conf.researchr.org / Liming Zhu
Registered user since Fri 10 May 2019
Name:Liming Zhu
Bio:
Dr/Prof Liming Zhu is a Research Director at CSIRO’s Data61 and a conjoint full professor at the University of New South Wales (UNSW). He is the chairperson of Standards Australia’s blockchain committee and on AI trustworthiness-related committees. He is a member of the Responsible AI think tank at the National AI Center. His research program innovates in the areas of AI/ML platforms, responsible/ethical AI, software engineering, blockchain, regulation technology, quantum software, privacy and cybersecurity. He has published more than 300 academic papers on software architecture, data/ML infrastructure, blockchain, governance and responsible AI.
Country:Australia
Affiliation:CSIRO’s Data61
Personal website: https://liming-zhu.org/
X (Twitter): https://x.com/limingz
Research interests:software architecture, software engineering, distributed systems, data platforms
Contributions
2025
2024
AIware
- Author of Towards Responsible Generative AI: A Reference Architecture for Designing Foundation Model based Agents within the Late Breaking Arxiv Track-track
- Steering Committee Member in Steering Committee
- Author of An AI System Evaluation Framework for Advancing AI Safety: Terminology, Taxonomy, Lifecycle Mapping within the Main Track-track
- Author of Agent Design Pattern Catalogue: A Collection of Architectural Patterns for Foundation Model based Agents within the Late Breaking Arxiv Track-track
- Author of Towards Responsible AI in the Era of Generative AI: A Reference Architecture for Designing Foundation Model based Systems within the Late Breaking Arxiv Track-track
ICSE
CAIN
- Author of Towards a Responsible AI Metrics Catalogue: A Collection of Metrics for AI Accountability within the Research and Experience Papers-track
- Author of A Taxonomy of Foundation Model based Systems through the Lens of Software Architecture within the Research and Experience Papers-track
- Committee Member in Program Committee within the Research and Experience Papers-track
- Author of Privacy and Copyright Protection in Generative AI: A Lifecycle Perspective within the Research and Experience Papers-track
2023
ICSE
- Author of Silent Vulnerable Dependency Alert Prediction with Vulnerability Key Aspect Explanation within the Technical Track-track
- Keynote Speaker of Software Engineering as the Linchpin of Responsible AI - Dr. Liming Zhu within the ICSE Keynotes-track
- Author of A Multi-faceted Vulnerability Searching Website Powered by Aspect-level Vulnerability Knowledge Graph within the DEMO - Demonstrations-track
- Author of A Decision Model for Choosing Patterns in Blockchain-Based Applications within the Showcase-track
- Author of RIdiom: Automatically Refactoring Non-idiomatic Python Code with Pythonic Idioms within the DEMO - Demonstrations-track
- Author of SoapOperaTG: A Tool for System Knowledge Graph Based Soap Opera Test Generation within the DEMO - Demonstrations-track
- Author of Faster or Slower? Performance Mystery of Python Idioms Unveiled with Empirical Evidence within the Technical Track-track
- Author of SeeHow: Workflow Extraction from Programming Screencasts through Action-Aware Video Analytics within the Technical Track-track
- Author of An Empirical Study on Software Bill of Materials: Where We Stand and the Road Ahead within the Technical Track-track
2022
ESEC/FSE
ASE
- Author of Constructing a System Knowledge Graph of User Tasks and Failures from Bug Reports to Support Soap Opera Testing within the Research Papers-track
- Author of Prompt-tuned Code Language Model as a Neural Knowledge Base for Type Inference in Statically-Typed Partial Code within the Research Papers-track
ICSE
2021
ECSA
Blockchain Software Engineering
2020
ICSE
- Author of Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learning within the Technical Papers-track
- Committee Member in Program Committee within the Doctoral Symposium-track
- Author of Seenomaly: Vision-Based Linting of GUI Animation Effects Against Design-Don’t Guidelines within the Technical Papers-track