World's Best Scientists 2026 revealed!
Markus Püschel

Markus Püschel

D-Index & Metrics

Computer Science

D-Index
50
Citations
9287
World Ranking
5645
National Ranking
115

Overview

Markus Püschel is affiliated with ETH Zurich in Switzerland and has contributed extensively to the field of computer science. Their research spans across several subfields, including artificial intelligence, computational theory and mathematics, statistical and nonlinear physics, management science and operations research, and computational mechanics.

The scientist's work focuses on advanced topics such as advanced graph neural networks, complex network analysis techniques, sparse and compressive sensing techniques, adversarial robustness in machine learning, advanced neural network applications, topological and geometric data analysis, and auction theory and applications.

Notable recent papers authored or co-authored by Markus Püschel include:

  • PRIMA: general and precise neural network certification via scalable convex hull approximations (2022), Proceedings of the ACM on Programming Languages
  • Causal Fourier Analysis on Directed Acyclic Graphs and Posets (2023), IEEE Transactions on Signal Processing
  • Digraph Signal Processing With Generalized Boundary Conditions (2021), IEEE Transactions on Signal Processing
  • Scaling Polyhedral Neural Network Verification on GPUs (2020), arXiv (Cornell University)
  • Compressive Sensing Using Iterative Hard Thresholding With Low Precision Data Representation: Theory and Applications (2020), IEEE Transactions on Signal Processing

Throughout their career, Markus Püschel has frequently published in venues such as:

  • arXiv (Cornell University)
  • IEEE Transactions on Signal Processing
  • Proceedings of the ACM on Programming Languages
  • Artifact Digital Object Group
  • Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence

Collaborations have been a significant aspect of their research, with frequent co-authors including Chris Wendler, Bastian Seifert, Gagandeep Singh, Martin Vechev, and Panagiotis Misiakos.

Best Publications

  • SPIRAL: Code Generation for DSP Transforms

    M. Puschel;J.M.F. Moura;J.R. Johnson;D. Padua

  • An abstract domain for certifying neural networks

    Gagandeep Singh;Timon Gehr;Markus Püschel;Martin Vechev

  • Multiplierless multiple constant multiplication

    Yevgen Voronenko;Markus Püschel

  • D-ADMM: A Communication-Efficient Distributed Algorithm for Separable Optimization

    J. F. C. Mota;J. M. F. Xavier;P. M. Q. Aguiar;M. Puschel

  • Fast and Effective Robustness Certification

    Gagandeep Singh;Timon Gehr;Matthew Mirman;Markus Püschel

  • Spiral: A Generator for Platform-Adapted Libraries of Signal Processing Algorithms

    Markus Püschel;José M. F. Moura;Bryan Singer;Jianxin Xiong

  • Distributed Basis Pursuit

    J. F. C. Mota;J. M. F. Xavier;P. M. Q. Aguiar;M. Puschel

  • Algebraic Signal Processing Theory: Foundation and 1-D Time

    M. Puschel;J. Moura

  • Computer Generation of Hardware for Linear Digital Signal Processing Transforms

    Peter Milder;Franz Franchetti;James C. Hoe;Markus Püschel

  • Applying the roofline model

    Georg Ofenbeck;Ruedi Steinmann;Victoria Caparros;Daniele G. Spampinato

  • The Algebraic Approach to the Discrete Cosine and Sine Transforms and Their Fast Algorithms

    Markus Püschel;José M. F. Moura

  • Discrete fourier transform on multicore

    F. Franchetti;M. Puschel;Y. Voronenko;S. Chellappa

  • Algebraic Signal Processing Theory: Cooley–Tukey Type Algorithms for DCTs and DSTs

    Y. Voronenko;M. Puschel

  • Algebraic Signal Processing Theory: 1-D Space

    M. Puschel;J.M.F. Moura

  • Fast polyhedra abstract domain

    Gagandeep Singh;Markus Püschel;Martin Vechev

  • Toward efficient static analysis of finite-precision effects in DSP applications via affine arithmetic modeling

    Claire Fang Fang;Rob A. Rutenbar;Markus Püschel;Tsuhan Chen

  • Distributed Optimization With Local Domains: Applications in MPC and Network Flows

    Joao F. C. Mota;Joao M. F. Xavier;Pedro M. Q. Aguiar;Markus Puschel

  • FFT program generation for shared memory: SMP and multicore

    Franz Franchetti;Yevgen Voronenko;Markus Puschel

  • Time-Multiplexed Multiple-Constant Multiplication

    P.. Tummeltshammer;J.C. Hoe;M.. Puschel

  • Real, tight frames with maximal robustness to erasures

    M. Puschel;J. Kovacevic

  • Boosting Robustness Certification of Neural Networks.

    Gagandeep Singh;Timon Gehr;Markus Püschel;Martin T. Vechev

  • Beyond the Single Neuron Convex Barrier for Neural Network Certification

    Gagandeep Singh;Rupanshu Ganvir;Markus Püschel;Martin T. Vechev

  • Special Issue on Program Generation, Optimization, and Platform Adaptation

    J.M.F. Moura;M. Puschel;D. Padua;J. Dongarra

Frequent Co-Authors

Franz Franchetti
Franz Franchetti Carnegie Mellon University
James C. Hoe
James C. Hoe Carnegie Mellon University
Jose M. F. Moura
Jose M. F. Moura Carnegie Mellon University
Martin Vechev
Martin Vechev ETH Zurich
Jelena Kovacevic
Jelena Kovacevic New York University
Tiark Rompf
Tiark Rompf Purdue University West Lafayette
David Padua
David Padua University of Illinois at Urbana-Champaign
Robert I. Killey
Robert I. Killey University College London
Manuela Veloso
Manuela Veloso Carnegie Mellon University
Thomas Beth
Thomas Beth Karlsruhe University of Applied Sciences

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

Exploring Computer Science in the USA opens a pathway to diverse online degree options, each tailored to different career goals and timelines. If you aim to fast-track your technology career, an associate degree online can get you started in the workforce quickly—sometimes in as little as six months.

For those considering business management roles in tech, consider an affordable online business degree. This can complement your technical skills and prepare you for leadership opportunities.

Ambitious professionals may look toward higher academics. There are a range of affordable doctoral programs available online, making advanced research and teaching roles more accessible.

Additionally, if you’re interested in educational leadership, a online edd provides a fast-track way into top-level positions within academic institutions.

Online education offers flexibility, cost savings, and a variety of pathways—making it easier to align your Computer Science journey with your career ambitions.

Best Scientists Citing Markus Püschel

Trending Scientists

Recently Published Articles