🎓 I am a Master’s graduate in Artificial Intelligence from The University of Manchester, with a strong commitment to developing intelligent systems that can perceive, reason, and act autonomously in complex environments.
🤖 My research lies at the intersection of deep learning, generative modeling, and autonomous systems, with a central focus on building robust, interpretable, and adaptable AI agents.
💨 I am especially passionate about Diffusion Models and their transformative potential in structured generation tasks — ranging from video synthesis 🎥 to goal-conditioned planning 🧭. I view diffusion as a foundational paradigm for next-generation AI systems due to its probabilistic grounding, high-fidelity outputs, and cross-modal versatility.
🌍 My long-term research goal is to advance autonomous agents capable of accurate perception 👁️, goal-driven decision making 🎯, and continual learning 🔁 in uncertain, real-world environments.
I am particularly drawn to topics such as:
- 🌀 Diffusion-based generative AI for video and behavior generation
- 🚗 End-to-end autonomous driving and personalized trajectory modeling
- 🎛️ Multimodal learning (vision, language, control)
🌐 Currently, I am seeking a PhD position in the UK or globally, where I can pursue cutting-edge research on Diffusion Models and their applications in robotics, and autonomous driving.
If you’re building the future of grounded, interpretable, and trustworthy AI — let’s connect.
Google Scholar • GitHub • LinkedIn
🔥 News
- 2025.11: 🏆 Our work StyleDrive has been accepted as an Oral presentation at AAAI 2026.
- 2025.10: 🏆 Our work StyleDrive received an average score of 7 in the AAAI 2026 review process!
- 2025.08: 🤖 Joined Tuojing Intelligence as a Researcher, focusing on diffusion-based scenario generation and embodied AI.
- 2025.02: 🎓 Joined Tsinghua AIR (Institute for AI Industry Research) as a Research Assistant, working on generative modeling for autonomous driving.
- 2025.07: 🚀 Our paper “StyleDrive: Towards Driving-Style Aware Benchmarking of End-to-End Autonomous Driving” is now on arXiv! Read here
- 2024.09: 🎉 Graduated from The University of Manchester (MSc AI, Distinction)
- 2023.07: 🎓 Completed BSc Computer Science at The University of Manchester
📝 Publications
StyleDrive: Driving-Style Aware Benchmarking of End-to-End Autonomous Driving
Ruiyang Hao, Bowen Jing, Haibao Yu, Zaiqing Nie
- 🚗 Introduced the first large-scale real-world dataset for driving-style–aware E2E autonomous driving.
- 🧠 Developed a hybrid annotation pipeline combining motion heuristics and VLM reasoning.
- 📊 Proposed the SM-PDMS metric and established the first benchmark for personalized E2EAD.
- 💡 Achieved notable improvements in human-like driving through style conditioning.
[SignBart: Gloss-Free Sign Language Translation for Human-Robot Interaction via Efficient Spatiotemporal Learning]
Hongpeng Wang, Bowen Jing, Hao Tang, Xiang Li, Fanying Kong
[Preprint (Submitted to ICRA 2026)]
- 🧏♂️ Introduced a gloss-free, end-to-end sign language translation framework for human–robot interaction.
- ⚙️ Designed a lightweight visual encoder (CSIFE-ConvNeXt) achieving 58% parameter reduction.
- 🧠 Proposed TTT-mBART, enabling test-time adaptation and robust domain transfer.
- 📈 Achieved state-of-the-art results on PHOENIX-2014T and CSL-Daily benchmarks.
- 🤖 Demonstrated strong deployability for assistive and service robots in real-world settings.
[MInM: Mask Instance Modeling for Visual Representation Learning] Bowen Jing
[Preprint (Submitted to AAAI 2026)]
- Proposed an instance-aware masked image modeling framework (MInM) that replaces random masking with SAM2-generated semantic masks to guide self-supervised learning.
- Integrated MInM into the MAE pipeline with zero architectural change, improving convergence speed and semantic alignment.
- Evaluated on ImageNet-1K, Pascal VOC 2007, MS COCO, and MedMNIST; demonstrated better semantic generalization and downstream transfer.
End-to-End Autonomous Driving System with Middle Fusion and Attention
Bowen Jing
- Built a multimodal AV system integrating LiDAR and RGB via channel-attentive fusion.
- Deployed in CARLA and validated via extensive ablation studies.
[VDiff-SR: Diffusion with Hierarchical Visual Autoregressive Priors for Image Super-Resolution]
- Proposed a novel hybrid framework combining diffusion models and pretrained Visual AutoRegressive (VAR) priors for real-world image super-resolution (Real-ISR).
- Designed two new modules: Condition-Gated Unit (CoGU) for feature conditioning, and Cross-Scale Prior-Aligned Attention (CSPA) for multi-scale structural alignment.
- Achieved SOTA performance on RealSR and RealSet5 with significant improvements in perceptual metrics (CLIP-IQ ↑, MUSIQ ↑).
- Conducted detailed ablation demonstrating the synergy of VAR priors and denoising-based generation.
📖 Educations
- 2023.09 – 2024.09, MSc in Artificial Intelligence, University of Manchester
- Focus: Deep Learning, Computer Vision, Reinforcement Learning, Robotics
- Dissertation: Comparative study of Deep Learning and Traditional Vision in Robotic Perception
- Graduated with Distinction (Top 10%)
- 2020.09 – 2023.06, BSc in Computer Science, University of Manchester
- Specialized in software development and machine learning foundations
- Final Year Project: Spiking Neural Network
💻 Internships
- 2025.08 – Present · Tuojing Intelligence — Research Intern in Traffic Simulation and Generative Modeling
- 2025.02 – 2025.08 · Tsinghua University, AIR — Research Intern in Large-Scale Autonomous Driving Data Mining