jersey

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