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Computer Science

D-Index
46
Citations
9595
World Ranking
6793
National Ranking
57

Overview

Hongseok Yang is a researcher affiliated with the Korea Advanced Institute of Science and Technology in South Korea. Their research primarily spans computer science, with a particular focus on artificial intelligence, statistics, probability, and civil and structural engineering subfields.

Their work covers topics such as machine learning and algorithms, neural networks and applications, Bayesian modeling and causal inference, machine learning and data classification, model reduction and neural networks, Gaussian processes and Bayesian inference, and adversarial robustness in machine learning.

Hongseok Yang has authored publications in a range of venues, notably:

  • arXiv (Cornell University)
  • Proceedings of the ACM on Programming Languages
  • Transactions of the Korean Society for Noise and Vibration Engineering
  • Forensic Science International Digital Investigation
  • Theoretical Computer Science

Recent papers include:

  • Some Formal Structures in Probability (Invited Talk), 2021, arXiv (Cornell University)
  • On Correctness of Automatic Differentiation for Non-Differentiable Functions, 2020, arXiv (Cornell University)
  • A generalization of hierarchical exchangeability on trees to directed acyclic graphs, 2021, Annales Henri Lebesgue
  • Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inference, 2023, Proceedings of the ACM on Programming Languages
  • The Study on Characteristic of Floor Impact Noise using the Structural Vibration on Floor Slab: Effective Plate, 2020, Transactions of the Korean Society for Noise and Vibration Engineering

Hongseok Yang's frequent collaborators include:

  • Wonyeol Lee
  • Juho Lee
  • Xavier Rival
  • Paul Jung
  • Sam Staton

The researcher's publication record reflects a consistent focus on integrating probabilistic methods with computational models, as reflected by their interests in Bayesian inference and machine learning algorithms. Their contributions to venues such as arXiv and the Proceedings of the ACM on Programming Languages indicate engagement with both foundational theoretical work and applied programming language studies.

Best Publications

  • Local Reasoning about Programs that Alter Data Structures

    Peter W. O'Hearn;John C. Reynolds;Hongseok Yang

  • Compositional Shape Analysis by Means of Bi-Abduction

    Cristiano Calcagno;Dino Distefano;Peter W. O’Hearn;Hongseok Yang

  • Automated concolic testing of smartphone apps

    Saswat Anand;Mayur Naik;Mary Jean Harrold;Hongseok Yang

  • A local shape analysis based on separation logic

    Dino Distefano;Peter W. O'Hearn;Hongseok Yang

  • Compositional shape analysis by means of bi-abduction

    Cristiano Calcagno;Dino Distefano;Peter O'Hearn;Hongseok Yang

  • Separation and information hiding

    Peter W. O'Hearn;Hongseok Yang;John C. Reynolds

  • Local Action and Abstract Separation Logic

    C. Calcagno;P.W. O'Hearn;Hongseok Yang

  • Scalable Shape Analysis for Systems Code

    Hongseok Yang;Oukseh Lee;Josh Berdine;Cristiano Calcagno

  • Separation and information hiding

    Peter W. O'Hearn;Hongseok Yang;John C. Reynolds

  • Shape analysis for composite data structures

    Josh Berdine;Cristiano Calcagno;Byron Cook;Dino Distefano

  • Replicated data types: specification, verification, optimality

    Sebastian Burckhardt;Alexey Gotsman;Hongseok Yang;Marek Zawirski

  • Possible worlds and resources: the semantics of BI

    David J. Pym;Peter W. O'Hearn;Hongseok Yang

  • Views: compositional reasoning for concurrent programs

    Thomas Dinsdale-Young;Lars Birkedal;Philippa Gardner;Matthew Parkinson

  • Abstraction for concurrent objects

    Ivana Filipovi;Peter OHearn;Noam Rinetzky;Hongseok Yang

  • Computability and Complexity Results for a Spatial Assertion Language for Data Structures

    Cristiano Calcagno;Hongseok Yang;Peter W. O'Hearn

  • A Semantic Basis for Local Reasoning

    Hongseok Yang;Peter W. O'Hearn

  • Relational separation logic

    Hongseok Yang

  • 'Cause I'm strong enough: Reasoning about consistency choices in distributed systems

    Alexey Gotsman;Hongseok Yang;Carla Ferreira;Mahsa Najafzadeh

  • Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints

    Sam Staton;Frank Wood;Hongseok Yang;Chris Heunen

  • An Introduction to Probabilistic Programming

    Jan-Willem van de Meent;Brooks Paige;Hongseok Yang;Frank Wood

  • Abstraction for Concurrent Objects

    Ivana Filipović;Peter O'Hearn;Noam Rinetzky;Hongseok Yang

Frequent Co-Authors

Frank Wood
Frank Wood University of British Columbia
Peter W. O'Hearn
Peter W. O'Hearn University College London
Lars Birkedal
Lars Birkedal Aarhus University
Cristiano Calcagno
Cristiano Calcagno Imperial College London
Mayur Naik
Mayur Naik University of Pennsylvania
Yee Whye Teh
Yee Whye Teh University of Oxford
Mooly Sagiv
Mooly Sagiv Tel Aviv University
Byron Cook
Byron Cook Amazon (United States)
Hagit Attiya
Hagit Attiya Technion – Israel Institute of Technology
Zoubin Ghahramani
Zoubin Ghahramani University of Cambridge

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