Rong Gu, Ph.D.

Research Profile

Research Description

My research is about applying formal methods and machine learning in autonomous systems. Machine learning bears the promise of letting machines learn by themselves given enough training data. However, it has no guarantee of correctness, safety, and security, which are crucial for autonomous systems. My research aims to overcome these shortcomings via formal methods as they are based on mathematics and can provide rigorous analysis such as formal verification.

Projects as PI

SATISFIES (Holistic Synthesis and Verification for Safe and Secure Autonomous Vehicles). 2024 - 2026. KKS funded. Industrial partners: Volvo Car, Zenseact, Mimer Information Technology.

Academic Activities

Reviewer/sub-reviewer: FM Symposium, AST, NFM, FMICS, SERENE, ISEC, Science of Computer Programming, Journal of Robotics, IEEE Transactions on Intelligent Transportation Systems, Journal of Supercomputing, Journal of Software and Systems Modeling, Journal of Systems & Software, etc.

Organizing conferences/workshops: IEEE SSE 2024 - present (PC member), SEAA 2024 - present (PC member), ASYDE 2024 (PC member), SPIN 2024 (PC member). ECBS 2023 (Tool Demo Chair), MODELS 2023 (Volunteer), ICST 2018 (Volunteer).

Invited seminars/conferences: Dagstuhl Seminar 24071 - Safety Assurance for Autonomous Mobility. FM Symposium 2024 (invited journal first). QUATIC 2023 (invited journal first).

Collaborations with Industry

Volvo Car. Volvo Construction Equipment. Zenseact. Mimer Information Technology, Scania.

Teaching Profile

Teaching:

• PhD course: Applied Formal Methods (to be announced)

Teaching assistant: 

• Catching Bugs by Formal Verification (2018 - now)

• Development of Web Application (2017 - 2019)

• Data Communication (2018 - 2024)

• Programming with Python (2022 - 2023)

Lecture: 

• Guest lecturer of course: DVA482 Embedded Systems II. 2021 - now.

• Guest lecturer of course: FLA433 Autonomous Vehicle. 2024 - now.

Thesis proposal:

• Generating Scenarios from Formal Specifications for Autonomous Vehicles. Company info: Scania.

• Safe Motion Planning and Reinforcement Learning for Self-Driving Vehicles and Robots.

Supervision: 

• Supervisor of Bachelor theses.

  • Model Checked Reinforcement Learning For Multi-Agent Planning. Erik Wetterholm. 2023.

• Supervisor of Master theses.

  • Fine-tuning LLMs for Scenario Generation for ADAS Systems. Ammara Asif. 2025. Company: Volvo Cars.

  • Integrating the power of large language models and model checking in requirement engineering. Gayomi Patikirige. 2025.

  • Overcoming the ambiguity and inconsistency of requirements by using generative AI. Ishimwe Kwizera. 2024. Company: Alstom.

  •  Safety-Guaranteed Mission Planner for Autonomous Vehicles. Đorđe Kalezić. 2020.

• Co-supervisor of PhD students

  • Lukas Dust

  • Emil Persson

Selected Presentations