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Computer Science

D-Index
46
Citations
10846
World Ranking
6751
National Ranking
2976

Overview

Carlos Scheidegger is affiliated with the University of Arizona in the United States. Their research primarily spans the field of Computer Science, with specializations across several subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Networks and Communications, Signal Processing, and Statistical and Nonlinear Physics.

The scientist's work addresses a range of topics, notably Data Visualization and Analytics, Anomaly Detection Techniques and Applications, Explainable Artificial Intelligence (XAI), Bayesian Modeling and Causal Inference, Machine Learning and Data Classification, Data Management and Algorithms, as well as Software System Performance and Reliability.

Carlos Scheidegger has contributed to a variety of scholarly publication venues. Frequent outlets for their work include arXiv (Cornell University), IEEE Transactions on Visualization and Computer Graphics, Communications of the ACM, The Astronomical Journal, and Distill.

Some of their recent papers are:

  • "The (Im)possibility of fairness," 2021, Communications of the ACM
  • "Problems with Shapley-value-based explanations as feature importance measures," 2020, arXiv (Cornell University)
  • "The ANTARES Astronomical Time-domain Event Broker," 2021, The Astronomical Journal
  • "Human-in-the-loop Extraction of Interpretable Concepts in Deep Learning Models," 2021, IEEE Transactions on Visualization and Computer Graphics

Carlos Scheidegger frequently collaborates with other researchers. Notable coauthors include Zhenge Zhao, Sorelle A. Friedler, Suresh Venkatasubramanian, Mingwei Li, and Joshua A. Levine.

Best Publications

  • Certifying and Removing Disparate Impact

    Michael Feldman;Sorelle A. Friedler;John Moeller;Carlos Scheidegger

  • VisTrails: visualization meets data management

    Steven P. Callahan;Juliana Freire;Emanuele Santos;Carlos E. Scheidegger

  • A comparative study of fairness-enhancing interventions in machine learning

    Sorelle A. Friedler;Carlos Scheidegger;Suresh Venkatasubramanian;Sonam Choudhary

  • VisTrails: enabling interactive multiple-view visualizations

    L. Bavoil;S.P. Callahan;P.J. Crossno;J. Freire

  • On the (im)possibility of fairness

    Sorelle A. Friedler;Carlos Scheidegger;Suresh Venkatasubramanian

  • Managing rapidly-evolving scientific workflows

    Juliana Freire;Cláudio T. Silva;Steven P. Callahan;Emanuele Santos

  • Auditing black-box models for indirect influence

    Philip Adler;Casey Falk;Sorelle A. Friedler;Tionney Nix

  • Nanocubes for Real-Time Exploration of Spatiotemporal Datasets

    Lauro Lins;James T. Klosowski;Carlos Scheidegger

  • Special Issue: The First Provenance Challenge

    Luc Moreau;Bertram Ludäscher;Ilkay Altintas;Roger S. Barga

  • The First Provenance Challenge

    Luc Moreau;Bertram Ludaescher;Ilkay Altintas;Roger S. Barga

  • Runaway Feedback Loops in Predictive Policing

    Danielle Ensign;Sorelle A. Friedler;Scott Neville;Carlos Eduardo Scheidegger

  • Multilevel agglomerative edge bundling for visualizing large graphs

    Emden R. Gansner;Yifan Hu;Stephen North;Carlos Scheidegger

  • The (Im)possibility of fairness: different value systems require different mechanisms for fair decision making

    Sorelle A. Friedler;Carlos Scheidegger;Suresh Venkatasubramanian

  • SynMap2 and SynMap3D: web-based whole-genome synteny browsers

    Asher Haug-Baltzell;Sean A. Stephens;Sean Davey;Carlos Eduardo Scheidegger

  • Managing the Evolution of Dataflows with VisTrails

    S.P. Callahan;J. Freire;E. Santos;C.E. Scheidegger

  • Tackling the Provenance Challenge one layer at a time

    Carlos Scheidegger;David Koop;Emanuele Santos;Huy Vo

  • Querying and Creating Visualizations by Analogy

    C.E. Scheidegger;H.T. Vo;D. Koop;J. Freire

  • Querying and re-using workflows with VsTrails

    Carlos E. Scheidegger;Huy T. Vo;David Koop;Juliana Freire

  • Problems with Shapley-value-based explanations as feature importance measures

    I. Elizabeth Kumar;Suresh Venkatasubramanian;Carlos Scheidegger;Sorelle Friedler

  • Machine Learning-based Brokers for Real-time Classification of the LSST Alert Stream

    Gautham Narayan;Tayeb Zaidi;Monika D. Soraisam;Zhe Wang

  • An Algebraic Process for Visualization Design

    Gordon L. Kindlmann;Carlos Eduardo Scheidegger

Frequent Co-Authors

Cláudio T. Silva
Cláudio T. Silva New York University
Juliana Freire
Juliana Freire New York University
Thomas Matheson
Thomas Matheson National Optical-Infrared Astronomy Research Lab
Richard T. Snodgrass
Richard T. Snodgrass University of Arizona
Remco Chang
Remco Chang Tufts University
Stephen G. Kobourov
Stephen G. Kobourov University of Arizona
Gordon Kindlmann
Gordon Kindlmann University of Chicago
Robert M. Kirby
Robert M. Kirby University of Utah
Edward W. Olszewski
Edward W. Olszewski University of Arizona

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