World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
83
Citations
105549
World Ranking
872
National Ranking
474

Research.com Recognitions

  • 2006 - Fellow of Alfred P. Sloan Foundation

Overview

Carlos Guestrin is affiliated with Stanford University in the United States. Their main field of study is Computer Science, with a specialized focus on several subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Endocrinology, Diabetes and Metabolism, Software, and Sociology and Political Science.

Their research covers a variety of topics. Notable among these are:

  • Topic Modeling
  • Machine Learning and Data Classification
  • Natural Language Processing Techniques
  • Diabetes Management and Research
  • Advanced Neural Network Applications
  • Explainable Artificial Intelligence (XAI)
  • Diabetes and associated disorders

Frequent co-authors include:

  • Tatsunori Hashimoto
  • Matei Zaharia
  • Emily B. Fox
  • David Scheinker
  • David M. Maahs

Carlos Guestrin has published predominantly in the venue arXiv (Cornell University), contributing 23 publications. Other publication venues include Proceedings of the ACM on Management of Data, Nature, bioRxiv (Cold Spring Harbor Laboratory), and JAMA Network Open.

Some of their recent papers include:

  • "AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback," 2023, arXiv (Cornell University)
  • "Mobility trends provide a leading indicator of changes in SARS-CoV-2 transmission," 2020, bioRxiv (Cold Spring Harbor Laboratory)
  • "Disparities in Hemoglobin A1c Levels in the First Year After Diagnosis Among Youths With Type 1 Diabetes Offered Continuous Glucose Monitoring," 2023, JAMA Network Open
  • "ACORN: Performant and Predicate-Agnostic Search Over Vector Embeddings and Structured Data," 2024, Proceedings of the ACM on Management of Data
  • "Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks," 2023, arXiv (Cornell University)

The scientist has been recognized as a Fellow of the Alfred P. Sloan Foundation in 2006.

Best Publications

  • XGBoost: A Scalable Tree Boosting System

    Tianqi Chen;Carlos Guestrin

  • “Why Should I Trust You?”: Explaining the Predictions of Any Classifier

    Marco Túlio Ribeiro;Sameer Singh;Carlos Guestrin

  • Cost-effective outbreak detection in networks

    Jure Leskovec;Andreas Krause;Carlos Guestrin;Christos Faloutsos

  • PowerGraph: distributed graph-parallel computation on natural graphs

    Joseph E. Gonzalez;Yucheng Low;Haijie Gu;Danny Bickson

  • Anchors: High-Precision Model-Agnostic Explanations

    Marco Tulio Ribeiro;Sameer Singh;Carlos Guestrin

  • Max-Margin Markov Networks

    Ben Taskar;Carlos Guestrin;Daphne Koller

  • Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies

    Andreas Krause;Ajit Singh;Carlos Guestrin

  • Distributed GraphLab: a framework for machine learning and data mining in the cloud

    Yucheng Low;Danny Bickson;Joseph Gonzalez;Carlos Guestrin

  • Model-driven data acquisition in sensor networks

    Amol Deshpande;Carlos Guestrin;Samuel R. Madden;Joseph M. Hellerstein

  • GraphChi: large-scale graph computation on just a PC

    Aapo Kyrola;Guy Blelloch;Carlos Guestrin

  • TVM: an automated end-to-end optimizing compiler for deep learning

    Tianqi Chen;Thierry Moreau;Ziheng Jiang;Lianmin Zheng

  • Beyond accuracy: Behavioral testing of NLP models with checklist

    Marco Tulio Ribeiro;Tongshuang Wu;Carlos Guestrin;Sameer Singh

  • Model-Agnostic Interpretability of Machine Learning.

    Marco Túlio Ribeiro;Sameer Singh;Carlos Guestrin

  • Training Deep Nets with Sublinear Memory Cost.

    Tianqi Chen;Bing Xu;Chiyuan Zhang;Carlos Guestrin

  • Learning structured prediction models: a large margin approach

    Ben Taskar;Vassil Chatalbashev;Daphne Koller;Carlos Guestrin

  • The battle of the water sensor networks (BWSN): A design challenge for engineers and algorithms

    Avi Ostfeld;James G. Uber;Elad Salomons;Jonathan W. Berry

  • GraphLab: a new framework for parallel machine learning

    Yucheng Low;Joseph Gonzalez;Aapo Kyrola;Danny Bickson

  • Stochastic Gradient Hamiltonian Monte Carlo

    Tianqi Chen;Emily Fox;Carlos Guestrin

  • Efficient solution algorithms for factored MDPs

    Carlos Guestrin;Daphne Koller;Ronald Parr;Shobha Venkataraman

  • Distributed regression: an efficient framework for modeling sensor network data

    Carlos Guestrin;Peter Bodik;Romain Thibaux;Mark Paskin

Frequent Co-Authors

Andreas Krause
Andreas Krause ETH Zurich
Sameer Singh
Sameer Singh University of California, Irvine
Joseph E. Gonzalez
Joseph E. Gonzalez University of California, Berkeley
Joseph M. Hellerstein
Joseph M. Hellerstein University of California, Berkeley
Daphne Koller
Daphne Koller insitro Inc.
Jure Leskovec
Jure Leskovec Stanford University
Luis Ceze
Luis Ceze University of Washington
Arvind Krishnamurthy
Arvind Krishnamurthy University of Washington
Amol Deshpande
Amol Deshpande University of Maryland, College Park
Eric Horvitz
Eric Horvitz Microsoft (United States)

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