Jingzhi Gong

Jingzhi Gong

Research Associate for King's College London, PhD from Loughborough University

About

I’m an AI4SE researcher building trustworthy, efficient, and sustainable software using AI. I currently work as a Research Associate in AI for Software Engineering at King’s College London, contributing to the ITEA GENIUS project—a multinational collaboration leveraging GenAI and LLMs to enhance software development life cycle. I am a member of the Software Systems (SSY) group in the Department of Informatics, supervised by Dr Jie M. Zhang, Dr Gunel Jahangirova, and Prof Mohammad Reza Mousavi. My work focuses on developing quality assurance methods for LLM-based software engineering, ensuring the functionality, quality, and architectural soundness of both human and AI-generated software systems.

Previously, from June 2024 to November 2025, I worked as a postdoctoral KTP Associate with both the University of Leeds and TurinTech AI, focusing on compiler- and LLM-based code optimisation. We successfully completed the two-year KTP plan in just one and a half years. At the University of Leeds, I was a member of the Intelligent Systems Software Lab (ISSL) and the Distributed Systems and Services (DSS) research group, supervised by Prof Jie Xu and Prof Zheng Wang. At TurinTech AI, I was a member of the Data Science team led by Dr Fan Wu and Dr Paul Brookes.

I completed my PhD in Dec 2024 in the Department of Computer Science at Loughborough University, supervised by Dr Tao Chen in the IDEAS Laboratory (Intelligent Dependability Engineering for Adaptive Software Laboratory). My PhD thesis received the SPEC Kaivalya Dixit Distinguished Dissertation Award 2024, a prominent award in computer benchmarking, performance evaluation, and experimental system analysis.

Research Focus

I work across different AI-powered techniques for software performance engineering, from foundational machine learning models to cutting‑edge GenAI systems.

  • Doctoral research — Software configuration performance engineering
    • Developed ML/DL approaches that learn the high‑dimensional configuration options to predict and optimise performance without exhaustive benchmarking, addressing critial challenges such as feature sparsity, rugged performance spaces, and cross‑environment drift (versions/hardware/workloads).
    • Why it matters: This enables earlier performance issue detection, software adaptability and autoscaling, and faster product evolution with far fewer measurements.
  • Current research — GenAI for industrial code performance optimisation
    • Lead work on search-based multi‑LLM optimisation and meta‑prompting for robust code scoring/optimization, combined with ensembling and compiler techniques; implemented in commercial platforms via the collaboration with TurinTech AI and evaluated on real production workloads.
    • Why it matters: Our methods deliver verifiable speedups and cost reductions on production codebases while making GenAI systems more reliable and auditable in practice.
  • Ongoing interests — AI-driven performance engineering, AI4SE, SE4AI
    • LLM performance modeling (hybrid models + online adaptive tuning), performance‑aware GenAI systems (dynamic prompt engineering + configuration tuning), trustworthy GenAI (RLHF + uncertainty verification), and industry standards/tooling (benchmarks, profiling + static analysis validation, CI/CD integration).
    • Why it matters: These directions make GenAI systems predictable and safe in real-world workloads, enabling reproducible evaluation, faster industrial adoption, and lower compute and carbon footprints.

If you’re interested in collaboration, please feel free to reach out!

News

Selected Publications

Further Background

I received first-class BSc degree from both the Information and Computing Science programme at Xi’an Jiaotong-Liverpool University (2014-16), and the Computer Science course at University of Liverpool (2016-18).