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Computer Science

D-Index
43
Citations
8143
World Ranking
7963
National Ranking
3430

Overview

Junfeng Yang is affiliated with Columbia University in the United States. Their research predominantly lies within the field of Computer Science, with a focus on several specialized subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Computer Networks and Communications, and Electrical and Electronic Engineering.

The main topics covered in their research include:

  • Adversarial Robustness in Machine Learning
  • Domain Adaptation and Few-Shot Learning
  • Anomaly Detection Techniques and Applications
  • Software Engineering Research
  • Advanced Malware Detection Techniques
  • Generative Adversarial Networks and Image Synthesis
  • Security and Verification in Computing

Junfeng Yang has contributed to various publication venues, with the most frequent being:

  • arXiv (Cornell University)
  • Sustainability
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • IEEE Access
  • IEEE Internet of Things Journal

The scientist's recent papers include:

  • Full Reference Image Quality Assessment by Considering Intra-Block Structure and Inter-Block Texture, 2020, IEEE Access
  • Trex: Learning Execution Semantics from Micro-Traces for Binary Similarity, 2020, arXiv (Cornell University)
  • Adversarial Attacks are Reversible with Natural Supervision, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Multitask Learning Strengthens Adversarial Robustness, 2020, arXiv (Cornell University)
  • Performance Analysis of Delay Distribution and Packet Loss Ratio for Body-to-Body Networks, 2021, IEEE Internet of Things Journal

Frequent coauthors collaborating with Junfeng Yang include:

  • Chengzhi Mao
  • Baishakhi Ray
  • Carl Vondrick
  • Suman Jana
  • Amogh Gupta

In addition to journal and conference papers, Junfeng Yang has contributed to book publications, including a work published by Springer Nature titled Resilience and Future of Smart Learning in 2022.

Best Publications

  • DeepXplore: Automated Whitebox Testing of Deep Learning Systems

    Kexin Pei;Yinzhi Cao;Junfeng Yang;Suman Jana

  • An empirical study of operating systems errors

    Andy Chou;Junfeng Yang;Benjamin Chelf;Seth Hallem

  • Towards Making Systems Forget with Machine Unlearning

    Yinzhi Cao;Junfeng Yang

  • Using model checking to find serious file system errors

    Junfeng Yang;Paul Twohey;Dawson Engler;Madanlal Musuvathi

  • Formal Security Analysis of Neural Networks using Symbolic Intervals

    Shiqi Wang;Kexin Pei;Justin Whitehouse;Junfeng Yang

  • MODIST: transparent model checking of unmodified distributed systems

    Junfeng Yang;Tisheng Chen;Ming Wu;Zhilei Xu

  • Efficient Formal Safety Analysis of Neural Networks

    Shiqi Wang;Kexin Pei;Justin Whitehouse;Junfeng Yang

  • EXPLODE: a lightweight, general system for finding serious storage system errors

    Junfeng Yang;Can Sar;Dawson Engler

  • NEUZZ: Efficient Fuzzing with Neural Program Smoothing

    Dongdong She;Kexin Pei;Dave Epstein;Junfeng Yang

  • Correlation exploitation in error ranking

    Ted Kremenek;Ken Ashcraft;Junfeng Yang;Dawson Engler

  • Automatically generating malicious disks using symbolic execution

    Junfeng Yang;Can Sar;P. Twohey;C. Cadar

  • Stable deterministic multithreading through schedule memoization

    Heming Cui;Jingyue Wu;Chia-Che Tsai;Junfeng Yang

  • Efficiently, effectively detecting mobile app bugs with AppDoctor

    Gang Hu;Xinhao Yuan;Yang Tang;Junfeng Yang

  • DeepXplore: automated whitebox testing of deep learning systems

    Kexin Pei;Yinzhi Cao;Junfeng Yang;Suman Jana

  • Practical software model checking via dynamic interface reduction

    Huayang Guo;Ming Wu;Lidong Zhou;Gang Hu

  • Metric Learning for Adversarial Robustness

    Chengzhi Mao;Ziyuan Zhong;Junfeng Yang;Carl Vondrick

  • Shuffler: fast and deployable continuous code re-randomization

    David Williams-King;Graham Gobieski;Kent Williams-King;James P. Blake

  • Towards Practical Verification of Machine Learning: The Case of Computer Vision Systems

    Kexin Pei;Yinzhi Cao;Junfeng Yang;Suman Jana

  • Fingerprinting event logs for system management troubleshooting

    Rina Panigrahy;Chad Verbowski;Yinglian Xie;Junfeng Yang

  • Parrot: a practical runtime for deterministic, stable, and reliable threads

    Heming Cui;Jiri Simsa;Yi-Hong Lin;Hao Li

  • Efficient deterministic multithreading through schedule relaxation

    Heming Cui;Jingyue Wu;John Gallagher;Huayang Guo

Frequent Co-Authors

Suman Jana
Suman Jana Columbia University
Baishakhi Ray
Baishakhi Ray Columbia University
Carl Vondrick
Carl Vondrick Columbia University
Dawson Engler
Dawson Engler Stanford University
Lidong Zhou
Lidong Zhou Microsoft (United States)
Salvatore J. Stolfo
Salvatore J. Stolfo Columbia University
Angelos D. Keromytis
Angelos D. Keromytis Georgia Institute of Technology
Hao Wang
Hao Wang Swinburne University of Technology
Jason Nieh
Jason Nieh Columbia University
Shuran Song
Shuran Song Stanford University

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