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Thomas Hofmann

Thomas Hofmann

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Computer Science
Switzerland
2025

D-Index & Metrics

Computer Science

D-Index
87
Citations
48375
World Ranking
701
National Ranking
19

Research.com Recognitions

  • 2025 - Research.com Computer Science in Switzerland Leader Award
  • 2022 - Research.com Computer Science in Switzerland Leader Award

Overview

Thomas Hofmann is affiliated with ETH Zurich in Switzerland. Their research spans multiple scientific domains, focusing notably on history and philosophy of science, artificial intelligence, food science, molecular biology, and nutrition and dietetics. Hofmann's scholarly output includes key publications and contributions that reflect this multidisciplinary interest.

Recent papers authored or co-authored by Hofmann cover a range of topics and publication venues:

  • Mass-spectrometry-based draft of the Arabidopsis proteome (2020, Nature)
  • Analysis of microplastics in drinking water and other clean water samples with micro-Raman and micro-infrared spectroscopy: minimum requirements and best practice guidelines (2021, Analytical and Bioanalytical Chemistry)
  • Regulatory myeloid cells paralyze T cells through cell-cell transfer of the metabolite methylglyoxal (2020, Nature Immunology)
  • Integrated microbiota and metabolite profiles link Crohn's disease to sulfur metabolism (2020, Nature Communications)
  • From the Well to the Bottle: Identifying Sources of Microplastics in Mineral Water (2021, Water)

Hofmann collaborates with several frequent co-authors, notably:

  • Cynthia J. Burrows
  • Shu Wang
  • Hyun Jae Kim
  • Gerald J. Meyer
  • Kirk S. Schanze

Publications by Hofmann have frequently appeared in these venues:

  • Journal of Agricultural and Food Chemistry (61 publications)
  • arXiv (Cornell University) (37 publications)
  • Lebensmittelchemie (18 publications)
  • Food Chemistry (5 publications)
  • Foods (5 publications)

Hofmann has also contributed to book publications. Two known books were published by Böhlau Verlag:

  • Abenteuer Wissenschaft (2020)
  • Wiener Naturgeschichten (2021)

Their research topics include:

  • Academic Writing and Publishing
  • Biochemical Analysis and Sensing Techniques
  • Fermentation and Sensory Analysis
  • Advanced Chemical Sensor Technologies
  • Phytochemicals and Antioxidant Activities
  • Olfactory and Sensory Function Studies
  • Analytical Chemistry and Chromatography

Best Publications

  • Probabilistic latent semantic indexing

    Thomas Hofmann

  • Unsupervised Learning by Probabilistic Latent Semantic Analysis

    Thomas Hofmann

  • Large Margin Methods for Structured and Interdependent Output Variables

    Ioannis Tsochantaridis;Thorsten Joachims;Thomas Hofmann;Yasemin Altun

  • Kernel methods in machine learning

    Thomas Hofmann;Bernhard Schölkopf;Alexander J. Smola

  • Probabilistic latent semantic analysis

    Thomas Hofmann

  • Latent semantic models for collaborative filtering

    Thomas Hofmann

  • Support vector machine learning for interdependent and structured output spaces

    Ioannis Tsochantaridis;Thomas Hofmann;Thorsten Joachims;Yasemin Altun

  • Support Vector Machines for Multiple-Instance Learning

    Stuart Andrews;Ioannis Tsochantaridis;Thomas Hofmann

  • Beyond sliding windows: Object localization by efficient subwindow search

    C.H. Lampert;M.B. Blaschko;T. Hofmann

  • Latent class models for collaborative filtering

    Thomas Hofmann;Jan Puzicha

  • Hidden Markov support vector machines

    Yasemin Altun;Ioannis Tsochantaridis;Thomas Hofmann

  • The Missing Link - A Probabilistic Model of Document Content and Hypertext Connectivity

    David A. Cohn;Thomas Hofmann

  • System and method for personalized search, information filtering, and for generating recommendations utilizing statistical latent class models

    Thomas Hofmann;Jan Christian Puzicha

  • Pairwise data clustering by deterministic annealing

    T. Hofmann;J.M. Buhmann

  • Predicting Structured Data

    GH Bakir;T Hofmann;B Schölkopf;Smola Aj, Taskar, B

  • Collaborative filtering via gaussian probabilistic latent semantic analysis

    Thomas Hofmann

  • Unifying collaborative and content-based filtering

    Justin Basilico;Thomas Hofmann

  • Efficient Subwindow Search: A Branch and Bound Framework for Object Localization

    C.H. Lampert;M.B. Blaschko;T. Hofmann

  • Hierarchical document categorization with support vector machines

    Lijuan Cai;Thomas Hofmann

  • Fully Character-Level Neural Machine Translation without Explicit Segmentation

    Jason Lee;Kyunghyun Cho;Thomas Hofmann

  • Stabilizing Training of Generative Adversarial Networks through Regularization

    Kevin Roth;Aurelien Lucchi;Sebastian Nowozin;Thomas Hofmann

  • The Mobile Robot Rhino

    J. Buhmann;W. Burgard;A. B. Cremers;D. Fox

  • Greedy Layer-Wise Training of Deep Networks

    Bernhard Schölkopf;John Platt;Thomas Hofmann

  • Analysis of Representations for Domain Adaptation

    Bernhard Schölkopf;John Platt;Thomas Hofmann

  • An Application of Reinforcement Learning to Aerobatic Helicopter Flight

    Bernhard Schölkopf;John Platt;Thomas Hofmann

Frequent Co-Authors

Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
John Platt
John Platt Google (United States)
Alexander J. Smola
Alexander J. Smola Amazon (United States)
S. V. N. Vishwanathan
S. V. N. Vishwanathan Purdue University West Lafayette
Ben Taskar
Ben Taskar University of Washington
Mark Johnson
Mark Johnson Macquarie University
Andreas Krause
Andreas Krause ETH Zurich

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