World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
49
Citations
8730
World Ranking
5934
National Ranking
278

Overview

Martin Hofmann is a researcher affiliated with Ludwig-Maximilians-Universität München in Germany. Their work primarily spans the field of Computer Science, with contributions in both Artificial Intelligence and Computational Theory and Mathematics as subfields.

The main topics addressed in Martin Hofmann's research include:

  • Logic, programming, and type systems
  • Logic, Reasoning, and Knowledge
  • Advanced Algebra and Logic

Their publication record includes research articles published in the venue Mathematical Structures in Computer Science. One notable paper is:

  • "A quantitative model for simply typed λ-calculus," published in 2021 in Mathematical Structures in Computer Science

Martin Hofmann has collaborated with other researchers, among whom Jérémy Ledent is a frequent co-author.

Best Publications

  • Background segmentation with feedback: The Pixel-Based Adaptive Segmenter

    Martin Hofmann;Philipp Tiefenbacher;Gerhard Rigoll

  • Multivariate amortized resource analysis

    Jan Hoffmann;Klaus Aehlig;Martin Hofmann

  • Static prediction of heap space usage for first-order functional programs

    Martin Hofmann;Steffen Jost

  • Linear types and non-size-increasing polynomial time computation

    Martin Hofmann

  • Syntax and semantics of dependent types

    Martin Hofmann

  • Mobile Resource Guarantees for smart devices

    David Aspinall;Stephen Gilmore;Martin Hofmann;Donald Sannella

  • The TUM Gait from Audio, Image and Depth (GAID) database

    Martin Hofmann;Jürgen Geiger;Sebastian Bachmann;Björn Schuller

  • Semantical analysis of higher-order abstract syntax

    M. Hofmann

  • Amortized resource analysis with polynomial potential: a static inference of polynomial bounds for functional programs

    Jan Hoffmann;Martin Hofmann

  • Static determination of quantitative resource usage for higher-order programs

    Steffen Jost;Kevin Hammond;Hans-Wolfgang Loidl;Martin Hofmann

  • On the Interpretation of Type Theory in Locally Cartesian Closed Categories

    Martin Hofmann

  • Symmetric lenses

    Martin Hofmann;Benjamin Pierce;Daniel Wagner

  • Type-based amortised heap-space analysis

    Martin Hofmann;Steffen Jost

  • A type system for bounded space and functional in-place update

    Martin Hofmann

  • Categorical Reconstruction of a Reduction Free Normalization Proof

    Thorsten Altenkirch;Martin Hofmann;Thomas Streicher

  • The groupoid model refutes uniqueness of identity proofs

    M. Hofmann;T. Streicher

  • Hypergraphs for Joint Multi-view Reconstruction and Multi-object Tracking

    Martin Hofmann;Daniel Wolf;Gerhard Rigoll

  • Mobile-Agent versus Client/Server Performance: Scalability in an Information-Retrieval Task

    Robert S. Gray;David Kotz;Ronald A. Peterson;Joyce Barton

  • Normalization by evaluation for typed lambda calculus with coproducts

    T. Altenkirch;P. Dybjer;M. Hofmann;P. Scott

  • A unifying type-theoretic framework for objects

    Martin Hofmann;Benjamin C. Pierce

  • Gait Recognition in the Presence of Occlusion: A New Dataset and Baseline Algorithms

    Martin Hofmann;Shamik Sural;Gerhard Rigoll

  • A Type System for Bounded Space and Functional In-Place Update--Extended Abstract

    Martin Hofmann

Frequent Co-Authors

Nick Benton
Nick Benton Facebook (United States)
Donald Sannella
Donald Sannella University of Edinburgh
Benjamin C. Pierce
Benjamin C. Pierce University of Pennsylvania
Stephen Gilmore
Stephen Gilmore University of Edinburgh
Helmut Seidl
Helmut Seidl Technical University of Munich
Greg Morrisett
Greg Morrisett Cornell University
Gilles Barthe
Gilles Barthe Max Planck Institute for Security and Privacy
Lars Birkedal
Lars Birkedal Aarhus University
David Kotz
David Kotz Dartmouth College
Matthias Felleisen
Matthias Felleisen Northeastern University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Studying Computer Science in the USA opens doors to a range of flexible, online degree options and career pathways. For those interested in data and analytics, pursuing a data scientist degree can be a smart, affordable choice with strong job prospects.

Engineering fields are also expanding online. You’ll find accredited online electrical engineering programs that offer quality coursework and recognized qualifications without on-campus attendance.

If you’re looking to enter the workforce quickly, consider 3-month certificate programs that pay well. These certifications are designed for in-demand skills, helping students launch well-paying careers in less time.

For those seeking advanced credentials, explore the quickest masters degree online options. Accelerated master’s programs can be completed within a year, letting you boost your career prospects efficiently.

Best Scientists Citing Martin Hofmann

Trending Scientists