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Yuede Ji

Title: Assistant Professor

Department: Computer Science and Engineering

College: College of Engineering

Curriculum Vitae

Curriculum Vitae Link

Education

  • PhD, George Washington University, 2021
    Major: Computer Engineering
    Specialization: Cybersecurity and High-Performance Computing
    Dissertation: High-Performance Graph Computing and Application in Cybersecurity
  • MS, Jilin University, 2015
    Major: Computer Science
    Specialization: Cybersecurity
    Dissertation: Host-Based Botnet Detection
  • BE, Jilin University, 2012
    Major: Software Engineering
    Specialization: Software Engineering
    Dissertation: Research of Host-based Bot Detection

Current Scheduled Teaching

CSCE 6933.001Advanced Topics in Computer Science and EngineeringSpring 2025

Previous Scheduled Teaching

CSCE 6933.001Advanced Topics in Computer Science and EngineeringSpring 2024 SPOT
CSCE 4890.709Directed StudySpring 2024
CSCE 6940.917Individual ResearchSpring 2024
CSCE 5565.002Secure Software DevelopmentSpring 2024 SPOT
CSCE 5150.002Analysis of Computer AlgorithmsFall 2023 SPOT
CSCE 4890.709Directed StudyFall 2023
CSCE 6940.809Individual ResearchFall 2023
CSCE 6933.001Advanced Topics in Computer Science and EngineeringSpring 2023 SPOT
CSCE 4890.762Directed StudySpring 2023
CSCE 6940.917Individual ResearchSpring 2023
CSCE 5150.002Analysis of Computer AlgorithmsFall 2022 Syllabus SPOT
CSCE 4890.709Directed StudyFall 2022
CSCE 5934.809Directed StudyFall 2022
CSCE 6940.809Individual ResearchFall 2022
CSCE 6940.917Individual ResearchSpring 2022
CSCE 5565.001Secure Software SystemsSpring 2022 Syllabus SPOT
CSCE 5150.002Analysis of Computer AlgorithmsFall 2021 Syllabus SPOT

Published Intellectual Contributions

    Conference Proceeding

  • Qiu, C., Yadav, S., Ji, Y., Squicciarini, A., Dantu, R., Zhao, J., Xu, C. Fine-Grained Geo-Obfuscation to Protect Workers’ Location Privacy in Large-Scale Time-Sensitive Spatial Crowdsourcing. 27th International Conference on Extending Database Technology (EDBT) 2024.
  • Feng, W., Chen, S., Liu, H., Ji, Y. (2023). PeeK: A Prune-Centric Approach for Shortest Path Computation. Denver, CO, International Conference for High Performance Computing, Networking, Storage, and Analysis (SC).
  • Chen, S., Zheng, D., Ding, C., Huan, C., Ji, Y., Liu, H. (2023). TANGO: re-thinking quantization for graph neural network training. International Conference for High Performance Computing, Networking, Storage, and Analysis (SC).
  • Caldwell, J., Feng, W., Byun, M., Albert, M.V., Shu, T., Ji, Y. (2023). Exploring Power and Thermal Dynamics in the Summit Supercomputer: A Data Visualization Study. 7th Annual Smoky Mountains Computational Sciences Data Challenge (SMCDC).
  • Haleem, Y., Wagenvoord, I., Wei, Q., Xiao, T., Shu, T., Ji, Y. (2023). Understanding Nationwide Power Outage and Restoration for Future Prediction. 7th Annual Smoky Mountains Computational Sciences Data Challenge (SMCDC).
  • Cui, L., Cui, J., Ji, Y., Hao, Z., Li, L., Ding, Z. (2023). API2vec: Learning Representations of API Sequences for Malware Detection. ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA'23).
  • Fu, Q., Ji, Y., Huang, H. (2022). TLPGNN: A Lightweight Two-Level Parallelism Paradigm for Graph Neural Network Computation on GPU. ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC). https://dl.acm.org/doi/abs/10.1145/3502181.3531467
  • He, H., Ji, Y., Huang, H. (2022). Illuminati: Towards Explaining Graph Neural Networks for Cybersecurity Analysis. IEEE European Symposium on Security and Privacy. https://ieeexplore.ieee.org/document/9797387
  • Ji, Y., Huang, H. NestedGNN: Detecting Malicious Network Activity with Nested Graph Neural Networks. IEEE.
  • Ji, Y., Elsabagh, M., Johnson, R., Stavrou, A. (2021). DEFInit: An Analysis of Exposed Android Init Routines. USENIX Security.
  • Ji, Y., Cui, L., Huang, H. (2021). Vestige: Identifying Binary Code Provenance for Vulnerability Detection. The 19th International Conference on Applied Cryptography and Network Security (ACNS).
  • Ji, Y., Cui, L., , H. (2021). BugGraph: Differentiating Source-Binary Code Similarity with Graph Triplet-Loss Network. The ACM Asia Conference on Computer and Communications Security (AsiaCCS).
  • Journal Article

  • Ji, Y., Liu, H., Hu, Y., Huang, H. iSpan: Parallel Identification of Strongly Connected Components with Spanning Trees. ACM.
  • Shang, L., Guo, Ji, Y., Li, Q. (2021). Discovering Unknown Advanced Persistent Threat Using Shared Features Mined by Neural Networks. Computer Networks.
  • Poster

  • Wang, L., Malladi, A., Ji, Y. Efficient Sparse Deep Neural Network Computation on GPU. ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis (SC).

Contracts, Grants and Sponsored Research

    Grant - Research

  • Ji, Y. (Principal), Bhowmick, S. (Co-Principal), "Collaborative Research: SHF: Small: LEGAS: Learning Evolving Graphs At Scale," sponsored by National Science Foundation, Federal, $308739 Funded. (2024 - 2026).
  • Ji, Y. (Principal), Gao, X. (Co-Principal), "CICI: UCSS: Secure Containers in High-Performance Computing Infrastructure," sponsored by National Science Foundation, Federal, $600000 Funded. (2023 - 2026).
  • Ji, Y., "Scalable and Efficient Computation of Graph Neural Networks on GPUs," sponsored by Google, Private, $5000 Funded. (2021 - 2022).
  • Grant - Teaching

  • Ji, Y., "Analysis of Computer Algorithms," sponsored by Google, Private, $5350 Funded. (2022 - 2023).
  • Ji, Y., "Analysis of Computer Algorithms," sponsored by Google, Private, $2900 Funded. (2021 - 2022).
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Overall
Summative Rating
Challenge and
Engagement Index
Response Rate

out of 5

out of 7
%
of
students responded
  • Overall Summative Rating (median):
    This rating represents the combined responses of students to the four global summative items and is presented to provide an overall index of the class’s quality. Overall summative statements include the following (response options include a Likert scale ranging from 5 = Excellent, 3 = Good, and 1= Very poor):
    • The course as a whole was
    • The course content was
    • The instructor’s contribution to the course was
    • The instructor’s effectiveness in teaching the subject matter was
  • Challenge and Engagement Index:
    This rating combines student responses to several SPOT items relating to how academically challenging students found the course to be and how engaged they were. Challenge and Engagement Index items include the following (response options include a Likert scale ranging from 7 = Much higher, 4 = Average, and 1 = Much lower):
    • Do you expect your grade in this course to be
    • The intellectual challenge presented was
    • The amount of effort you put into this course was
    • The amount of effort to succeed in this course was
    • Your involvement in course (doing assignments, attending classes, etc.) was
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