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D-Index & Metrics

Computer Science

D-Index
44
Citations
10529
World Ranking
7453
National Ranking
3247

Overview

Sewoong Oh is affiliated with the University of Washington in the United States. Their research primarily spans the field of Computer Science, with substantial contributions across several subfields. These subfields include Artificial Intelligence, Molecular Biology, Spectroscopy, Computer Networks and Communications, and Computer Vision and Pattern Recognition.

The scientist's work addresses numerous topics, with a particular emphasis on:

  • Privacy-Preserving Technologies in Data
  • Advanced Proteomics Techniques and Applications
  • Mass Spectrometry Techniques and Applications
  • Stochastic Gradient Optimization Techniques
  • Adversarial Robustness in Machine Learning
  • Error Correcting Code Techniques
  • Domain Adaptation and Few-Shot Learning

They have contributed to a variety of published research articles. Notable recent papers include:

  • Sequence-to-sequence translation from mass spectra to peptides with a transformer model, 2024, Nature Communications
  • DataComp: In search of the next generation of multimodal datasets, 2023, arXiv (Cornell University)
  • Deepcode: Feedback Codes via Deep Learning, 2020, IEEE Journal on Selected Areas in Information Theory
  • PacGAN: The Power of Two Samples in Generative Adversarial Networks, 2020, IEEE Journal on Selected Areas in Information Theory
  • Physical Layer Communication via Deep Learning, 2020, IEEE Journal on Selected Areas in Information Theory

The scientist frequently publishes in venues such as:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • IEEE Journal on Selected Areas in Information Theory
  • Journal of Proteome Research
  • Cancer Research

Frequent collaborators in their research include:

  • Pramod Viswanath
  • William Stafford Noble
  • Xiyang Liu
  • Peter Kairouz
  • Jonathan Hayase

Best Publications

  • Matrix Completion From a Few Entries

    Raghunandan H Keshavan;Andrea Montanari;Sewoong Oh

  • Matrix Completion from Noisy Entries

    Raghunandan H. Keshavan;Andrea Montanari;Sewoong Oh

  • Iterative Learning for Reliable Crowdsourcing Systems

    David R. Karger;Sewoong Oh;Devavrat Shah

  • The Composition Theorem for Differential Privacy

    Peter Kairouz;Sewoong Oh;Pramod Viswanath

  • Budget-Optimal Task Allocation for Reliable Crowdsourcing Systems

    David R. Karger;Sewoong Oh;Devavrat Shah

  • Extremal mechanisms for local differential privacy

    Peter Kairouz;Sewoong Oh;Pramod Viswanath

  • PacGAN: The power of two samples in generative adversarial networks

    Zinan Lin;Ashish Khetan;Giulia C. Fanti;Sewoong Oh

  • Attention-based Graph Neural Network for Semi-supervised Learning

    Kiran Koshy Thekumparampil;Chong Wang;Sewoong Oh;Li-Jia Li

  • Iterative ranking from pair-wise comparisons

    Sahand Negahban;Sewoong Oh;Devavrat Shah

  • Rank Centrality: Ranking from Pairwise Comparisons

    Sahand Negahban;Sewoong Oh;Devavrat Shah

  • Matrix completion from a few entries

    Raghunandan H. Keshavan;Sewoong Oh;Andrea Montanari

  • Efficient crowdsourcing for multi-class labeling

    David R. Karger;Sewoong Oh;Devavrat Shah

  • Provable Tensor Factorization with Missing Data

    Prateek Jain;Sewoong Oh

  • The Staircase Mechanism in Differential Privacy

    Quan Geng;Peter Kairouz;Sewoong Oh;Pramod Viswanath

  • Communication algorithms via deep learning

    Hyeji Kim;Yihan Jiang;Ranvir B. Rana;Sreeram Kannan

  • Budget-optimal crowdsourcing using low-rank matrix approximations

    David R. Karger;Sewoong Oh;Devavrat Shah

  • Deepcode: Feedback Codes via Deep Learning

    Hyeji Kim;Yihan Jiang;Sreeram Kannan;Sewoong Oh

  • Counting with the crowd

    Adam Marcus;David Karger;Samuel Madden;Robert Miller

  • Demystifying Fixed $k$ -Nearest Neighbor Information Estimators

    Weihao Gao;Sewoong Oh;Pramod Viswanath

  • Estimating Mutual Information for Discrete-Continuous Mixtures

    Weihao Gao;Sreeram Kannan;Sewoong Oh;Pramod Viswanath

  • Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels

    Yihan Jiang;Hyeji Kim;Himanshu Asnani;Sreeram Kannan

Frequent Co-Authors

Pramod Viswanath
Pramod Viswanath Princeton University
Andrea Montanari
Andrea Montanari Stanford University
Yung Yi
Yung Yi Korea Advanced Institute of Science and Technology
Jinwoo Shin
Jinwoo Shin Korea Advanced Institute of Science and Technology
Sham M. Kakade
Sham M. Kakade Harvard University
Praneeth Netrapalli
Praneeth Netrapalli Google (United States)
Prateek Jain
Prateek Jain Google (United States)
Martin Vetterli
Martin Vetterli École Polytechnique Fédérale de Lausanne

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