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Stefanie Jegelka

Stefanie Jegelka

D-Index & Metrics

Computer Science

D-Index
44
Citations
12921
World Ranking
7406
National Ranking
3230

Research.com Recognitions

  • 2018 - Fellow of Alfred P. Sloan Foundation

Overview

Stefanie Jegelka is affiliated with MIT in the United States and specializes in computer science, with a focus on artificial intelligence. Their research encompasses subfields such as computer vision and pattern recognition, molecular biology, materials chemistry, and computational theory and mathematics.

Their work extensively covers topics including advanced graph neural networks, domain adaptation and few-shot learning, neural networks and their applications, stochastic gradient optimization techniques, machine learning and algorithms, adversarial robustness in machine learning, and topic modeling.

Selected recent papers authored or co-authored by Stefanie Jegelka include:

  • Formal Semantics for Kolmogorov-Arnold Network Representations of Operational Games, 2025, Zenodo (CERN European Organization for Nuclear Research)
  • Graph neural networks, 2024, Nature Reviews Methods Primers
  • Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks, 2020, Journal of Chemical Information and Modeling
  • How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks, 2020, arXiv (Cornell University)
  • Robust Contrastive Learning against Noisy Views, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Frequent co-authors collaborating with Stefanie Jegelka include:

  • Ching-Yao Chuang
  • Yisen Wang
  • Khashayar Gatmiry
  • Antonio Torralba
  • Joshua Robinson

Stefanie Jegelka has published primarily in venues such as arXiv (Cornell University), Nature Reviews Methods Primers, Journal of Chemical Information and Modeling, the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), and Nature Communications.

The researcher has been recognized as a Fellow of the Alfred P. Sloan Foundation in 2018.

Best Publications

  • How Powerful are Graph Neural Networks

    Keyulu Xu;Weihua Hu;Jure Leskovec;Stefanie Jegelka

  • Deep Metric Learning via Lifted Structured Feature Embedding

    Hyun Oh Song;Yu Xiang;Stefanie Jegelka;Silvio Savarese

  • Representation Learning on Graphs with Jumping Knowledge Networks

    Keyulu Xu;Chengtao Li;Yonglong Tian;Tomohiro Sonobe

  • Deep Metric Learning via Facility Location

    Hyun Oh Song;Stefanie Jegelka;Vivek Rathod;Kevin Murphy

  • Debiased Contrastive Learning

    Ching-Yao Chuang;Joshua Robinson;Yen-Chen Lin;Antonio Torralba

  • Max-value Entropy Search for Efficient Bayesian Optimization

    Zi Wang;Stefanie Jegelka

  • On learning to localize objects with minimal supervision

    Hyun Oh Song;Ross Girshick;Stefanie Jegelka;Julien Mairal

  • Submodularity beyond submodular energies: Coupling edges in graph cuts

    Stefanie Jegelka;Jeff Bilmes

  • Virtual screening of inorganic materials synthesis parameters with deep learning

    Edward Kim;Kevin Huang;Stefanie Jegelka;Elsa Olivetti

  • Weakly-supervised Discovery of Visual Pattern Configurations

    Hyun Oh Song;Yong Jae Lee;Stefanie Jegelka;Trevor Darrell

  • ResNet with one-neuron hidden layers is a Universal Approximator

    Hongzhou Lin;Stefanie Jegelka

  • How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks

    Keyulu Xu;Mozhi Zhang;Jingling Li;Simon Shaolei Du

  • Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks.

    Edward Kim;Zach Jensen;Alexander van Grootel;Kevin Huang

  • Fast Semidifferential-based Submodular Function Optimization

    Rishabh Iyer;Stefanie Jegelka;Jeff Bilmes

  • Batched Large-scale Bayesian Optimization in High-dimensional Spaces

    Zi Wang;Clement Gehring;Pushmeet Kohli;Stefanie Jegelka

  • Generalization and Representational Limits of Graph Neural Networks

    Vikas K Garg;Stefanie Jegelka;Tommi Jaakkola

  • What Can Neural Networks Reason About

    Keyulu Xu;Jingling Li;Mozhi Zhang;Simon S. Du

  • Contrastive Learning with Hard Negative Samples

    Joshua David Robinson;Ching-Yao Chuang;Suvrit Sra;Stefanie Jegelka

  • Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions

    Rishabh K Iyer;Stefanie Jegelka;Jeff A Bilmes

  • A Principled Deep Random Field Model for Image Segmentation

    Pushmeet Kohli;Anton Osokin;Stefanie Jegelka

  • Adversarially Robust Optimization with Gaussian Processes

    Ilija Bogunovic;Jonathan Scarlett;Stefanie Jegelka;Volkan Cevher

  • Distributionally Robust Optimization and Generalization in Kernel Methods

    Matthew Staib;Stefanie Jegelka

Frequent Co-Authors

Jeff A. Bilmes
Jeff A. Bilmes University of Washington
Andreas Krause
Andreas Krause ETH Zurich
Ken-ichi Kawarabayashi
Ken-ichi Kawarabayashi National Institute of Informatics
Trevor Darrell
Trevor Darrell University of California, Berkeley
Pushmeet Kohli
Pushmeet Kohli DeepMind (United Kingdom)
Arthur Gretton
Arthur Gretton University College London
Jiashi Feng
Jiashi Feng ByteDance

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